MOLECULAR MARKER DEVELOPMENT, QTL PYRAMIDING, AND

COMPARATIVE ANALYSIS OF PHENOTYPIC AND MARKER-ASSISTED

SELECTION IN CUCUMBER

by

Matthew D. Robbins

A dissertation submitted in partial fulfillment of

the requirements for the degree of

Doctor of Philosophy

(Plant Breeding and Plant Genetics)

at the

UNIVERSITY OF WISCONSIN-MADISON

2006

i

Dedication

To my family: Cody, Hayden, Camila, and most especially, Heidi

ii

Acknowledgements There have been many who have helped make this dissertation possible. I am thankful for several undergraduates, Danya Hooker, Caroline Gatheca, Brian Holzman, Katrina Pfaff, Sarah Yokobowski, John Alaniz, Julie Weidner, and Julianna Whan, who directly worked with me on this project. I also want to thank the graduate students, Gennaro Fazio, Sang-Min Chung, Anabel López-Sesé, Zhanyong Sun, Jaun Zalapa, Isabelle Delannay, Vanessa Gordon, Shanna Mason, Miriam Paris, and Hugo Cuevas who worked with me and gave me encouragement. I am thankful for Linda Crubaugh and her patience with me and the other graduate students. Thanks goes to my committee members, Drs. Phil Simon, Mike Casler, Mike Havey, and Jim Coors for their patience and guidance. A special thanks to my advisor, Dr. Jack Staub for his tutorage and mentorship. I am grateful for the support of my parents, Val and Judy Robbins, and my in-laws Mikel and JoLynn Stevens. I am most indebted to my children who have sacrificed time with their dad, and my wife, Heidi, without whom this work would not be possible. Finally, but most importantly, I thank my Heavenly Father for his guidance and loving care.

iii

Table of Contents

Dedication ............................................................................................................................ i Acknowledgements............................................................................................................. ii Table of Contents............................................................................................................... iii List of Tables .................................................................................................................... vii List of Figures .................................................................................................................. viii Abstract ............................................................................................................................... x Introduction......................................................................................................................... 1

Cucumber........................................................................................................................ 1 Cucumber Yield .............................................................................................................. 5 Yield Components .......................................................................................................... 8

Multiple lateral branching........................................................................................... 9 Sex expression .......................................................................................................... 11 Earliness.................................................................................................................... 16 Fruit length to diameter ratio .................................................................................... 17 Yield components considerations ............................................................................. 18

Molecular Markers........................................................................................................ 21 Molecular marker types ............................................................................................ 21 Marker conversion .................................................................................................... 26 Genetic mapping in cucumber .................................................................................. 29

Marker-assisted Selection ............................................................................................. 31 Epistasis ........................................................................................................................ 34 Literature Cited ............................................................................................................. 36

Chapter 1. Comparative analysis of marker-assisted and phenotypic selection for yield components in cucumber .................................................................................................. 45

Abstract ......................................................................................................................... 45 Introduction................................................................................................................... 46 Materials and Methods.................................................................................................. 48

Germplasm................................................................................................................ 48 Selection scheme....................................................................................................... 49 Open-field evaluation of selection ............................................................................ 52 Statistical analysis..................................................................................................... 54

Results........................................................................................................................... 54 Discussion..................................................................................................................... 57

Considerations for MAS ........................................................................................... 58 Selection effectiveness.............................................................................................. 59 Selection methods in breeding programs.................................................................. 63

Literature Cited ............................................................................................................. 65 Chapter 2. The development of molecular markers with increased efficacy for genetic analysis in cucumber......................................................................................................... 79

iv

Abstract ......................................................................................................................... 79 Introduction................................................................................................................... 80 Materials and Methods.................................................................................................. 82

RAPD to SCAR conversion...................................................................................... 82 SCAR multiplexing................................................................................................... 84 Codominant marker development............................................................................. 85

Identification of SNPs by SCAR sequencing ......................................................... 85 Identification of SNPs by BAC end sequencing .................................................... 86 SNP marker creation............................................................................................. 87

SNP marker evaluation and verification ................................................................... 90 Results........................................................................................................................... 91

RAPD to SCAR conversion...................................................................................... 91 SCAR multiplexing................................................................................................... 92 Codominant marker development............................................................................. 92

Identification of SNPs by SCAR sequencing ......................................................... 92 Identification of SNPs by BAC end sequencing .................................................... 93 SNP marker creation............................................................................................. 94

SNP marker evaluation ............................................................................................. 94 Summary of RAPD conversion ................................................................................ 95

Discussion..................................................................................................................... 96 RAPD to SCAR conversion...................................................................................... 97 SCAR multiplexing................................................................................................... 99 Identification of SNPs............................................................................................. 100 SNP marker creation and evaluation....................................................................... 102

Literature Cited ........................................................................................................... 105 Chapter 3. Pyramiding QTL for multiple lateral branching in cucumber using nearly isogenic lines................................................................................................................... 121

Abstract ....................................................................................................................... 121 Introduction................................................................................................................. 122 Materials and Methods................................................................................................ 124

NIL creation ............................................................................................................ 124 Molecular marker analysis...................................................................................... 125 Open-field evaluation of NIL for MLB .................................................................. 126 Statistical analysis................................................................................................... 126

Results......................................................................................................................... 127 Discussion................................................................................................................... 129 Literature Cited ........................................................................................................... 134

Conclusions and Future Work ........................................................................................ 143 Literature Cited ........................................................................................................... 147

Appendices...................................................................................................................... 149 Appendix A. Means and linear response at two planting dates (June 23, 2004 and July 7, 2004) of five traits in four base cucumber populations (C0) of cucumber which

v

underwent three cycles of recurrent mass selection (C1-C3) using three breeding methods (see Chapter 1).............................................................................................. 149

Appendix B. The linear response of selection for five traits in four cucumber populations by marker (MAS), phenotype (PHE), and random mating (no selection; RAN) over three cycles. The five traits are earliness (measured as the number of fruits per plant in first harvest), gynoecy (measured as the percent female flowers in the first ten nodes), fruit length to diameter ratio (measured as the mean length to diameter ratio of 5-10 randomly selected fruit averaged over three harvests), multiple lateral branching (measured as the number of lateral branches of at least three internodes long on the mainstem in the first 10 nodes), and yield (measured as the number of fruits per plant averaged over four harvests; see Chapter 1). ..................................................... 152

Appendix C. Sequences of RAPD bands used to make SCAR primers. Sequences are presented in FASTA format with the name of the RAPD marker, and the parental line (Gy-7 or H-19) from which the band was produced in parentheses. Incomplete sequences are indicated by “FORWARD ONLY” or “REVERSE ONLY”. Sequences that correspond to a band other than the polymorphic RAPD band (as determined by the segregation pattern of the SCAR created from the sequence and the original RAPD marker) are indicated by “DOES NOT MATCH RAPD” (Table 2.1; see Chapter 2)...................................................................................................................................... 157

Appendix D. SCAR marker database. An html (web-based) database of the SCAR markers created in Chapter 2, as well as additional SCAR markers, is available that contains primer sequences, annealing temperature gradient PCR (ATG-PCR) profiles, and spreadsheets with information on each of the SCAR markers. The structure and contents of this database are explained in the following screenshots and can be accessed at: http://www.vcru.wisc.edu/staublab/Matt/SCAR%20web%20page2/Scar%20database.htm ................................................................................................................................ 169

Appendix E. BAC clone end sequences used to create SCAR markers in Table 2.2 (Chapter 2). Sequences are presented in FASTA format with the sequence name followed by the number of bases in parentheses. Sequences are named with a one or two letter designation for the marker that hybridized to the BAC clone (AJ = AJ6SCAR, B = BC523SCAR, C = CSWCTT14, L = L19SCAR, M = M8SCAR, and W = OP-W7-1), the number of the clone that was sequenced, BE for “BAC end”, and L or R to signify the left or right end of the BAC clone, respectively (L2 or R2 indicates the second attempt to obtain the sequence). AJ-1-BE-L, for example, is the sequence of the left end of the first clone sequenced that hybridized to AJ6SCAR. . 171

Appendix F. SCAR primers from BAC clone end sequences as developed in cucumber (see Chapter 2). ........................................................................................................... 183

vi

Appendix G. SNPs identified between Gy-7 and H-19 in markers from two sequencing sources [sequencing of SCAR fragments (SCAR) and utilizing markers to identify BAC clones for sequencing (BAC end); see Chapter 2]. Asterisks (*) in either the Gy-7 or H-19 sequence (cucumber parental lines used in map construction) indicate an insertion/deletion (indel). ....................................................................................... 186

Appendix H. Characteristics of single nucleotide polymorphism (SNP) markers (comprising an allele-specific primer based on a SNP, and a non-specific primer) and their allele-specific primers as developed in cucumber (see Chapter 2). ................... 190

Appendix I. Primer names and sequences of SNP markers as developed in cucumber (see Chapter 2). ........................................................................................................... 194

Appendix J. Sample alignment of sequences from the RAPD marker OP-C1 generated in cucumber by Horesji et al. (1999; silver staining sequencing) and Chapter 2. Asterisks highlight differences in sequences and undetermined bases (N) are indicated by ^.............................................................................................................................. 199

Appendix K. The effect of increasing the number of quantitative trait loci (QTL) on the number of lateral branches in two leaf types of cucumber in two locations as determined by near-isogenic lines (NIL; Table 3.2). Lines indicate the incremental addition of QTL, and the comparisons in the legends refer to means comparisons performed (Table 3.4; Chapter 3). .............................................................................. 200

Appendix L. Analysis of variance (ANOVA) table of a test for main effects and interactions on the number of lateral branches in cucumber (MLB; Chapter 3). Effects examined were location (Hancock, Wisc. and Arlignton, Wisc.), replication (reps), with-in row spacings (10, 15, and 20 cm between plants), leaf type (standard and little leaf) and genotype [near-isogenic lines (NIL) that vary in the number of QTL (Table 3.2) for MLB]. The coefficient of variation (CV) was 19.6%................................... 201

vii

List of Tables Table 1. Sex phenotypes from combinations of the three major genes of sex expression in cucumber (Cucumis sativus L.). Adapted from Mibus and Tatlioglu (2004)…………………………………………13 Table 1.1. Mean values of yield component traits of commercial checks and parental inbred lines used to create four cucumber populations for comparison of response to selection by phenotype and marker. Means are from the replicated trial described herein…………………………………………………………….…70 Table 1.2. Cumulative selection differential over three cycles between Stage 1 and Stage 2 of phenotypic selection (PHE) for four traits in four populations of cucumber……………………. …………………..…71 Table 1.3. Characteristics of molecular markers defined in a genetic map of cucumber constructed by Fazio et al. (2003b) and used in marker-assisted selection for population improvement…………………………72 Table 1.4. Means and linear response of five traits in four base populations (C0) of cucumber which underwent three cycles of recurrent mass selection (C1-C3) using three methods………………………….73 Table 1.5. Phenotypic correlations (r) among traits in cucumber over three cycles of selection by markers (MAS) and phenotype (PHE)……………………………………………………………………………….75 Table 2.1. RAPD markers converted to SCAR markers in cucumber……………………………………110 Table 2.2. SCAR markers created from BAC end sequences in cucumber………………………………112 Table 2.3. Allele-specific markers created by four design approaches based on SNPs between two cucumber lines (Gy-7 and H-19) identified from two sequence sources………………………………….113 Table 2.4 Primer names and sequences of the polymorphic markers converted from RAPDs and verified by segregation in cucumber………………………………………………………………………………..114 Table 2.5. Results of RAPD to SCAR conversion in cucumber of RAPDs common to Horejsi et al. (1999) and this study………………………………………………………………………………………………117 Table 3.1. Characteristics of previously identified cucumber quantitative trait loci (QTL) associated with multiple lateral branching (MLB) that were introgressed to create nearly isogenic lines (NIL)………….137 Table 3.2. QTL composition and mean number of branches in near isogenic lines (NIL) of cucumber...138 Table 3.3. Marker-QTL associations used to introgress quantitative trait loci (QTL) for multiple lateral branching (MLB) into nearly isogenic lines (NIL) in cucumber…………………………………………..139 Table 3.4. Means comparisons to determine specific quantitative trait loci (QTL) effects in nearly isogenic lines (NIL) of cucumber…………………………………………………………………………………...140

viii

List of Figures

Figure 1.1. Schematic of the selection and evaluation scheme in cucumber where PHE is phenotypic selection, MAS is selection by marker, and RAN is random mating……………………………………….76

Figure 1.2. Response to selection as measured by the slope of linear regression (Y axes) over three cycles of MAS (selection by marker), PHE (phenotypic selection) or RAN (random mating) for five traits in four cucumber populations. *, **, and *** denote slopes are significant at P ≤ 0.05, P ≤ 0.01, and P ≤ 0.001, respectively………………………………………………………………………………………………….77

Figure 1.3. Time required to complete MAS (selection by marker) and PHE (phenotypic selection) for three cycles in four cucumber populations. Gray areas indicate evaluation of the populations and black areas represent recombination among selections……………………………………………………………78 Figure 2.1. SCAR multiplex reactions in cucumber. Panel C: banding patterns of individual SCAR primer pairs, including molecular weight in base pairs (bp) across a temperature gradient. Vertical numbers denote PCR annealing temperatures (oC) for each lane. Panels A, B, D, and E contain two, three, three, and four primer pairs, respectively, added to the same PCR reaction. Molecular weight of the EcoRI+HindIII digested lambda marker are to the right of panels B and E………………………………………………..118 Figure 2.2. Allele specific primer design used to create a codominant marker in cucumber from SNPs within a locus employing the optimal approach. Panel A: SNPs between H19 (bottom sequence) and Gy-7 (top sequence) in a portion of the AD14SCAR sequence are indicated by an asterisk (*). Allele specific primers match the SNP of one parent at the 3’ end with an additional mismatch (^) to both alleles within 4 bases of the 3’ end. Universal non-specific primers have no mismatch to either allele. Primer orientation and direction of extension by a polymerase (horizontal arrows) during PCR are indicated under each primer name. Panel B: Photograph after agarose gel electrophoresis of PCR reactions using Gy-7 and H-19 as template with the dominant Gy-7 allele specific marker (Gy-7 allele specific primer and Universal non-specific primer) labeled G, the dominant H-19 allele specific marker (H-19 allele specific primer and Universal non-specific primer) labeled H, and the G-y7 and H-19 allele specific markers combined (Gy-7 allele specific primer, H-19 allele specific primer, and Universal non-specific primer) in a codominant assay labeled “C”. The codominant assay was also tested on F1 and F2 individuals from a cross between Gy-7 and H-19. A 100 bp ladder (MW ladder) flanks PCR products……………….……………………119 Figure 2.3. Graphical representation of four design approaches developed herein to create Gy-7 and H-19 allele-specific markers in cucumber from a single locus depending on location and number of single nucleotide polymorphisms (SNPs). Solid horizontal lines represent a genomic fragment containing a SNP (asterisk) between Gy-7 and H-19. Arrows represent the direction of primer extension by a polymerase in PCR. Primers are designated as allele-specific (GS) and non allele-specific (GN) to amplify the Gy-7 allele, allele-specific (HS) and non allele-specific (HN) to amplify the H-19 allele, and non-allele-specific universal (UN) to amplify both alleles. The dotted line on the GS primer of the tail approach represents additional base pairs that do not anneal to the template during PCR, but are designed to add length to the PCR product. The horizontal dotted lines above and below the genomic fragment represent PCR products of Gy-7 and H-19 template, respectively. The panels on the far right represent the gel banding patterns of both approaches in each row after agarose gel electrophoresis with Gy-7, H-19, and an F1 hybrid as templates…………………………………………………………………………………………………...120

ix Figure 3.1. The effect of plant density on the number of lateral branches in cucumber near-isogenic lines (NIL) of two leaf types (little-leaf and standard-leaf). The linear and residual P-values, as well as the R2 of each leaf type are presented to the right of each leaf type. Within row spacings of 10, 15 and 20 cm correspond to plant densities of 66,700, 44,400, and 33,300 plants/hectare, respectively………………...141 Figure 3.2. The effect of increasing the number of quantitative trait loci (QTL) on the number of lateral branches in two leaf types of cucumber as determined by near-isogenic lines (NIL; Table 3.2). Lines indicate the incremental addition of QTL, and the comparisons in the legends refer to means comparisons performed (Table 3.4). Asterisks (*) indicate significant means comparisons (P < 0.05)………………..142

x

Abstract Several theoretically-based simulation studies suggest that the effectiveness of marker-

assisted selection (MAS) for polygenic traits can be greater than phenotypic selection

(PHE), but empirical comparisons are scarce and often conflicting. Therefore, existing

molecular tools [i.e., genetic linkage maps with defined quantitative trait loci (QTL)-

marker associations] were leveraged to compare the effectiveness of MAS to PHE for

several quantitative (conditioned by 2-6 QTL), yield-related traits [multiple lateral

branching (MLB), gynoecious sex expression (GYN), earliness (EAR), and fruit length to

diameter ratio (L:D)] in cucumber. Four complementary inbred lines were intermated to

produce four populations which underwent MAS, PHE, and random mating (no

selection) for three cycles of recurrent selection. Although both MAS and PHE improved

all traits, except for EAR by MAS, their effectiveness depended upon the traits and

populations under selection. PHE was most effective for GYN, EAR, and L:D, while

MAS was most effective for MLB and yield (fruit per plant). To increase the efficiency

of existing molecular markers, 43 random amplified polymorphic DNA (RAPD) markers

were sequenced to produce 22 polymorphic sequence characterized amplified region

(SCAR) markers. Sequences from bacterial artificial chromosome (BAC) clones and

monomorphic SCARs were obtained to identify single nucleotide polymorphisms (SNPs),

and four novel marker design approaches were utilized to create allele-specific markers

based on SNPs with an 80% success rate (20 markers created from 25 loci containing

xi SNPs). A total of 32 RAPDs were converted into SCAR or SNP markers that will

increase the efficiency of MAS in cucumber. Although QTL-marker associations have

provided for improvement of MLB by MAS, the epistatic effects of individual QTL have

not been characterized. Sets of nearly-isogenic lines (NIL) were created in two genetic

backgrounds (standard- and little-leaf type) with varying numbers of QTL associated

with MLB. Comparative analysis of specific QTL combinations among NIL

characterized epistatic interactions which were detected among QTL in the little leaf

background, but not in standard-leaf NIL. Genotype and QTL by environment

interactions were also identified, indicating that lateral branch production is determined

by environmental effects, interactions among other cucumber traits, and interactions

among QTL conditioning MLB.

1

Introduction

Cucumber

Commercial cucumber (Cucumis sativus L.; 2n=2x=14) has been of culinary

importance to humans for millennia. Although the cucumber is thought to originate in

India or Southern Asia, evidence from Northern Thailand suggests the earliest use of

cucumber by humans was approximately 9,750 B.C. (cucumber history reviewed by

Lower and Edwards 1986, Wehner 1989, Tatlioglu 1993, Meglic and Staub 1996a, and

Staub and Bacher 1997). The initial domestication of cucumber, however, is thought to

have occurred in India circa 3,000 years ago (Lower and Edwards 1986), which makes it

one of the oldest cultivated vegetable crops (Shetty and Wehner 2002). The

domestication of cucumber spread east from India to Western Asia, then west to Asia

Minor, North Africa, and Southern Europe before written history (Tatlioglu 1993).

Cucumber was cultivated by the Chinese (second century B.C.), Sumerians (2,500 B.C.),

ancient Greeks and Romans (300 B.C.), ancient Egyptians, French (9th century), and

English (15th century) before being carried to Haiti and New England by Christopher

Columbus at the end of the 15th century (Lower and Edwards 1986; Wehner 1989;

Tatlioglu 1993; Meglic and Staub 1996a). After its introduction into the Americas,

cucumber was grown in colonial gardens and by several North American Indian tribes

(Meglic and Staub 1996a; Staub and Bacher 1997). Cucumber is now grown in nearly all

countries in temperate zones (Tatlioglu 1993).

2 Cucumber belongs to the Cucurbitaceae or vine-crop family, which includes

watermelon [Citrullus lanatus (Thunb.) Matsumura & Nakai], melon (Cucumis melo L.),

squashes and pumpkin (Cucurbita spp.), and gourds (multiple genera). In the genus

Cucumis, the “African horned cucumber” or “jelly melon” (C. metuliferus E. Meyer ex

Naudin), West Indian gherkin (C. anguria L.), melon, and cucumber are the only species

commonly cultivated for their fruit (Morton 1987; Baird and Thieret 1988). Although

there are 32 species in Cucumis, cucumber is genetically isolated within the genus since it

is not readily cross-compatible with any other species (Kirkbride 1993). Chromosome

number (x = 7) is a major crossing impediment since cucumber deviates from other

Cucumis species, which posses 12 (or its multiples) haploid chromosomes (x = 12; Lower

and Edwards 1986). Although cucumber is cross-compatible with a feral, sympatric,

botanical variety of the same species [C. sativus var. hardwickii (R.) Alef. (x = 7;

hereafter referred to as C. s. var. hardwickii)], cross-compatibilities between cucumber

and x = 12 Cucumis species are extremely rare. Employing biotechnological techniques

such as somatic hybridization and protoplast fusion to overcome such crossing barriers

have been largely unsuccessful. Successful hybridization between C. hystrix Chakr. (2n

= 2x = 24) is a notable exception (Chen et al. 1997). Interspecific hybrids were obtained

through embryo rescue and chromosome doubling to create a synthetic amphidiploid

species (2n = 4x = 38) called C. hytivus (Chen and Kirkbride 2000; Chen et al. 2002).

The reproductive isolation of cucumber within Cucumis has made it difficult to

broaden the narrow genetic base of cucumber. Cucumber genetic diversity has been

assessed by several types of genetic markers, and the percentage of individuals

3 polymorphic for any given marker in adapted germplasm is relatively low (3 to 8% on

average) compared to other allogamous Cucumis species (10 to 25%) and C. s. var.

hardwickii (17 to 25%; Knerr et al. 1989; Dijkhuizen et al. 1996; Serquen et al. 1997a;

Horejsi and Staub 1999; Horejsi et al. 1999). Germplasm from C. s. var. hardwickii has

been employed in several cucumber breeding programs (Lower and Edwards 1986), and

the new C. hytivus species (Chen et al. 2002) may be valuable as a bridge species to

broaden genetic diversity in cucumber.

Cucumber is the fifth most widely grown vegetable crop worldwide behind

tomato (Solanum lycopersicum L.), watermelon, cabbage (Brassica oleracea L.), and

onion (Allium cepa L.), with a total of 2,427,436 hectares harvested in 2004 producing

40,860,985 Mt (FAOSTAT, 2005). Cucumber types can vary widely by country and

some types, such as the long, dark-green skinned, slicing types important in central and

western Europe, are uncommon in other growing regions such as the Middle East and

Turkey where shorter, lighter skinned, “Beit alpha” types are preferred (Tatlioglu 1993).

Cucumbers are produced both in greenhouses and open fields. The former production

environment is more common in Europe, while the latter makes up the majority of

production in the US (Wehner 1989). Cucumber ranks seventh among vegetable crops in

the US in area harvested with 68,660 total hectares producing 969,400 Mt in 2004

(FAOSTAT, 2005).

The two main cucumber types produced in the US are fresh market (slicing) and

processed (pickling; Lower and Edwards 1986; Wehner 1989; Staub and Bacher 1997).

Slicing cucumbers are white-spined with relatively thick, uniformly colored skins, and

4 fruit length to diameter ratios (L:D) of ≥ 4.0 are preferred. Pickling cucumbers are

also white-spined, where thin skinned fruits possess an L:D ≥ 3.0 (Lower and Edwards

1986; Staub and Bacher 1997). Cucumbers grown for processing are much more

prevalent than those for fresh market in the US (46,013 and 23,136 hectares harvested in

2005, respectively). However, the value of fresh market ($10,136/ha) is greater than that

of processed cucumber ($3,223/ha; USDA NASS 2006).

Pickling cucumbers are harvested in two ways: mechanically (once-over) and by

hand (multiple harvest). Mechanically harvested fruits of desirable size are separated

from their attending vines in one destructive pass over the entire field. In contrast, plants

are left intact during hand harvest, allowing additional harvests (up to 30) after further

fruit development (Lower and Edwards 1986; Tatlioglu 1993). Although the percentage

of fields harvested by machine varies across the United States, the percentage continues

to increase, especially in the northern states where up to 60% of pickling cucumber is

harvested by machine (Staub and Bacher 1997). The increase in the use of machines to

harvest pickling cucumber in the 1960’s prompted a change in cultural practices and

plant types to fit a crop production system adapted to machine harvest. To obtain the

maximum number of marketable fruit from once-over mechanical harvesting, fruit set

needs to be uniform and abundant. High plant density cultural practices and improved

harvest machinery were important contributing factors which led to the adoption of

mechanical harvesting. Earliness and gynoecious sex expression were critical to the early

success of this new harvesting method. Higher plant densities [> 20,000 (mechanical

harvest) vs. 6,000 to 8,000 (hand harvest) plants/ha] allowed for an increase in fruit

5 number per unit area, while early, gynoecious lines and hybrids provided a more

productive and uniform fruit set than their monoecious counterparts (Lower and Edwards

1986).

The cucumber processing industry produces a wide variety of products using

three principal processing technologies: brine (fermented), fresh-pack (pasteurized), and

cold-pack (refrigerated; Lower and Edwards 1986; Staub and Bacher 1997). Brining

involves the preservation of harvested fruit in a high salt solution (5-16% sodium

chloride) for several months. Flavoring, such as dill, garlic and spices, are typically

added during or after the completion of fermentation, and the pickles are usually desalted

before being packed for commercial sale. Pickles are held in the brine and packaged

according to market demand. For fresh-pack pickles, cucumbers are preserved by

acidification (addition of vinegar or acetic acid) with subsequent pasteurization (heating

to 71-75oC for 10-15 min). Cold-pack products are produced by combining cucumbers,

vinegar, flavorings, and usually a preservative directly during packaging, and then

preserved by constant refrigeration. Brined products have the longest shelf life (24

months), followed by fresh-pack (18 months), and cold-pack (8 months; Staub and

Bacher 1997).

Cucumber Yield

Yield has been a focus of cucumber breeders for over 50 years (Lower and

Edwards 1986; Wehner 1989; Wehner et al. 1989). During the middle part of the 20th

century, yield of US processing cucumber maintained a steady increase from 4,685 kg/ha

6 in 1949 to 11,455 kg/ha in 1979, an average increase of 224 kg/ha per year (Lower and

Edwards 1986). Similarly, yield trials of five popular gynoecious pickling varieties from

the Southeastern United States released between 1969 and 1987 revealed an average yield

increase of 400 kg/ha per year (Wehner 1989). By 1980, the average yield of US

processing cucumbers was 12,550 kg/ha, triple that observed in 1920 (4,076 kg/ha;

USDA Agricultural Statistics, 1940, 1981). Most of the increase in yield during this

period can be attributed to improved cultural practices and breeding for disease resistance

(Lower and Edwards 1986; Wehner 1989; Wehner et al. 1989). The introduction of the

gynoecious flowering habit increased early yield, but did not affect total yield measured

over multiple harvests (Wehner et al. 1989).

As cucumber yield became increasingly important, it became the focus of many

studies beginning in the late 1970’s. Research conducted on many aspects of yield

including breeding methodologies (e.g., selection methods and selection criteria),

optimizing yield trials (e.g., methods to measure yield and optimal plot size), and the

genetics of yield (e.g., heritability and genotype by environment interactions) from the

late 1970’s to the late 1980’s was reviewed by Wehner (1989). These studies provided

important information for breeding for increased yield in cucumber, and indicated that

improvement by direct selection for yield is difficult. Yield is quantitatively inherited

with a low, narrow-sense heritability (h2) of 0.07 to 0.25, and is influenced mainly by

genotype and environment, and to a lesser degree by genotype by environment

interactions. Selection for yield should occur in intermediate stages of a recurrent

selection scheme on a plot basis rather than individual plants. Yield may be evaluated

7 effectively in small (one row, rep and harvest), multi-location (two to three) trials over

seasons or years. The optimal harvest time in such trials depends upon the harvest index,

which is based on the number and weight of oversized fruit (> 50 mm in diameter) in

control plots.

Measuring cucumber yield is often difficult because fruits are harvested before

they reach physiological maturity (yield measurement is reviewed by Wehner 1989).

Cucumber growers usually measure yield by volume or weight per unit area, but the

volume and weight of immature fruit can change rapidly from day to day, thus yield is

dependent on the time of harvest. Converting yield to market value of processing

cucumbers is further complicated because harvested fruit are graded by diameter where

the smallest fruits have the greatest value, while oversized fruit have no commercial

value. Although several methods for measuring yield (i.e., volume, mass, number, or

dollar value) have been investigated, the most efficient measurement of yield in research

studies is the total (marketable and oversize) number of fruits per plant, since it has a

higher heritability, is more stable over time, and is easier to measure than other yield

measurements. Furthermore, fruit number is highly correlated (genetic correlation =

0.87) with fruit weight. Therefore, the number of fruit per plant is considered

synonymous with yield in the research presented herein.

Increased research efforts since the early 1980’s to improve yield of US

processing cucumber have not been effective (Shetty and Wehner 2002; Fazio et al.

2003a). Selecting directly for yield is difficult and has produced mixed results (Wehner

1989) which may partially be explained by the low heritability and environmental

8 influence, combined with the difficulty in measuring yield. Since many other traits are

correlated with yield and have a higher heritability, the most effective approach to

breeding for yield may be selecting for these traits (Wehner 1989; Cramer and Wehner

1998a; Cramer and Wehner 2000b). Traits correlated with yield are commonly referred

to as yield components, and include number of plants per hectare, number of harvests per

plant, stem length, number of branches per plant, number of nodes per branch, time until

first flowering, percentage of pistillate flowers, and percentage of fruit set (Cramer and

Wehner 1998a; Cramer and Wehner 2000b).

Yield Components

Correlations among yield component traits as well as with yield have been

investigated in a variety of cucumber germplasm including slicing populations (Cramer

and Wehner 1998a; Cramer and Wehner 1998b), pickling populations (Serquen et al.

1997b; Cramer and Wehner 1998b; Cramer and Wehner 2000b; Fazio 2001), hybrids

(Cramer and Wehner 1999), and germplasm derived from C. s. var. hardwickii (Fredrick

and Staub 1989). In addition, genetic and quantitative trait loci (QTL) mapping studies

have contributed to the understanding of the genetics of yield components, and have

identified QTL conditioning these traits. Two inbred lines, Gy-7 (synom. G421; R.L.

Lower, University of Wisconsin, Madison, Wisc.) and H-19 (synom. AR 7975; Goode et

al. 1980), were used as parents to create F3 families for genetic analysis (Serquen et al.

1997b) and QTL mapping (Serquen et al. 1997a) of several yield components including

multiple lateral branching, gynoecious sex expression, fruit length to diameter ratio (L:D),

9 and earliness. These yield components were further characterized by QTL analysis of

recombinant inbred lines (RIL) from the same parental lines (Fazio et al. 2003b).

Multiple lateral branching Evidence from several studies indicates that selection for multiple lateral

branching (MLB) types (plants with several lateral branches) should increase cucumber

yield (fruit per plant). The number of lateral branches was found to be positively

correlated (r = 0.58 to 0.42, p = 0.001) with the number of fruit per plant in a pickling

cucumber population in two locations over two years (Fazio 2001). Significant, positive

correlations (r = 0.53 to 0.78) between yield and MLB were also detected in several other

populations of various types (Fredrick and Staub 1989; Cramer and Wehner 1998a;

Cramer and Wehner 1999; Cramer and Wehner 2000b). Path analysis was employed in

eight pickling and slicing cucumber populations to determine the magnitude of

correlations of yield component traits with each other as well as with yield (Cramer and

Wehner 2000a). Of the yield components tested (branches per plant, nodes per branch,

pistillate nodes, and fruit set), only branches per plant was consistently, highly correlated

(r > 0.7) with yield (i.e., over populations, cycles of selection, and environments).

Furthermore, the correlation between MLB and yield increased (from r = 0.67 to 0.82)

with continued selection (from early to later cycles) for yield. Cramer and Wehner

(2000a), therefore, suggest that efforts to improve yield in cucumber should focus on

increasing MLB.

Two main germplasm sources have been utilized in the US for the incorporation

of MLB in cucumber. A feral relative of cucumber (C. sativus var. sativus), C. sativus

10var. hardwickii (R) Alef. (hereafter referred to as C. s. var. hardwickii; Horst and

Lower 1978), and the cucumber inbred line ‘Little John’ (line H-19; synom. AR 79-75;

Goode et al. 1980) both possess a multiple lateral branching habit not present in

commercial cucumber. Multiple lateral branching in both sources is quantitatively

inherited (Wehner et al. 1978; Serquen et al. 1997b; Fazio et al. 2003b) demonstrating

mostly additive genetic variance with narrow-sense heritabilities (h2) ranging from 0.00

to 0.61 (Wehner et al. 1978; Serquen et al. 1997b). In an F3 population created from Gy-

7 (synom. G421) and H-19, Serquen et al. (1997b) indicated that MLB is controlled by at

least four genes, where four QTL explained 48% to 66% of the observed variation (R2)

depending upon environment (Serquen et al. 1997a). Although a total of 13 QTL for

MLB were identified by Fazio et al. (2003b) using recombinant inbred lines (RIL)

derived from the same parents, only five were detected in at least two locations with a

combined R2 of 37% to 55% depending on location. In both QTL studies, one major

QTL was detected that accounted for 32% (Fazio et al. 2003b) to 40% (Serquen et al.

1997a) of the variation, which mapped near the little leaf locus (ll).

In evaluations of multiple growing environments, MLB was not affected by

growing location (Georgia, Utah, and Hancock, Wisc.; Serquen et al. 1997b; Fazio et al.

2003b) or planting date (early and late; Fredrick and Staub 1989). However, Fazio et al.

(2003b) identified a QTL specific to Hancock, Wisc. (LOD 2.7-3.0 in two years), and

seven other QTL (LOD 2.8-6.1) unique to a single environment. In addition, the

moderate narrow sense heritability (the ratio of additive to total phenotypic variation) of

MLB (0.48) and the additive gene action of this trait (Serquen et al. 1997b) indicate other

11factors (i.e., the environment) play a role in trait expression. Indeed, MLB varied

across years in another study in Hancock, Wisc. (López-Sesé and Staub 2002), and the

number of lateral branches decreased with increased plant density in C. s. var. hardwickii

germplasm (Fredrick and Staub 1989). Therefore, MLB is conditioned by a few major

loci with effects from minor QTL and growing environment.

Sex expression Associations between the number of female flowers per plant (sex expression)

and fruit per plant (yield) have been identified in several studies. In four slicing

cucumber populations over several cycles of selection, Cramer and Wehner (1998a)

found that the number of female flowers was positively correlated with yield in some

population/season combinations. A highly significant, positive correlation (r = 0.79)

between percent pistillate nodes and yield was also identified in one of four pickling

populations, with moderate, positive correlations (r = 0.51) in another (Cramer and

Wehner 2000b), suggesting sex expression has potential for increasing yield through

indirect selection. In the other two populations, however, slight negative correlations (r =

-0.21 and -0.14) between the two traits were identified. In populations derived from the

same inbred parents, Serquen et al. (1997b) found a relatively low, negative phenotypic

correlation (r = –0.27, p = 0.05) between sex expression and the number of fruits per

plant, while Fazio (2001) found a positive correlation (r = 0.24, p = 0.01) with the

number of female nodes on lateral branches and total fruit per plant. Examining similar

germplasm, Fan et al. (2006) identified a positive correlation (r = 0.40, p = 0.01) between

12gynoecy and fruit number. These data suggest that the association between yield and

sex expression varies between populations and growing environments.

The most noticeable effect of sex expression on yield is not in total yield over

multiple harvests, but on early yield. Gynoecious × gynoecious and gynoecious ×

monoecious hybrids produced significantly higher yields in the first harvest than

monoecious × monoecious hybrids, but there was no significant difference among all

hybrids for total yield over multiple harvests (Wehner and Miller 1985). Thus, early-

flowering, gynoecious hybrids with concentrated fruit set, were instrumental in

establishing mechanical harvesting of processing cucumber (Lower and Edwards 1986;

Wehner 1989; Staub and Bacher 1997). Presently, almost all once-over mechanical

harvest operations exclusively use gynoecious hybrids (Staub and Bacher 1997).

The genetics of sex expression in cucumber has been widely studied and reviewed

by several authors (Lower and Edwards 1986; Tatlioglu 1993; Staub and Bacher 1997;

Mibus and Tatlioglu 2004), since cucumber is considered a model organism for sex

expression in plants. Although all flower buds contain both male and female primordia,

mature cucumber flowers may be perfect, staminate, or pistillate. The combination of

these three flower types gives rise to a range of phenotypes including monoecious

(staminate and pistillate flowers), predominantly female (mostly pistillate with some

staminate flowers), gynoecious (pistillate flowers only), andreoecious (staminate flowers

only), andromonoecious (staminate and perfect flowers) or hermaphroditic (perfect

flowers only). The genetic control of sex expression can generally be explained by a

13three-gene model involving the F (Female), m (andromonoecious), and a

(androecious) genes (Table 1).

Table 1. Sex phenotypes from combinations of the three major genes of sex expression in cucumber (Cucumis sativus L.). Adapted from Mibus and Tatlioglu (2004).

FF Ff ff ff

A_ or aa A_ or aa A_ aa

M_ Gynoecious Predominantly female Monoecious Androecious

mm Hermaphroditic Hermaphroditic Andromonoecious Androecious

The F locus promotes femaleness, but is incompletely dominant (FF > Ff > ff)

and strongly influenced by the environment. The m locus controls the development of

perfect (mm) or unisexual (M_) flowers. Plants with the F allele are gynoecious (FF) or

predominantly female (Ff) in a M_ background, but hermaphroditic as mm. The a locus

is hypostatic to the F locus, in that the effects of a are only seen in the ff genotype. With

ff, the aa genotype promotes maleness, leading to androecy.

The three major genes are not the only genetic factors involved in sex expression.

Other genes reported to be involved in sex expression include gy (gynoecious), In-F

(Intensifier of female sex expression), m-2 (andromonoecious-2), and Tr (Trimonoecious;

Xie and Wehner 2001). In addition to these secondary genes, genetic background affects

the expression of femaleness through the F locus (Lower and Edwards 1986; Tatlioglu

1993; Staub and Bacher 1997; Xie and Wehner 2001). Gynoecious sex expression, for

example, is more stable in plants with the determinate habit (i.e., homozygous for the de

gene; Lower and Edwards 1986; Staub and Bacher 1997). Gynoecious inbred lines with

14the same genotype at the major sex expression genes vary in their level of gynoecy, as

well as that of their F1 progeny, suggesting the presence of genes that modify the F locus.

The facts that different sources of the same commercial hybrid vary in their sex

expression (Lower and Edwards 1986) and selection for the number of nodes to the first

pistillate flower increases femaleness in monoecious germplasm (Staub and Bacher 1997)

support the hypothesis that modifying genes affect sex expression.

Genetic analysis and QTL mapping studies also indicate several loci are involved

in sex expression. In a population fixed for the M and A genes (i.e., segregating only at

the F locus), Serquen et al. (1997b) estimated five effective factors involved in sex

expression in each of two locations. Most of the variance was attributed to dominance

variance, with an approximate 1:3 ratio of additive to dominance variance. The narrow-

sense heritability (h2) was relatively low (between 0.14 and 0.16), suggesting selection

for sex expression may be difficult. In the same population, Serquen et al. (1997a)

identified four QTL for sex expression common across two environments, and a fifth

QTL unique to one environment. These QTL accounted for over 85% of the observed

variation in each environment with 67% and 74% of the variation attributed to a QTL

near the F locus. In a similar QTL study of a population derived from the same parents,

three QTL were detected for the number of female nodes on the mainstem, accounting for

31% of the variation, 16% of which was attributed to a QTL at the F locus (Fazio et al.

2003b). Two of these QTL, including the one near the F locus, plus two others showed

significant effects on the production of female nodes on primary lateral branches. The F

locus QTL accounted for only 5% of the 20% total variation explained by all four QTL.

15Although a large portion of the genetics of sex expression is controlled by the F locus,

it is clear there are other regions of the genome involved.

The determination of floral development is under genetic control, but the

environment plays a significant role in sex expression. Factors such as photoperiod,

temperature, and stress affect the proportion of female flowers produced (Lower and

Edwards 1986; Tatlioglu 1993; Staub and Bacher 1997). Generally, long days and high

temperatures promote male flowers and short days and low temperatures promote female

flowers. Because stress conditions tend to increase male flowers, stress is sometimes

used to test for stability of the gynoecious character in breeding lines (Lower and

Edwards 1986). When compared over multiple environments, the sex expression of

several commercial varieties varied widely between locations with relatively little

variation at the same location over different years. Thus, growing environment can have

a large effect on cucumber sex expression. The degree of variation in sex expression,

however, depends upon the genetic background (number and kind of modifying loci) as

some lines genetically identical at the major sex expression genes are less variable than

others.

An important tool for breeding cucumber is the ability to chemically manipulate

sex expression using plant growth regulators. Although the plant hormones ethylene and

auxin induce female flowers, and gibberellins promote male flowers, other chemical

compounds are generally more effective for sex conversion (Lower and Edwards 1986;

Tatlioglu 1993; Staub and Bacher 1997; Trebitsh et al. 1997). Ethylene releasing

compounds, such as alpha-naphthalene acetic acid and ethephon (2-

16chloroethylphosphonic acid), are routinely used for female flower induction while

ethylene inhibitors, such as silver nitrate, silver thiosulfate, carbon dioxide, and

aminoethoxyvinylglycine, are used to stimulate the production of male flowers. The

preferred chemicals for sex alteration in cucumber are ethophon for female flower

induction and silver nitrate for male flower development (Lower and Edwards 1986;

Tatlioglu 1993; Staub and Bacher 1997).

Earliness Earliness and stable gynoecious sex expression are important components of yield

in pickling cucumber, especially in once-over machine harvest operations. The

introduction of early, gynoecious lines enabled the production of a uniform, concentrated

fruit set necessary for effective machine harvest systems (Lower and Edwards 1986;

Wehner 1989). Earliness is often measured as days to anthesis or days to first harvest.

Serquen et al. (1997b) found that days to anthesis was negatively correlated (r = –0.23, p

= 0.05) with the number of fruit per plant (fewer days to harvest correlates to more fruit

per plant). Fazio (2001) found a comparable result in a similar population in one year

(2000; r = –0.31, p = 0.001), but these two characteristics were not significantly

correlated in another (1999). Additionally, a significant, positive correlation (r = 0.26, p

= 0.01) was identified between days to first harvest and number of fruit per plant (fewer

days to first harvest is correlated with fewer fruit per plant) in a single year (1999).

Significant correlations were not identified between days to first harvest and days to

anthesis.

17A QTL analysis for days to anthesis revealed a single QTL explaining 13% of

the variation common in two environments, and a second QTL of smaller magnitude (R2

= 8.1) in a second environment (Serquen et al. 1997a). Fazio et al. (2003b) identified

four QTL for days to anthesis, two of which were common in two environments tested.

These two QTL accounted for 12% to 15% of the variation observed, with environment

specific QTL explaining an additional 15% and 4% of the variation. Fazio et al. (2003b)

also identified four QTL in a single environment for days to first harvest. These QTL

accounted for 21% of the variation observed, one of which mapped to the same genomic

region as a QTL for days to anthesis. These data suggest that few (1-2) QTL consistently

(i.e., over locations) affect earliness, but the QTL detected in specific environments and

the low total R2 indicates there are other genomic locations and/or environmental factors

involved in earliness.

Fruit length to diameter ratio Fruit length to diameter ratio (L:D) is considered a yield component because, as a

fruit grading measurement, it determines marketable yield of pickling cucumber. In the

United States, processing cucumbers are graded based on their size, with the smaller fruit

usually bringing a higher market price (Lower and Edwards 1986; Tatlioglu 1993).

Pickling cucumbers must have an L:D between 2.9 and 3.3 to be commercially

acceptable (Serquen et al. 1997b). Fruit with a low L:D are removed during grading and

sorting, and are used for relish (Staub and Bacher 1997). Although important for

marketable yield, comparatively larger L:D (> 3.2) is generally associated with lower

fruit number per plant. In one genetic analysis of yield components, the phenotypic

18correlation between number of fruit per plant and L:D was not significant, but they

were negatively correlated genotypically (r = –0.98; Serquen et al. 1997b). In a separate

study, L:D was negatively correlated with total fruit per plant (r = –0.27, p = 0.01 and r =

–0.36, p = 0.001) in the two years examined (Fazio 2001).

As with earliness, QTL analysis suggests that fruit L:D is conditioned by a few

stable QTL with effects from additional genomic locations and the environment. Serquen

et al. (1997a), identified QTL for fruit length, fruit diameter, and L:D, and one QTL was

identified for fruit length in the two environments tested (R2 = 21% and 31%). Three

QTL were identified for fruit diameter, one in both environments (R2 = 15.7% and 9.6%)

and one unique to each environment (R2 = 21.9% and 9.6%). Two QTL were identified

for L:D, but only in one of the environments examined (R2 = 13.7% and 14.4%), and both

mapped to the same genomic regions as QTL for fruit diameter. Although a total of 12

QTL for L:D were declared significant by Fazio et al. (2003b), only five were identified

in both locations examined with a combined R2 of 31% and 30%. The total R2 from all

QTL was 36% and 57% in the two environments evaluated.

Yield components considerations MLB, sex expression, earliness, and L:D have been shown to be correlated with

fruit per plant in certain populations and environments, but correlations have also been

identified between these four characteristics. No consistent correlations between MLB

and female flowers were identified in eight (four slicing and four pickling) different

populations (Cramer and Wehner 1998a; Cramer and Wehner 2000b), or at two different

plant spacings in germplasm derived from C. s. var. hardwickii (Fredrick and Staub 1989).

19However, consistent and significant negative correlations (r = -0.27 to -0.54) between

MLB and female flowers were identified in Gy-7 × H19 populations (Serquen et al.

1997b; Fazio 2001; Fan et al. 2006). While significant correlations were both positive

and negative between MLB and early yield in eight diverse populations (Cramer and

Wehner 1998a; Cramer and Wehner 2000b), negative correlations were not significant in

germplasm derived from C. s. var. hardwickii (Fredrick and Staub 1989). Likewise,

Serquen et al. (1997b) found a negative correlation that was not significant, while the

negative correlations detected by Fazio (2001) were significant over two years. Only one

(at low plant density) of the two negative correlations between MLB and L:D was

significant in germplasm derived from C. s. var. hardwickii (Fredrick and Staub 1989),

while Fazio (2001) similarly detected a negative correlation during one of the two years

evaluated. Fan et al. (2006), however, detected significant positive correlations between

MLB and L:D. The percent female nodes and early yield were significantly and

positively correlated in several of the eight populations tested (Cramer and Wehner

1998a; Cramer and Wehner 2000b). Sex expression was found to be negatively

correlated with days to flower (more females promotes fewer days to flower) and

earliness by Serquen et al. (1997b) and Fazio (2001), respectively. Fazio (2001) also

found a positive correlation between days to flower and the number of female nodes on

the mainstem. Femaleness is consistently negatively correlated with L:D, while L:D and

earliness are consistently uncorrelated (Serquen et al. 1997b; Fazio 2001; Fan et al. 2006).

Traits other than MLB, sex expression, earliness, and L:D are associated with

yield, but the large investment in research on these four characteristics leverages their

20application for the improvement of cucumber yield. These four traits are

quantitatively inherited, and a few (2-6), consistent (i.e., over environments) QTL with

relatively large effects have been associated with each trait (Serquen et al. 1997a; Fazio

et al. 2003b). The heritabilities of these traits are low to moderate, as Serquen et al.

(1997b) found that narrow-sense heritabilities (h2) of MLB and sex expression are 0.48

and 0.14 respectively, and broad sense-heritabilities (H2) of days to anthesis and L:D are

0.13 and 0.11, respectively. In addition to genetic factors involved in the expression of

each of these four yield components, they are also affected by growing environment.

Under high plant density (> 20,000 plants/ha), for instance, the number of branches and

the percentage of female flowers decrease, and fruit tends to be shorter (i.e., lower L:D;

Lower and Edwards 1986; Fredrick and Staub 1989; Wehner 1989). The most effective

method for improvement of these characters is through recurrent selection, which is well

suited to quantitative traits with low heritability, (Wehner 1989; Cramer and Wehner

1998a). Recurrent selection has provided improvement when selecting for yield directly

in some cases (Nienhuis and Lower 1988; Wehner and Cramer 1996a; Wehner and

Cramer 1996b), but not in others (reviewed by Wehner 1989). In addition to phenotypic

recurrent selection, recently developed laboratory tools may be of benefit for cucumber

yield improvement. In this regard, the QTL mapping studies of Serquen et al. (1997a)

and Fazio et al. (2003b) have provided a framework to test the efficacy of molecular

methods for indirect yield improvement through marker-assisted selection of yield

components.

21Molecular Markers

Molecular markers have several uses in plant breeding programs, including

genetic diversity assessment, cultivar identity, genetic similarity estimation,

fingerprinting, genetic map construction, gene tagging, and marker-assisted selection

(MAS; Collard et al. 2005). Several reviews have been published on markers regarding

their use in gene (or QTL) mapping and gene tagging, and their deployment in MAS

(Tanksley 1993; Staub et al. 1996; Jones et al. 1997; Gupta et al. 1999; Collard et al.

2005; Francia et al. 2005). Therefore, these subjects will not be reviewed here, but a

summary of important points relevant to the research presented herein will be provided.

The ideal markers for use in plant breeding programs are codominant (able to

distinguish heterozygotes), easy to develop and use, robust (repeatable and tolerable to

slight changes in detection), abundant, amenable to high-throughput systems, and low

cost (Staub et al. 1996; Jones et al. 1997; Gupta et al. 1999; Collard et al. 2005; Francia et

al. 2005). Although there are several types of markers, each has advantages and

disadvantages for their deployment in plant breeding.

Molecular marker types The three main types of genetic markers are morphological, biochemical (protein),

and DNA-based (i.e., molecular markers; Staub et al. 1996; Collard et al. 2005).

Although morphological (visualized as a phenotype, such as flower color) and

biochemical markers (allelic variants of functional enzymes, also referred to as isozymes)

were historically valuable, their paucity and variability due to environmental conditions

and developmental stages limit their effectiveness in plant genetics and breeding. The

22large majority of currently utilized markers are DNA-based because they are relatively

abundant, not influenced by the environment, and do not effect phenotype (Staub et al.

1996; Gupta et al. 1999; Collard et al. 2005).

The first widely used DNA-based markers were restriction fragment length

polymorphisms (RFLP; Botstein et al. 1980; Tanksley 1993). Although RFLPs are

codominant, fairly robust, and more prevalent than isozymes, they are costly, time-

consuming, laborious (not high-throughput), and not as abundant as other marker systems.

They also require large amounts of DNA, as well as the use of radiolabeled isotopes, and

cloning is a necessary part of marker development.

To overcome the time and labor requirements of RFLP markers, random

amplified polymorphic DNA (RAPD) markers were developed (Williams et al. 1990).

As their name implies, RAPDs are much quicker and easier to develop and utilize than

RFLPs, and they are comparatively more abundant, much less expensive, require less

DNA, and, in many cases, provide multiple markers per assay. RAPDs, however, are

typically dominant, not robust, and often methodologically problematic (Staub et al.

1996; Paran and Michelmore 1993).

Sequence characterized amplified region (SCAR) markers were initially designed

by Paran and Michelmore (1993) to convert a polymorphic RAPD marker into a robust,

single-copy marker. SCAR markers are produced by sequencing the RAPD band and

using the sequence at both ends of the fragment to extend the 10 bp RAPD primer an

additional 14 base pairs to produce a specific pair of primers. Since a SCAR marker is

defined as a fragment from genomic DNA generated from specific primers through PCR

23(Paran and Michelmore 1993), SCARs can also be derived from markers other than

RAPDs (e.g., RFLPs). The only requirement is that cloning and sequencing are needed

to design primers to specifically amplify a single product. Once developed, however,

SCARs are much more robust and repeatable than RAPDs, and are as easy and

inexpensive to use (Polashock and Vorsa 2002; Randig et al. 2002). Although SCAR

markers are usually dominant, codominant SCARs are not uncommon (Staub et al. 1996).

Because most SCAR markers produce a single band, they are amenable to multiplexing

(including two or more markers simultaneously in the same PCR reaction), which further

increases their efficiency during genotyping (Polashock and Vorsa 2002; Randig et al.

2002), and makes them amenable to high-throughput systems.

Amplified fragment length polymorphism (AFLP) markers are dominant, more

robust than RAPDs, and can provide several markers per assay (Vos et al. 1995).

Although the AFLP methodology is more technologically complicated than RAPDs, no

cloning or prior sequence knowledge is required. Initially, AFLPs required

polyacrylamide gel electrophoresis and labeling with radiolabeled isotopes, but they have

been adapted for automated sequencing platforms with fluorescent labeling (fAFLP;

Desai et al. 1998). AFLP markers are more expensive than RAPDs, and, except for

RFLPs, require only slightly more DNA than other marker systems for utilization.

There are several types of markers that require sequence information for

development in addition to SCARs. Simple sequence repeat (SSR or microsatellite)

markers take advantage of the fact that small (usually di-, tri-, tetra-, or penta-nucleotide),

tandemly repeated sequences tend to vary in length among haplotypes in a population

24(Gupta et al. 1999). These repeats are relatively abundant and highly polymorphic in

plants (Staub et al. 1996). SSRs are usually developed by creating a library enriched with

genomic fragments containing repeats, sequencing the fragments, then designing primers

flanking the repeats, which is expensive and time consuming. SSRs are codominant by

nature, and can have multiple alleles per locus because the tandem repeats vary in length

in genetically diverse populations. Once developed, SSRs are robust, but small

differences in molecular weight among band morphotypes often necessitate their

visualization by polyacrylamide gel electrophoresis. Like fAFLPs, SSRs can be

visualized in automated sequencing platforms, but unlike AFLPs or RAPDs, they can be

multiplexed in high-throughput systems.

Sequenced tag site (STS) markers were originally proposed as a standard for

simple, PCR-based markers created from RFLP probes in humans (Olson et al. 1989).

An STS is a short, single-copy marker that is associated with a specific locus and can be

amplified by PCR. Although STS and SCAR have been used synonymously in the

literature at times, STS is conventionally reserved for PCR markers made from RFLPs

(Gupta et al. 1999). STS markers are robust, relatively inexpensive, easy to use, and

amenable to high-throughput systems through multiplexing. STSs are usually dominant,

but can be codominant depending on their design and use.

Another marker type based on previously identified markers is cleaved amplified

polymorphic sequences or CAPS (Konieczny and Ausubel 1993). These markers are

based on sequence polymorphisms at restriction enzyme sites. To detect a CAPS marker,

a PCR amplified product is digested by a restriction enzyme to create a codominant

25length polymorphism that can be resolved by agarose gel electrophoresis. Sequence

information is necessary for the development of CAPS markers, and not all PCR products

contain restriction enzyme sites, which limits their development. The restriction enzyme

digestion of PCR fragments adds an extra step and expense when genotyping with CAPS

markers. In addition, the incomplete digestion of PCR products can reduce the reliability

of CAPS markers and complicate their scoring in some cases (Zheng et al. 1999; Burger

et al. 2003).

Markers based on single nucleotide polymorphisms (SNP) are gaining popularity

and are the current marker of choice for several species including crop plants (Gupta et al.

2001). This popularity is based on the idea that as more genomic resources are being

made available, SNPs are best able to fit the ideal marker for use in plant breeding. SNPs

are usually codominant and robust markers. The number of SNPs in any given genome is

much higher than any other marker type (estimated at 1 in 100 to 1 in 1000 base pairs),

including an order of magnitude higher than SSRs (Gupta et al. 2001). The rise in SNP

popularity has lead to several different methods of discovery and genotyping (Gupta et al.

2001). Some of these methods, such as pyrosequencing for SNP detection, are focused

on high-throughput systems. These and other non-gel based assays such as TaqMan,

Molecular Beacons, and array-based assays, are usually supported by proprietary

technologies which may be cost prohibitive to many plant breeding programs. SNP

genotyping, however, can be adapted to low cost methods using basic laboratory

equipment such as PCR followed by agarose gel electrophoresis in allele-specific PCR

(AS-PCR) or single-nucleotide amplified polymorphism (SNAP) assays (Drenkard et al.

262000; Moreno-Vazquez et al. 2003). The major disadvantages to the development of

SNPs markers are that sequence information is necessary for their design, and SNPs are

bi-allelic, unlike SSRs, which usually have multiple alleles per locus. The abundance of

SNPs, however, compensates for the limited number of alleles, making their development

cost-effective.

The selection of marker types for use in plant breeding depends on several factors

including project objectives, population and mating structure, genomic complexity, the

intended use of the markers, and the resources available (Staub et al. 1996; Gupta et al.

1999). For example, RAPD and AFLP are useful technologies for new marker

identification and molecular map construction because multiple markers can be identified

in each sample and no a priori sequence knowledge is needed (Paran and Michelmore

1993; Brugmans et al. 2003). Once established, however, SCAR, SNP, STS, and SSR

markers are much more useful in genotyping populations because of their robustness and

potential ability to be mutliplexed. The continued increase in sequence availability and

EST databases, allows for the creation of SNP, SSR, CAPS, and SCAR type markers

without having to generate sequence date. Furthermore, markers created from EST

databases are based upon transcribed loci, and may, therefore, be more suited to gene

tagging.

Marker conversion Although AFLPs and RAPDs are well suited for relatively rapid identification of

new markers, the low reproducibility of RAPDs and the technical methodology of AFLPs

has prompted the conversion of these markers into other, more robust and simple marker

27types better suited for tracking alleles in MAS (Brugmans et al. 2003; Collard et al.

2005). For instance, Shirasawa et al. (2004) converted 46 AFLP markers to 8 dominant

SCAR markers, six codominant SCAR markers, 13 CAPS markers, and 13 PCR-

restriction fragment single strand conformation polymorphism (PCR-RF-SSCP or PRS)

markers. Brugmans et al. (2003) report a systematic approach to AFLP marker

conversion based on novel polymorphisms within the AFLP band or the polymorphism

that produced the original polymorphic AFLP fragment. Through this approach all ten of

the randomly selected AFLP markers were converted into a single-copy, robust marker.

The term SCAR was originally applied to single-copy, reliable markers converted

from RAPDs (Paran and Michelmore 1993). In this initial study, nine RAPDs were

converted into five dominant, three codominant, and one monomorphic SCAR, and eight

out of the nine SCAR primer pairs produced a single band. Because the majority of

RAPD polymorphisms are in the priming site (Williams et al. 1990), SCAR primer pairs

included the original RAPD primer sequence plus an additional 14 base pairs in order to

transfer the RAPD polymorphism to the SCAR. Only three of the dominant markers,

however, retained the original polymorphism. The extension of the RAPD primers to

create a SCAR primer pair allowed amplification of contrasting genotypes from the other

six SCAR markers. One of these was monomorphic, but three length polymorphisms

resulted in the three codominant markers. A fifth SCAR marker that amplified both

genotypes at the molecular weight of the RAPD, also amplified a second, dominant band

that cosegregated with the original RAPD. The sixth marker amplified both genotypes at

60o C, but raising the PCR annealing temperature to 67o C recovered a dominant

28polymorphism. One of the codominant markers was detectable by agarose

electrophoresis only after digestion with a restriction enzyme. Another codominant

marker was not polymorphic until tested on genotypes other than those that produced the

original RAPD polymorphism. Thus, the conversion from RAPD to SCAR was highly

successful (89%), but required marker optimization.

The conversion rate was not as high in a RAPD to SCAR conversion study in

cucumber (Horejsi et al. 1999). Of the 75 RAPDs attempted, 48 (64%) were successfully

sequenced by silver staining of polyacrylamide gels, from which 48 primer (18 to 22 bp)

pairs (96 primers) were designed. Only 11 of the 48 (15%) primer pairs resulted in a

polymorphism and 20 (42%) produced more than one band per DNA template. This low

RAPD to SCAR conversion rate may be partially explained by the observation that the

majority of the SCAR primers did not contain the original RAPD primer. Three of the 96

SCAR primers contained all 10 bp of the original RAPD primer, while 13 contained at

least one bp, and 80 did not contain any part of the RAPD primer. Of the 48 SCAR

markers from Horejsi et al. (1999), 20 (42%) produced more than one band per DNA

template, a percentage which is greater than the 11% obtained by Paran and Michelmore

(1993). This difference may be the result of reduced primer specificity in the cucumber

SCARs because of the comparatively shorter primers (18 bp vs. 24 bp), which allowed

additional bands to be amplified in each template. Similar to Paran and Michelmore

(1993), an increase in PCR annealing temperature by Horejsi et al. (1999) identified an

additional dominant SCAR marker, illustrating the need to empirically determine optimal

annealing temperatures of the SCAR primers in order to recover polymorphisms.

29The results of both Paran and Michelmore (1993) and Horejsi et al. (1999)

illustrate that not all SCAR markers are polymorphic, even after marker optimization. In

such cases, both alleles of the SCAR marker may be sequenced to identify additional

polymorphisms that might be exploited to genotype the locus by PCR. Sequencing the

monomorphic SCAR, for example, in the original SCAR to RAPD conversion study

revealed two SNPs (Paran and Michelmore 1993). Such polymorphisms can be utilized

to create SNP markers through a variety of methods. Three such methods are based on

CAPS (Konieczny and Ausubel 1993), AS-PCR (Newton et al. 1989; Sarkar et al. 1990)

and SNAP assays (Drenkard et al. 2000). SNP markers that utilize the CAPS assay are

codominant, based on a SNP in a restriction site, and are detected by PCR, a restriction

enzyme digest, and then agarose gel electrophoresis. The AS-PCR and SNAP assays rely

on primers designed to specifically amplify one allele at a SNP and can be codominant if

a marker is created for each allele. The advantage of the AS-PCR or SNAP assay is that

they do not require a restriction enzyme step, as PCR is followed directly by agarose gel

electrophoresis.

Genetic mapping in cucumber The utility of molecular markers is greatly enhanced when they are placed on

genetic linkage maps, which allows the identification of simply inherited genes and

genomic regions involved in agronomically important traits through QTL mapping

(Collard et al. 2005). The first genetic linkage maps in cucumber were reported almost

20 years ago and were based solely on phenotypic markers (Fanourakis and Simon 1987;

Vakalounakis 1992; Pierce and Wehner 2000). The first biochemical markers mapped in

30cucumber were isozymes (Knerr and Staub 1992), and were later combined with

phenotypic markers to produce maps of low saturation (Meglic and Staub 1996b). As

DNA-based molecular markers were developed (RFLP and RAPD), they were combined

with existing marker types in linkage maps (Kennard et al. 1994). More recently

developed maps include phenotypic and several types of DNA based markers (RAPD,

RFLP, AFLP, SCAR, SSR, and SNP; Serquen et al. 1997a; Park et al. 2000; Fazio et al.

2003b). As genetic maps continued to be refined and molecular markers were included,

the total map distance generally expanded to match the estimated range of 750 to 1000

cM (Staub and Meglic 1993). Genetic distances in such maps were reported as 166

(Fanourakis and Simon 1987), 95 (Vakalounakis 1992), 168 (Knerr and Staub 1992), 766

(narrow-based), 480 (wide-based; Kennard et al. 1994), 584 (Meglic and Staub 1996b),

600 (Serquen et al. 1997a), 816 (Park et al. 2000), and 706 cM (Fazio et al. 2003b).

The incorporation of molecular markers has also increased the saturation of

cucumber genetic linkage maps. Park et al. (2000) employed 347 RAPD, RFLP, AFLP,

and loci conditioning virus resistances to construct a map with 12 linkage groups (LOD ≤

3.5) and a mean marker interval of 4.2 cM. A map constructed by Serquen et al. (1997a)

defined nine linkage groups and spanned ca. 600 cM with an average distance between

RAPD markers of 8.4 cM. Information from this map was recently merged with other

maps (Fanourakis and Simon 1987; Knerr and Staub 1992; Kennard et al. 1994; Meglic

and Staub 1996b; Horejsi et al. 2000) to synthesize a consensus map containing 255

markers, including morphological traits, disease resistance loci, isozymes, RFLPs,

RAPDs, and AFLPs spanning 10 linkage groups (Bradeen et al. 2001). The mean marker

31interval in this consensus map was 2.1 cM spanning a total length of 538 cM. More

recently, Fazio et al. (2003b) constructed a map containing 14 SSR, 24 SCAR, 27 AFLP,

62 RAPD, one SNP, and three morphological markers (131 total markers) spanning seven

linkage groups (the theoretical number based on the haploid chromosome number) using

RIL. This map spanned 706 cM with a mean marker interval of 5.6 cM.

The development of genetic linkage maps have provided tools for the molecular

analysis of important characteristics in cucumber including fruit quality (Wenzel et al.

1995), disease resistance, (Park et al. 2000) and yield components (Serquen et al. 1997a;

Fazio et al. 2003b). The marker-QTL associations identified in these studies form the

foundation for crop improvement through marker-assisted selection.

Marker-assisted Selection

One of the primary purposes of creating genetic linkage maps coupled with QTL

analysis, is to utilize marker-QTL associations in MAS. Several simulation studies

suggest that the effectiveness of MAS for polygenic traits can be greater than traditional

breeding (Lande and Thompson 1990; Zhang and Smith 1992; Edwards and Page 1994;

Gimelfarb and Lande 1994a; Gimelfarb and Lande 1994b). In general, these studies

agree that MAS efficiency is enhanced when markers are tightly linked (< 5.0 cM) to

quantitative trait loci (QTL) and selection is performed in early generations prior to

recombination between markers and QTL, on large sample sizes, and on traits with low

heritability. In practice, MAS has been effective for the introgression of simple traits or a

small number of genes in several crop species [e.g., disease resistance in common bean

32(Phaseolus vulgaris L.; de Oliveira et al. 2005), grain protein concentration in durum

wheat (Triticum turgidum L. var. durum; Chee et al. 2001), and root depth in rice (Oryza

sativa L.; Shen et al. 2001)], but less effective for complex traits [e.g., yield in barley

(Hordeum vulgare L.; Kandemir et al. 2000), and soybean (Glycine max (L.) Merrill;

Reyna and Sneller 2001)]. Studies reporting the empirical comparison of MAS to

phenotypic selection (PHE), however, are scarce and often conflicting (Yousef and Juvik

2001; Willcox et al. 2002; Hoeck et al. 2003).

MAS has been found to be more (Yousef and Juvik 2001; Fazio et al. 2003a;

Zhang et al. 2006), equivalent (Stromberg et al. 1994; Romagosa et al. 1999; Van Berloo

and Stam 1999; Willcox et al. 2002; Moreau et al. 2004), or less (Hoeck et al. 2003)

efficient and/or effective for increasing gain from selection when compared to PHE in

various plant species. Additional comparisons of MAS and PHE have provided mixed

results within the same study (Schneider et al. 1997; Flint-Garcia et al. 2003).

Cucumber possesses several characteristics that are favorable for MAS, including

a small genome size (genetic map length of 750 to 1,000 cM, 882 Megabases; Staub and

Meglic 1993) low chromosome number (n = 7), and rapid life cycle (four cycles per year).

In addition, moderately saturated genetic linkage maps have been developed, and QTL

analyses have identified several genomic locations associated with important traits in

cucumber. Two experiments have been reported using the marker-QTL associations

identified by Serquen et al. (1997a) and Fazio et al. (2003b) for MAS of yield

components.

33Fazio et al. (2003a) compared the response of the number of lateral branches

(MLB) to phenotypic selection under open-field conditions (PHE), random intermating

without selection (RAN), and MAS employing five markers (two SSRs, two RAPDs and

one SNP) in two backcross generations. No significant differences (p < 0.001) were

detected in either backcross generation between the mean values of MLB from PHE and

MAS, which were both significantly higher than RAN (control). Since two cycles of

MAS required one year compared to three for PHE, MAS increased overall breeding

efficiency.

The effect of MAS for four yield components (MLB, gynoecy, fruit L:D, and

earliness) was evaluated in two backcross populations (line extraction) after two cycles of

phenotypic recurrent selection (population improvement). Even after PHE provided

gains in MLB and L:D, MAS continued to improve both these traits in one backcross

population and L:D in the other. MAS also provided an increase in gynoecy in both

populations (Fan et al. 2006). Thus, MAS operated to fix favorable alleles that were not

exploited by phenotypic selection.

The results of these two studies demonstrate the utility of MAS for several

quantitative traits in cucumber. Furthermore, a response to selection from MAS for MLB,

L:D, and gynoecy confirms the marker-QTL associations for these traits. Relatively little

is known, however, about the individual QTL involved these traits, such as their

architecture (i.e., single genes or gene families), specific functions, interactions with the

environment, or epistatic effects (i.e., interactions among QTL of the same trait or with

QTL for other traits).

34Epistasis

Knowledge of epistasis is critical to comprehensive genetic analysis (Kinghorn

1987; Yano 2001) and breeding (Schnell and Cockerham 1992). Although epistasis is

considered in classical quantitative genetic theory (Falconer and Mackay 1996; Bernardo

2002), it is generally ignored in QTL mapping studies (Carlborg and Haley 2004). The

term ‘epistasis’ was originally applied to simply inherited traits where the actions of one

locus mask the effects of another locus (Carlborg and Haley 2004). Epistasis can be

more broadly defined, however, as a difference in phenotype from the same genotype

when in different genetic backgrounds. Epistatic interactions can occur among QTL

affecting the same trait, or between loci involved in several traits (Carlborg and Haley

2004).

Epistatic interactions have historically been detected by classical quantitative

genetic methods at the whole genome level, but the use of molecular markers and QTL

mapping studies has provided the ability to study epistasis between individual loci

(Tanksley 1993). The small population size, and interference with other QTL make the

detection of epistasis difficult in the primary populations [F2, F2-derived F3 (F2:3), or RIL]

utilized for QTL analyses. The analysis of near-isogenic lines (NIL) that differ in

specific QTL, however, provides a more powerful examination of epistasis, including

interactions between specific QTL (Lin et al. 2000). Molecular markers linked to QTL

for heading data in rice (Oryza sativa L.) were employed during backcrossing to create

NIL that contained one, two, or all three QTL under investigation (Lin et al. 2000). An

analysis of these NIL under different daylength conditions revealed epistatic interactions

35between specific QTL. One of the QTL did not affect photoperiod sensitivity alone,

but enhanced the expression of another QTL for heading date. This approach was

utilized to create other NIL with different QTL involved in heading date, from which,

additional epistatic interactions were identified (Yamamoto et al. 2000).

The development of markers (Horejsi et al. 1999; Fazio et al. 2002) and the

subsequent construction of genetic linkage maps and QTL analyses of Serquen et al.

(1997a) and Fazio et al. (2003b) have provided tools for the molecular improvement of

yield components in cucumber. Improvements have been made for several yield

components during inbred line development by MAS in backcross breeding (Fazio et al.

2003a; Fan et al. 2006). However, there is a need for additional, efficient markers, a

more complete understanding of gene interactions, and comparative analyses of DNA-

based multi-trait selection methods. Therefore, the effectiveness of MAS was evaluated

for three cycles of population improvement by recurrent selection for MLB, earliness,

L:D, and gynoecy (Chapter 1). Four inbred lines were intermated to create four base

populations that each underwent MAS, phenotypic selection and random mating under

the same selection scheme to determine whether responses to selection from markers and

phenotype for several quantitative traits were equally effective (greater than random

mating). To increase the efficiency and effectiveness of future applications of MAS in

cucumber (Chapter 2), a number of RAPD markers from the map of Fazio et al. (2003b)

were cloned and sequenced to create polymorphic SCAR markers, and the Gy-7 and H-

19 alleles of several monomorphic and dominant SCARs were sequenced. Sequences

were also obtained from BAC clones that hybridized to markers linked to important yield

36component QTL, and SNPs between Gy-7 and H-19 sequences from both sources

were identified and utilized to create codominant markers. Lastly, several NIL with

various numbers of QTL involved in MLB were developed and evaluated to characterize

the role of epistasis among individual QTL and genetic background on the expression of

MLB (Chapter 3).

Literature Cited

Baird JR, Thieret JW (1988) The bur gherkin (Cucumis anguria var. Anguria, Cucurbitaceae). Econ Bot 42:447-451

Bernardo R (2002) Breeding for Quantitative Traits in Plants. 1st edn. Woodbury, Stemma Press

Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32:314-331

Bradeen JM, Staub JE, Wye C, Antonise R, Peleman J (2001) Towards an expanded and integrated linkage map of cucumber (Cucumis sativus L.). Genome 44:111-119

Brugmans B, van der Hulst RGM, Visser RGF, Lindhout P, van Eck HJ (2003) A new and versatile method for the successful conversion of AFLP markers into simple single locus markers. Nucleic Acids Res 31:e55

Burger Y, Katzir N, Tzuri G, Portnoy V, Saar U, Shriber S, Perl-Treves R, Cohen R (2003) Variation in the response of melon genotypes to Fusarium oxysporum f.sp melonis race 1 determined by inoculation tests and molecular markers. Plant Pathol 52:204-211

Carlborg O, Haley CS (2004) Epistasis: too often neglected in complex trait studies? Nat Rev Genet 5:618-U4

Chee PW, Elias EM, Anderson JA, Kianian SF (2001) Evaluation of a high grain protein QTL from Triticum turgidum L. var. dicoccoides in an adapted durum wheat background. Crop Sci 41:295-301

Chen JF, Staub J, Adelberg J, Lewis S, Kunkle B (2002) Synthesis and preliminary characterization of a new species (amphidiploid) in Cucumis. Euphytica 123:315-322

37Chen JF, Kirkbride JH (2000) A new synthetic species of Cucumis (Cucurbitaceae) from interspecific hybridization and chromosome doubling. Brittonia 52:315-319

Chen JF, Staub JE, Tashiro Y, Isshiki S, Miyazaki S (1997) Successful interspecific hybridization between Cucumis sativus L. and C. hystrix Chakr. Euphytica 96:413-419

Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica 142:169-196

Cramer CS, Wehner TC (2000a) Path analysis of the correlation between fruit number and plant traits of cucumber populations. HortScience 35:708-711

Cramer CS, Wehner TC (2000b) Fruit yield and yield component correlations of four pickling cucumber populations. Cucurbit Genet Coop Rpt 23:12-15

Cramer CS, Wehner TC (1999) Little heterosis for yield and yield components in hybrids of six cucumber inbreds. Euphytica 110:99-108

Cramer CS, Wehner TC (1998a) Fruit yield and yield component means and correlations of four slicing cucumber populations improved through six to ten cycles of recurrent selection. J Am Soc Hort Sci 123:388-395

Cramer CS, Wehner TC (1998b) Fruit yield and yield components of cucumber populations grown at low plant density. In: McCreight JD (eds) Cucurbitaceae '98: Evaluation and enhancement of cucurbit germplasm. 1st ed. Alexandria, ASHS Press, pp 277-285

de Oliveira EJ, Alzate-Marin AL, Borem A, Fagundes SD, de Barros EG, Moreira MA (2005) Molecular marker-assisted selection for development of common bean lines resistant to angular leaf spot. Plant Breed 124:572-575

Desai M, Tanna A, Wall R, Efstratiou A, George R, Stanley J (1998) Fluorescent amplified-fragment length polymorphism analysis of an outbreak of group A streptococcal invasive disease. J Clin Microbiol 36:3133-3137

Dijkhuizen A, Kennard WC, Havey MJ, Staub JE (1996) RFLP variation and genetic relationships in cultivated cucumber. Euphytica 90:79-87

Drenkard E, Richter BG, Rozen S, Stutius LM, Angell NA, Mindrinos M, Cho RJ, Oefner PJ, Davis RW, Ausubel FM (2000) A simple procedure for the analysis of single nucleotide polymorphisms facilitates map-based cloning in Arabidopsis. Plant Physiol 124:1483-1492

38Edwards MD, Page NJ (1994) Evaluation of marker-assisted selection through computer simulation. Theor Appl Genet 88:376-382

Falconer DS, Mackay TFC (1996) Introduction to Quantitative Genetics. 4th edn. Essex, Longman

Fan Z, Robbins MD, Staub JE (2006) Population development by phenotypic selection with subsequent marker-assisted selection for line extraction in cucumber (Cucumis sativus L.). Theor Appl Genet 112:843-855

Fanourakis NE, Simon PW (1987) Analysis of genetic linkage in the cucumber. J Hered 78:238-242

FAOSTAT (2005) Agriculture. FAO Statistical Databases (http://faostat.fao.org, last accessed December 2005)

Fazio G, Chung SM, Staub JE (2003a) Comparative analysis of response to phenotypic and marker-assisted selection for multiple lateral branching in cucumber (Cucumis sativus L.). Theor Appl Genet 107:875-883

Fazio G, Staub JE, Chung SM (2002) Development and characterization of PCR markers in cucumber. J Am Soc Hort Sci 127:545-557

Fazio G, Staub JE, Stevens MR (2003b) Genetic mapping and QTL analysis of horticultural traits in cucumber (Cucumis sativus L.) using recombinant inbred lines. Theor Appl Genet 107:864-874

Fazio G (2001) Comparative study of marker-assisted and phenotypic selecton and genetic analysis of yield components in cucumber. PhD dissertation University of Wisconsin Madison

Flint-Garcia SA, Darrah LL, McMullen MD, Hibbard BE (2003) Phenotypic versus marker-assisted selection for stalk strength and second-generation European corn borer resistance in maize. Theor Appl Genet 107:1331-1336

Francia E, Tacconi G, Crosatti C, Barabaschi D, Bulgarelli D, Dall'Aglio E, Vale G (2005) Marker assisted selection in crop plants. Plant Cell Tissue Organ Cult 82:317-342

Fredrick LR, Staub JE (1989) Combining ability analyses of fruit yield and quality in near-homozygous lines derived from cucumber. J Am Soc Hort Sci 114:332-338

Gimelfarb A, Lande R (1994a) Simulation of marker assisted selection for nonadditive traits. Genet Res 64:127-136

Gimelfarb A, Lande R (1994b) Simulation of marker assisted selection in hybrid populations. Genet Res 63:39-47

39Goode MJ, Bowers JL, Bassi Jr. A (1980) Little leaf, a new kind of pickling cucumber plant. Arkansas Farm Res 29:4

Gupta PK, Roy JK, Prasad M (2001) Single nucleotide polymorphisms: A new paradigm for molecular marker technology and DNA polymorphism detection with emphasis on their use in plants. Curr Sci 80:524-535

Gupta PK, Varshney RK, Sharma PC, Ramesh B (1999) Molecular markers and their applications in wheat breeding. Plant Breed 118:369-390

Hoeck JA, Fehr WR, Shoemaker RC, Welke GA, Johnson SL, Cianzio SR (2003) Molecular marker analysis of seed size in soybean. Crop Sci 43:68-74

Horejsi T, Staub JE, Thomas C (2000) Linkage of random amplified polymorphic DNA markers to downy mildew resistance in cucumber (Cucumis sativus L.). Euphytica 115:105-113

Horejsi T, Box JM, Staub JE (1999) Efficiency of randomly amplified polymorphic DNA to sequence characterized amplified region marker conversion and their comparative polymerase chain reaction sensitivity in cucumber. J Am Soc Hort Sci 124:128-135

Horejsi T, Staub JE (1999) Genetic variation in cucumber (Cucumis sativus L.) as assessed by random amplified polymorphic DNA. Genet Resour Crop Evol 46:337-350

Horst EK, Lower RL (1978) Cucumis hardwickii: A source of germplasm for the cucumber breeder. Cucurbit Genet Coop Rpt 1:5

Jones N, Ougham H, Thomas H (1997) Markers and mapping: we are all geneticists now. New Phytol 137:165-177

Kandemir N, Jones BL, Wesenberg DM, Ullrich SE, Kleinhofs A (2000) Marker-assisted analysis of three grain yield QTL in barley (Hordeum vulgare L.) using near isogenic lines. Mol Breed 6:157-167

Kennard WC, Poetter K, Dijkhuizen A, Meglic V, Staub JE, Havey MJ (1994) Linkages among RFLP, RAPD, isozyme, disease resistance, and morphological markers in narrow and wide crosses of cucumber. Theor Appl Genet 89:42-48

Kinghorn BP (1987) The nature of 2-locus epistatic interactions in animals: evidence from Sewall Wright's guinea pig data. Theor Appl Genet 73:595-604

Kirkbride JH (1993) Biosystematic monograph of the genus Cucumis (Cucurbitaceae). Boone, N.C., Parkway Publishers, pp 159

Knerr LD, Staub JE (1992) Inheritance and linkage relationships of isozyme loci in cucumber (Cucumis sativus L.). Theor Appl Genet 84:217-224

40Knerr LD, Staub JE, Holder DJ, May BP (1989) Genetic diversity in Cucumis sativus L. assessed by variation at 18 allozyme coding loci. Theor Appl Genet 78:119-128

Konieczny A, Ausubel FM (1993) A procedure for mapping Arabidopsis mutations using codominant ecotype-specific PCR-based markers. Plant J 4:403-410

Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743-756

Lin HX, Yamamoto T, Sasaki T, Yano M (2000) Characterization and detection of epistatic interactions of 3 QTLs, Hd1, Hd2, and Hd3, controlling heading date in rice using nearly isogenic lines. Theor Appl Genet 101:1021-1028

López-Sesé AI, Staub J (2002) Combining ability analysis of yield components in cucumber. J Am Soc Hort Sci 127:931-937

Lower RL, Edwards MD (1986) Cucumber breeding. In: Bassett MJ (eds) Breeding Vegetable Crops. Westport, AVI Publishing Co., pp 173-207

Meglic V, Staub JE (1996a) Genetic diversity in cucumber (Cucumis sativus L.): II. An evaluation of selected cultivars released between 1846 and 1978. Genet Resour Crop Evol 43:547-558

Meglic V, Staub JE (1996b) Inheritance and linkage relationships of isozyme and morphological loci in cucumber (Cucumis sativus L). Theor Appl Genet 92:865-872

Mibus H, Tatlioglu T (2004) Molecular characterization and isolation of the F/f gene for femaleness in cucumber (Cucumis sativus L.). Theor Appl Genet 109:1669-1676

Moreau L, Charcosset A, Gallais A (2004) Experimental evaluation of several cycles of marker-assisted selection in maize. Euphytica 137:111-118

Moreno-Vazquez S, Ochoa OE, Faber N, Chao SM, Jacobs JME, Malsonneuve B, Kesseli RV, Michelmore RW (2003) SNP-based codominant markers for a recessive gene conferring resistance to corky root rot (Rhizomonas suberifaciens) in lettuce (Lactuca sativa). Genome 46:1059-1069

Morton JF (1987) The horned cucumber, alias Kiwano (Cucumis metuliferus, Cucurbitaceae). Econ Bot 41:325-327

Newton CR, Graham A, Heptinstall LE, Powell SJ, Summers C, Kalsheker N, Smith JC, Markham AF (1989) Analysis of any point mutation in DNA. The amplification refractory mutation system (ARMS). Nucleic Acids Res 17:2503-2516

Nienhuis J, Lower RL (1988) Comparison of two recurrent selection procedures for yield in two pickling cucumber populations. J Am Soc Hort Sci 113:272-276

41Olson M, Hood L, Cantor C, Botstein D (1989) A common language for physical mapping of the human genome. Science 245:1434-1435

Paran I, Michelmore RW (1993) Development of reliable PCR-based markers linked to downy mildew resistance genes in lettuce. Theor Appl Genet 85:985-993

Park YH, Sensoy S, Wye C, Antonise R, Peleman J, Havey MJ (2000) A genetic map of cucumber composed of RAPDs, RFLPs, AFLPs, and loci conditioning resistance to papaya ringspot and zucchini yellow mosaic viruses. Genome 43:1003-1010

Pierce LK, Wehner TC (2000) Review of genes and linkage groups in cucumber. HortScience 25:605-615

Polashock JJ, Vorsa N (2002) Development of SCAR markers for DNA fingerprinting and germplasm analysis of American cranberry. J Am Soc Hort Sci 127:677-684

Randig O, Bongiovanni M, Carneiro RMDG, Castagnone-Sereno P (2002) Genetic diversity of root-knot nematodes from Brazil and development of SCAR markers specific for the coffee-damaging species. Genome 45:862-870

Reyna N, Sneller CH (2001) Evaluation of marker-assisted introgression of yield QTL alleles into adapted soybean. Crop Sci 41:1317-1321

Romagosa I, Han F, Ullrich SE, Hayes PM, Wesenberg DM (1999) Verification of yield QTL through realized molecular marker-assisted selection responses in a barley cross. Mol Breed 5:143-152

Sarkar G, Cassady J, Bottema CDK, Sommer SS (1990) Characterization of polymerase chain reaction amplification of specific alleles. Anal Biochem 186:64-68

Schneider KA, Brothers ME, Kelly JD (1997) Marker-assisted selection to improve drought resistance in common bean. Crop Sci 37:51-60

Schnell FW, Cockerham CC (1992) Multiplicative vs. arbitrary gene action in heterosis. Genetics 131:461-469

Serquen FC, Bacher J, Staub JE (1997a) Mapping and QTL analysis of horticultural traits in a narrow cross in cucumber (Cucumis sativus L.) using random-amplified polymorphic DNA markers. Mol Breed 3:257-268

Serquen FC, Bacher J, Staub JE (1997b) Genetic analysis of yield components in cucumber at low plant density. J Am Soc Hort Sci 122:522-528

Shen L, Courtois B, McNally KL, Robin S, Li Z (2001) Evaluation of near-isogenic lines of rice introgressed with QTLs for root depth through marker-aided selection. Theor Appl Genet 103:75-83

42Shetty NV, Wehner TC (2002) Screening the cucumber germplasm collection for fruit yield and quality. Crop Sci 42:2174-2183

Shirasawa K, Kishitani S, Nishio T (2004) Conversion of AFLP markers to sequence-specific markers for closely related lines in rice by use of the rice genome sequence. Mol Breed 14:283-292

Staub JE, Bacher J (1997) Cucumber as a Processed Vegetable. In: Smith DS, Cash JN, Nip W, Hui YH (eds) Processing Vegetables: Science and Technology. Lancaster, Technomic Publishing Co., Inc., pp 129-193

Staub JE, Serquen FC, Gupta M (1996) Genetic markers, map construction, and their application in plant breeding. HortScience 31:729-741

Staub JE, Meglic V (1993) Molecular genetic markers and their legal relevance for cultigen discrimination: A case study in cucumber. HortTechnology 3:291-300

Stromberg LD, Dudley JW, Rufener GK (1994) Comparing conventional early generation selection with molecular marker assisted selection in maize. Crop Sci 34:1221-1225

Tanksley SD (1993) Mapping polygenes. Annu Rev Genet 27:205-233

Tatlioglu T (1993) Cucumber Cucumis sativus L. In: Kalloo G, Bergh BO (eds) Genetic Improvement of Vegetable Crops. Tarrytown, Pergamon Press Ltd., pp 197-234

Trebitsh T, Staub JE, ONeill SD (1997) Identification of a 1-aminocyclopropane-1-carboxylic acid synthase gene linked to the female (F) locus that enhances female sex expression in cucumber. Plant Physiol 113:987-995

USDA NASS (2006) Vegetables: 2005 summary. United States Department of Agriculture, National Agricultural Statistics Service Vg 1-2 (06)

Vakalounakis DJ (1992) Heart leaf, a recessive leaf shape marker in cucumber: Linkage with disease resistance and other traits. J Hered 83:217-221

Van Berloo R, Stam P (1999) Comparison between marker-assisted selection and phenotypical selection in a set of Arabidopsis thaliana recombinant inbred lines. Theor Appl Genet 98:113-118

Vos P, Hoger R, Bleeker M, Reijans M, van de Lee T, Hornes M, Frijters A, Pot J, Peleman J, Kuiper M, Zabeau M (1995) AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res 23: 4407-4414

Wehner TC, Cramer CS (1996a) Gain for pickling cucumber yield and fruit shape using recurrent selection. Crop Sci 36:1538-1544

43Wehner TC, Cramer CS (1996b) Ten cycles of recurrent selection for fruit yield, earliness, and quality in three slicing cucumber populations. J Am Soc Hort Sci 121:362-366

Wehner TC (1989) Breeding for improved yield in cucumber. Plant Breed Rev 6:323-359

Wehner TC, Lower RL, Staub JE, Tolla GE (1989) Convergent-divergent selection for cucumber fruit yield. HortScience 24:667-669

Wehner TC, Miller CH (1985) Effect of gynoecious expression on yield and earliness of a fresh-market cucumber hybrid. J Am Soc Hort Sci 110:464-466

Wehner TC, Staub JE, Peterson CE (1978) Inheritance of littleleaf and multi-branched plant type in cucumber. Cucurbit Genet Coop Rpt 10:33-34

Wenzel G, Kennard WC, Havey MJ (1995) Quantitative trait analysis of fruit quality in cucumber: QTL detection, confirmation, and comparison with mating-design variation. Theor Appl Genet 91:53-61

Willcox MC, Khairallah MM, Bergvinson D, Crossa J, Deutsch JA, Edmeades GO, Gonalez-de-Leon D, Jiang C, Jewell DC, Mihm JA, Williams WP, Hoisington D (2002) Selection for resistance to southwestern corn borer using marker-assisted and conventional backcrossing. Crop Sci 42:1516-1528

Williams JGK, Kubelik AR, Livak KJ, Rafalski JA, Tingey SV (1990) DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Res 18:6531-6535

Xie J, Wehner TC (2001) Gene list 2001 for cucumber. Cucurbit Genet Coop Rpt 24:110-136

Yamamoto T, Lin HX, Sasaki T, Yano M (2000) Identification of heading date quantitative trait locus Hd6 and characterization of its epistatic interactions with Hd2 in rice using advanced backcross progeny. Genetics 154:885-891

Yano M (2001) Genetic and molecular dissection of naturally occurring variation. Curr Opin Plant Biol 4:130-135

Yousef GG, Juvik JA (2001) Comparison of phenotypic and marker-assisted selection for quantitative traits in sweet corn. Crop Sci 41:645-655

Zhang C, Tar'an B, Warkentin T, Tullu A, Bett KE, Vandenberg B, Somers DJ (2006) Selection for lodging resistance in early generations of field pea by molecular markers. Crop Sci 46:321-329

44Zhang W, Smith C (1992) Computer simulation of marker-assisted selection utilizing linkage disequilibrium. Theor Appl Genet 83:813-820

Zheng XY, Wolff DW, Baudracco-Arnas S, Pitrat M (1999) Development and utility of cleaved amplified polymorphic sequences (CAPS) and restriction fragment length polymorphisms (RFLPs) linked to the Fom-2 fusarium wilt resistance gene in melon (Cucumis melo L.). Theor Appl Genet 99:453-463

45

Chapter 1. Comparative analysis of marker-assisted and phenotypic selection for yield components in cucumber

Abstract

Comparative analysis of marker-assisted (MAS) and phenotypic (PHE) selection

efficacy is important for deployment of MAS in plant breeding. For direct comparison of

the effectiveness of MAS and PHE, four cucumber (Cucumis sativus L.; 2n=2x=14)

inbred lines were intermated then bulked maternally to create four base populations for

recurrent mass selection. Each of these populations underwent three cycles of PHE

(open-field evaluations) and MAS (genotyping at 18 marker loci), as well as random

mating (RAN; no selection). The four yield-related traits that underwent selection

[multiple lateral branching (MLB), gynoecious sex expression (GYN), earliness (EAR),

and fruit length to diameter ratio (L:D)] are quantitatively inherited (2-6 QTL per trait).

Both MAS and PHE were useful for multi-trait improvement in cucumber, but their

effectiveness depended upon the traits and populations under selection. The populations

with maternal parents that were inferior for a trait responded favorably to selection, while

those with maternal parents of superior trait values either did not change or decreased

during selection. Both MAS and PHE provided improvements in all traits under selection

in at least one population, except for EAR by MAS. Generally, PHE was most effective

for GYN, EAR, and L:D, while MAS was most effective for MLB and provided the only

increase in yield (fruit per plant).

46Introduction

Yield has been a focus of cucumber (Cucumis sativus L.; 2n=2x=14) breeding for

over 50 years (Lower and Edwards 1986; Wehner 1989; Wehner et al. 1989). Although

the yield of U.S. processing cucumber increased steadily from 1950 to 1980, it has

reached a plateau since the early 1980’s (Shetty and Wehner 2002). Selecting directly for

yield is difficult (Wehner 1989) which is partially due to its relatively low narrow-sense

heritability (0.07 to 0.25) and the dramatic influence of the environment on trait

expression. The most effective breeding approach for yield improvement in cucumber

may be selection for traits directly related to yield (Wehner 1989; Cramer and Wehner

1998; Cramer and Wehner 2000b).

Several theoretically-based simulation studies suggest that the effectiveness of

marker-assisted selection (MAS) for polygenic traits can be greater than traditional

breeding (Lande and Thompson 1990; Zhang and Smith 1992; Edwards and Page 1994;

Gimelfarb and Lande 1994a; Gimelfarb and Lande 1994b). In general, these studies

agree that MAS efficiency is enhanced when markers are tightly linked (< 5.0 cM) to

quantitative trait loci (QTL), and selection is performed in early generations prior to

recombination between markers and QTL, in relatively large populations, and on traits

with low heritability. In practice, MAS has been effective for the introgression of simple

traits or a small number of genes in several crop species [e.g., disease resistance in

common bean (Phaseolus vulgaris L.; de Oliveira et al. 2005), grain protein

concentration in durum wheat (Triticum turgidum L. var. durum; Chee et al. 2001), and

root depth in rice (Oryza sativa L.; Shen et al. 2001)], but less effective for complex traits

47[e.g., yield in barley (Hordeum vulgare L.; Kandemir et al. 2000), and soybean (Glycine

max (L.) Merrill; Reyna and Sneller 2001)]. Empirical comparisons of MAS to

phenotypic selection (PHE), however, are scarce and often conflicting (Yousef and Juvik

2001; Willcox et al. 2002; Hoeck et al. 2003).

Four important yield components in cucumber are earliness (EAR), gynoecious

sex expression (GYN), fruit length to diameter ratio (L:D), and multiple lateral branching

(MLB; Cramer and Wehner 2000a; Fazio et al. 2003a). Each of these traits is under the

control of two to six major genes with relatively large effects, where narrow-sense

heritabilities (h2) range from 0.14 to 0.48 depending on trait and environment (Serquen et

al. 1997a; Fazio et al. 2003b). EAR, GYN, and MLB have been shown to be positively

correlated with the number of fruit per plant (Cramer and Wehner 2000a; Cramer and

Wehner 2000b; Fazio 2001), and L:D is an important determinant of marketable fruit

yield (Serquen et al. 1997b). Negative correlations exist however, between these yield

components (e.g., GYN with MLB and EAR with L:D) making the simultaneous

improvement of these traits a challenge.

The use of MAS in plant breeding has potential for increasing the efficiency and

effectiveness of plant improvement (Francia et al. 2005). Moderately saturated linkage

maps have been developed for cucumber and genomic regions have been identified that

have proven useful for selection of yield components by MAS (Fazio et al. 2003a; Fan et

al. 2006). These studies utilized yield-associated QTL identified initially by Serquen et

al. (1997a) and then by Fazio et al. (2003b) in a mapping population derived from a cross

between lines Gy-7 (synom. G421) and H-19. Selectable markers included those linked

48to QTL for EAR (LOD ≥ 4.1), GYN (LOD ≥ 3.0), L:D (LOD ≥ 4.2), and MLB (LOD ≥

3.0).

MAS in cucumber has proven to be effective in line extraction during backcross

breeding (Fazio et al. 2003a; Fan et al. 2006), but has not been evaluated for its efficacy

in population improvement. Given the potential utility of MAS, a study was designed to

increase cucumber yield by simultaneous selection of multiple yield components by MAS

and PHE, and to compare these methods for response to selection. In order to test their

efficacy, both methods used the same selection scheme which was designed to overcome

known negative correlations between yield components. Four populations were created

by intermating four inbred lines, and each population underwent three cycles of recurrent

selection by PHE and MAS, as well as random mating (RAN; no selection). Results will

allow for managerial decisions regarding the value and use of MAS in population

improvement of cucumber.

Materials and Methods

Germplasm Four genetically diverse but complementary inbred lines were used as parents for

the development of four populations that subsequently underwent selection. These

contrasting lines were drawn from the U.S. Department of Agriculture (USDA) cucumber

breeding program, Madison, Wisc., because the combination of their complementary

phenotypes (Table 1.1) provided the basis for selection of desirable aspects of EAR,

GYN, MLB, and L:D. Lines 6996A and 6995C were drawn from a recombinant inbred

49line (RIL; Gy-7 × H-19) population (F9; Staub et al. 2002). Line 6823B originated from

a cross between a parent (H-19) of the RIL population and a USDA elite processing line

whose progeny were then selected for H-19 attributes. Line 6632E is morphologically

similar to the other parent (Gy-7) of the RIL population, but does not have either parent

in its pedigree (Staub and Crubaugh 2001).

Selection scheme The four parental inbreds were intermated in a greenhouse in Madison, Wisc., in

2000 by pollinating female flowers of each inbred with bulked male flowers from the

other three lines (Figure 1.1). The resulting seeds were bulked by maternal parent to

create four populations (i.e., Population 1-4). Since these populations did not undergo

any selection, they are designated as cycle 0 (e.g., Population 1 C0). Each of these

populations subsequently underwent PHE, MAS, and RAN for three cycles (C1-C3). All

selection and mating was performed within each of the four populations, independent of

the other three populations (i.e., intrapopulation improvement only). Each population

was independently subjected to each of the two selection methods. PHE was performed

based on phenotype alone (i.e., without marker information), and MAS was applied

without regards to phenotypic information (i.e., marker information only).

Phenotypic selection was practiced on 400 (2001) C0, 600 (2002) C1, or 600

(2003) C2 plants in each population for EAR, GYN, MLB, L:D, and standard leaf type

under open-field conditions at the University of Wisconsin Experiment Station, Hancock,

Wisc. [UWESH; Plainfield loamy sand (Typic Udipsamment; sandy, mixed, mesic)].

Data were taken on individual plants, where leaf type was classified as standard (LL) or

50little leaf (ll = 30-40 cm2; Staub et al. 1992). Although EAR is usually measured as the

number of fruit per plant in the first harvest on a per plot basis, EAR was assessed on

individual plants as the number of days from planting to anthesis of the first female

flower, because the fruit number of single plants is a poor predictor of fruit number in

replicated trials (Wehner 1989). Sex expression was measured as the percentage of the

first 10 flowering nodes bearing female flowers (nodes with male and female flowers

were classified as male) where 100% was designated gynoecious, 50% to 90% was

considered predominantly female (PF), and less than 50% was classified as monoecious.

Fruit L:D was estimated by visual inspection of at least four immature fruit (USDA grade

size 3A-3B; 3.0 to 5.0 cm in diameter). MLB was recorded at or after anthesis as the

number of lateral branches (at least three internodes in length) in the first ten nodes of the

mainstem. PHE was accomplished in two stages within each cycle of selection using

minimum trait thresholds for the first stage, and index selection for the second stage. For

Stage 1, individual plants were first evaluated for leaf type, EAR, GYN, and MLB, since

these are the first traits to be expressed developmentally in cucumber. Only individuals

that met pre-established thresholds (i.e., standard-leaf, EAR < 48 days to the first female

flower, GYN > 50% female flowers, and MLB > 3 branches) were evaluated for L:D.

Those plants with an L:D above the threshold (> 2.8) were designated selections of Stage

1. An informal index was employed for Stage 2 of PHE where MLB and EAR were

weighted approximately 2 (MLB) and 1.5 (EAR) times that of GYN and L:D, which were

weighted equally. Individual plants were ranked by their values of MLB, then EAR, and

the values of GYN and L:D were used to make Stage 2 selections among the highest

51ranked individuals. The relative weights among the traits are illustrated by the selection

differentials (difference in trait means of the selections from Stage 2 and the selections

from Stage 1) for each trait in each population (Table 1.2). Twenty plants were selected

from Stage 2 in each cycle (C1-C3) of PHE within each population, representing a

standardized selection intensity (i) of 2.063, 2.219, and 2.219 for C1, C2, and C3,

respectively. Meristems of each Stage 2 selection were taken for cloning as rooted

cuttings. Once these cuttings were established, the apical meristems and surrounding

leaves were treated with two applications (7 days apart) of 3 mM silver thiosulfate

[Ag(S2O3)2]3- as a foliar spray to induce male flower production (Nijs and Visser 1980).

Selections were then randomly mated by pollination of each female flower with five

random male flowers.

For MAS, individual plants were genotyped using 18 markers linked to F

(femaleness), de (determinate), ll and previously identified QTL (Serquen et al. 1997a;

Fazio et al. 2003b) for EAR, GYN, MLB, and L:D (Table 1.3), and then selections were

made based on marker genotype. All markers employed were drawn from Fazio et al.

(2003b), except AJ6SCAR, and M8SCAR which were SCARs converted from previously

mapped RAPDs (Nam et al. 2005; Chapter 2). Marker type, genetic distance from QTL,

and number of QTL in proximity to the marker were considered when identifying

markers for use in MAS. Generally, dominant markers flanking QTL or codominant

markers tightly linked to QTL were selected (Robbins et al. 2002; Robbins and Staub

2004). All four parental lines carried only Gy-7 or H-19 alleles at each marker locus.

Thus, the desired genotype, or ideotype, was created based on parental allele constitution

52at each marker locus and the relationship of the QTL surrounding the marker locus (Table

1.3).

Tissue from all individuals and parental lines was harvested and DNA extraction,

polymerase chain reaction (PCR) amplification, and agarose gel electrophoresis was

conducted according to Fazio et al. (2003b). To increase marker efficiency, markers

were multiplexed in empirically determined groups (Table 1.3) according to Staub et al.

(2004) and Chapter 2. All individuals within a population were genotyped at each

marker locus, and individuals were selected whose genotype most closely matched the

ideotype at the greatest number of marker loci. The number of individuals tested, the

selection intensity, and crossing scheme for each cycle of MAS within each of the four

populations were identical to that of PHE.

Random mating was accomplished by sowing 20 randomly selected seeds from

each of the four C0 populations, and then chemically inducing male flowers in

gynoecious plants prior to intermating using the same scheme as that for MAS and PHE

to create C1. The resulting seeds were equally bulked, and 20 seeds were randomly

selected in the same manner to create C2 and C3.

Open-field evaluation of selection Response to selection was evaluated in the open-field trial at UWESH in the

summer of 2004 at two planting dates. Seeds were sown in a greenhouse in Madison,

Wisc. on June 4, 2004 and June 16, 2004, then transplanted on June 23, 2004 and July 7,

2004, respectively. Each planting date was arranged in a split-plot design with eight

replications of each population (whole plot factor) in randomized complete blocks, with a

53combination of cycle (i.e., C0,-C3) and method of selection (i.e., MAS, PHE, and RAN)

completely randomized as subplots with 10 plants per subplot. Plots were arranged in

single rows with 18 cm between plants and 1.5 m between rows (~37,000 plants/ha).

This plant density was chosen because it optimized potential yield in MLB genotypes in

multiple harvest operations (Fredrick and Staub 1989; Staub et al. 1992). The four inbred

lines that served as parents, as well as Gy-7, H-19, and the commercial cultivar ‘Vlasset’

(Seminis Vegetable Seeds, Inc, Oxnard, Calif.) were included as controls for comparison.

The traits evaluated were MLB, GYN, EAR, L:D, and total yield. The most

efficient measurement of yield in cucumber research is the total (marketable and

oversize) number of fruits per plant, since it has a higher heritability, is more stable over

time, and is easier to measure than other yield measurements (i.e., volume, mass, or

dollar value). Furthermore, fruit number is highly correlated (genetic correlation = 0.87)

with fruit weight (Wehner et al. 1989). The number of fruit per plot was counted at each

of four harvests [59, 66, 76, and 96 (first planting date) and 54, 64, 75, and 91 (second

planting date) days after planting] to calculate four-harvest means adjusted for plant stand.

Each of the four harvests occurred as two to three oversized fruit (>51 mm in diameter)

were observed within a plot (Wehner et al. 1989), where all immature fruits >20 mm in

diameter and >10 cm in length were included in total fruit number. Both MLB and GYN

were evaluated on each plant exactly as during PHE. Mean fruit L:D was obtained per

plot by measuring the length and diameter of 5–10 randomly selected fruits (USDA 2B-

3A grade; 2.5-3.0 cm in diameter), and then averaging over three harvests. EAR was

defined as the average number of fruits per plant in the first harvest.

54Statistical analysis

All response variables were initially analyzed by analysis of variance (ANOVA)

using PROC GLM of SAS (2003) to determine treatment effects. Treatments of planting

date, populations, cycles, and methods were considered fixed effects, while blocks were

considered random. Specific single-degree of freedom contrasts within analyses of

variance were employed to determine general response to selection for biologically

important comparisons (e.g., PHE and MAS). Selection responses (linear and quadratic

effects) were computed by regression of trait means on selection cycles within each

population for each selection method by employing single-degree of freedom contrasts

within ANOVA (Steele et al. 1996). To determine the relationship between the traits

under selection, phenotypic correlations among traits were calculated by Pearson

correlation using PROC CORR of SAS (2003).

Results

All main effects (planting date, populations, and combinations of cycles and

methods) were highly significant (P < 0.001) for all traits. In general, planting date

affected the magnitude of the mean value of a trait and not the entry ranking in response

to selection over cycles (Appendix A). Generally, the means of all traits were higher for

all populations in the first planting than the second, except for MLB, which was lower.

Although the planting date by population interaction was significant for L:D (P = 0.01)

and EAR (P = 0.001), general trends over cycles were the same for both plantings for all

traits. Selection was performed in each population independent of the other populations,

55and response to selection varied by population. Therefore, results are presented by

population with both plantings combined (Table 1.4; Figure 1.2; Appendix B).

Population 1 showed the widest range of response to selection over cycles when

compared to the other populations examined (Figure 1.2). All trait values decreased

significantly from RAN in this population, except L:D, which did not change. Both

MAS and PHE were effective at increasing MLB and L:D means, but EAR was

increased only by PHE. The only trait value to decrease through PHE was that of GYN,

which was diminutive when compared to MAS. The means of both EAR and GYN

decreased dramatically by MAS when compared to RAN and PHE. Yield values

remained fairly constant during PHE, but were reduced during MAS and RAN.

The effect of RAN was the most dramatic in Population 2 (Figure 1.2). The value

of two traits, L:D and MLB, increased slightly after three cycles of RAN, while the other

three trait means were reduced. The same directional trends were also apparent after

MAS. In contrast, PHE increased the mean of EAR and GYN, while no significant

change was detected in the other three traits.

In Population 3 after three cycles of RAN, the values of all traits except L:D were

unchanged (yield and MLB) or decreased (EAR and GYN; Figure 1.2). The increase in

the mean of L:D by PHE was similar to that of RAN. The mean of MLB also increased

after PHE, while EAR, GYN and yield values decreased. The decrease in the mean of

GYN after PHE, although significant, was dramatically less than the reduction detected

after MAS. The only trait value reduced by MAS was GYN. The means of EAR and

L:D were unchanged by MAS, while MLB and yield values increased.

56The mean values of three traits (EAR, L:D, and yield) in Population 4 were

decreased after RAN, while GYN and MLB remained unchanged over cycles (Figure 1.2).

The responses to selection from MAS and PHE were similar for all traits, except EAR. A

reduction in the means of L:D, MLB, and yield was detected during MAS, but such

reductions were not as large as those resulting from PHE. The gain from PHE for GYN

was, likewise, larger than that from MAS. The mean of EAR increased by PHE, but was

reduced by MAS and RAN. When compared to the other populations examined,

Population 4 showed the least improvement as trait values were increased in only three

instances (EAR by PHE, GYN by MAS, and GYN by PHE).

The four inbred lines (Table 1.1; Figure 1.1) used as parents in this study were

specifically chosen because high values for some of the traits under selection

complimented low values found in other lines (e.g., 6632E is high for GYN and EAR, but

low for MLB and L:D). Although this disparity among trait values was predictably

minimized in the C0 populations (Table 1.1), response to MAS and PHE varied by trait

and population. In general, the populations with maternal parents (i.e., inbred lines) that

were inferior for a trait responded favorably to selection while those with maternal

parents of superior trait values either did not change or decreased for certain traits. This

was most clearly observed in PHE for all traits except for EAR, which increased in

Population 1 even though the maternal parent (6632E) was superior for EAR. Trait

values for EAR remained comparatively low after MAS in Populations 2 and 4 (both

parents inferior for EAR), and results of MAS for GYN in Population 2 were similar. In

contrast, trait values after RAN generally decreased or remained unchanged. Trait values

57increased after RAN in only three cases: L:D and MLB in Population 2, and L:D in

Population 3. In several cases, trait values decreased after RAN in populations with

inferior maternal parents (e.g., MLB in Population 1).

The correlated response to selection [phenotypic correlation values (r)] after MAS

and PHE were somewhat different, and are presented by population in Table 1.5.

Consistent, positive correlations were detected for EAR with GYN and yield (r = 0.25 to

0.70), but EAR was always negatively correlated with MLB (r = -0.14 to -0.14).

Generally, EAR and L:D were not correlated, but a negative (Population 1 after MAS; r =

-0.26) and positive (Population 3 after MAS; r = 0.30) correlation was detected between

the two traits. Negative correlations were generally detected for GYN with L:D and

MLB (r = -0.07 to -0.64), but GYN was not correlated with yield, except in Population 1

after MAS (r = 0.47). Generally, L:D was positively correlated with MLB (r = 0.06 to

0.38), although more correlations were significant after PHE than MAS. In only two

populations [Populations 2 and 4 after both MAS (r = 0.34) and PHE (r = 0.32)] were

positive correlations detected between L:D and yield, while MLB and yield were not

correlated, except for a positive correlation in Population 4 after MAS (r = 0.27).

Discussion

Marker-assisted selection has been found to be more (Yousef and Juvik 2001;

Fazio et al. 2003a; Zhang et al. 2006), equivalent (Stromberg et al. 1994; Romagosa et al.

1999; Van Berloo and Stam 1999; Willcox et al. 2002; Moreau et al. 2004), or less

(Hoeck et al. 2003) efficient and/or effective for increasing gain from selection when

58compared to PHE in various plant species. Additional comparisons of MAS and PHE

have provided mixed results within the same study (Schneider et al. 1997; Flint-Garcia et

al. 2003). Moreover, these studies did not evaluate these selection methods for their

efficacy in the improvement of multiple, quantitatively inherited traits over multiple

cycles of recurrent selection. Data presented herein provide the first comprehensive,

comparative evaluation of MAS and PHE for such traits in a vegetable crop species.

Considerations for MAS The QTL for EAR, GYN, L:D, and MLB identified for selection in this study had

a relatively large effect (cumulative R2 > 37%-85% depending on trait and environment),

high LOD scores (>3.0; Table 1.3), and were consistent over several environments

(Serquen et al. 1997a; Fazio et al. 2003b). However, in several instances, QTL were so

tightly clustered that multiple QTL for different traits were located between adjacent

marker loci (e.g., QTL for all traits were linked to CSWCT28 and L18-SNP-H19 as well

as OP-AD12-1; Table 1.3). As the desired QTL allele came from different parental lines

for separate traits (e.g., EAR and GYN from Gy-7; MLB and L:D from H-19 at

CSWCT28), strategic decisions were made based on QTL effects and neighboring genes

to determine the most appropriate parental type for each marker locus. For example, the

Gy-7 allele was selected at OP-AD12-1, the marker linked to the little leaf gene (ll) from

H-19, in order to avoid the deleterious effects of the little leaf type on GYN and EAR

(Fazio et al. 2003b). Little leaf types, however, typically have more branches than

standard leaf types, and the QTL (from H-19) with the greatest effect on MLB (LOD =

32.9, R2 = 32%) is tightly linked (0.7 cM) to ll (Fazio et al. 2003b). Selection of the H-19

59allele at OP-AD12-1, therefore, may have resulted in greater gains in MLB from MAS,

but may, in turn, have negatively affected EAR, GYN, and L:D, which are associated

with the Gy-7 allele.

Genetic distance between QTL and marker, marker inheritance, and marker type

were also considered when choosing markers for MAS. The majority of marker-QTL

associations in this study were < 5.0 cM (Fazio et al. 2003b), and codominant markers

were utilized when available. In regions where marker-QTL associations were wide,

markers flanking the QTL of interest (Edwards and Page 1994) were employed (e.g.,

AK5SCAR and M8SCAR for MLB; Table 1.3). Certain marker types (SCAR, SNP, and

SSR) were chosen over others (RAPD and AFLP) because of their inherent robustness,

ease of use, and ability to be multiplexed (Polashock and Vorsa 2002; Tang et al. 2003;

Mohring et al. 2004; Staub et al. 2004). The majority of the RAPD markers used in MAS

were repeated several times to provide certainty during genotyping. In contrast, all but

one (M8SCAR) of the SNP and SCAR markers could be multiplexed, allowing for

increased genotyping efficiency (Table 1.3). The low repeatability of RAPDs and the

advantage of multiplexing for high-throughput genotyping demonstrate the need for SNP,

SCAR, and SSR markers for MAS in cucumber.

Selection effectiveness Each of the four base populations underwent random mating (RAN) following the

same mating scheme as MAS and PHE to provide four estimates of genetic drift. When

considered over all five traits in each of the four populations, 15 of the 20 slopes were

significant after RAN (Table 1.4; Figure 1.2). The significant changes in trait values of

60L:D and MLB after RAN are most likely due to genetic drift, since regression slopes

were positive, negative, or not significant, depending on the population, and the R2 values

were generally low. In the three instance where trait values increased after RAN (L:D in

Populations 2 and 3 and MLB in Population 3), the similar increase from MAS or PHE

cannot be attributed to selection. In contrast, the regression slopes for EAR, GYN, and

yield were significantly negative in almost every population after RAN, and the R2 values

are relatively high, indicating a small, but steady reduction in these traits inconsistent

with the random nature of genetic drift. A probable explanation for the reduction in GYN

is that gynoecious plants needed to be chemically induced to produce male flowers for

pollination during RAN. The production of male flowers varies in time and quantity

among gynoecious individuals, which introduces flowering time and fecundity

differences, possibly leading to a reproductive disadvantage for gynoecious plants.

Chemical induction of male flowers may also have also affected EAR and yield indirectly

[e.g., correlated responses similar to GYN and EAR after MAS and PHE (Table 1.5)], but

other physiological factors (i.e., source-sink relationships, reduced fitness of higher

yielding individuals) may have also affected these traits. Although trait values were

generally not static in the absence of selection, the general reduction in trait values after

RAN indicates that increases after MAS or PHE can be attributed to a response from

selection.

Both MAS and PHE provided improvements in all traits under selection in at least

one population, except EAR by MAS (Table 1.4, Figure 1.2). Generally, PHE was most

effective for GYN, EAR, and L:D, while MAS was slightly more effective for MLB.

61The similar response to MAS and PHE for MLB confirms the results from Fazio et al.

(2003a) where MAS and PHE equally improved MLB in two cycles of backcross

selection in cucumber. Both PHE and MAS were generally effective at improving

populations with inferior traits, but not as effective at maintaining traits with high values.

Based on trait value changes in response to selection, PHE was more effective than MAS

in Populations 1, 2, and 4, but MAS was slightly more effective than PHE in Population 3.

Thus, the choice of selection methods for cucumber improvement through plant

architectural manipulation (i.e., yield components) will depend upon the populations and

traits under selection.

Yield was not under direct selection in this study, but was evaluated to test the

efficacy of indirect yield improvement by selection for yield components. Yield was

higher in every C0 population than the maternal parent that produced it, except the

highest yielding parent (6823B; Population 2), suggesting the possibility of a heterotic

yield effect in these populations. Cucumber is considered a cross pollinated crop, and

although it exhibits little inbreeding depression, heterosis for yield has been observed in a

number of cases (Wehner 1989). Using the mean of the four parents (1.81) as the mid-

parent value, the mid-parent percent heterosis for yield is 22%, 12%, 2.6%, and 27% for

Populations 1-4, respectively. These values are similar to those reported for fruit number

in previous studies (Wehner 1989). Given this heterotic yield effect, and the difficulty of

simultaneously increasing several yield components, inbred lines with high values for

specific yield components (e.g., GYN with EAR, or MLB with L:D) could be developed

in parallel, and then crossed to create high yielding hybrids. This approach would

62involve the extensive combining ability or test cross evaluation of inbred lines in multiple

environments, and would likely be population specific.

Indirect selection by MAS or PHE was generally not effective at increasing yield

in this study. Nevertheless, the hypothesis that yield increases with the improvement of

all four yield components cannot be rejected, since in no instance did improvement of all

four traits occur. This contention is supported by the results of Fan et al. (2006), who

independently evaluated the effectiveness of MAS for MLB, GYN, and L:D in a crossing

scheme using selections from C2 of PHE in Population 1 of this study as recurrent parents

to produce two backcross populations. In addition to the gains made by PHE for MLB

and L:D after two cycles of selection, MAS continued to improve MLB and L:D in one

backcross population, and L:D in the other, while GYN was improved in both

populations after MAS. These results, coupled with the observation that MAS increased

GYN, L:D, and MLB in this study, confirms the potential value of the marker-QTL

associations for these three traits in these cucumber populations. The challenge to

improving yield in cucumber will likely be the simultaneous improvement of yield

components using both MAS and PHE.

The simultaneous increase in all four traits under selection in this study will be

predictably difficult given the negative correlations among some yield component traits.

The strength and direction of these correlations have been documented in a wide range of

genetic backgrounds (Kupper and Staub 1988; Serquen et al. 1997b; Cramer and Wehner

1998; Cramer and Wehner 1999; Cramer and Wehner 2000b; Fazio et al. 2003b).

Recombination of four inbred lines, recurrent selection, and four different populations

63were used herein to mitigate negative correlations among yield components. These

strategies were generally ineffective, however, because GYN and EAR were positively

correlated as were MLB and L:D in all four populations (Table 1.5). The correlations

among these yield components are most likely due to a combination of pleitropy with the

F, de, and ll genes (Fazio et al. 2003b), and linkage among individual QTL (Robbins and

Staub 2004). Selection by MAS or PHE will not overcome pleitropic effects, but may

identify recombinants between QTL that may help to diminish negative correlations

among yield components. Fine mapping in regions with clustered QTL would assist in

determining the extent of linkage between QTL and identify molecular markers that

could be useful for selecting recombinants between tightly linked QTL (Nam et al. 2005).

Selection methods in breeding programs For MAS to be employed in plant improvement programs, it must provide

resource (cost/benefit) and/or technical (improved effectiveness or efficiency) advantages

over PHE. In this study, the cumulative time required to complete three cycles of MAS

in all four populations, was 19 months as compared to one cycle per year for PHE (Figure

1.3). Populations were evaluated for PHE simultaneously, while they were offset for

MAS such that genotyping usually occurred in one population while other populations

were intermated. The increased efficiency of MAS may, in some cases, be an advantage

over PHE under Wisconsin conditions. For example, the improvement of GYN per year

in Population 4 was fairly similar between MAS (4.9%/cycle × 3 cycles/yr = 14.7%/yr)

and PHE (12.6%/cycle × 1 cycle/yr = 12.6%/yr). The efficiency of MAS could be further

improved by the use of codominant, single-copy markers that can be multiplexed, such as

64SCARs, SNPs, and SSRs in combination with automation technologies (e.g., robotics,

gel-less PCR assays, microarrays, etc.; Gupta et al. 2001; Collard et al. 2005).

Substantial investments required for automation technologies are currently cost

prohibitive for minor crops such as cucumber, but may be mitigated as genomic tools

become more available and affordable.

Recurrent selection is the method of choice for traits with low heritability and has

been used extensively for yield improvement in cucumber (Lower and Edwards 1986;

Wehner 1989; Cramer and Wehner 1998). Two important considerations for recurrent

selection are selection intensity and genetic drift. Selection intensity must be stringent

enough to increase desired alleles (make gain from selection), but modest enough to

allow diversity to continue improvement in subsequent cycles of selection (Casler 1999;

Bernardo 2002). These two factors must be balanced since increasing selection intensity

by decreasing the number of individuals selected increases the effect of drift. The results

from RAN indicate that selecting 20 out of 600 individuals to obtain fairly high selection

intensities results in genetic drift for some traits. Genetic drift may be minimized by

relaxing the selection intensity so that more individuals are intermated (e.g., select 50-75

individuals), which also increases the probability of recombination among tightly linked

QTL. Using this approach, gains from selection per cycle would be expected to be lower,

and thus, increased cycles of selection should be employed. In addition, the finding that

the values of GYN, EAR, and yield were reduced in the absensce of selection indicates

the importance of continual selection for all three traits. An alternative to relaxing the

selection intensity is to increase the number of individuals evaluated and selected

65proportionately, thereby maintaining the selection intensity. The evaluation of 600

individuals in each population was the maximum allowable for each method with the

resources available in this study. However, selecting 600 individuals by both MAS and

PHE in same cycle and intermating 40 selections is possible. Using this approach, high

selection intensities are maintained, but evaluating a greater number of individuals may

allow for recombination among tightly linked QTL, while intermating more individuals

may overcome genetic drift. Thus, selection for improved yield in cucumber may be

most effective by combining both MAS and PHE.

Literature Cited

Bernardo R (2002) Breeding for Quantitative Traits in Plants. 1st edn. Woodbury, Stemma Press

Casler MD (1999) Phenotypic recurrent selection methodology for reducing fiber concentration in smooth bromegrass. Crop Sci 39:381-390

Chee PW, Elias EM, Anderson JA, Kianian SF (2001) Evaluation of a high grain protein QTL from Triticum turgidum L. var. dicoccoides in an adapted durum wheat background. Crop Sci 41:295-301

Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica 142:169-196

Cramer CS, Wehner TC (2000a) Path analysis of the correlation between fruit number and plant traits of cucumber populations. HortScience 35:708-711

Cramer CS, Wehner TC (2000b) Fruit yield and yield component correlations of four pickling cucumber populations. Cucurbit Genet Coop Rpt 23:12-15

Cramer CS, Wehner TC (1999) Little heterosis for yield and yield components in hybrids of six cucumber inbreds. Euphytica 110:99-108

Cramer CS, Wehner TC (1998) Fruit yield and yield component means and correlations of four slicing cucumber populations improved through six to ten cycles of recurrent selection. J Am Soc Hort Sci 123:388-395

66de Oliveira EJ, Alzate-Marin AL, Borem A, Fagundes SD, de Barros EG, Moreira MA (2005) Molecular marker-assisted selection for development of common bean lines resistant to angular leaf spot. Plant Breed 124:572-575

Edwards MD, Page NJ (1994) Evaluation of marker-assisted selection through computer simulation. Theor Appl Genet 88:376-382

Fan Z, Robbins MD, Staub JE (2006) Population development by phenotypic selection with subsequent marker-assisted selection for line extraction in cucumber (Cucumis sativus L.). Theor Appl Genet 112:843-855

Fazio G, Chung SM, Staub JE (2003a) Comparative analysis of response to phenotypic and marker-assisted selection for multiple lateral branching in cucumber (Cucumis sativus L.). Theor Appl Genet 107:875-883

Fazio G, Staub JE, Stevens MR (2003b) Genetic mapping and QTL analysis of horticultural traits in cucumber (Cucumis sativus L.) using recombinant inbred lines. Theor Appl Genet 107:864-874

Fazio G (2001) Comparative study of marker-assisted and phenotypic selecton and genetic analysis of yield components in cucumber. PhD dissertation University of Wisconsin Madison

Flint-Garcia SA, Darrah LL, McMullen MD, Hibbard BE (2003) Phenotypic versus marker-assisted selection for stalk strength and second-generation European corn borer resistance in maize. Theor Appl Genet 107:1331-1336

Francia E, Tacconi G, Crosatti C, Barabaschi D, Bulgarelli D, Dall'Aglio E, Vale G (2005) Marker assisted selection in crop plants. Plant Cell Tissue Organ Cult 82:317-342

Fredrick LR, Staub JE (1989) Combining ability analyses of fruit yield and quality in near-homozygous lines derived from cucumber. J Am Soc Hort Sci 114:332-338

Gimelfarb A, Lande R (1994a) Simulation of marker assisted selection for nonadditive traits. Genet Res 64:127-136

Gimelfarb A, Lande R (1994b) Simulation of marker assisted selection in hybrid populations. Genet Res 63:39-47

Gupta PK, Roy JK, Prasad M (2001) Single nucleotide polymorphisms: A new paradigm for molecular marker technology and DNA polymorphism detection with emphasis on their use in plants. Curr Sci 80:524-535

Hoeck JA, Fehr WR, Shoemaker RC, Welke GA, Johnson SL, Cianzio SR (2003) Molecular marker analysis of seed size in soybean. Crop Sci 43:68-74

67Kandemir N, Jones BL, Wesenberg DM, Ullrich SE, Kleinhofs A (2000) Marker-assisted analysis of three grain yield QTL in barley (Hordeum vulgare L.) using near isogenic lines. Mol Breed 6:157-167

Kupper RS, Staub JE (1988) Combining ability between lines of Cucumis sativus L. and Cucumis sativus var. hardwickii (R.) Alef. Euphytica 38:197-210

Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743-756

Lower RL, Edwards MD (1986) Cucumber breeding. In: Bassett MJ (eds) Breeding Vegetable Crops. Westport, AVI Publishing Co., pp 173-207

Mohring S, Salamini F, Schneider K (2004) Multiplexed, linkage group-specific SNP marker sets for rapid genetic mapping and fingerprinting of sugar beet (Beta vulgaris L.). Mol Breed 14:475-488

Moreau L, Charcosset A, Gallais A (2004) Experimental evaluation of several cycles of marker-assisted selection in maize. Euphytica 137:111-118

Nam YW, Lee JR, Song KH, Lee MK, Robbins MD, Chung SM, Staub JE, Zhang HB (2005) Construction of two BAC libraries from cucumber (Cucumis sativus L.) and identification of clones linked to yield component quantitative trait loci. Theor Appl Genet 111:150-161

Nijs APMD, Visser DL (1980) Induction of male flowering in gynoecious cucumbers (Cucumis sativus L.) by silver ions. Euphytica 29:273-280

Polashock JJ, Vorsa N (2002) Development of SCAR markers for DNA fingerprinting and germplasm analysis of American cranberry. J Am Soc Hort Sci 127:677-684

Reyna N, Sneller CH (2001) Evaluation of marker-assisted introgression of yield QTL alleles into adapted soybean. Crop Sci 41:1317-1321

Robbins MD, Staub JE (2004) Strategies for selection of multiple, quantitatively inherited yield components in cucumber. In: Lebeda A, Paris HS (eds) Progress in Cucurbit Genetics and Breeding Research. Proceedings of Cucurbitaceae 2004, the 8th EUCARPIA Meeting on Cucurbit Genetics and Breeding. Olomouc, Czech Republic, Palacky University, pp 401-408

Robbins MD, Staub JE, Fazio G (2002) Deployment of molecular markers for multi-trait selection in cucumber. In: Cucurbitaceae 2002. Alexandria, Amer. Soc. Hort. Sci. Press, pp 41-47

68Romagosa I, Han F, Ullrich SE, Hayes PM, Wesenberg DM (1999) Verification of yield QTL through realized molecular marker-assisted selection responses in a barley cross. Mol Breed 5:143-152

SAS (2003) SAS software, Version 9.1 for Windows. Copyright © 2002-2003 by SAS Institute Inc., Cary, NC.

Schneider KA, Brothers ME, Kelly JD (1997) Marker-assisted selection to improve drought resistance in common bean. Crop Sci 37:51-60

Serquen FC, Bacher J, Staub JE (1997a) Mapping and QTL analysis of horticultural traits in a narrow cross in cucumber (Cucumis sativus L.) using random-amplified polymorphic DNA markers. Mol Breed 3:257-268

Serquen FC, Bacher J, Staub JE (1997b) Genetic analysis of yield components in cucumber at low plant density. J Am Soc Hort Sci 122:522-528

Shen L, Courtois B, McNally KL, Robin S, Li Z (2001) Evaluation of near-isogenic lines of rice introgressed with QTLs for root depth through marker-aided selection. Theor Appl Genet 103:75-83

Shetty NV, Wehner TC (2002) Screening the cucumber germplasm collection for fruit yield and quality. Crop Sci 42:2174-2183

Staub JE, Robbins MD, Chung S, López-Sesé AI (2004) Molecular methodologies for improved genetic diversity assessment in cucumber and melon. Acta Hort 637:41-47

Staub JE, Crubaugh LK, Fazio G (2002) Cucumber recombinant inbred lines. Cucurbit Genet Coop Rpt 25:1-2

Staub JE, Crubaugh LK (2001) Cucumber inbred line USDA 6632E. Cucurbit Genet Coop Rpt 24:6-7

Staub JE, Knerr LD, Hopen HJ (1992) Plant density and herbicides affect cucumber productivity. J Am Soc Hort Sci 117:48-53

Steele RGD, Torrie JH, Dickey DA (1996) Principles and Procedure in Statistics. 3rd edn. New York, McGraw-Hill

Stromberg LD, Dudley JW, Rufener GK (1994) Comparing conventional early generation selection with molecular marker assisted selection in maize. Crop Sci 34:1221-1225

Tang SX, Kishore VK, Knapp SJ (2003) PCR-multiplexes for a genome-wide framework of simple sequence repeat marker loci in cultivated sunflower. Theor Appl Genet 107:6-19

69Van Berloo R, Stam P (1999) Comparison between marker-assisted selection and phenotypical selection in a set of Arabidopsis thaliana recombinant inbred lines. Theor Appl Genet 98:113-118

Wehner TC (1989) Breeding for improved yield in cucumber. Plant Breed Rev 6:323-359

Wehner TC, Lower RL, Staub JE, Tolla GE (1989) Convergent-divergent selection for cucumber fruit yield. HortScience 24:667-669

Willcox MC, Khairallah MM, Bergvinson D, Crossa J, Deutsch JA, Edmeades GO, Gonalez-de-Leon D, Jiang C, Jewell DC, Mihm JA, Williams WP, Hoisington D (2002) Selection for resistance to southwestern corn borer using marker-assisted and conventional backcrossing. Crop Sci 42:1516-1528

Yousef GG, Juvik JA (2001) Comparison of phenotypic and marker-assisted selection for quantitative traits in sweet corn. Crop Sci 41:645-655

Zhang C, Tar'an B, Warkentin T, Tullu A, Bett KE, Vandenberg B, Somers DJ (2006) Selection for lodging resistance in early generations of field pea by molecular markers. Crop Sci 46:321-329

Zhang W, Smith C (1992) Computer simulation of marker-assisted selection utilizing linkage disequilibrium. Theor Appl Genet 83:813-820

70 Table 1.1. Mean values of yield component traits of commercial checks and parental inbred lines used to create four cucumber populations for comparison of response to selection by phenotype and marker. Means are from the replicated trial described herein.

Inbred line/Check Population EARa GYNb L:Dc MLBd Yielde Leaf typef

6632E 1 2.47 99.1 2.57 2.1 2.04 Standard 6823B 2 0.76 5.7 3.51 4.5 2.11 Little 6996A 3 1.84 99.2 2.71 0.9 1.50 Standard 6995C 4 0.43 10.6 3.01 3.1 1.60 Standard Gy-7 (check) line 2.13 99.8 2.74 1.0 1.70 Standard H-19 (check) line 0.42 6.1 3.03 5.7 1.84 Little ‘Vlasset’g (check) hybrid 1.98 77.9 2.70 3.0 2.28 Standard a Earliness measured as the number of fruits per plant in first harvest b Gynoecy measured as the percent female flowers in the first ten nodes c Fruit length to diameter ratio measured as the mean length to diameter ratio of 5-10 randomly selected fruit averaged over three harvests d Multiple lateral branching measured as the number of lateral branches (at least three internodes long) on the mainstem in the first 10 nodes e Yield measured as the noumber of fruits per plant averaged over four harvests f Leaf type classified as Standard (> 40 cm2) or Little leaf (30-40 cm2; Staub et al. 1992) g Commercial cultivar from Seminis Vegetable Seeds, Inc, Oxnard, Calif.

71Table 1.2. Cumulative selection differential over three cycles between Stage 1 and Stage 2 of phenotypic selection (PHE) for four traits in four populations of cucumber.

Traita Population Selection differentialb P-valuec Percentd

EAR 1 -3.79 0.019 3.38 2 -2.19 0.080 2.00 3 -2.68 0.008 2.45 4 -3.53 0.011 3.16 GYN 1 7.91 0.205 3.98 2 38.37 0.021 18.40 3 -20.44 0.222 10.24 4 -25.03 0.187 13.48 MLB 1 2.94 <0.001 19.68 2 2.40 <0.001 16.22 3 2.51 <0.001 17.96 4 3.20 <0.001 22.90 L:D 1 0.04 0.169 0.70 2 0.08 0.078 0.86 3 0.04 0.078 0.71 4 0.08 0.027 1.00

a Traits are EAR = earliness measured as the number of days to anthesis of the first female flower, GYN = gynoecy measured as the percentage of plants classified as gynoecious (100% female flowers in the first 10 nodes), MLB = multiple lateral branching measured as the number of lateral branches (at least three internodes long) on the mainstem in the first 10 nodes, and L:D = fruit length to diameter ratio measured as the mean length to diameter ratio of 5-10 randomly selected fruit averaged over three harvests, b Sum over three cycles of the difference between the mean of Stage 2 selections and the mean of the selections from Stage 1 at each cycle. The harmonic mean number of individuals selected at Stage 1 over three cycles was 125.2, 128.9, 114.2, and 130.1 for Populations 1, 2, 3, and 4, respectively. c P-value of a t-test for the significance ot the selection differential d Selection differential expressed as the percent of the mean of Stage 1 selections

72

Table 1.3. Characteristics of molecular markers defined in a genetic map of cucumber constructed by Fazio et al. (2003b) and used in marker-assisted selection for population improvement.

Marker TypeaLinkage group

Map position (cM) Parentb

Multiplex groupc Ideotype QTL (mapping parent and LOD score) and gene associations d

CSWCT28 SSR 1 5.0 G&H G&H EAR(G, 7.1), MLB(H, 10.4), GYN(G, 13.0), L:D(H, 5.7), F L18-SNP-H19 SNP 1 7.4 H 1 H EAR(G, 7.1), MLB(H, 10.4), GYN(G, 13.0), L:D(H, 5.7) OP-AG1-1 RAPD 1 31.8 G H EAR(G, 6.4), MLB(H, 11.6), GYN(G, 7.3), de AJ6SCAR SCAR 1 61.4 G 3 H MLB(H, 3.3) BC523SCAR SCAR 1 66.5 G 2 H MLB(H, 3.3) OP-AD12-1 RAPD 1 70.2 H G EAR(G, 4.1), MLB(H, 32.9), GYN(G, 3.7), L:D(G, 8.6), ll AW14SCAR SCAR 3 3.9 G&H 1 G GYN(G, 5.1) CSWTAAA01 SSR 4 34.1 G&H 2 H MLB(H, 4.6) OP-AI4 RAPD 5 101.0 G G GYN(G, 3.0) OP-AO12 RAPD 5 117.3 G G GYN(G, 3.0) OP-AI10 RAPD 6 22.5 H G L:D(G, 7.3) AK5SCAR SCAR 6 33.0 G 2 H MLB(H, 3.0) M8SCAR SCAR 6 39.1 H H MLB(H, 3.0) OP-W7-1 RAPD 6 83.4 H G GYN(G, 4.1) L19-2-SCAR SCAR 6 115.0 H 1 G MLB(G, 4.2), GYN(G, 4.1) NR60 SSR 6 137.4 G&H G MLB(G, 4.2) BC515 RAPD 7 0.0 H H L:D(H, 4.2) L19-1-SCAR SCAR 7 9.9 H 3 H L:D(H, 4.2) a SSR simple sequence repeat, SNP single nucleotide polymorphism, RAPD random amplified polymorphic DNA, and SCAR sequence characterized amplified region

b Allelic constitution based on mapping parents H-19 and Gy-7 (synom. G421) (Fazio et al. 2003b), where G = present in Gy-7, H = present in H-19, G&H = present in Gy-7 and H-19 (codominant marker) c Markers used in multiplex were placed in multiplexing groups (1, 2, or 3) d Markers associated with QTL for DTF = earliness, MLB = multiple lateral branching, GYN = gynoecious, and L:D = length to diameter ratio. The parentheses contain the parent contributing the QTL (G = Gy-7, H = H-19) followed by the highest LOD score for each QTL obtained from multiple field trials (Serquen et al. 1997a; Fazio et al. 2003b). Genes are F = femaleness, de = determinate, and ll = little leaf

73Table 1.4. Means and linear response of five traits in four base populations (C0) of cucumber which underwent three cycles of recurrent mass selection (C1-C3) using three methods.

Traita Methodb C0 C1 C2 C3 bc R2 P-valued

Population 1e EAR MAS 2.00 1.74 1.54 1.13 -0.281 0.976 <0.001 PHE 2.00 1.90 2.13 2.12 0.058 0.494 0.047 RAN 2.00 1.87 1.81 1.76 -0.076 0.940 <0.001 GYN MAS 89.9 72.3 49.9 29.6 -20.333 0.998 <0.001 PHE 89.9 76.2 94.0 81.8 -0.645 0.011 0.009 RAN 89.9 81.6 87.6 82.7 -1.547 0.256 <0.001 L:D MAS 2.77 2.83 2.92 3.05 0.091 0.973 <0.001 PHE 2.77 2.77 2.76 3.01 0.070 0.565 <0.001 RAN 2.77 2.74 2.73 2.79 0.006 0.073 0.878 MLB MAS 2.41 2.70 3.02 3.17 0.259 0.980 <0.001 PHE 2.41 2.65 2.56 3.08 0.192 0.744 <0.001 RAN 2.41 2.29 2.16 2.20 -0.077 0.792 0.001 Yield MAS 2.21 2.26 2.21 1.81 -0.125 0.608 <0.001 PHE 2.21 2.15 2.17 2.24 0.009 0.084 0.988 RAN 2.21 1.92 2.03 1.97 -0.061 0.368 <0.001 Population 2 EAR MAS 1.66 1.31 1.29 1.30 -0.110 0.629 <0.001 PHE 1.66 1.83 2.13 1.82 0.078 0.264 <0.001 RAN 1.66 1.54 1.45 1.39 -0.091 0.977 <0.001 GYN MAS 54.9 23.4 17.9 25.4 -9.380 0.532 <0.001 PHE 54.9 72.6 93.5 90.4 12.747 0.848 <0.001 RAN 54.9 41.5 40.9 40.8 -4.292 0.642 <0.001 L:D MAS 3.10 3.23 3.24 3.13 0.011 0.037 0.001 PHE 3.10 2.97 2.91 3.22 0.030 0.076 0.429 RAN 3.10 3.10 3.14 3.18 0.029 0.928 <0.001 MLB MAS 2.97 3.48 3.32 3.08 0.017 0.009 0.005 PHE 2.97 2.80 2.67 3.02 0.001 0.000 0.317 RAN 2.97 3.39 3.20 3.16 0.039 0.087 0.003 Yield MAS 2.03 1.96 2.00 1.83 -0.054 0.659 0.007 PHE 2.03 2.00 2.11 2.09 0.029 0.548 0.183 RAN 2.03 2.10 1.90 1.93 -0.047 0.484 0.044 Population 3 EAR MAS 1.87 1.87 1.77 1.87 -0.011 0.085 0.556 PHE 1.87 1.55 1.63 1.57 -0.081 0.516 <0.001 RAN 1.87 1.85 1.83 1.59 -0.087 0.722 0.001 GYN MAS 93.5 82.2 60.6 69.6 -9.336 0.698 <0.001 PHE 93.5 76.9 83.1 89.5 -0.587 0.011 <0.001 RAN 93.5 89.1 83.2 76.0 -5.853 0.989 <0.001 L:D MAS 2.80 2.83 2.89 2.73 -0.015 0.084 0.366 PHE 2.80 2.83 2.92 3.13 0.105 0.868 <0.001 RAN 2.80 2.76 2.85 3.04 0.079 0.692 <0.001

74 Traita Methodb C0 C1 C2 C3 bc R2 P-valued

MLB MAS 2.33 2.25 2.59 2.57 0.106 0.632 <0.001 PHE 2.33 2.14 2.52 2.53 0.098 0.461 0.004 RAN 2.33 2.07 2.21 2.38 0.029 0.073 0.922 Yield MAS 1.86 1.88 2.01 1.99 0.051 0.755 0.010 PHE 1.86 1.82 1.79 1.72 -0.047 0.971 0.018 RAN 1.86 1.77 1.91 1.86 0.014 0.092 0.764 Population 4 EAR MAS 1.76 1.55 1.47 1.59 -0.062 0.397 <0.001 PHE 1.76 1.61 1.95 1.86 0.062 0.303 0.050 RAN 1.76 1.49 1.52 1.59 -0.050 0.274 0.001 GYN MAS 49.7 61.3 54.5 68.1 4.856 0.604 <0.001 PHE 49.7 67.3 87.7 84.7 12.548 0.849 <0.001 RAN 49.7 56.6 40.5 52.5 -0.757 0.020 0.442 L:D MAS 3.01 2.74 2.92 2.75 -0.060 0.349 <0.001 PHE 3.01 2.74 2.70 2.75 -0.081 0.546 <0.001 RAN 3.01 2.88 2.98 2.94 -0.010 0.052 0.005 MLB MAS 3.00 2.71 2.82 2.79 -0.053 0.312 0.003 PHE 3.00 2.62 2.54 2.60 -0.130 0.629 <0.001 RAN 3.00 2.82 3.03 2.99 0.017 0.049 0.983 Yield MAS 2.35 1.89 2.04 2.18 -0.037 0.058 <0.001 PHE 2.35 1.94 2.13 1.97 -0.097 0.433 <0.001 RAN 2.35 1.99 2.05 1.94 -0.117 0.664 <0.001

a Traits are EAR = earliness measured as the number of fruits per plant in first harvest, GYN = gynoecy measured as the percent female flowers in the first ten nodes, L:D = fruit length to diameter ratio measured as the mean length to diameter ratio of 5-10 randomly selected fruit averaged over three harvests, MLB = multiple lateral branching measured as the number of lateral branches (at least three internodes long) on the mainstem in the first 10 nodes, and Yield measured as the number of fruits per plant averaged over four harvests b Methods are MAS = selection by marker, PHE = phenotypic selection, and RAN = random mating (no selection) c Slope of linear regression of means over cycles d P-values from F-tests of linear response to selection e Populations were created by intermating four inbred lines, and then bulking by the maternal parent (Figure 1.1)

75

Table 1.5. Phenotypic correlations (r) among traits in cucumber over three cycles of selection by markers (MAS) and phenotype (PHE). Selection by markers (MAS) Selection by phenotype (PHE) Traita EAR GYN L:D MLB Traita EAR GYN L:D MLB Population 1b Population 1 GYN 0.70*** GYN 0.35** L:D -0.26* -0.57*** L:D 0.19 -0.33** MLB -0.53*** -0.53*** 0.13 MLB -0.29* -0.31* 0.36** Yield 0.56*** 0.47*** -0.18 -0.04 Yield 0.25* 0.04 0.17 -0.02 Population 2 Population 2 GYN 0.37** GYN 0.30* L:D 0.21 -0.44*** L:D 0.01 -0.20 MLB -0.54*** -0.43*** 0.14 MLB -0.35** -0.32** 0.38** Yield 0.54*** 0.17 0.34** -0.07 Yield 0.56*** -0.01 0.28* 0.12 Population 3 Population 3 GYN 0.30* GYN 0.45*** L:D 0.30* -0.26* L:D -0.11 -0.07 MLB -0.38** -0.37** 0.06 MLB -0.26* -0.14 0.31* Yield 0.54*** 0.12 -0.01 -0.17 Yield 0.55*** 0.16 -0.03 -0.08 Population 4 Population 4 GYN 0.37** GYN 0.42*** L:D 0.03 -0.53*** L:D 0.07 -0.64*** MLB -0.46*** -0.41*** 0.32** MLB -0.43*** -0.42*** 0.23 Yield 0.39** 0.08 0.32** 0.27* Yield 0.49*** -0.12 0.32** 0.13

a Traits are EAR = earliness measured as the number of fruits per plant in first harvest, GYN = gynoecy measured as the percent female flowers in the first ten nodes, L:D = fruit length to diameter ratio measured as the mean length to diameter ratio of 5-10 randomly selected fruit averaged over three harvests, MLB = multiple lateral branching measured as the number of lateral branches (at least three internodes long) on the mainstem in the first 10 nodes, and Yield measured as the number of fruits per plant averaged over four harvests b Populations were created by intermating four inbred lines, and then bulking by the maternal parent (Figure 1.1) * = P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001

76

6632E(Inbred 1)

Cross with2,3,4

Pop. 1 C0

6823B(Inbred 2)

6632E(Inbred 1)

Cross with1,3,4

Pop. 2 C0

6996A(Inbred 3)

Cross with1,2,4

Pop. 3 C0

6995C(Inbred 4)

Cross with1,2,3

Pop. 4 C0

Cycle 1

Replicated Trial

MASPHE RAN MASPHE RAN MASPHE RAN MASPHE RAN

MASPHE RANCycle 2

MASPHE RANCycle 3

MASPHE RAN

MASPHE RAN

MASPHE RAN

MASPHE RAN

MASPHE RAN

MASPHE RAN

Cross with2,3,4

Pop. 1 C0

6823B(Inbred 2)

Cross with1,3,4

Pop. 2 C0

6996A(Inbred 3)

Cross with1,2,4

Pop. 3 C0

6995C(Inbred 4)

Cross with1,2,3

Pop. 4 C0

Cycle 1

Replicated Trial

MASPHE RAN MASPHE RAN MASPHE RAN MASPHE RAN

MASPHE RAN MASPHE RAN MASPHE RAN MASPHE RANCycle 2

MASPHE RANCycle 3

MASPHE RAN MASPHE RAN MASPHE RAN

Figure 1.1. Schematic of the selection and evaluation scheme in cucumber where PHE is phenotypic selection, MAS is selection by marker, and RAN is random mating.

77

Earliness (EAR)

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

Pop. 1 Pop. 2 Pop. 3 Pop. 4

Slop

e of

line

ar re

gres

sion

***

***

*

***

***

*** ******** **

*

Gynoecious Sex Expression (GYN)

-25.0

-20.0

-15.0

-10.0

-5.0

0.0

5.0

10.0

15.0

Pop. 1 Pop. 2 Pop. 3 Pop. 4

Slop

e of

line

ar re

gres

sion

***

***

***

***

***

***

***

***

***

***

*

Fruit L:D (L:D)

-0.10

-0.05

0.00

0.05

0.10

0.15

Pop. 1 Pop. 2 Pop. 3 Pop. 4

Slop

e of

line

ar re

gres

sion

******

******

******

******

**

Multiple Lateral Branching (MLB)

-0.15

-0.10

-0.05

0.00

0.050.10

0.15

0.20

0.25

0.30

Pop. 1 Pop. 2 Pop. 3 Pop. 4

Population

Slop

e of

line

ar re

gres

sion

***

***

**

* *

*****

**

***

Yield (Number of fruit per plant)

-0.14-0.12-0.10-0.08-0.06-0.04-0.020.000.020.040.06

Pop. 1 Pop. 2 Pop. 3 Pop. 4

Population

Slop

e of

line

ar re

gres

sion

***

*** ** *

**

****

******

MAS PHE RAN

Selection method

Figure 1.2. Response to selection as measured by the slope of linear regression (Y axes) over three cycles of MAS (selection by marker), PHE (phenotypic selection) or RAN (random mating) for five traits in four cucumber populations. *, **, and *** denote slopes are significant at P ≤ 0.05, P ≤ 0.01, and P ≤ 0.001, respectively.

78

Figure 1.3. Time required to complete MAS (selection by marker) and PHE (phenotypic selection) for three cycles in four cucumber populations. Gray areas indicate evaluation of the populations and black areas represent recombination among selections.

79

Chapter 2. The development of molecular markers with increased efficacy for genetic analysis in cucumber

Abstract

The genetic base of cucumber (Cucumis sativus L.; 2n=2x=14) is narrow, thus the

recovery of useful markers from genetic analyses is typically low. Although the largely

RAPD-based genetic linkage maps of cucumber have proven effective for marker-

assisted selection (MAS), marker genotyping is inefficient. Conversion of RAPD to

SCAR markers has not been extremely successful in cucumber and, thus, methods were

developed to increase conversion of these RAPDs into more efficient and effective SCAR

and SNP markers. Forty-three dominant RAPD bands were sequenced to create 22

polymorphic (17 dominant, five codominant) SCAR markers. Evaluation of three

multiplexing sets revealed that the optimal number of SCARs that could be combined

during PCR was four to five. Twenty-three monomorphic or dominant SCARs were

sequenced to identify SNPs. Additional sequences were obtained using the amplicons of

six dominant markers (five SCAR and one RAPD) linked to important yield traits to

probe a cucumber BAC library, that led to the sequencing of a subset of positive clones.

Four different approaches were utilized to create allele-specific markers based on SNPs

for 20 (19 codominant, one dominant) of the 25 loci that contained SNPs. Of the 43

initial RAPDs, 32 were converted into SCAR or SNP markers, six of which have proven

effective in MAS, while others provide a starting point for molecular investigation of

several QTL associated with yield-related traits in cucumber.

80Introduction

Molecular markers are becoming proven, valuable tools for the improvement of

many crop species (Collard et al. 2005; Francia et al. 2005). Markers have been

employed in plant breeding programs for genetic diversity assessment, cultivar identity,

genetic similarity estimation, fingerprinting, genetic map construction, gene tagging, and

marker-assisted selection (MAS; Collard et al. 2005). The ideal markers for use in plant

breeding programs are codominant, easily adapted, robust (repeatable), abundant, and

amenable to low cost, high-throughput systems (Staub et al. 1996; Mohan et al. 1997;

Gupta et al. 1999; Collard et al. 2005).

In cucumber (Cucumis sativus L.; 2n=2x=14), a moderately saturated genetic

linkage map has been constructed (Fazio et al. 2003b) that defined economically

important marker-trait associations which have been useful in MAS. Gain from selection

using MAS has been demonstrated for quantitative traits (multiple lateral branching,

gynoecious sex expression, and fruit length to diameter ratio) in backcross breeding

(Fazio et al. 2003a; Fan et al. 2006) and in recurrent selection (Chapter 1).

Approximately 70% of the 131 markers on the Fazio et al. (2003b) map, however, are

random amplified polymorphic DNA (RAPD) or amplified fragment length

polymorphism (AFLP) markers, and roughly 80% of all markers are dominant. Although

AFLPs and RAPDs are well suited for relatively rapid identification of new markers, the

low reproducibility of RAPDs and the technical methodology of AFLPs as well as their

dominant nature make them less efficient than more robust sequence characterized

amplified region (SCAR), and codominant simple sequence repeat (SSR) and single

81nucleotide polymorphism (SNP) based markers, which are better suited for tracking

alleles during MAS (Brugmans et al. 2003; Shirasawa et al. 2004; Collard et al. 2005).

The historically low rate of new marker identification due to the narrow genetic base of

cucumber (3-8% polymorphism among adapted cultivars for any given marker; Knerr et

al. 1989; Dijkhuizen et al. 1996; Horejsi and Staub 1999; Fazio et al. 2002) suggests that

existing resources should be used to convert mapped RAPD markers to more efficient

markers for use in MAS.

SCAR markers were initially designed to convert polymorphic RAPD markers

into robust, single-copy markers by extending the original 10 bp RAPD primer an

additional 14 bases pairs on the 3’ end based on the internal sequence of both ends of the

RAPD fragment (Paran and Michelmore 1993). Although the SCAR marker may retain

the original RAPD polymorphism in some cases, lengthening RAPD primers to increase

PCR specificity may mask the original RAPD polymorphism in others. If the latter

occurs, raising the PCR annealing temperature may reveal polymorphisms that are not

apparent under standard PCR conditions (Paran and Michelmore 1993; Horejsi et al.

1999). Additionally, both alleles of the SCAR marker may be sequenced to identify

polymorphisms for exploitation in genotypic analysis. Although initially created from

RAPDs, SCAR markers can be created from any genomic sequence by designing specific

primer pairs for PCR (Paran and Michelmore 1993).

Codominant SNP-based markers are particularly attractive for use in MAS

because they are robust, abundant, and can be detected by several methods, including low

cost methods using basic laboratory equipment (Gupta et al. 2001). Cleaved amplified

82polymorphic sequence (CAPS; Konieczny and Ausubel 1993), allele-specific PCR

(AS-PCR; Newton et al. 1989; Sarkar et al. 1990) and single-nucleotide amplified

polymorphism (SNAP) markers (Drenkard et al. 2000) are all based on SNPs and are

detected by PCR and agarose gel electrophoresis. The advantage of the AS-PCR or

SNAP markers, however, is that they do not require a restriction enzyme step after PCR,

which is essential for CAPS markers.

The limited success of a previous RAPD to SCAR conversion study in cucumber

using silver staining technology for sequencing (Horejsi et al. 1999) compared to that of

Paran and Michelmore (1993), prompted a reevaluation of the conversion of existing

RAPDs into more efficient markers using current sequencing methods (automated

fluorescent sequencing). This was accomplished by: 1) converting several RAPDs into

polymorphic SCAR markers; 2) determining if the new SCAR markers could be

multiplexed to increase their efficiency; 3) creating codominant markers based on SNPs

from sequences of monomorphic or dominant SCARs (a) and specific BAC clones (b);

and 4) evaluating allele-specific SNP markers from both sequence sources. The

development, optimization, and evaluation of these markers for PCR will allow for

increased MAS efficiency during plant improvement in cucumber.

Materials and Methods

RAPD to SCAR conversion Forty-three RAPD markers mapped by Serquen et al. (1997) and Fazio et al.

(2003b) were selected for conversion to SCAR markers (Objective 1; Table 2.1).

83Parental lines of these linkage maps, Gy-7 (synon. G421) and H-19, were used as

templates for amplification of RAPD markers employing PCR conditions used by

Serquen et al. (1997). The PCR products were resolved on agarose (1.6%) gels, and

polymorphic bands were excised and purified with the QIAquick gel extraction kit

(QIAGEN Inc., Valencia, CA) following the manufacturer’s protocol. The resulting

fragments were subsequently cloned into the pGEM®-T vector (Promega Corporation,

Madison, Wisc.) following manufacturer’s instructions, and single colonies were picked

and used to inoculate 3 mL of LB broth with ampicillin (100μg/mL) for overnight

incubation at 37o C. Plasmids were then isolated using the QIAprep spin miniprep kit

(QIAGEN Inc., Valencia, CA) according to the manufacturer’s protocol, and the inserts

were amplified in preparation for sequencing using M13 forward (5’-CGC CAG GGT

TTT CCC AGT CAC GAC-3’) and M13 reverse (5’-TCA CAC AGG AAA CAG CTA

TGA C-3’) sequencing primers. PCR conditions were 1× Taq DNA Polymerase

Reaction Buffer without MgCl2 (Promega Corporation, Madison, Wisc.), 5 mM MgCl2,

0.25 mM of each dNTP, 15-20 ng template DNA, 0.3 μM of each primer, and 1 U of Taq

DNA polymerase in 15 μL reactions cycled at 94o C for 3 min, 35 cycles of 94o C for 50

sec, 60o C for 1 min, and 72o C for 1 min 50 sec, followed by 72o C for 7 min. A sample

of the PCR product was visualized by agarose (1.6%) gel electrophoresis to verify

amplification of a single band at the expected molecular weight. Once verified, the

remaining product was purified, used for template in sequencing PCR, and the

sequencing products analyzed using standard protocols developed at the DNA

Sequencing Laboratory, University of Wisconsin Biotechnology Center (Madison, Wisc.).

84Sequences of the cloned RAPD fragments were examined to verify that they

contained the original RAPD primer sequence on both ends of the fragment. SCAR

primers were then created for each end of the fragment by adding 14 base pairs of unique

sequence to the 10 base pairs of the original RAPD primer sequence (Paran and

Michelmore 1993). SCAR primer pairs were subsequently tested in PCR with Gy-7 and

H19 as templates using annealing temperature gradient PCR (ATG-PCR) to determine

the optimal annealing temperature of each SCAR marker. ATG-PCR reactions included

1X Taq DNA Polymerase Reaction Buffer without MgCl2 (Promega Corporation,

Madison, Wisc.), 3 mM MgCl2, 0.2 mM of each dNTP, 15 ng template DNA, 0.3 μM of

each primer, and 1 U of Taq DNA polymerase in 15 μL reactions. The ATG-PCR

reactions were run on a Mastercycler gradient (Eppendorf AG, Hamburg, Germany) PCR

thermal cycler with cycling conditions as 94o C for 4 min, 40 cycles of 94o C for 30 sec, a

gradient of 60 ± 10o C for 45 sec, and 72o C for 1 min 30 sec, followed by 72o C for 7

min, and then held at 4o C. Each marker was tested at 12 different annealing

temperatures across the 60 ± 10o C gradient (50.6°, 50.6°, 51.6°, 53.4°, 55.0°, 58.5°,

61.4°, 64.2°, 66.7°, 68.7°, 69.9°, and 70.1° C). To determine the polymorphic nature of

the SCAR and verify that its molecular weight matched the original RAPD marker, the

ATG-PCR products were visualized by agarose (1.6%) gel electrophoresis and compared

to the original RAPD products using Gy-7 and H-19 as template DNA.

SCAR multiplexing Three different sets of SCARs were combined in multiplex to determine if

multiple SCAR markers could be evaluated in the same PCR reaction (Objective 2). The

85creation of multiplexing sets followed the logic of Henegariu et al. (1997), where

SCAR primers in the same multiplex set possessed minimal primer-primer interactions,

had similar optimal annealing temperatures and were of differing molecular weights.

While Set 1 consisted of BC469SCAR, BC551SCAR, BC592SCAR, L19SCAR, and

W7SCAR, Set 2 included AF7SCAR, C10SCAR, C1SCAR, and N8SCAR, and Set 3

comprised AO8SCAR, AW14SCAR, and BC526SCAR (all codominant markers).

SCAR markers were run separately and in all duplex, triplex, quadruplex, and pentaplex

(Set 1 only) combinations possible within a set during ATG-PCR reactions. The ATG-

PCR reactions were prepared and products visualized as stated above, where 0.3 μM of

each primer was added to the same PCR multiplex reaction. Multiplex performance was

evaluated as a visual inspection in the same gel of the band intensity of a marker in

multiplex compared to its intensity when used in PCR alone (Figure 2.1).

Codominant marker development

Identification of SNPs by SCAR sequencing All monomorphic SCARs and six dominant SCARs were sequenced to create

codominant markers from SNPs between Gy-7 and H-19 (Objective 3a; Table 2.1). The

dominant markers are linked to QTL for yield components and were chosen because of

their utility in MAS (Fazio et al. 2003b; Chapter 1). Each monomorphic marker was

amplified under the PCR conditions stated above for ATG-PCR, except the annealing

temperature was not a gradient, but a single temperature previously determined to

produce one Gy-7 and one H-19 band (Table 2.1). The PCR reaction was verified by

agarose (1.6%) gel electrophoresis and sequenced by previously described methods. In

86order to obtain Gy-7 and H-19 specific fragments from dominant SCAR markers, PCR

annealing temperatures were relaxed to 35o C. Under these conditions, multiple bands

were usually produced, necessitating the excision of specific fragments from the gel

using the Wizard® SV gel and PCR clean-up system (Promega Corporation, Madison,

WI), followed by sequencing as previously described. To distinguish true SNPs from

sequencing artifacts such as polymerase errors, three independent sequences in both the

forward and reverse direction were generated for both Gy-7 and H-19.

Identification of SNPs by BAC end sequencing In addition to direct sequencing of SCAR markers, specific BAC clones were

employed to provide sequence information for Gy-7 and H-19 SNP detection (Objective

3b). Several markers linked to important yield component QTL, including L19SCAR

[denoted as L19-2-SCAR by Fazio et al. (2003b)], AJ6SCAR, BC523SCAR, and

M8SCAR (four SCAR markers created herein), a RAPD marker, OP-W7-1, and an SSR

marker, CSWCTT14 (Fazio et al. 2003b), had been previously used to probe a cucumber

BAC library (Nam et al. 2005). A subset of positive clones identified by each of these

markers was randomly selected (Table 2.2) and end sequenced by Macrogen Inc. (Seoul,

Korea). From each BAC end sequence, SCAR primer pairs were created using Primer3

(Rozen and Skaletsky 2000) to amplify fragments from Gy-7 and H-19 genomic DNA

(Table 2.2). All primers were tested using Gy-7 and H-19 DNA by ATG-PCR

methodologies as stated above, except that the annealing temperature gradient was 55 ±

10o C for the initial test, and 65 ± 5o C and/or 45 ± 5o C for subsequent tests as needed to

determine the optimal annealing temperature of each primer pair. While several primer

87pairs produced polymorphic bands without further modification (Table 2.2), all other

primer pairs produced monomorphic bands, which were sequenced to identify SNPs

following methodologies described above.

SNP marker creation The Gy-7 and H-19 DNA sequences from both sources [SCAR markers

(Objective 3a) and BAC ends (Objective 3b)] were compared to identify SNPs. Raw

ABI trace files were processed, and contigs were made using the Staden Package for

Windows Version 2002.0 (Staden 1996). Only unambiguous differences from all

overlapping sequences (at least two overlapping sequences from each parent in any given

region) were recorded as SNPs. To reduce genotyping costs, SNP marker requirements

were that they be easily detected by PCR and agarose gel electrophoresis without any

additional processing steps (i.e., restriction enzyme digest common to CAPS markers).

Thus, allele-specific markers were designed for each genotype (Gy-7 and H-19) at each

SCAR locus that was amenable to independent (assays G and H in Figure 2.2) or

combined (assay C in Figure 2.2) evaluation in a single PCR reaction (Objective 4). To

visualize both allele-specific markers by agarose gel electrophoresis, the two markers

were designed to produce PCR products of different molecular weight (> 10 bp

difference) within a range of 100 to 500 bp. Four approaches were developed herein

(Figure 2.3) to design allele-specific markers that fit these requirements based on the

number and location of SNPs in a sequence. Each allele-specific marker contained one

allele-specific primer based on a SNP polymorphism (e.g., Gy-7 allele-specific primer;

88Figure 2.2) and one non-specific primer for each allele (e.g., Universal non-specific

primer in Figure 2.2 to amplify Gy-7 allele).

Allele-specific primers were created based on SNAP marker protocols using

WebSNAPER (Drenkard et al. 2000) where the final base at the 3’ end of the primer

matched the sequence of one of the parents at a SNP. To improve the allelic

discrimination of the primers, WebSNAPER includes an additional internal mismatch to

both parents within the first four base pairs from the 3’ end of the primer (Newton et al.

1989; Sarkar et al. 1990; Drenkard et al. 2000; Figure 2.2). Three allele-specific primers

were chosen for each targeted SNP from the output of WebSNAPER based on the

position and nature of the internal mismatch. Preference was given to stronger

mismatches (A:G = C:C > A:A > G:G > C:A > G:T = C:T = T:T; Kwok et al. 1990;

Moreno-Vazquez et al. 2003) closest to the 3’ end of the primer. Rather than design a

new primer, the non-specific primer was, in most cases, one of the original SCAR

primers used to produce the fragment for sequencing. All allele-specific primers and

non-specific primers that could potentially be used in a PCR reaction were analyzed by

Oligo Analyzer version 1.0.2 to determine tendencies of the primer to form secondary

structures with itself and/or with all other primers (ΔG). In cases where secondary

structures were likely (ΔG < -5.0), new allele-specific primers were designed, or an

alternative non-specific primer was designed using Primer3 as described above.

In most cases, multiple SNPs were detected within a locus (Tables 2.1 and 2.2).

The choice of which SNP to use to create allele-specific primers was based on several

factors including location of the SNP, product size, and SNP mismatch stability. The

89optimal scenario was to create Gy-7 allele-specific primers based on one SNP near

another SNP used to create H-19 allele-specific primers (optimal approach; Figures 2.2

and 2.3). The allele-specific primers for both Gy-7 and H-19 were designed in the same

orientation such that a single, non-specific primer (usually one of the original SCAR

primers) could be utilized for both alleles. In such cases, only three primers were

required to produce PCR products of different lengths from both Gy-7 and H-19 alleles in

a single assay (assay C, Figure 2.2, Panel B). In cases where multiple SNPs were

available, the SNPs chosen for targets of allele-specific primers provided the lowest

possible 3’ SNP mismatch stability and contained flanking sequence amenable to primer

design (i.e., no repeated sequence, balanced GC content, and primers without significant

secondary structure).

The SNP position and number varied across marker loci, requiring several

variations to the optimal strategy described above, which were named based on the

position of the allele-specific primers relative to the SNP (Figure 2.3). The opposite

approach was utilized when SNPs were on opposite ends of a long fragment or when

sequence characteristics prevented allele-specific primers from being designed in the

same direction. As a separate, non-specific primer was required for each allele, four

primers were required for the combined assay (assay C, Figure 2.2, Panel B) for markers

designed by this approach. Two design approaches, designated tail and sandwich, were

utilized when fragments contained only one SNP and required the use of three and four

primers, respectively, to detect both alleles in the same reaction (assay C, Figure 2.2,

Panel B). The tail approach was utilized in one case (AC17SCAR) where the sequence

90on only one side of the SNP was suitable for PCR primer design. To create a length

polymorphism that was detectable by agarose gel electrophoresis, 12 extra bases

(GATACAGATACA) were added to the 5’ end of the Gy-7 allele-specific primer

(Sheffield et al. 1989; Papp et al. 2003). This extra “tail” did not promote secondary

structure with any other primers for this locus, and was not complementary to the

template so that the PCR annealing temperatures of both the longer Gy-7 and shorter H-

19 allele-specific primers were similar. Thus, the Gy-7 and H-19 allele-specific primers

could be combined in the same reaction. In cases where primers could be designed from

sequences on both sides of a SNP, the sandwich approach was utilized.

SNP marker evaluation and verification Three allele-specific primers were designed for each target SNP because the

combination of 3’ mismatch and internal mismatch does not always provide allele

specificity during PCR (Drenkard et al. 2000). Each allele-specific marker was tested in

triplicate to determine which of the three allele-specific primers provided the best results

at optimized annealing temperatures (Objective 4). The PCR reagent concentrations were

identical to those for ATG-PCR tests for SCAR primer pairs, and thermal cycling

conditions were 94o C for 5 min, 35 cycles of 94o C for 30 sec, 60o C for 45 sec, and 72o

C for 1 min, followed by 72o C for 6 min, and then held at 4o C. If allele specificity was

not achieved for at least one allele-specific marker, the annealing temperature was

adjusted accordingly, and in some cases, ATG-PCR was performed to identify optimal

annealing temperatures. Once the best Gy-7 (assay G) and H-19 (assay H) allele-specific

markers were identified at a locus, they were combined for evaluation in a single assay

91(assay C, Figure 2.2, Panel B). If necessary, annealing temperatures were readjusted to

obtain allele specificity in the combined assay.

To verify marker utility and conversion, all RAPD-based polymorphic markers

created herein were used to genotype Gy-7, H-19, an F1, and 20 F2 individuals from the

original mapping population of Serquen et al. (1997). PCR reaction conditions of each

marker followed those determined by empirical testing. Only those markers whose F2

genotypes matched those of the original polymorphic RAPD band were considered the

same locus as the RAPD.

Results

RAPD to SCAR conversion Sequence information and SCAR primers were obtained for 40 of the 43 (93%)

RAPD markers selected for conversion to SCARs (Objective 1; Table 2.1). Only three

(7.5%) SCAR primer pairs (BC388SCAR, BC403SCAR, and AS5SCAR) produced more

than one band in each parent at the optimum annealing temperature determined by ATG-

PCR (Table 2.1). All SCAR primer pairs produced a band that matched the molecular

weight of the original RAPD, except AG1-1SCAR and AT1SCAR. These markers are

not the same locus as their original RAPDs, since AG1-1SCAR and a SNP created from

AT1SCAR did not co-segregate with their respective RAPD in 20 F2 plants. All other

polymorphic SCARs co-segregated with their respective RAPD marker. Thus, 38 SCAR

markers (95%) matched the original RAPD, of which, 16 were monomorphic, 17 were

dominant (eight amplified Gy-7, nine amplified H-19), and five were codominant (PCR

92products from Gy-7 and H-19 were of different molecular weights). A total of 22 (17

dominant and five codominant) of the 43 RAPDs (51%) were converted to SCARs.

SCAR multiplexing The testing of three multiplexing sets revealed that SCAR markers can be

multiplexed (Objective 2). In most cases, markers multiplexed in pairs did not show

adverse effects to either marker. However, some reduction in band intensity was

observed for some duplex pairs (e.g., C1SCAR when combined with C10SCAR; Figure

2.1, Panel B). Generally, the more markers used in multiplex reactions, the greater the

reduction in band intensities. Likewise, the band intensities of higher molecular weight

markers were generally more affected than markers of low molecular weight (e.g.,

AF7SCAR and N8SCAR in multiplex with C1SCAR and C10SCAR; Figure 2.1, panel

E). Although a reduction in intensity was observed for at least one marker in every

multiplex set, an optimal annealing temperature could be identified in each set where all

markers were readily discernable. These annealing temperatures were 67oC for Set 1,

64oC for Set 2 (Figure 2.1), and 64oC for Set 3.

Codominant marker development

Identification of SNPs by SCAR sequencing In several cases, the Gy-7 and H-19 SCAR alleles were sequenced to identify

SNPs for the development of codominant markers (Objective 3a; Table 2.1). Sequence

for both Gy-7 and H-19 was obtained for 19 markers, 12 of which contained at least one

SNP and 11 contained multiple SNPs. The number of SNPs per marker ranged from 1 to

15. A total of 15,913 bases were sequenced from which 76 SNPs and 5

93insertion/deletions (indel) were identified with an average of approximately one SNP

every 210 bp, and one indel every 3,183 bp. While the majority (52, 64%) of the

polymorphisms identified were transition mutations, 24 (30%) transversions, four single

base indels (5%), and a single TC indel (1%) were also identified (Appendix G).

Identification of SNPs by BAC end sequencing A total of 30 BAC clones were selected for end sequencing as a source to identify

SNPs (Objective 3b; Table 2.2). From these sequences, 43 unique SCAR primer pairs

produced a PCR product with Gy-7 or H-19 as template. Nine of the 43 SCAR markers

were polymorphic between Gy-7 and H-19, six of which were dominant (present in Gy-7)

and three were codominant (Table 2.2). Since they were polymorphic, the PCR products

of these markers were not sequenced for SNP identification, except for W-2-BE-R

(codominant) which was difficult to score by agarose gel electrophoresis. When tested

on F2 individuals, B-3-BE-R, B-5-BE-L, and M-5-BE-L segregated with the original

SCAR markers used to isolate their BAC clones. In contrast, AJ-1-BE-L and AJ1-BE-R2

did not segregate with AJ6SCAR, and AJ-2-BE-L, AJ-2-BE-R, and AJ-3-BE-L, although

polymorphic between the parents, did not produce a PCR product in the F2 individuals

examined. Sequences of both the Gy-7 and H-19 bands were obtained for 34 (79%) of

the SCAR markers, 12 (28%) of which contained at least one SNP (Table 2.2). A total of

84 SNPs and six indels were identified in 14,505 bases sequenced, averaging one SNP

every 173 bp and one indel every 2,418 bp, with one to 31 SNPs per sequence. Of the 90

polymorphisms, one in W-2-BE-R was a 12 bp indel (TTAATTTTTTTA), two were two

bp indels, three were single bp indels, 63 (70%) were transition mutations, and 21 (23%)

94were transversion mutations (Appendix G). The Gy-7 and H-19 sequences from C8-

BE-R were not from the same locus since their sequences did not match.

SNP marker creation Allele-specific markers were created from the sequences of all SCAR markers

[from RAPDs (Table 2.1; Objective 3a) and BAC ends (Table 2.2; Objective 3b)] that

contained SNPs utilizing one of four different marker design strategies, depending on the

location and number of SNPs in each marker (Figures 2.2 and 2.3). Of the 25 loci that

contained SNPs (Table 2.3), 17 were amenable to the optimal approach, while the

sandwich, opposite, and tail approach were specifically applied to three, two, and one

locus, respectively. None of the four marker design strategies were applicable to C8-BE-

R or W-2-BE-R. Since the C8-BE-R sequences of Gy-7 and H-19 were unique, a SCAR

marker was designed for each of them. Although four H-19 allele-specific primers were

created for W-2-BE-R, no suitable Gy-7 specific primers were identified because the AT

content of the sequence was too high for primer design.

SNP marker evaluation Of the 138 putative allele-specific markers created, 98 (71%) appropriately

distinguished the Gy-7 and H-19 alleles, 29 (21%) were not allele specific (produced a

PCR product from Gy-7 and H-19), and 11 (8%) did not produce a PCR product in either

Gy-7 or H-19 (Objective 4; Appendix H). A suitable Gy-7 specific marker (assay G,

Figure 2.2) was identified for all loci, except B-1-BE-L, C-6-BE-L, and M-4-BE-R

(Table 2.3). Suitable H-19 specific markers were not identified for B-1-BE-L, C-1-BE-R,

C-6-BE-L, M-4-BE-R, and W-2-BE-R. Except for W-2-BE-R, all markers at each locus

95were not allele-specific as they produced a PCR product in both Gy-7 and H-19.

Although no PCR products were obtained from any allele-specific marker at W-2-BE-R,

both SCAR markers created for C8-BE-R successfully amplified their respective

fragments. From the 19 loci with both Gy-7 and H-19 allele specific markers (Table 2.3),

the two alleles were reliably detected at 15 loci when the allele-specific markers were

combined (assay C, Figure 2.2). Allele specificity was lost when the allele-specific

markers were combined for C-3-BE-L, C3-BE-R2, M-4-BE-L, and M7-BE-L, since both

alleles were amplified by one or both of the allele-specific markers. Thus, from the 25

SCAR sequences that contained a SNP (Table 2.3), two new SCAR markers (C8RG and

C8RH) and a total of 20 SNP markers (19 codominant and one dominant), were created

(Table 2.3). All SNP markers that can be traced back to RAPD markers (all but those

from BAC end sequences of clones that hybridized to CSWCTT14) matched the

segregation pattern of the corresponding RAPD, except AT1SNPG3H3.

Summary of RAPD conversion Fifty-three SCAR markers [19 from RAPDs (Objective 3a) and 34 from BAC

sequences (Objective 3b)] were sequenced, but only 25 (47%) contained SNPs (Tables

2.1 and 2.2). The identification of a SNP, however, usually resulted in a polymorphic

marker [20 out of 25 (80%); Table 2.3]. Only three (BC523SCAR, L19SCAR, and

M8SCAR) of the six dominant SCARs derived from RAPDs were converted to a

codominant SNP, and all of these were derived from BAC end sequences. A codominant

SNP marker was also created from the sequence of M8SCAR, the only dominant SCAR

marker that contained a SNP. The identification of SNPs in monomorphic SCARs

96resulted in SNP markers for 10 RAPD markers that were not converted to SCARs.

These 10, along with the 22 SCARs derived from RAPDs, make a total of 32 (74%) of

the original 43 RAPDs that were converted into a SCAR or SNP marker (Table 2.4).

Three markers were created for BC523, two for OP-L19-2, and five for OP-M8 (Table

2.4), making a total of 39 new markers. Over half of the new markers (21 or 54%) are

codominant, while the other 18 are dominant (9 dominant for Gy-7 and 9 dominant for H-

19).

Discussion

Paran and Michelmore (1993) developed SCAR primers that contained the

original RAPD primer sequence since most RAPD polymorphisms originate as mutations

or deletions in the priming site (Paran and Michelmore 1993; Willcox et al. 2002). This

approach resulted in the conversion of nine RAPDs to three codominant, five dominant,

and one monomorphic SCAR marker, and eight out of the nine polymorphic SCAR

primer pairs produced a single band. In a previous RAPD to SCAR conversion study in

cucumber, 75 RAPDs were sequenced by silver staining of polyacrylamide gels to

produce 48 primer (18 to 22 bp) pairs (96 primers), resulting in 11 polymorphic SCAR

markers (Horejsi et al. 1999). For both conversion studies, SCAR marker optimization

(i.e., increasing the PCR annealing temperature or a restriction enzyme digestion after

PCR) was required to detect polymorphisms in some cases.

97RAPD to SCAR conversion

The sequences of RAPD markers common between Horejsi et al. (1999) and this

study were aligned, and, although some sequences did not match, the similarity of the

matching sequences ranged from 89.8% to 99.0% (Table 2.5). Because the RAPD bands

were sequenced only once herein and by Horejsi et al. (1999; silver staining), sequencing

errors could be present in both studies. The number of questionable and unknown base

determinations from sequencing by silver staining, however, was much higher than

sequences produced by automated fluorescent sequencing utilized herein (Appendix J).

Sequence differences were detected in the SCAR priming sites of SCAD14800,

SCAK51275, SCAS5800, and SCBC403750, (Horejsi et al. 1999), which is the likely reason

for the lack of a PCR product from SCAD14800 and SCAS5800. Moreover, a much higher

percentage of RAPDs were sequenced herein (93%; Table 2.1) compared to Horejsi et al.

(1999; 64%). These results indicate that the improved sequencing methodology utilized

herein increased the success of RAPD to SCAR conversion compared to that of Horejsi et

al. (1999).

The sequences of nine of the 21 RAPDs common to both studies did not match

(Table 2.5), suggesting they are separate loci. The finding that BC526SAR, I20SCAR,

P14SCAR, and W7aSCAR co-segregate with their original RAPD (Table 2.1) suggests

that a band other than the polymorphic RAPD was cloned and sequenced for SCBC526900,

SCI201300, SCP141380, and SCW71150, even though their molecular weights matched those

of their respective RAPDs. This conclusion is further supported by the finding that

SCW71150 and OP-W7-1 map to different linkage groups (Linkage Group 4 and 6,

respectively; Fazio et al. 2003b). Furthermore, two markers from this study, AG1-

981SCAR and AT1SCAR, did not match their respective RAPD. Although SCARs

should be verified by molecular weight after cloning the RAPD band, they must also be

evaluated in a segregating population to be certain that the SCAR amplifies the same

locus as its RAPD progenitor (Paran and Michelmore 1993).

The same three SCAR markers that were polymorphic from Horejsi et al. (1999)

were also polymorphic in this study, and seven additional polymorphic SCARs were

recovered (Table 2.5). Since SCAJ181000 and SCBC388350 were polymorphic and their

primers did not contain any of the original RAPD primer sequence, the original RAPD

polymorphism is not in the RAPD priming site. However, the polymorphisms at OP-

AK5, OP-AS5, BC403, and BC469, are most likely in the RAPD priming site, since the

only major difference between the monomorphic SCAR markers of Horejsi et al. (1999)

and the polymorphic SCARs described herein is the inclusion of the RAPD primer

sequence in the SCAR primers. Although not all RAPD polymorphisms originate in the

RAPD priming site, the probability of recovering a polymorphism is increased when the

original RAPD primer is included in the SCAR primers. However, only three of the 96

primers created by Horejsi et al. (1999) contained all 10 bp of the original RAPD primer,

while 13 contained at least one bp, and 80 did not contain any part of the RAPD primer.

The fact that all SCAR primers examined herein contain the original RAPD primer

sequence is a major reason the RAPD to SCAR conversion rate of this study (51%) was

dramatically higher than that of Horejsi et al. (1999; 15%).

Only 7.5% of SCAR markers created herein produced more than one band per

parent (Table 2.1) compared to 42% reported by Horejsi et al. (1999). This difference

99(Table 2.5) may be attributable to the increased length of the SCAR primers [18-22 bp

(Horejsi et al. 1999) vs. 24 bp herein] or the identification of optimal PCR annealing

temperatures by ATG-PCR. The specificity advantage of comparatively long SCAR

primers may, however, be offset by their toleration of mismatches in the original RAPD

priming site, resulting in a band where the RAPD band was absent. Increasing PCR

annealing temperature can exacerbate SCAR primer mismatches and in some cases

prevent PCR amplification, thereby, allowing for the recovery of a polymorphism. In

fact, several SCAR markers (e.g., BC526SCAR, BC515SCAR, and AO14SCAR) were

not polymorphic until annealing temperatures were increased to the point that a band was

present in only one parent (Table 2.1). Thus, the evaluation of SCAR markers across a

range of annealing temperatures by ATG-PCR was central to SCAR marker optimization

(e.g., the narrow range of annealing temperatures for AO14SCAR).

SCAR multiplexing Multiplexing has been shown to increase genotyping efficiency in grape (Vitis

vinifera L.; Merdinoglu et al. 2005), apple (Malus domestica; Frey et al. 2004), cranberry

(Vaccinium macrocarponp; Polashock and Vorsa 2002), sugar beet (Beta vulgaris L.;

Mohring et al. 2004), common bean (Phaseolus vulgaris L.; Masi et al. 2003),

Arabidopsis (Torjek et al. 2003), rice (Oryza sativa L.; Blair et al. 2002), sunflower

(Helianthus annuus L.; Tang et al. 2003), and Brassica spp. (Mitchell et al. 1997).

Annealing temperature is one of the most important parameters in multiplexing

(Henegariu et al. 1997). Therefore, determining the annealing temperature range of

SCAR markers by ATG-PCR was necessary for identifying markers that could be

100successfully multiplexed. The multiplex evaluations performed herein were designed

to optimize the use of SCAR markers for genotyping. Although multiplexing was not

tested over all possible PCR conditions (i.e., PCR reagent concentrations and thermal

cycling conditions other than annealing temperature), the evaluations conducted provided

for critical insights, which subsequently led to the design of multiplexing primer sets. All

markers (up to five) were scoreable within all three multiplexing sets at an optimal

annealing temperature. These results are similar to those of Polashock and Vorsa (2002)

who found the optimal SCAR multiplex marker number was four to five in cranberry.

Optimization of multiplex PCR conditions proved useful for increasing the efficiency of

MAS in cucumber (Fan et al. 2006; Chapter 1), and provides guidelines for the

identification of potential multiplex groups where large-scale genotyping is demanded.

However, since the interactions of markers are typically unknown and the multiplexing

potential of primer sets is unpredictable (Polashock and Vorsa 2002), empirical testing of

potential multiplex groups is essential.

Identification of SNPs SCAR-based multiplexing provides the ability to evaluate the equivalent of four

to five RAPD markers simultaneously. Not all RAPD markers were converted to

polymorphic SCARs, however, and the majority of polymorphic SCARs were dominant.

Two sources of sequence data [SCAR markers (Objective 3a) and BAC ends (Objective

3b)] were utilized, therefore, to create more stable and efficient RAPD marker

replacements. Sequencing SCAR markers provided a marker at the same physical

location as its RAPD counterpart. Sequencing BAC ends, however, created a set of

101markers physically near the RAPD locus linked to yield component QTL that could

then be utilized for fine mapping or map-based cloning (Fazio et al. 2003b; Nam et al.

2005), and to identify SNPs where no polymorphism was detected by a SCAR and no

internal sequence polymorphism exists.

The two sources of sequences to identify SNPs (SCAR markers and BAC ends)

provided contrasting SNP frequency results. The percentage of sequences that contained

at least one SNP was higher from SCAR markers (63%) than from the BAC end

sequences (35%). This may be explained by the fact that the average number of base

pairs sequenced per locus was much higher in the SCARs (838 bp/locus) than in the BAC

end sequences (427 bp/locus), which increases the chance of identifying a SNP. The

SNP frequency was lower in the SCARs (one SNP every 210 bp) than in the BAC end

sequences (one SNP every 173 bp). However, the large number of SNPs detected in C-3-

BE-L (31 SNPs) and C6-BE-L (24 SNPs) likely inflated the average SNP frequency in

the BAC end sequences such that the true average SNP frequency is most likely about

one SNP every 210 bp. In coding regions of melon (Cucumis melo L.), SNPs and indels

were identified at an average frequency of one SNP every 441 bp and one indel every

1,666 bp (Morales et al. 2004). In 22 diverse soybean genotypes, SNPs were identified at

a frequency of one SNP every 503 bp in coding regions and one SNP every 237 bp in

non-coding regions (Zhu et al. 2003). Likewise, the SNP frequency in elite maize (Zea

mays) lines was estimated at one SNP per 31 bp (non-coding regions) or 124 bp (coding

regions; Ching et al. 2002). In contrast, SNP frequencies of tomato, have been

determined to be one SNP every 7,000-9,500 bp in coding regions (Nesbitt and Tanksley

1022002; Yang et al. 2004; Labate and Baldo 2005). The SNP frequency identified

herein (non-coding regions) is much greater than tomato, and is similar to that detected in

soybean and to what may be expected for non-coding regions of melon. Lines Gy-7 and

H-19 are two of the most genetically diverse accessions known in processing cucumber

(Horejsi and Staub 1999), and thus, SNP frequencies in other germplasm would be

predictably lower. Nevertheless, the abundance of SNPs in cucumber provides an

opportunity to create a large number of new markers utilizing the methodologies

presented herein. The fact that a SNP marker was successfully created for 20 of the 25

sequences that contained a SNP indicates that the recovery rate of SNP-based markers

will be greater than that previously reported in the same germplasm for RAPDs (4.8%;

Serquen et al. 1997) and SSRs (7.2%; Fazio et al. 2002).

SNP marker creation and evaluation SNP-based, allele-specific primers were designed such that the final 3’ nucleotide

matched only one allele (Gy-7 or H-19), but an additional mismatch to both alleles was

included within the last four nucleotides to increase allele specificity in PCR (Newton et

al. 1989; Sarkar et al. 1990; Drenkard et al. 2000; Figure 2.2). Because not all internal

mismatches provide the same level of specificity, Drenkard et al. (2000) has

recommended testing four allele-specific primers at each SNP to obtain a polymorphic

marker. Indeed, 21% of the SNP-based primers created herein were not allele-specific.

However, the fact that an allele-specific marker was recovered for the Gy-7 and H-19

allele in 87.5% and 80.0% of loci attempted (Table 2.3), respectively, indicates that three

allele-specific primers for both Gy-7 and H-19 were usually sufficient. For those

103sequences where allele-specific markers were not identified, additional allele-specific

primers could be tested, which would likely produce a suitable marker (Drenkard et al.

2000). The high recovery rate of allele-specific markers, coupled with the finding that

71% of the SNP-based markers were allele specific, demonstrates that the 3’ and

additional, internal mismatches are highly, but not universally, effective at distinguishing

alleles. Thus, when designing allele-specific, SNP-based markers by methodologies

described herein, only a few (3-4) allele-specific primers are required to detect most

SNPs, while others may require designing additional allele-specific primers.

Four different design approaches to create allele-specific markers (Figure 2.3)

were utilized since none of the individual approaches were applicable in all cases due to

the variability in location (sequence surrounding SNP for primer design, and proximity to

other SNPs) and number of SNPs in a given sequence. When considered together,

however, the four different approaches provided allele-specific markers for almost any

SNP. In only one case was an allele-specific marker not created from a SNP-containing

sequence (Gy-7 allele at W-2-BE-R) because the AT content of the sequence was too

high to design a primer flanking the SNP. Thus, the only main requirements of the four

design approaches are that sequences on at least one side of the SNP need to be amenable

to primer annealing, and sufficient sequence flanking the SNP is needed to create a non-

specific primer.

The optimal marker design approach was utilized for the majority of loci (17 of

25; Table 2.3) because they contained more than one SNP in close proximity to each

other. This approach was preferred over the other three approaches from a marker design

104standpoint since the allele-specific primers for Gy-7 and H-19 were anchored to two

different genomic DNA sites (in contrast to the tail approach) to reduce primer site

competition and both allele-specific markers were designed to utilize the same non-

specific (universal) primer. Thus, only three primers are required to detect both alleles

(i.e., a codominant marker) when both markers are combined in PCR, which reduces the

complexity of the reaction (in contrast to the sandwich and opposite approaches). The

optimal approach was effective and produced a codominant marker in 14 of 17 cases

(Table 2.3). All three of the markers produced by the sandwich approach were

codominant, but they could not be combined in a single assay. This was most likely due

to the increased PCR complexity of combining four primers in a single reaction.

Codominant markers were also produced by both the tail and opposite approaches. The

creation of 19 codominant markers from 25 sequences that contained SNPs demonstrates

the effectiveness of these four approaches, which are predicted to produce similar results

in any crop species because of their applicability to sequences with one or more SNPs in

any position.

The conversion of RAPDs to more efficient and effective SCAR and SNP

markers was highly successful. Compared to a previous RAPD to SCAR conversion

study, the results reported herein illustrate the importance of: 1) including the RAPD

primer sequence in the SCAR primers to increase the recovery of polymorphisms; 2)

creating SCAR primers of sufficient length (24 bp) and utilizing ATG-PCR to increase

marker specificity (recovering single bands); and 3) verifying the new SCAR markers in

a segregating population. The methodologies utilized to create SNP markers to detect

105two alleles at a locus in one or two assays by relatively simple and inexpensive

procedures (conventional PCR and agarose gel electrophoresis), should be applicable to a

diverse array of crop species. In addition, BAC libraries can be used as a tool to obtain

sequence physically proximal to a RAPD marker from which SNPs can be identified.

Markers derived from BAC clones also provide a potential starting point for fine

mapping and map-based cloning of yield component QTL.

Literature Cited

Blair MW, Hedetale V, McCouch SR (2002) Fluorescent-labeled microsatellite panels useful for detecting allelic diversity in cultivated rice (Oryza sativa L.). Theor Appl Genet 105:449-457

Brugmans B, van der Hulst RGM, Visser RGF, Lindhout P, van Eck HJ (2003) A new and versatile method for the successful conversion of AFLP markers into simple single locus markers. Nucleic Acids Res 31:e55

Ching A, Caldwell KS, Jung M, Dolan M, Smith OS, Tingey S, Morgante M, Rafalski AJ (2002) SNP frequency, haplotype structure and linkage disequilibrium in elite maize inbred lines. BMC Genet 3:19

Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica 142:169-196

Dijkhuizen A, Kennard WC, Havey MJ, Staub JE (1996) RFLP variation and genetic relationships in cultivated cucumber. Euphytica 90:79-87

Drenkard E, Richter BG, Rozen S, Stutius LM, Angell NA, Mindrinos M, Cho RJ, Oefner PJ, Davis RW, Ausubel FM (2000) A simple procedure for the analysis of single nucleotide polymorphisms facilitates map-based cloning in Arabidopsis. Plant Physiol 124:1483-1492

Fan Z, Robbins MD, Staub JE (2006) Population development by phenotypic selection with subsequent marker-assisted selection for line extraction in cucumber (Cucumis sativus L.). Theor Appl Genet 112:843-855

106Fazio G, Chung SM, Staub JE (2003a) Comparative analysis of response to phenotypic and marker-assisted selection for multiple lateral branching in cucumber (Cucumis sativus L.). Theor Appl Genet 107:875-883

Fazio G, Staub JE, Stevens MR (2003b) Genetic mapping and QTL analysis of horticultural traits in cucumber (Cucumis sativus L.) using recombinant inbred lines. Theor Appl Genet 107:864-874

Fazio G, Staub JE, Chung SM (2002) Development and characterization of PCR markers in cucumber. J Am Soc Hort Sci 127:545-557

Francia E, Tacconi G, Crosatti C, Barabaschi D, Bulgarelli D, Dall'Aglio E, Vale G (2005) Marker assisted selection in crop plants. Plant Cell Tissue Organ Cult 82:317-342

Frey JE, Frey B, Sauer C, Kellerhals M (2004) Efficient low-cost DNA extraction and multiplex fluorescent PCR method for marker-assisted selection in breeding. Plant Breed 123:554-557

Gupta PK, Roy JK, Prasad M (2001) Single nucleotide polymorphisms: A new paradigm for molecular marker technology and DNA polymorphism detection with emphasis on their use in plants. Curr Sci 80:524-535

Gupta PK, Varshney RK, Sharma PC, Ramesh B (1999) Molecular markers and their applications in wheat breeding. Plant Breed 118:369-390

Henegariu O, Heerema NA, Dlouhy SR, Vance GH, Vogt PH (1997) Multiplex PCR: Critical parameters and step-by-step protocol. BioTechniques 23:504-511

Horejsi T, Box JM, Staub JE (1999) Efficiency of randomly amplified polymorphic DNA to sequence characterized amplified region marker conversion and their comparative polymerase chain reaction sensitivity in cucumber. J Am Soc Hort Sci 124:128-135

Horejsi T, Staub JE (1999) Genetic variation in cucumber (Cucumis sativus L.) as assessed by random amplified polymorphic DNA. Genet Resour Crop Evol 46:337-350

Knerr LD, Staub JE, Holder DJ, May BP (1989) Genetic diversity in Cucumis sativus L. assessed by variation at 18 allozyme coding loci. Theor Appl Genet 78:119-128

Konieczny A, Ausubel FM (1993) A procedure for mapping Arabidopsis mutations using codominant ecotype-specific PCR-based markers. Plant J 4:403-410

Kwok S, Kellogg DE, Mckinney N, Spasic D, Goda L, Levenson C, Sninsky JJ (1990) Effects of primer template mismatches on the polymerase chain reaction: Human immunodeficiency virus type 1 model studies. Nucleic Acids Res 18:999-1005

107Labate JA, Baldo AM (2005) Tomato SNP discovery by EST mining and resequencing. Mol Breed 16:343-349

Masi P, Zeuli PLS, Donini P (2003) Development and analysis of multiplex microsatellite markers sets in common bean (Phaseolus vulgaris L.). Mol Breed 11:303-313

Merdinoglu D, Butterlin G, Bevilacqua L, Chiquet V, Adam-Blondon AF, Decroocq S (2005) Development and characterization of a large set of microsatellite markers in grapevine (Vitis vinifera L.) suitable for multiplex PCR. Mol Breed 15:349-366

Mitchell SE, Kresovich S, Jester CA, Hernandez CJ, SzewcMcFadden AK (1997) Application of multiplex PCR and fluorescence-based, semi-automated allele sizing technology for genotyping plant genetic resources. Crop Sci 37:617-624

Mohan M, Nair S, Bhagwat A, Krishna TG, Yano M, Bhatia CR, Sasaki T (1997) Genome mapping, molecular markers and marker-assisted selection in crop plants. Mol Breed 3:87-103

Mohring S, Salamini F, Schneider K (2004) Multiplexed, linkage group-specific SNP marker sets for rapid genetic mapping and fingerprinting of sugar beet (Beta vulgaris L.). Mol Breed 14:475-488

Morales M, Roig E, Monforte AJ, Arus P, Garcia-Mas J (2004) Single-nucleotide polymorphisms detected in expressed sequence tags of melon (Cucumis melo L.). Genome 47:352-360

Moreno-Vazquez S, Ochoa OE, Faber N, Chao SM, Jacobs JME, Malsonneuve B, Kesseli RV, Michelmore RW (2003) SNP-based codominant markers for a recessive gene conferring resistance to corky root rot (Rhizomonas suberifaciens) in lettuce (Lactuca sativa). Genome 46:1059-1069

Nam YW, Lee JR, Song KH, Lee MK, Robbins MD, Chung SM, Staub JE, Zhang HB (2005) Construction of two BAC libraries from cucumber (Cucumis sativus L.) and identification of clones linked to yield component quantitative trait loci. Theor Appl Genet 111:150-161

Nesbitt TC, Tanksley SD (2002) Comparative sequencing in the genus Lycopersicon: Implications for the evolution of fruit size in the domestication of cultivated tomatoes. Genetics 162:365-379

Newton CR, Graham A, Heptinstall LE, Powell SJ, Summers C, Kalsheker N, Smith JC, Markham AF (1989) Analysis of any point mutation in DNA. The amplification refractory mutation system (ARMS). Nucleic Acids Res 17:2503-2516

108Papp AC, Pinsonneault JK, Cooke G, Sadee W (2003) Single nucleotide polymorphism genotyping using allele-specific PCR and fluorescence melting curves. BioTechniques 34:1068-1072

Paran I, Michelmore RW (1993) Development of reliable PCR-based markers linked to downy mildew resistance genes in lettuce. Theor Appl Genet 85:985-993

Polashock JJ, Vorsa N (2002) Development of SCAR markers for DNA fingerprinting and germplasm analysis of American cranberry. J Am Soc Hort Sci 127:677-684

Rozen S, Skaletsky HJ (2000) Primer3 on the WWW for general users and for biologist programmers. In: Krawetz S, Misener S (eds) Bioinformatics Methods and Protocols: Methods in Molecular Biology. Totowa, Humana Press, pp 365-386

Sarkar G, Cassady J, Bottema CDK, Sommer SS (1990) Characterization of polymerase chain reaction amplification of specific alleles. Anal Biochem 186:64-68

Serquen FC, Bacher J, Staub JE (1997) Mapping and QTL analysis of horticultural traits in a narrow cross in cucumber (Cucumis sativus L.) using random-amplified polymorphic DNA markers. Mol Breed 3:257-268

Sheffield VC, Cox DR, Lerman LS, Myers RM (1989) Attachment of a 40-base-pair G+C-rich sequence (GC-clamp) to genomic DNA fragments by the polymerase chain reaction results in improved detection of single-base changes. Proc Natl Acad Sci U S A 86:232-236

Shirasawa K, Kishitani S, Nishio T (2004) Conversion of AFLP markers to sequence-specific markers for closely related lines in rice by use of the rice genome sequence. Mol Breed 14:283-292

Staden R (1996) The Staden sequence analysis package. Mol Biotechnol 5:233-241

Staub JE, Serquen FC, Gupta M (1996) Genetic markers, map construction, and their application in plant breeding. HortScience 31:729-741

Tang SX, Kishore VK, Knapp SJ (2003) PCR-multiplexes for a genome-wide framework of simple sequence repeat marker loci in cultivated sunflower. Theor Appl Genet 107:6-19

Torjek O, Berger D, Meyer RC, Mussig C, Schmid KJ, Sorensen TR, Weisshaar B, Mitchell-Olds T, Altmann T (2003) Establishment of a high-efficiency SNP-based framework marker set for Arabidopsis. Plant J 36:122-140

Willcox MC, Khairallah MM, Bergvinson D, Crossa J, Deutsch JA, Edmeades GO, Gonalez-de-Leon D, Jiang C, Jewell DC, Mihm JA, Williams WP, Hoisington D (2002)

109Selection for resistance to southwestern corn borer using marker-assisted and conventional backcrossing. Crop Sci 42:1516-1528

Yang WC, Bai XD, Kabelka E, Eaton C, Kamoun S, van der Knaap E, Francis D (2004) Discovery of single nucleotide polymorphisms in Lycopersicon esculentum by computer aided analysis of expressed sequence tags. Mol Breed 14:21-34

Zhu YL, Song QJ, Hyten DL, Van Tassell CP, Matukumalli LK, Grimm DR, Hyatt SM, Fickus EW, Young ND, Cregan PB (2003) Single-nucleotide polymorphisms in soybean. Genetics 163:1123-1134

Table 2.1. RAPD markers converted to SCAR markers in cucumber. Original RAPD SCAR namea

Match RAPDb Verifiedc

GMW (bp)d

HMW (bp)d Poly.e

Gy-7 bands (no.)f

H-19 bands (no.)f

Anneal rangeg

Opt. annealh

Seq. (bp.)i

SNP (no.)

BC231 BC231SCAR Yes Seg 950 Dom-H 1 65-70 70 BC388 BC388SCAR Yes Seg 421 Dom-G 3 1 50-70 70 BC403 BC403SCAR Yes Seg 774 Dom-G 2 1 59-70 60 BC469 BC469SCAR Yes Seg 610 Dom-H 1 70-70 70 BC515 BC515SCAR Yes Seg 652 Dom-H 1 70-70 70 No seq BC523 BC523SCAR Yes Seg 879 Dom-G 1 70-70 70 No seq BC526 BC526SCAR Yes Seg 940 968 Codom 1 1 50-70 70 BC592 BC592SCAR Yes Seg 715 Dom-H 1 70-70 70 OP-AB14 AB14SCAR Yes Seg 802 802 None 1 1 60-70 70 802 7 OP-AC17 AC17SCAR Yes Seg 612 612 None 1 1 58-67 60 612 1 OP-AC9 AC9SCAR Yes Seg 663 663 None 1 1 64-70 70 663 7 OP-AD12 AD12SCAR Yes MW 682 682 None 1 1 70-70 70 563 None OP-AD14 AD14SCAR Yes Seg 867 867 None 1 1 61-70 70 867 15 OP-AF15 AF15SCAR Yes Seg 1600 Dom-H 1 65-70 65 OP-AF7 AF7SCAR Yes MW 1700 1700 None 1 1 59-70 70 1080 None OP-AG1 AG1-1SCAR No MW, Seg 1600 1550 Codom 1 1 50-70 65 OP-AI4 AI4SCAR Yes Seg 1136 1136 None 1 1 64-64 64 1136 9 OP-AJ18 AJ18SCAR Yes Seg 988 Dom-G 1 55-66 60 OP-AJ6 AJ6SCAR Yes Seg 541 Dom-G 1 55-70 55 541 None OP-AK16 AK16SCAR Yes Seg 1370 1370 None 1 1 51-70 70 1370 9 OP-AK5 AK5SCAR Yes Seg 1081 Dom-G 1 50-64 60 No seq OP-AM2 AM2SCAR Yes MW 1650 1650 None 1 1 62-70 67 460 None OP-AO12 AO12SCAR Yes MW 1288 1288 None 1 1 50-70 55 No seq OP-AO14 AO14SCAR Yes MW 633 Dom-G 1 50-53 50 OP-AO8 AO8SCAR Yes Seg 655 694 Codom 1 1 54-70 70 OP-AS5 AS5SCAR Yes Seg 865 Dom-G 2 1 61-67 63 OP-AT1 AT1SCAR No MW, Seg 800 800 None 1 1 50-69 50 800 5 OP-AT15 No primers

110

OP-AW14 AW14SCAR Yes Seg 1200 800 Codom 1 1 50-64 60 OP-C1 C1SCAR Yes Seg 372 372 None 1 1 50-70 65 372 None OP-C10 C10SCAR Yes Seg 726 726 None 1 1 55-70 70 726 3 OP-D11 D11SCAR Yes Seg 1800 1800 None 1 1 55-70 65 940 5

Original RAPD SCAR namea

Match RAPDb Verifiedc

GMW (bp)d

HMW (bp)d Poly.e

Gy-7 bands (no.)f

H-19 bands (no.)f

Anneal rangeg

Opt. annealh

Seq. (bp.)i

SNP (no.)

OP-I20 I20SCAR Yes Seg 1300 1600 Codom 1 1 59-70 65 OP-J5-1 J5SCAR Yes Seg 1300 1100 Codom 1 1 54-70 60 OP-L19-2 L19SCAR Yes Seg 1009 Dom-H 1 59-70 60 1009 None OP-M8 M8SCAR Yes Seg 1400 Dom-H 1 51-64 60 940 3 OP-N8 N8SCAR Yes Seg 1384 1384 None 1 1 50-70 65 1384 11 OP-P13 P13SCAR Yes MW 1265 1265 None 1 1 50-64 55 1200 None OP-P14 P14SCAR Yes Seg 1380 Dom-H 1 55-70 55 OP-T2 No primers OP-W7-1 W7aSCAR Yes Seg 1070 Dom-H 1 50-66 55 OP-W7-2 W7SCAR Yes Seg 448 448 None 1 1 55-69 55 448 6 OP-Y10 No primers

a No primers were created for OP-AT15, OP-T2, and OP-Y10 because reliable sequence information was not obtained b SCAR markers were either the same locus as the original RAPD (Yes) or another locus (No) c SCAR markers verified to match original RAPD by segregation on 20 F2 individuals either directly or from SNP markers created from the SCAR marker (Seg = Segregation), or by molecular weight determined by agarose gel electrophoresis (MW) d The molecular weight of the PCR product amplified from Gy-7 (GMW) or H-19 (HMW) e Polymorphism type: dominant for Gy-7 (Dom-G), or H-19 (Dom-H), or codominant (Codom) f The number of PCR products produced from Gy-7 or H-19 g The range of PCR annealing temperatures that produces the lowest number of bands for Gy-7 and H-19 as determined by annealing temperature gradient PCR (ATG-PCR) h The optimal PCR annealing temperature i The number of base pairs sequenced from both Gy-7 and H-19 to identify SNPs. No seq = sequencing was attempted but could not be obtained for both genotypes

111

112Table 2.2. SCAR markers created from BAC end sequences in cucumber. Marker used as probe

Library addressa SCAR marker

MW (bp)b Polymorphismc

Seq (bp.)d

SNP (no.)

AJ6SCAR B07A24 AJ-1-BE-L 458 Dom-G B07A24 AJ1-BE-R2 434 Dom-G B18A17 AJ-2-BE-L 504 Dom-G B18A17 AJ-2-BE-R 332 Dom-G B50O22 AJ-3-BE-L 592 Dom-G B50O22 AJ-3-BE-R 435 None 435 None BC523SCAR B12P24 B-1-BE-L 409 None 391 2 B19M17 B-2-BE-L 579 None 579 None B19M17 B-2-BE-R 374 None 356 None B44D16 B-3-BE-L 506 None 506 None B44D16 B-3-BE-R 582 Codom B58D06 B-4-BE-L 302 None 302 None B58D06 B4-BE-R2 413 None 306 None B60N01 B-5-BE-L 577 Dom-G M8SCAR E15E16 M-1-BE-L 579 None 579 None E15E16 M-1-BE-R 419 None 419 None B22O04 M-2-BE-L 516 None 516 None B24B17 M-3-BE-L 510 None No seq B24B17 M-3-BE-R 221 Same as M-4-BE-R B37K17 M-4-BE-L 518 None 400 2 B37K17 M-4-BE-R 268 None 268 2 B43B20 M-5-BE-L 520 Codom B07J14 M6-BE-L 528 None 528 None B07J14 M6-BE-R 520 Same as M-5-BE-L B22B19 M7-BE-L 314 None 314 1 L19SCAR B08C06 L-1-BE-L 501 None 501 3 B08C06 L1-BE-R2 410 None 410 None CSWCTT14 E05C10 C-1-BE-R 501 None 501 6 E12P12 C-2-BE-L 405 None 405 None E12P12 C-2-BE-R 542 None 542 None E18J09 C-3-BE-L 582 None 582 31 E18J09 C3-BE-R2 502 None 302 1 B10P11 C-4-BE-L 510 None 510 9 B31C15 C-5-BE-L 512 None 512 4 E03L13 C6-BE-L 401 None 401 24 E03L13 C6-BE-R 468 None 468 None E11P14 C7-BE-R No amplification E41P03 C8-BE-R 373 None 348 unique OP-W7-1 E40B02 W-1-BE-L 505 None 415 None E40B02 W-1-BE-R 230 None 230 None E40G16 W-2-BE-L 535 None 535 None E40G16 W-2-BE-R 387 Codom 387 5 E44J08 W-4-BE-L 528 None 412 None E44J08 W-4-BE-R 212 None 212 None B23B20 W-5-BE-L 533 None 533 None B23B20 W5-BE-R2 513 None 400 None

a The name of the BAC library clone from Nam et al. (2005) b Molecular weight c Polymorphism type: dominant for Gy-7 (Dom-G), or H-19 (Dom-H), or codominant (Codom) d The number of base pairs sequenced from both Gy-7 and H-19 to identify SNPs

Table 2.3. Allele-specific markers created by four design approaches based on SNPs between two cucumber lines (Gy-7 and H-19) identified from two sequence sources. Gy-7 allele-specific marker H-19 allele-specific marker Codominant SNP markere

Original marker

Sequence methoda

SNP marker designb

SNP marker typec Marker

MW (bp) Anneald Marker

MW (bp) Anneald Marker Anneald

AB14SCAR SCAR optimal Codom AB14SNPG1 470 60 AB14SNPH1 519 60 AB14SNPG1H1 60 AC17SCAR SCAR tail Codom AC17SNPG1 117 68 AC17SNPH2 102 68 AC17SNPG1H2 68 AC9SCAR SCAR optimal Codom AC9SNPG3 412 60 AC9SNPH3 363 60 AC9SNPG3H3 60 AD14SCAR SCAR optimal Codom AD14SNPG1 292 60 AD14SNPH2 243 60 AD14SNPG1H2 60 AI4SCAR SCAR optimal Codom AI4SNPG1 523 58 AI4SNPH1 481 58 AI4SNPG1H1 58 AK16SCAR SCAR optimal Codom AK16SNPG1 417 60 AK16SNPH2 290 60 AK16SNPG1H2 60 AT1SCAR SCAR optimal Codom AT1SNPG3 252 60 AT1SNPH3 307 60 AT1SNPG3H3 60 B-1-BE-L BAC optimal None Not allele specific Not allele specific C10SCAR SCAR optimal Codom C10SNPG1 245 59 C10SNPH1 108 59 C10SNPG1H1 59 C-1-BE-R BAC opposite Dom-G C1RG3 349 65 Not allele specific C-3-BE-L BAC optimal Codom (2 assay) C3LG3 285 60 C3LH3 329 60 Allele specificity lost C3-BE-R2 BAC sandwich Codom (2 assay) C3R2G1 350 60 C3R2H1 185 60 Allele specificity lost C-4-BE-L BAC optimal Codom C4LG1 145 60 C4LH2 208 60 C4LG1H2 60 C-5-BE-L BAC optimal Codom C5LG3 200 60 C5LH2 339 60 C5LG3H2 60 C-6-BE-L BAC optimal None Not allele specific Not allele specific C8-BE-R BAC two loci Dom-G, Dom-H C8RG 182 55 C8RH 132 50 D11SCAR SCAR opposite Codom D11SNPG3 246 64 D11SNPH1 297 64 D11SNPG3H1 64 L-1-BE-L BAC optimal Codom L1LG3 451 60 L1LH3 327 60 L1LG3H3 60 M-4-BE-L BAC sandwich Codom (2 assay) M4LG2 173 58 M4LH2 392 58 Allele specificity lost M-4-BE-R BAC optimal None Not allele specific Not allele specific M7-BE-L BAC sandwich Codom (2 assay) M7LG3 182 58 M7LH3 166 58 Allele specificity lost M8SCAR SCAR optimal Codom M8SNPG3 176 60 M8SNPH1 256 60 M8SNPG3H1 60 N8SCAR SCAR optimal Codom N8SNPG2 569 55 N8SNPH3 517 55 N8SNPG2H3 55 W7SCAR SCAR optimal Codom W7SNPG1 273 60 W7SNPH3 384 60 W7SNPG1H3 60 W-2-BE-R BAC H allele only None None designed No amplification

a Sequences were obtained by sequencing SCARs converted from RAPDs (SCAR) or BAC ends (BAC) b Allele-specific markers based on SNPs were created by one of four design approaches (Figure 2.3). The Gy-7 and H-19 sequences of C8-BE-R were completely different, and markers for only H-19 allele were designed for W-2-BE-R c SNP markers were dominant for Gy-7 (Dom-G), or H-19 (Dom-H), codominant by a single assay (Codom), or codominant by two assays [Codom (2 assay; Figure 2.2)] d Optimal PCR annealing temperature e If the Gy-7 and H-19 allele-specific markers were successfully combined in a single assay they were considered as a single marker and their names were combined (e.g., AB14SNPG1 and AB14SNPH1 becomes AB14SNPG1H1)

113

Table 2.4 Primer names and sequences of the polymorphic markers converted from RAPDs and verified by segregation in cucumber. Original RAPD New Marker Marker type Polymorphisma Primer name Primer sequence (5’ to 3’) BC231 BC231SCAR SCAR Dom-H BC231SCARF AGGGAGTTCCAAACTTTTCAGTAC BC231SCARR AGGGAGTTCCCCTGTGATCTCTCT BC388 BC388SCAR SCAR Dom-G BC388SCARF CGGTCGCGTCCTTAGACCAACCAC BC388SCARR CGGTCGCGTCATTCTGTATGAGGC BC403 BC403SCAR SCAR Dom-G BC403SCARF GGAAGGCTGTCTTCCTTATGTCTT BC403SCARR GGAAGGCTGTGCAAGGTCGAGGGA BC469 BC469SCAR SCAR Dom-H BC469SCARF CTCCAGCAAACTAACAATTGAGGG BC469SCARR CTCCAGCAAAGATTTCAAAAGGCT BC515 BC515SCAR SCAR Dom-H BC515SCARF GGGGGCCTCATTATGAGGAATGAA BC515SCARR GGGGGCCTCAAGTGAAAACAATCA BC523 B-3-BE-R SCAR Codom B-3-BE-R_F CCAAAACATACGACCCATCC B-3-BE-R_R TTCAATCGGTTTCCATGTTC B-5-BE-L SCAR Dom-G B-5-BE-L_F CCCGAGTTTATGTGGAAATG B-5-BE-L_R AAGAGGTGCTTGGGAAAGTG BC523SCAR SCAR Dom-G BC523SCARF ACAGGCAGACCCGACGAGGGGCAG BC523SCARR ACAGGCAGACAAGAGTTTGAGGAT BC526 BC526SCAR SCAR Codom BC526SCARF AACGGGCACCCGTCTCACTGGAAA BC526SCARR AACGGGCACCCACATAGTGAAAAC BC592 BC592SCAR SCAR Dom-H BC592SCARF GGGCGAGTGCAATATCTAAAATGG BC592SCARR GGGCGAGTGCATGCGAACACAAAA OP-AB14 AB14SNPG1H1 SNP Codom AB14SCARF AAGTGCGACCGGGTCAGTAAATTA AB14SNP451G1 ACTTGGAAAGCGGACATAGA AB14SNP491H1 GTTATCATATCTATCAGTAACAGAAGGAA OP-AC17 AC17SNPG1H2 SNP Codom AC17SNPG1T GATACAGATACACTCGGTTACCTGTAGTCTTGAACTA AC17SNPH4 GGTTACCTGTAGTCTTGAACGG AC17SNPUR TTTTTCCTGTTCTGTCATCGTG OP-AC9 AC9SNPG3H3 SNP Codom AC9SCARF AGAGCGTACCACTATGAGTGAGAA AC9SNP274G6 TCCGCATATTGATCCTTCTATTA AC9SNP322H6 TCCTTGAGATTCGACCAAACTA OP-AD14 AD14SNPG1H2 SNP Codom AD14SCARR GAACGAGGGTGAATGTTGCGAAAC AD14SNP602G1 ATTTTGTAAACATCTCGAAGTGAAC AD14SNP648H3 TCTGCTCGTCCATCTCGTCA 114

115Original RAPD New Marker Marker type Polymorphisma Primer name Primer sequence (5’ to 3’) OP-AF15 AF15SCAR SCAR Dom-H AF15SCARF CACGAACCTCAGCTGCACTATTTC AF15SCARR CACGAACCTCCTTACTAAGGCTTC OP-AI4 AI4SNPG1H1 SNP Codom AI4SCARR CTATCCTGCCTCTTAATAATCATT AI4SNP460H2 ATGATGAATTTGCATTCCATT AI4SNP499G1 TTAGCTTAATGAAATCTGGGTTAAA OP-AJ18 AJ18SCAR SCAR Dom-G AJ18SCARF GGCTAGGTGGTATGGGGATGACAT AJ18SCARR GGCTAGGTGGGCTTAAGTTCTTTC OP-AJ6 AJ6SCAR SCAR Dom-G AJ6SCARF GTCGGAGTGGCTTTCCACTAAGAT AJ6SCARR GTCGGAGTGGGGGTGAAGACTGAA OP-AK16 AK16SNPG1H2 SNP Codom AK16SCARR CTGCGTGCTCTTGGGCGATTTCAT AK16SNP1106H3 AATCGTTTTCGACATTCATGTC AK16SNP985G1 TATTCTTTAGCCATAATTTAGTAGGTGA OP-AK5 AK5SCAR SCAR Dom-G AK5SCARF GATGGCAGTCTGATAACTATGTGA AK5SCARR GATGGCAGTCGGGAAGGTCAGTTG OP-AO14 AO14SCAR SCAR Dom-G AO14SCARF CTACTGGGGTATGATTAAGCATTT AO14SCARR CTACTGGGGTAATATAACAAATAA OP-AO8 AO8SCAR SCAR Codom AO8SCARF ACTGGCTCTCTACATATTGTGAGG AO8SCARR ACTGGCTCTCCCATTAATCAGAAG OP-AS5 AS5SCAR SCAR Dom-G AS5SCARF GTCACCTGCTATATTTATGGATTT AS5SCARR GTCACCTGCTTCAACCAAAATTCA OP-AW14 AW14SCAR SCAR Codom AW14SCARF GGTTCTGCTCTTCATTCATTTTCA AW14SCARR GGTTCTGCTCTAAATAACCAAAAA OP-C10 C10SNPG1H1 SNP Codom C10SCARR TGTCTGGGTGCATTGAAACAGAGA C10SNP508G1 AATTTTGTGATCAATATACCAAATATGA C10SNP643H2 GGATATCAAACAAAGAATAATTGTGC D11SCARF AGCGCCATTGTAGTTCAACCAGTA OP-D11 D11SNPG3H1 SNP Codom D11SNP116G5 ACCAACACCAACGTCAAACC D11SNP116GRU CAAACATTCCAACGACATGTAAC D11SNP270H1 ACTATCTATCGCCCTACTTTCTATTAGA OP-I20 I20SCAR SCAR Codom I20SCARF AAAGTGCGGGACTCACGCTTAATA I20SCARR AAAGTGCGGGCCCAAACGCGGCCG OP-J5-1 J5SCAR SCAR Codom J5SCARF CTCCATGGGGTGCACGTTAACGTT J5SCARR CTCCATGGGGCAGCTAAACAGCGG

116Original RAPD New Marker Marker type Polymorphisma Primer name Primer sequence (5’ to 3’) OP-L19-2 L19SCAR SCAR Dom-H L19SCARF GAGTGGTGACCATATATTAAAGTG L19SCARR GAGTGGTGACTGTAATATCACAAA L1LG3H3 SNP Codom L-1-BE-L_R ACACTAGAGCTGGAAAGTGAAGC L1L195H6 ACGATGTTGAAATGGAAACG L1L76G9 TGTTTATCATCTTTGTCTTTTATCGA OP-M8 M4LG2 SNP Dom* M4L152G2 GTTTGTAAAAGAGAGGGAAATCA M4LH2 SNP Dom* M4L152H3 CAAACTATTTCATTTCAGTCCGTA M-5-BE-L SCAR Codom M-5-BE-L_F GAATAAGCGCTCCAGCTCAG M-5-BE-L_R TAATCGGACGGTGTGAAAGG M7LG3 SNP Dom* M7L171G6 GTACCGTGATGGGAGTAAAC M7LH3 SNP Dom* M7L171H8 TTACAGCTACAGAGCTGCCTCTA M8SCAR SCAR Dom-H M8SCARF TCTGTTCCCCATACAAGAATTAAA M8SCARR TCTGTTCCCCATGATGTAGACTTC M8SNPG3H1 SNP Codom M8SCARR TCTGTTCCCCATGATGTAGACTTC M8SNP146G5 TCTAATTTAGTTGAAAATGTTAATCAATATC M8SNP234H2 GCAACTTAATTACAATTTGGTCAT OP-N8 N8SNPG2H3 SNP Codom N8SCARF ACCTCAGCTCCCAAACATTAAAAT N8SNP497H5 GCGTGTCAAACACACTCTACC N8SNP545G4 AATGTCAATAGGATTATTCACTGATG OP-P14 P14SCAR SCAR Dom-H P14SCARF CCAGCCGAACACAAGAAGGTCTGA P14SCARR CCAGCCGAACGCCTCACTCTTGAT OP-W7-1 W7aSCAR SCAR Dom-H W7aSCARF CTGGACGTCAACTAAAAGGTAATT W7aSCARR CTGGACGTCAATGTAGAGAGGGCT OP-W7-2 W7SNPG1H3 SNP Codom W7SCARF CTGGACGTCACATATCAGTAAGTA W7SNP248G1 CACCCCTCTTTAATTATTAATTGAAA W7SNP364H3 GAGCGTTGGGAGTTCCTATAT a Polymorphism type: dominant for Gy-7 (Dom-G), or H-19 (Dom-H), or codominant (Codom) * The two dominant markers sets of M4LG2/M4LH2 and M7LG3/M7LH3 are each considered a codominant marker but require genotyping with both markers to determine heterozygosity (i.e., a two assay, codominant marker)

Table 2.5. Results of RAPD to SCAR conversion in cucumber of RAPDs common to Horejsi et al. (1999) and this study. SCARs from Horejsi et al. (1999) SCARs from this study

RAPD SCAR name RAPD in SCARa Poly.b

Bands (no.)c SCAR name

RAPD in SCARa Poly.b

Bands (no.)c

Similarity (%)d

OP-AB14 SCAB14780 0 None 4 AB14SCAR 20 None 1 92.8 OP-AD12 SCAD12700 0 None 1 AD12SCAR 20 None 1 NA OP-AD14 SCAD14800 0 None 0 AD14SCAR 20 None 1 98.6* OP-AF7 SCAF71580 0 None 7 AF7SCAR 20 None 1 NA OP-AI4 SCAI41100 0 None 1 AI4SCAR 20 None 1 95.4 OP-AJ18 SCAJ181000 0 Dom-G 2 AJ18SCAR 20 Dom-G 1 96.1 OP-AK5 SCAK51275 0 None 1 AK5SCAR 20 Dom-G 1 97.4* OP-AM2 SCAM21550 0 None 3 AM2SCAR 20 None 1 NA OP-AO12 SCAO121370 0 None 1 AO12SCAR 20 None 1 NA OP-AS5 SCAS5800 0 None 0 AS5SCAR 20 Dom-G 2 98.2* BC388 SCBC388350 0 Dom-G 5 BC388SCAR 20 Dom-G 3 98.7 BC403 SCBC403750 0 None 3 BC403SCAR 20 Dom-G 2 98.0* BC469 SCBC469400 0 None 1 BC469SCAR 20 Dom-H 1 99.0 BC526 SCBC526900 0 None 1 BC526SCAR 20 Codom 1 NA OP-C1 SCC1400 9 None 1 C1SCAR 20 None 1 89.8 OP-C10 SCC101000 6 None 2 C10SCAR 20 None 1 87.9 OP-I20 SCI201300 0 None 2 I20SCAR 20 Codom 1 NA OP-N8 SCN81400 9 None 1 N8SCAR 20 None 1 97.7 OP-P13 SCP131200 5 None 1 P13SCAR 20 None 1 NA OP-P14 SCP141380 0 None 3 P14SCAR 20 Dom-H 1 NA OP-W7-1 SCW71150 18 Dom-H 3 W7aSCAR 20 Dom-H 1 NA

a The number of base pairs of the original RAPD primer (10 bp) that are part of the two SCAR primers. If the entire RAPD primer is included in both SCAR primers, the value is 20 (highest possible value) b Polymorphism type: dominant for line Gy-7 (Dom-G), or H-19 (Dom-H), or codominant (Codom) c The number of bands (including polymorphic band) produced by the SCAR marker d The similarity of the sequences obtain between the two studies. NA indicates the sequences were not aligned and are completely different. An asterisk (*) indicates that differences were detected in the priming site of at least one of the SCAR primers created by Horejsi et al. (1999)

117

C1SCAR (372 bp)

N8SCAR (1300 bp)

C10SCAR (726 bp)50

.650

.651

.653

.455

.058

.561

.464

.266

.768

.769

.970

.1 oC

AF7SCAR (1700 bp)

N8SCAR

AF7SCAR

564

947831

137515841904

3530 bp

2027

564

947831

137515841904

3530 bp

2027

C1SCAR

C10SCAR

A

C1SCAR

AF7SCAR

C1SCAR

C10SCAR

AF7SCAR

C1SCAR

C10SCAR

N8SCAR

B

ED

C50

.650

.651

.653

.455

.058

.561

.464

.266

.768

.769

.970

.1 oC 50.6

50.6

51.6

53.4

55.0

58.5

61.4

64.2

66.7

68.7

69.9

70.1 oC

50.6

50.6

51.6

53.4

55.0

58.5

61.4

64.2

66.7

68.7

69.9

70.1 oC50.6

50.6

51.6

53.4

55.0

58.5

61.4

64.2

66.7

68.7

69.9

70.1 oC

C1SCAR (372 bp)

N8SCAR (1300 bp)

C10SCAR (726 bp)50

.650

.651

.653

.455

.058

.561

.464

.266

.768

.769

.970

.1 oC50.6

50.6

51.6

53.4

55.0

58.5

61.4

64.2

66.7

68.7

69.9

70.1 oC

AF7SCAR (1700 bp)

N8SCAR

AF7SCAR

564

947831

137515841904

3530 bp

2027

564

947831

137515841904

3530 bp

2027

C1SCAR

C10SCAR

A

C1SCAR

AF7SCAR

C1SCAR

C10SCAR

AF7SCAR

C1SCAR

C10SCAR

N8SCAR

B

ED

C50

.650

.651

.653

.455

.058

.561

.464

.266

.768

.769

.970

.1 oC50.6

50.6

51.6

53.4

55.0

58.5

61.4

64.2

66.7

68.7

69.9

70.1 oC 50.6

50.6

51.6

53.4

55.0

58.5

61.4

64.2

66.7

68.7

69.9

70.1 oC50.6

50.6

51.6

53.4

55.0

58.5

61.4

64.2

66.7

68.7

69.9

70.1 oC

50.6

50.6

51.6

53.4

55.0

58.5

61.4

64.2

66.7

68.7

69.9

70.1 oC50.6

50.6

51.6

53.4

55.0

58.5

61.4

64.2

66.7

68.7

69.9

70.1 oC50.6

50.6

51.6

53.4

55.0

58.5

61.4

64.2

66.7

68.7

69.9

70.1 oC50.6

50.6

51.6

53.4

55.0

58.5

61.4

64.2

66.7

68.7

69.9

70.1 oC

Figure 2.1. SCAR multiplex reactions in cucumber. Panel C: banding patterns of individual SCAR primer pairs, including molecular weight in base pairs (bp) across a temperature gradient. Vertical numbers denote PCR annealing temperatures (oC) for each lane. Panels A, B, D, and E contain two, three, three, and four primer pairs, respectively, added to the same PCR reaction. Molecular weight of the EcoRI+HindIII digested lambda marker are to the right of panels B and E.

118

119

……Gy-7

H-19

Gy-7 allele specific primer5’-3’ (sense)

H-19 allele specific primer5’-3’ (sense)

Universal non-specific primer3’-5’ (antisense)

3’ mismatch*

^

** **

^

3’ mismatch

Gy-7

H-19

Gy-7

H-19

Gy-7

H-19 F

1 F2 Individuals

G H C

MW

ladder

MW

ladder

300 bp

200 bp

A

B

……Gy-7

H-19

Gy-7 allele specific primer5’-3’ (sense)

H-19 allele specific primer5’-3’ (sense)

Universal non-specific primer3’-5’ (antisense)

3’ mismatch*

^

** **

^

3’ mismatch

……Gy-7

H-19

Gy-7 allele specific primer5’-3’ (sense)

H-19 allele specific primer5’-3’ (sense)

Universal non-specific primer3’-5’ (antisense)

3’ mismatch*

^

** **

^

3’ mismatch

Gy-7

H-19

Gy-7

H-19

Gy-7

H-19 F

1 F2 Individuals

G H C

MW

ladder

MW

ladder

300 bp

200 bp

Gy-7

H-19

Gy-7

H-19

Gy-7

H-19 F

1 F2 Individuals

G H C

MW

ladder

MW

ladder

300 bp

200 bp

A

B Figure 2.2. Allele specific primer design used to create a codominant marker in cucumber from SNPs within a locus employing the optimal approach. Panel A: SNPs between H19 (bottom sequence) and Gy-7 (top sequence) in a portion of the AD14SCAR sequence are indicated by an asterisk (*). Allele specific primers match the SNP of one parent at the 3’ end with an additional mismatch (^) to both alleles within 4 bases of the 3’ end. Universal non-specific primers have no mismatch to either allele. Primer orientation and direction of extension by a polymerase (horizontal arrows) during PCR are indicated under each primer name. Panel B: Photograph after agarose gel electrophoresis of PCR reactions using Gy-7 and H-19 as template with the dominant Gy-7 allele specific marker (Gy-7 allele specific primer and Universal non-specific primer) labeled G, the dominant H-19 allele specific marker (H-19 allele specific primer and Universal non-specific primer) labeled H, and the G-y7 and H-19 allele specific markers combined (Gy-7 allele specific primer, H-19 allele specific primer, and Universal non-specific primer) in a codominant assay labeled “C”. The codominant assay was also tested on F1 and F2 individuals from a cross between Gy-7 and H-19. A 100 bp ladder (MW ladder) flanks PCR products.

*GS

HS

GN

HN

Sandwich

*GS UN

HS*

*GS GN

* HSHN

Opposite

Optimal

*GS UN

HS

Tail

Gy-7

H-19 F

1

Gy-7

H-19 F

1

*GS

HS

GN

HN

*GS

HS

GN

HN

Sandwich

*GS UN

HS**

GS UN

HS*

*GS GN

* HSHN

*GS GN

* HSHN

Opposite

Optimal

*GS UN

HS

*GS UN

HS

Tail

Gy-7

H-19 F

1

Gy-7

H-19 F

1

Figure 2.3. Graphical representation of four design approaches developed herein to create Gy-7 and H-19 allele-specific markers in cucumber from a single locus depending on location and number of single nucleotide polymorphisms (SNPs). Solid horizontal lines represent a genomic fragment containing a SNP (asterisk) between Gy-7 and H-19. Arrows represent the direction of primer extension by a polymerase in PCR. Primers are designated as allele-specific (GS) and non allele-specific (GN) to amplify the Gy-7 allele, allele-specific (HS) and non allele-specific (HN) to amplify the H-19 allele, and non-allele-specific universal (UN) to amplify both alleles. The dotted line on the GS primer of the tail approach represents additional base pairs that do not anneal to the template during PCR, but are designed to add length to the PCR product. The horizontal dotted lines above and below the genomic fragment represent PCR products of Gy-7 and H-19 template, respectively. The panels on the far right represent the gel banding patterns of both approaches in each row after agarose gel electrophoresis with Gy-7, H-19, and an F1 hybrid as templates. 120

121

Chapter 3. Pyramiding QTL for multiple lateral branching in cucumber using nearly isogenic lines

Abstract

Multiple lateral branching (MLB) is a quantitatively inherited trait associated with

yield in cucumber (Cucumis sativus L.; 2n=2x=14). Although quantitative trait loci

(QTL) have been identified for MLB and QTL-marker associations have been verified by

marker-assisted selection, the epistatic effects of these QTL have not been characterized.

Therefore, markers linked to MLB were utilized to create two sets (standard- and little-

leaf types) of nearly isogenic lines (NIL) possessing various numbers of QTL to test for

epistatic interactions among QTL. As the number of QTL increased, the number of

branches generally increased in the little-leaf NIL, but decreased in the standard-leaf NIL,

demonstrating an epistatic effect of genetic background on lateral branch development.

Comparative analysis of NIL that differed by a single QTL indicated that the effects of

two specific QTL were dependent on genetic background in the little-leaf NIL, but

similar epistatic effects were not detected among QTL in standard-leaf NIL. The

evaluation of NIL in two Wisconsin environments at three plant densities revealed a

genotype independent decrease in the number of branches at higher plant densities, as

well as genotype by environment and QTL by environment interactions involved in MLB.

Thus, the production of lateral branches is determined by growing environment,

interactions among other cucumber traits, and interactions among QTL conditioning

branching.

122Introduction

Cucumber (Cucumis sativus var. sativus L.) is the fifth most widely grown

vegetable crop worldwide (2,427,436 hectares harvested in 2004) and ranks seventh in

the US in area harvested (68,660 hectares; FAOSTAT, 2005). Although the yield of US

processing cucumber has reached a plateau in the last twenty years, evidence from

several studies indicates that selection for multiple lateral branching (MLB) types (i.e.,

plants with several lateral branches) may increase cucumber yield (i.e., fruit per plant;

Fredrick and Staub 1989; Cramer and Wehner 1998; Cramer and Wehner 1999; Cramer

and Wehner 2000b). Path analysis of yield component traits revealed that the number of

branches per plant was consistently and highly correlated (r > 0.7) with yield when tested

in several populations, cycles of selection, and environments (Cramer and Wehner

2000a). Moreover, highly branched, determinate processing cucumber genotypes are

desirable for once-over machine harvest operations (Lower and Edwards 1986; Wehner

1989; Staub et al. 1992).

A feral relative of cucumber, C. sativus var. hardwickii (R) Alef. (hereafter

referred to as C. s. var. hardwickii; Horst and Lower 1978), and a cucumber inbred line

‘Little John’ (line H-19; synom. AR 79-75; Goode et al. 1980) both possess a multiple

lateral branching habit not present in commercial cucumber. Multiple lateral branching

in both sources is quantitatively inherited (Wehner et al. 1978; Serquen et al. 1997b;

Fazio et al. 2003b) demonstrating mostly additive genetic variance with narrow-sense

heritabilities (h2) ranging between 0.00 and 0.61 (Wehner et al. 1978; Serquen et al.

1997b). Genetic evaluation of an F3 population derived from lines Gy-7 (synom. G421)

123and H-19, indicated that MLB is controlled by at least four genes (Serquen et al.

1997b), where four QTL explained 48% to 66% of the observed variation (R2) depending

upon environment (Serquen et al. 1997a). Using recombinant inbred lines (RIL) derived

from the same parents, Fazio et al. (2003b) identified five QTL with a combined R2 of

37% to 55% depending on location. In both QTL studies, one major QTL was detected

that accounted for 32% (Fazio et al. 2003b) to 40% (Serquen et al. 1997a) of the variation,

which mapped near the little-leaf locus (ll).

The QTL analyses of Gy-7 × H-19 derived populations (Serquen et al. 1997a;

Fazio et al. 2003b) provided marker-QTL relationships that have been exploited in

marker-assisted selection (MAS) of MLB (Fazio et al. 2003a; Fan et al. 2006). The

increase in the number of branches from MAS was comparable to phenotypic selection

after two generations of backcrossing to Gy-7, the low branching parent (Fazio et al.

2003a). Likewise, two generations of MAS backcrossing after two cycles of phenotypic

recurrent selection for MLB in a similar population continued to increase the number of

lateral branches, and operated to fix favorable alleles that were not exploited by

phenotypic selection (Fan et al. 2006). Moreover, MAS was slightly more effective than

phenotypic selection for the improvement of MLB during recurrent selection in four

genetically distinct populations (Chapter 1).

A knowledge of epistasis is critical to comprehensive genetic analysis (Kinghorn

1987; Yano 2001) and breeding (Schnell and Cockerham 1992) of crop species.

Relatively little is known, however, about epistatic interactions among individual QTL

involved in MLB in cucumber. Therefore, nearly isogenic lines (NIL) possessing

124differing numbers of QTL for MLB were created to test for epistasis among QTL

involved in MLB in two leaf types, little- (30-40 cm2) and standard-leaf (> 40 cm2; Staub

et al. 1992). These NIL were evaluated at two locations and three plant densities to test

the effect of genetic background (leaf type) and environment on lateral branch production.

Materials and Methods

NIL creation Six major QTL affecting MLB identified by Serquen et al. (1997a) and Fazio et al.

(2003b) were chosen for the development of NIL because of their relatively high LOD

scores, R2 percentages, and genetic effects (Table 3.1). The effect of specific QTL on

MLB was estimated by comparing NIL that differed by a single QTL. NIL with specific

sets of QTL were compared in different growing environments to define epistatic effects

and to provide for an understanding of environmental factors governing MLB.

The same parents (Gy-7 and H-19) used in cross-progeny QTL analysis (Serquen

et al. 1997a; Fazio et al. 2003b) were utilized to create NIL with varying numbers of QTL

for MLB (Table 3.2). Lines Gy-7 (standard-leaf type; LL) and H-19 (little-leaf type; ll)

were crossed to create F1 individuals that were subsequently backcrossed to both parents

resulting in BC1 populations that were standard-leaf (Gy-7 as recurrent parent) and

segregating for leaf type (H-19 as recurrent parent). Individuals from both BC1

populations were phenotypically selected for high lateral branch number and leaf type in

an open-field nursery at the University of Wisconsin Agricultural Research Station,

Hancock, Wisc. [Plainfield loamy sand (Typic Udipsamment; sandy, mixed, mesic)] and

125backcrossed to the recurrent parents to create little- and standard-leaf BC2 and BC3

families. These families were evaluated for MLB at Hancock in 2001, and 50 highly

branched individuals (30 standard-leaf and 20 little-leaf) were selfed to create BC2S1 and

BC3S1 families of each leaf type.

Twenty-four seedlings from each of the 50 BC2S1 and BC3S1 families were

genotyped using markers linked to QTL for MLB (Table 3.3). Since none of the

individuals contained all six QTL, the marker information was used to identify

individuals with complementing QTL compositions that could be crossed to provide for

as many QTL as possible in segregating progeny. A total of 11 little-leaf and nine

standard-leaf crosses were made, and 10 to 20 seeds of each cross were evaluated at

Hancock for MLB to identify 10 (five little-leaf and five standard-leaf) crosses where the

marker genotype (parental types) was confirmed by the branch number of progeny. All

the progeny (two to 96 individuals) from each of these 10 crosses were genotyped to

select plants that possessed as many QTL as possible, and then these selections were

genotyped and selfed for four to five generations to produce BC2S5/6 or BC3S5/6 NIL

differing in the number of QTL associated with MLB in both standard- and little-leaf

backgrounds (Table 3.2).

Molecular marker analysis Nine molecular markers and the ll gene linked to QTL for MLB were employed to

track the introgression of QTL into NIL (Table 3.3). All markers employed were from

Fazio et al. (2003b), except AJ6SCAR and M8SCAR, which were SCAR markers

(Chapter 2) converted from the RAPD marker mapped by Fazio et al. (2003b). Markers

126flanking each QTL were utilized, where available, to eliminate potential marker-QTL

recombination events. Leaf tissue for all genotyped lines was harvested, and DNA was

extracted, and then subjected to polymerase chain reaction (PCR) amplification and

agarose gel electrophoresis according to Fazio et al. (2003b).

Open-field evaluation of NIL for MLB To examine the effects of individual QTL, QTL number, and growing

environment on lateral branch production, NIL were evaluated in an open-field trial in the

summer of 2005 at three spacings within two locations; the University of Wisconsin

Agricultural Research Stations at Hancock and Arlington [Plano silt loam (Typic

Argiudoll)], Wisc. Seeds were sown in a greenhouse in Madison, Wisc. on June 11 and

June 14, 2005, and then transplanted on June 27 and July 29 to Hancock and Arlington,

respectively. Each location was arranged in a split-plot design with four replications of

spacing (whole plot factor) in randomized complete blocks, with the NIL completely

randomized as subplots with 10 plants per subplot. Plots were arranged in single rows

with 1.5 m between rows and 10, 15, and 20 cm between plants, corresponding to

approximately 66,700, 44,400, and 33,300 plants/ha, respectively. Lateral branch

number (at least three internodes in length) of each plant was recorded at or after anthesis

in the first ten nodes of the mainstem.

Statistical analysis Branching data were analyzed by analysis of variance using PROC GLM of SAS

(2003) to test for the main effects and interactions of location, spacing, leaf type, and

entry (NIL) nested within leaf type. All effects, except blocks, were considered fixed

127effects. Location was considered a fixed effect because the two locations chosen did

not represent a sampling of all cucumber growing environments, and inferences were

desired for Hancock, specifically, because it was the location originally used for the

detection of QTL (Serquen et al. 1997a; Fazio et al. 2003b). The two degrees of freedom

for spacings were partitioned into single degree of freedom contrasts within analyses of

variance to test for the linear (regression) and residual effect of spacing. The regression

coefficients of MLB on spacing were then obtained using PROC REG of SAS (2003).

Least squares means (lsmeans) are presented because of missing plots [22 of 288 (7.6%)].

To test for effects of specific QTL, comparisons were made between specific NIL that

differed in a single QTL (i.e., 12 NIL allowed for 11 single degree of freedom contrasts;

Table 3.4). For example, all little-leaf NIL in Table 3.2 have MLB1 and MLB6. In

addition, NIL-146 has MLB4, NIL-1246 has MLB2 and MLB4, and NIL-12456 has MLB2,

MLB4, and MLB5. Thus, NIL-146 and NIL-1246 have all MLB QTL in common, except

for MLB2. The addition effect of MLB2, therefore, is tested by comparing the means of

NIL-146 and NIL-1246 (Comparison A; Table 3.4). Likewise, the effect of adding

MLB5 to MLB2 and MLB4 is tested by Comparison B (NIL-12456 vs. NIL-1246).

Results

All main effects (location, spacing, leaf type, and entry nested within leaf type)

were highly significant (P < 0.001; Appendix L). The interactions of location with

spacing and leaf type were not significant, but the location by entry interaction was

highly significant (P < 0.001). Therefore, location lsmeans of MLB for each NIL (Table

1283.2) were used to interpret location effects. The number of branches of little-leaf NIL

tended to decrease in Hancock, but increase in Arlington with increasing number of QTL

(Table 3.2; Appendix K). In contrast, the number of branches of the standard-leaf NIL

tended to decrease in Arlington with increasing numbers of QTL, but no trend was

evident in Hancock as the number of QTL increased. Although the spacing by entry

interaction was not significant, the spacing by leaf type interaction was marginally

significant (P = 0.049). Therefore, the effect of spacing on MLB is presented separately

for each leaf type (Figure 3.1). Both the linear and residual effects of spacing were

significant in both leaf types (Figure 3.1). The lsmeans for MLB were 6.2, 7.6, and 7.8

(little-leaf NIL) and 0.9, 1.6, and 1.8 (standard-leaf NIL) branches per plant at within row

spacings of 10, 15, and 20 cm, respectively [LSD (α = 0.05) = 0.3].

Two of the six means comparisons for little-leaf NIL (Comparisons C and E)

were significant (Table 3.4). Of the three means comparisons (A, C, and F) that

examined the addition effect of MLB2 in little-leaf NIL, only one was significant

(Comparison C). Similarly, one of the three comparisons that evaluated the addition

effect of MLB5 (Comparison E) was significant. QTL by location interactions (Table

3.4) for MLB2 were highly significant (P < 0.001; Comparison C), marginally significant

(P = 0.04; Comparison A), or not significant (Comparison F), while one such interaction

for MLB5 was highly significant (P < 0.001; Comparison E) and two were marginally

significant [P = 0.06 (Comparison D) and 0.09 (Comparison B)]. Of the standard-leaf

NIL (Comparisons G-J), the effect of MLB3, when evaluated with MLB2 (Comparison G)

or alone (Comparison J), was not significant. In contrast, the effect of MLB5 was

129significant when alone (Comparison H) or with MLB2 (Comparison I). Only one

QTL by location interaction tested in the standard-leaf NIL was significant (P = 0.05;

Comparison J).

Increasing the number of QTL in the little-leaf background from three to four

increased the number of branches in two cases (NIL-136 to NIL-1236, and NIL-136 to

NIL-1356), while no such change was detected in a third case (NIL-146 to NIL-1246;

Figure 3.2). In all instances, the number of branches did not change when the number of

QTL was increased from four to five in little-leaf NIL. The increase from zero to two

QTL either did not change (NIL-0 to NIL-23) or decreased (NIL-0 to NIL-25) the

number of branches in standard-leaf plants (Figure 3.2). Similarly, the number of lateral

branches decreased (NIL-23 to NIL-235) or did not change (NIL-25 to NIL-235) when a

third QTL was added.

Discussion

Segregating populations such as F2, F2-derived F3 (F2:3), or recombinant inbred

lines (RIL) typically used for QTL identification are inadequate for the detection of

epistatic interactions among individual QTL (Lin et al. 2000). In contrast, the use of NIL

to detect epistatic interactions among QTL for heading date in rice (Oryza sativa L.)

illustrates the power of NIL over such populations for inter-allelic analyses. Therefore,

the marker-QTL relationships for MLB, previously identified and verified by MAS (Fan

et al. 2006; Chapter 1), were utilized to create NIL for the characterization of epistatic

interactions among QTL for MLB in cucumber. Based on the effects estimated from the

130QTL analyses of Serquen et al. (1997a) and Fazio et al. (2003b), the alleles of the

highly-branched H-19 parent at all but one QTL (MLB3) should contribute to higher

lateral branch number (Table 3.1). Although the estimated effects of each QTL are

dissimilar, incrementally combining the Gy-7 allele of MLB3 with the H-19 allele at all

other QTL should predictably increase the number of lateral branches in either little- or

standard-leaf NIL under a no-epistasis inheritance model.

The number of lateral branches in H-19 or C. s. var. hardwickii derived

germplasm was not affected by environment (Georgia and Hancock, Wisc.; Serquen et al.

1997b) or planting date (early and late; Fredrick and Staub 1989). In addition, the same

four QTL were identified in three different environments (Hancock, Wisc, in 1999 and

2000 and Utah in 1999; Fazio et al. 2003b), indicating that MLB habit can be relatively

stable across environments. However, Fazio et al. (2003b) identified a QTL specific to

Hancock (LOD 2.7-3.0 in both years), and seven other QTL (LOD 2.8-6.1) unique to a

single environment. This result, coupled with an estimate of narrow-sense heritability of

0.48 for MLB (Serquen et al. 1997b), indicates that MLB is affected by the environment.

Indeed, the number of lateral branches varied across years (López-Sesé and Staub 2002)

and planting dates (Chapter 1) when grown at Hancock, Wisc. The expression of MLB

was affected by environment in this study (Table 3.2), as evidenced by the location main

effect and location by entry interaction. Not only were genotype by location effects

evident, but QTL by location interactions were detected in both leaf-type backgrounds

(Table 3.4). Thus, although selection for MLB in one environment under a given plant

131density may increase the number of lateral branches, selection for MLB should be

performed in several commercial growing environments to optimize gain from selection.

The finding that the number of lateral branches decreased with increased plant

density (Figure 3.1) is consistent with previous results in C. s. var. hardwickii germplasm

(Fredrick and Staub 1989). This spacing effect was similar among all lines and across

locations examined. The highly significant residual effect of spacing on MLB suggests

their relationship is not linear (Figure 3.1). The decrease in the number of branches when

the spacing between plants was reduced from 20 to 15 cm was smaller than when spacing

was reduced from 15 to 10 cm in both leaf types. This difference, however, was less

pronounced for the little-leaf NIL than for standard-leaf NIL (i.e., spacing by leaf type

interaction; Figure 3.1). This reduction in number of lateral branches at higher plant

densities, coupled with the positive correlation of branching with fruit per plant, suggests

that the optimal plant density for yield of highly branched genotypes may be lower than

that of unbranched genotypes in machine harvest operations (100,000 to 200,000 plants/

ha; Wehner 1989; Staub et al. 1992). Thus, the relationship of branching and yield must

be evaluated in highly branched genotypes at plant densities above 44,400 plants/ha to

determine the optimal density.

The pyramiding of QTL for MLB did not necessarily increase the number of

lateral branches (Figure 3.2). In fact, increasing the number of QTL in the standard-leaf

NIL decreased the number of branches (Figure 3.2). The same QTL when in the little-

leaf NIL, however, generally increased the number of branches, and branching increased

when the number of QTL increased from three to four. Thus, the number of lateral

132branches is enhanced by the linkage block that contains the little-leaf gene. The QTL

with the greatest effect on MLB (MLB1, R2 = 32.4%; Table 3.1) was mapped to the

genomic region of the ll gene (Fazio et al. 2003b). Because ll was used as a marker for

the introgression of MLB1 (Table 3.3), only little-leaf NIL contained this QTL. The

effect of this genomic region on MLB is illustrated by the substantial difference in branch

number between the standard-leaf NIL-36 and the little-leaf NIL-136, which differ only

in MLB1 and ll (Table 3.2).

Fazio et al. (2003b) estimated that the effects of individual QTL are not equal

(Table 3.1), which was confirmed by this study. The means comparisons used to

estimate the effect of specific QTL (Table 3.4) revealed that the addition of some QTL

(i.e., MLB2 and MLB5 in little-leaf NIL) affected the number of lateral branches, while

others did not (i.e., MLB3 in standard-leaf NIL). In little-leaf NIL, the effect of a QTL

was dependent on the QTL with which it was combined. The differential effect of MLB2

and MLB5 when in combination with other QTL indicate that epistasis among QTL is a

major factor in the expression of MLB in little-leaf germplasm, and may be due to

duplicate gene action of MLB2 and MLB5. Epistasis was not detected, however, among

QTL in the standard-leaf NIL. The addition of MLB3, in every case (alone or with

another QTL), caused no change in the number of lateral branches (Table 3.2, Figure 3.2).

When MLB5, was added (either alone or with MLB2), however, a decrease in lateral

branches number was observed. The finding that MLB5 increased (Comparison E), did

not change (Comparisons B and D), or decreased (Comparisons H and I) the number of

lateral branches, depending on genetic background (i.e., QTL present and leaf type),

133indicates that epistasis plays a major role in the expression of MLB5. Thus, selection

for MLB will require the identification of favorable QTL combinations depending on the

genetic background employed.

The increased number of branches is desirable for once-over machine harvest and

branch number is higher in little-leaf than standard-leaf types. However, little-leaf types

tend to have poor fruit quality, and are monoecious and later flowering, resulting in low

first harvest yield, which is undesirable for machine harvest operations (Lower and

Edwards 1986; Wehner 1989). Previous studies indicate that MLB is also affected by the

determinate (de) and female (F) loci (Serquen et al. 1997a; Fazio et al. 2003a; Fazio et al.

2003b). Although determinant and/or gynoecious individuals with several branches have

been identified in little- and standard-leaf backgrounds, they generally have fewer

branches than their indeterminate and/or monoecious counterparts. Thus, even if all the

QTL that promote MLB are pyramided, the expression of MLB will depend upon plant

architecture (e.g., de and F) and genetic background.

The specific function of the QTL associated with MLB is largely unknown. It is

possible that the QTL mapped to the ll and de loci are not primarily involved with

branching, but represent pleitropic effects of ll and de on MLB (Fazio et al. 2003b).

Many of the QTL affecting MLB have been mapped near QTL for other traits, such as

sex expression, fruit length to diameter ratio, and fruits per plant, and MLB is

consistently correlated (either negatively or positively) with these traits (Kupper and

Staub 1988; Serquen et al. 1997b; Cramer and Wehner 1998; Cramer and Wehner 1999;

Cramer and Wehner 2000b; Fazio et al. 2003b). In fact, the simultaneous improvement

134of MLB and negatively correlated traits (e.g., gynoecy and earliness) using both

phenotypic selection and MAS has been largely unsuccessful (Chapter 1). Thus, some of

the factors involved in MLB may not be genes whose primary function is involved in

branching, but may be involved in the regulation of developmental processes and source

(i.e., photosynthetic base) to sink (i.e., fruit) relationships. Therefore, other traits (e.g.,

leaf type, determinate character, gynoecious sex expression, earliness, and fruit length to

diameter ratio) must be considered when developing highly branched cucumber

phenotypes to improve yield in cucumber.

Literature Cited

Cramer CS, Wehner TC (2000a) Path analysis of the correlation between fruit number and plant traits of cucumber populations. HortScience 35:708-711

Cramer CS, Wehner TC (2000b) Fruit yield and yield component correlations of four pickling cucumber populations. Cucurbit Genet Coop Rpt 23:12-15

Cramer CS, Wehner TC (1999) Little heterosis for yield and yield components in hybrids of six cucumber inbreds. Euphytica 110:99-108

Cramer CS, Wehner TC (1998) Fruit yield and yield component means and correlations of four slicing cucumber populations improved through six to ten cycles of recurrent selection. J Am Soc Hort Sci 123:388-395

Fan Z, Robbins MD, Staub JE (2006) Population development by phenotypic selection with subsequent marker-assisted selection for line extraction in cucumber (Cucumis sativus L.). Theor Appl Genet 112:843-855

FAOSTAT (2005) Agriculture. FAO Statistical Databases (http://faostat.fao.org, last accessed December 2005)

Fazio G, Chung SM, Staub JE (2003a) Comparative analysis of response to phenotypic and marker-assisted selection for multiple lateral branching in cucumber (Cucumis sativus L.). Theor Appl Genet 107:875-883

135Fazio G, Staub JE, Stevens MR (2003b) Genetic mapping and QTL analysis of horticultural traits in cucumber (Cucumis sativus L.) using recombinant inbred lines. Theor Appl Genet 107:864-874

Fredrick LR, Staub JE (1989) Combining ability analyses of fruit yield and quality in near-homozygous lines derived from cucumber. J Am Soc Hort Sci 114:332-338

Goode MJ, Bowers JL, Bassi Jr. A (1980) Little leaf, a new kind of pickling cucumber plant. Arkansas Farm Res 29:4

Horst EK, Lower RL (1978) Cucumis hardwickii: A source of germplasm for the cucumber breeder. Cucurbit Genet Coop Rpt 1:5

Kinghorn BP (1987) The nature of 2-locus epistatic interactions in animals: evidence from Sewall Wright's guinea pig data. Theor Appl Genet 73:595-604

Kupper RS, Staub JE (1988) Combining ability between lines of Cucumis sativus L. and Cucumis sativus var. hardwickii (R.) Alef. Euphytica 38:197-210

Lin HX, Yamamoto T, Sasaki T, Yano M (2000) Characterization and detection of epistatic interactions of 3 QTLs, Hd1, Hd2, and Hd3, controlling heading date in rice using nearly isogenic lines. Theor Appl Genet 101:1021-1028

López-Sesé AI, Staub J (2002) Combining ability analysis of yield components in cucumber. J Am Soc Hort Sci 127:931-937

Lower RL, Edwards MD (1986) Cucumber breeding. In: Bassett MJ (eds) Breeding Vegetable Crops. Westport, AVI Publishing Co., pp 173-207

Pierce LK, Wehner TC (2000) Review of genes and linkage groups in cucumber. HortScience 25:605-615

SAS (2003) SAS software, Version 9.1 for Windows. Copyright © 2002-2003 by SAS Institute Inc., Cary, NC.

Schnell FW, Cockerham CC (1992) Multiplicative vs. arbitrary gene action in heterosis. Genetics 131:461-469

Serquen FC, Bacher J, Staub JE (1997a) Mapping and QTL analysis of horticultural traits in a narrow cross in cucumber (Cucumis sativus L.) using random-amplified polymorphic DNA markers. Mol Breed 3:257-268

Serquen FC, Bacher J, Staub JE (1997b) Genetic analysis of yield components in cucumber at low plant density. J Am Soc Hort Sci 122:522-528

136Staub JE, Knerr LD, Hopen HJ (1992) Plant density and herbicides affect cucumber productivity. J Am Soc Hort Sci 117:48-53

Wehner TC (1989) Breeding for improved yield in cucumber. Plant Breed Rev 6:323-359

Wehner TC, Staub JE, Peterson CE (1978) Inheritance of littleleaf and multi-branched plant type in cucumber. Cucurbit Genet Coop Rpt 10:33-34

Yano M (2001) Genetic and molecular dissection of naturally occurring variation. Curr Opin Plant Biol 4:130-135

137

Table 3.1. Characteristics of previously identified cucumber quantitative trait loci (QTL) associated with multiple lateral branching (MLB) that were introgressed to create nearly isogenic lines (NIL)

Recombinant inbred line analysisa F2:3 progeny analysisb

Hancock, WI 1999 Hancock, WI 2000 Utah 1999 Tifton, GA Hancock, WI QTL Namec LODd R2 (%)e Effectf LOD R2 (%) Effect LOD R2 (%) Effect LOD R2 (%) LOD R2 (%) MLB1 mlb1.4 32.9 32.4 0.63 7.0 8.1 0.23 9.8 17.2 0.42 4.6 13.6 nd nd MLB2 mlb1.1 11.6 9.1 0.36 8.2 10.6 0.24 6.8 11.5 0.33 10.4 39.6 10.1 37.0 MLB3 mlb6.2 4.2 3.7 -0.17 3.7 4.3 -0.15 2.7 4.9 -0.18 MLB4 nd nd 3.3 11.0 MLB5 mlb4.4 3.0 1.7 0.17 4.6 4.6 0.37 2.7 3.3 0.20 MLB6 mlb6.1 2.7 1.5 0.11 3.0 2.9 0.17 ndg nd nd a QTL identified by Fazio et al 2003b b QTL identified by Serquen et al 1997a c QTL name given by Fazio et al 2003b d Log of likelihood ratio e Percentage of the phenotypic variation explained f The effect of the H-19 allele on the number of lateral branches g Not detected

138

Table 3.2. QTL composition and mean number of branches in near isogenic lines (NIL) of cucumber. Quantitative trait locic Number of lateral branchesd

NILa Leaf typeb MLB1 MLB2 MLB3 MLB4 MLB5 MLB6 Generation Hancock, Wisc. Arlington, Wisc. Mean NIL-1246 little MLB1 MLB2 MLB4 MLB6 BC2S5 6.75 7.30 7.03 NIL-136 little MLB1 MLB3 MLB6 BC2S5 7.89 4.68 6.29 NIL-1236 little MLB1 MLB2 MLB3 MLB6 BC2S5 7.10 8.28 7.69 NIL-1356 little MLB1 MLB3 MLB5 MLB6 BC2S6 7.20 7.44 7.32 NIL-12456 little MLB1 MLB2 MLB4 MLB5 MLB6 BC2S5 7.13 6.77 6.95 NIL-12356 little MLB1 MLB2 MLB3 MLB5 MLB6 BC2S5 7.24 8.19 7.71 NIL-146 little MLB1 MLB4 MLB6 BC2S6 7.64 7.18 7.41 NIL-36 standard MLB3 MLB6 BC3S5 1.45 1.38 1.41 NIL-23 standard MLB2 MLB3 BC2S5 2.13 1.98 2.05 NIL-25 standard MLB2 MLB5 BC2S5 0.93 1.25 1.09 NIL-235 standard MLB2 MLB3 MLB5 BC3S5 1.30 0.59 0.94 NIL-0 standard BC2S5 1.38 1.89 1.64

a NIL names reflect their QTL composition (i.e., NIL-1246 contains MLB1, MLB2, MLB4, and MLB6) b Leaf type classified as standard- (> 40 cm2) or little-leaf (30-40 cm2; Staub et al. 1992) c QTL names are from Table 3.1 d Lsmeans for Hancock, Wisconsin, Arlington, Wisconsin, and combined locations. LSD’s (α= 0.05) are 0.96, 0.60, and 0.57, and Coefficient of variation (CV) are 22.5%, 15.1%, and 19.6%, respectively.

139Table 3.3. Marker-QTL associations used to introgress quantitative trait loci (QTL) for multiple lateral branching (MLB) into nearly isogenic lines (NIL) in cucumber Flanking markera Distance (cM)b QTL Distance (cM) b Flanking markerc

OP-AD12-1 9.1 MLB1 3.7 lld

MLB2 6.1 OP-AG1-1 L19-2-SCAR 6.3 MLB3 16.1 NR60 AJ6SCAR MLB4e BC523SCAR CSWTAAA01 2.3 MLB5 AK5SCAR 4.1 MLB6 2.0 M8SCAR a The marker above the QTL in the genetic map of Fazio et al. (2003b) b The genetic map distance between the marker and the QTL c The marker below the QTL in the genetic map of Fazio et al. (2003b) d The little-leaf gene (Pierce and Wehner 2000) e MLB4 was mapped between AJ6SCAR and BC523SCAR, which are separated by 5.1 cM (Fazio et al. 2003b)

140Table 3.4. Means comparisons to determine specific quantitative trait loci (QTL) effects in nearly isogenic lines (NIL) of cucumber

Test hypothesisb

Means comparisona Comparison QTL effect Background P-valuecQTL × loc P-valued

NIL-1246 vs. NIL-146 A MLB2 MLB4 0.1542 0.0416 NIL-12456 vs. NIL-1246 B MLB5 MLB2 & MLB4 0.7716 0.0866 NIL-1236 vs. NIL-136 C MLB2 MLB3 <0.0001 <0.0001 NIL-12356 vs. NIL-1236 D MLB5 MLB2 & MLB3 0.2173 0.0598 NIL-1356 vs. NIL-136 E MLB5 MLB3 0.0001 <0.0001 NIL-12356 vs. NIL-1356 F MLB2 MLB3 & MLB5 0.1323 0.1756 NIL-23 vs. NIL-0 G MLB2 & MLB3 0.1130 0.2044 NIL-235 vs. NIL-23 H MLB5 MLB2 & MLB3 <0.0001 0.2946 NIL-25 vs. NIL-0 I MLB2 & MLB5 0.0375 0.7117 NIL-235 vs. NIL-25 J MLB3 MLB2 & MLB5 0.5782 0.0522 a The means (over both locations) of the two lines in the means comparison column were tested by single degree of freedom contrasts with the null hypothesis as equal means b Null hypothesis, where adding the QTL in QTL effect column to the QTL already present in the background column has no effect on the number of lateral branches c The P-value of the means comparison d The P-value of the QTL by location (loc; Hancock and Arlington, Wisc.) interaction

141

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

PPR

-linear < 0.0001 -residual <0.0001 2 = 0.81

10 15 20

Within row spacing (cm)

Num

ber

of la

tera

l bra

nche

s

Little leaf NIL (30-40 sq cm)Standard leaf NIL (> 40 sq cm)

Figure 3.1. The effect of plant density on the number of lateral branches in cucumber near-isogenic lines (NIL) of two leaf types (little-leaf and standard-leaf). The linear and residual P-values, as well as the R2 of each leaf type are presented to the right of each leaf type. Within row spacings of 10, 15 and 20 cm correspond to plant densities of 66,700, 44,400, and 33,300 plants/hectare, respectively.

P-linear < 0.0001 P-residual = 0.0022 R2 = 0.92

142

Little-leaf NIL

NIL-146

NIL-1246 NIL-12456

NIL-1236

NIL-136

NIL-1356

NIL-12356

5.5

6.0

6.5

7.0

7.5

8.0

2 3 4 5 6

Num

ber

of la

tera

l bra

nche

s

Comparisons A & B

Comparisons C & D

Comparisons E & F

**

Standard-leaf NIL

NIL-235

NIL-23

NIL-0

NIL-25

NIL-36

0.0

0.5

1.0

1.5

2.0

2.5

0 1 2 3 4

Number of QTL

Num

ber o

f lat

eral

bra

nche

s

Comparisons G & H

Comparisions I & J

NIL-36

*

*

Figure 3.2. The effect of increasing the number of quantitative trait loci (QTL) on the number of lateral branches in two leaf types of cucumber as determined by near-isogenic lines (NIL; Table 3.2). Lines indicate the incremental addition of QTL, and the comparisons in the legends refer to means comparisons performed (Table 3.4). Asterisks (*) indicate significant means comparisons (P < 0.05).

143

Conclusions and Future Work

The expression of cucumber (Cucumis sativus L.; 2n=2x=14) yield component

traits investigated herein [multiple lateral branching (MLB), gynoecious sex expression

(GYN), earliness (EAR), and fruit length to diameter ratio (L:D)] is complex and

influenced by genetic and environmental effects at several levels. Epistatic interactions

were detected among quantitative trait loci (QTL) for MLB, as well as between MLB and

leaf type. The expression of MLB was affected by the environment and QTL by

environment interactions were detected. Thus, manipulation of these traits by phenotypic

selection (PHE) or marker-assisted selection (MAS) will not be straightforward. Indeed,

both MAS and PHE provided improvements for these traits, but response to selection was

dependent upon genetic background (population). In some populations, trait means

decreased, but in others, improvements were made from both selection methods. MAS

and PHE were considered separately herein for a direct comparison of their efficacy, but

since both methods provided improvements, selection for yield component traits may be

most effective by combining both MAS and PHE.

Using the methods and resources of this study, MAS and PHE could be combined

by evaluating 600 individuals and selecting 20 by PHE during the summer. During the

establishment of cuttings from PHE, another 20 selections could be made by MAS from

600 individuals of the same population and all 40 selections could be intermated in the

greenhouse in the fall. Another option would be to genotype a large number of seedlings

with a select group of reliable sequenced characterized amplified region (SCAR), simple

144sequence repeat (SSR), or single nucleotide polymorphism (SNP) markers before

transplanting in the field for subsequent phenotypic selection. The multiplexing

guidelines and new SCAR and SNP markers developed herein would allow for efficient

genotyping of seedlings to greatly reduce the number of plants evaluated by phenotypic

selection. MAS and PHE could also be combined using and index based on both marker

and phenotypic information. Both phenotypic and genotypic data could be taken for all

traits on each plant, or plants could be phenotyped for GYN, EAR, and L:D and

genotyped for MLB, since these method/trait combinations were generally most effective

in this study. The combination of MAS and PHE using one or a combination of these

methods would allow a test of the hypothesis that MAS and PHE are more effective at

improving yield components when utilized together than separately.

Yield was generally not increased, nor was the simultaneous improvement of

MLB, GYN, EAR, and L:D accomplished during population improvement (recurrent

selection) from MAS or PHE, so the hypothesis that yield increases with the

improvement of all four traits cannot be rejected. These results suggest that obtaining

high-parent values of all four traits (i.e., > 6 branches, 100% female flowers, 2.0

fruit/plant in the first harvest, and L:D > 3.0) simultaneously in this germplasm by

population improvement will be difficult given the correlations between traits, which are

due, in large part, to pleitropic effects and/or linkage between QTL conditioning these

traits. When considering this hypothesis, the results of a parallel study are informative.

Fan et al. (2006) independently evaluated the effectiveness of two cycles of MAS for

MLB, GYN, and L:D in a backcrossing scheme using selections from cycle 2 (C2) of

145PHE in Population 1 of this study as recurrent parents to produce two backcross

populations. In addition to the gains for MLB and L:D after two cycles of recurrent

selection by PHE, MAS continued to improve MLB and L:D in one backcross population,

and L:D in the other, while GYN was improved in both populations and yield increased

in the former population after only two cycles of MAS. These results suggest that line

extraction (backcrossing) is more efficient than population improvement (recurrent

selection) for simultaneous multi-trait improvement. Indeed, individual plants with

standard leaves, > 6 branches, 100% female flowers, < 32 days to the first female flower,

and L:D > 3.0 were identified during phenotypic selection of this study. Thus, combining

all four traits is possible, and the development of inbred lines from these individuals

would allow a test of the hypothesis that combining all four traits increases yield.

The results of this study have demonstrated that MAS can be effective for the

improvement of quantitative yield components and illustrated that epistasis is an

important factor in the expression of MLB. Still, relatively little is known about the

individual QTL involved MLB and other yield components. Epistasis is known to play a

significant role among the three major genes involved in sex expression in cucumber, but

little is known about modifiers of the F locus. The characterization of interactions among

these loci and with the F locus, similar to the detection of epistatic interactions using NIL

presented herein, would provide valuable information for breeding for gynoecious sex

expression in cucumber. The clustering of QTL on Linkage Groups 1 and 6, the mapping

of QTL near known genes, and the correlation among yield component traits suggest that

some of these QTL may not be solely involved in one trait, but may be pleitropically

146involved in several yield traits. A closer inspection of these QTL by fine mapping would

help determine if relationships among traits are due to pleitropy or tight linkage. Several

BAC clones have been identified from markers linked to important yield component QTL,

and new markers have been created from several of these BAC clones that provide a

potential starting point for fine mapping and map-based cloning of yield component QTL.

In addition, the function of some of these QTL could be determined by comparative

analysis of characterized genes from other plant species. For example, the identification

and mapping of homologs of known branching genes from Arabidopsis or corn (Zea

mays), may help determine if the primary function of QTL associated with MLB is

indeed the production of branches. The characterization of the QTL associated with yield

components, including a knowledge of their function, will help define the relationships

among yield components and provide valuable information for trait manipulation by

selection.

The conversion of random amplified polymorphic DNA (RAPD) markers to

SCAR and SNP markers utilizing the methods described herein was highly successful.

Although the new markers were verified by co-segregation with the original RAPD from

which they were derived, they have not been placed on the current cucumber genetic

linkage map. In addition, these new markers have not been tested for their utility in

germplasm other than Gy-7 × H-19 crosses. The conversion of several RAPD markers

on the current map that were not included in the conversion study is recommended and

should be highly successful utilizing the methods described herein.

147The frequency of SNPs identified in this study (1 SNP every ~210 bp) and the

recovery rate of SNP-based markers in sequences that contain SNPs (80%) suggest that

the recovery rate of new markers based on SNPs in cucumber is expected to be much

greater than that previously reported for RAPDs (4.8%; Serquen et al. 1997) and SSRs

(7.2%; Fazio et al. 2002). Sequences for SNP identification can be obtained from current

markers, or by BAC clones. Sequences obtained from a randomly selected set of BAC

clones should provide SNP markers that are randomly distributed throughout the

cucumber genome, while sequences from BACs in targeted regions (i.e., linked to

important QTL), will provide physically clustered SNP markers useful for fine mapping

and QTL dissection. The creation of allele-specific markers based on SNPs utilizing the

four marker design approaches reported herein was highly successful and applicable to

almost any SNP. Although these methods are very useful for creating markers in specific

germplasm (i.e., Gy-7 × H-19 crosses), the creation of SNP markers that can distinguish

any allele would be useful for a wide range of germplasm. Several methods are available

(i.e., differential hybridization or heteroduplex analysis) to create broadly applicable SNP

markers. The new markers created herein will increase the efficiency of MAS in

cucumber, while the marker development methods will be useful to create new markers

in cucumber.

Literature Cited Fan Z, Robbins MD, Staub JE (2006) Population development by phenotypic selection with subsequent marker-assisted selection for line extraction in cucumber (Cucumis sativus L.). Theor Appl Genet 112:843-855

148Fazio G, Staub JE, Chung SM (2002) Development and characterization of PCR markers in cucumber. J Am Soc Hort Sci 127:545-557

Serquen FC, Bacher J, Staub JE (1997) Mapping and QTL analysis of horticultural traits in a narrow cross in cucumber (Cucumis sativus L.) using random-amplified polymorphic DNA markers. Mol Breed 3:257-268

149

Appendices

Appendix A. Means and linear response at two planting dates (June 23, 2004 and July 7, 2004) of five traits in four base cucumber populations (C0) of cucumber which underwent three cycles of recurrent mass selection (C1-C3) using three breeding methods (see Chapter 1).

First Planting Second planting

Traita Methodb C0 C1 C2 C3 bc R2 Pd C0 C1 C2 C3 bc R2 Pd

Population 1e EAR MAS 2.23 1.92 1.87 1.35 -0.269 0.908 <0.001 1.77 1.56 1.21 0.90 -0.294 0.991 <0.001 PHE 2.23 2.15 2.13 2.43 0.059 0.297 0.412 1.77 1.66 2.14 1.80 0.057 0.129 0.244 RAN 2.23 2.00 2.02 2.03 -0.059 0.501 0.099 1.77 1.74 1.60 1.50 -0.094 0.951 0.059 GYN MAS 92.5 76.9 55.5 32.4 -20.162 0.993 <0.001 87.3 67.6 44.4 26.7 -20.504 0.998 <0.001 PHE 92.5 79.2 95.9 82.4 -1.335 0.047 0.071 87.3 73.1 92.1 81.1 0.044 0.000 0.410 RAN 92.5 85.5 93.9 84.6 -1.527 0.173 0.117 87.3 77.6 81.3 80.9 -1.568 0.251 0.039 L:D MAS 2.77 2.89 2.97 3.08 0.099 0.992 <0.001 2.77 2.77 2.87 3.02 0.083 0.865 <0.001 PHE 2.77 2.74 2.74 3.02 0.076 0.524 <0.001 2.77 2.80 2.77 2.99 0.065 0.610 <0.001 RAN 2.77 2.74 2.69 2.81 0.007 0.034 0.997 2.77 2.75 2.77 2.78 0.005 0.253 0.874 MLB MAS 1.88 2.11 2.56 2.80 0.322 0.984 <0.001 2.94 3.29 3.48 3.53 0.196 0.901 <0.001 PHE 1.88 2.13 2.11 2.59 0.213 0.839 <0.001 2.94 3.18 3.01 3.56 0.170 0.627 0.001 RAN 1.88 1.73 1.41 1.62 -0.110 0.510 0.016 2.94 2.85 2.91 2.78 -0.043 0.580 0.380 Yield MAS 2.18 2.24 2.34 1.75 -0.119 0.350 0.022 2.24 2.28 2.07 1.88 -0.132 0.839 0.003 PHE 2.18 2.21 2.27 2.33 0.050 0.967 0.223 2.24 2.10 2.07 2.15 -0.032 0.296 0.228 RAN 2.18 1.98 2.11 2.05 -0.029 0.190 0.256 2.24 1.85 1.96 1.90 -0.092 0.456 0.002 Population 2 EAR MAS 2.00 1.63 1.70 1.78 -0.058 0.224 0.049 1.33 1.00 0.88 0.82 -0.162 0.876 <0.001 PHE 2.00 2.03 2.46 1.98 0.038 0.044 0.252 1.33 1.63 1.80 1.66 0.119 0.587 0.002 RAN 2.00 2.10 1.88 1.69 -0.115 0.705 0.048 1.33 0.98 1.02 1.09 -0.067 0.306 0.037

150 First Planting Second planting

Traita Methodb C0 C1 C2 C3 bc R2 Pd C0 C1 C2 C3 bc R2 Pd

GYN MAS 57.0 24.5 21.4 27.3 -9.236 0.523 <0.001 52.7 22.4 14.5 23.6 -9.523 0.537 <0.001 PHE 57.0 75.6 96.4 94.1 13.206 0.861 <0.001 52.7 69.5 90.6 86.6 12.288 0.833 <0.001 RAN 57.0 42.3 44.4 39.4 -5.083 0.715 <0.001 52.7 40.6 37.4 42.1 -3.501 0.463 <0.001 L:D MAS 3.12 3.29 3.34 3.17 0.022 0.077 0.004 3.08 3.17 3.14 3.09 -0.001 0.001 0.543 PHE 3.12 2.97 2.89 3.16 0.007 0.006 0.296 3.08 2.97 2.92 3.27 0.052 0.185 0.067 RAN 3.12 3.15 3.22 3.25 0.049 0.972 0.001 3.08 3.06 3.06 3.11 0.009 0.236 0.748 MLB MAS 2.50 2.99 2.91 2.73 0.058 0.123 0.038 3.43 3.96 3.73 3.43 -0.025 0.015 0.482 PHE 2.50 2.20 2.23 2.41 -0.026 0.055 0.249 3.43 3.40 3.11 3.63 0.029 0.031 0.878 RAN 2.50 2.81 2.64 2.75 0.056 0.287 0.125 3.43 3.96 3.76 3.58 0.022 0.015 0.137 Yield MAS 2.09 2.01 2.17 1.93 -0.031 0.153 0.460 1.97 1.91 1.83 1.73 -0.078 0.983 0.051 PHE 2.09 2.07 2.27 2.06 0.012 0.022 0.674 1.97 1.93 1.94 2.12 0.047 0.486 0.362 RAN 2.09 2.20 1.93 1.92 -0.077 0.547 0.110 1.97 1.99 1.88 1.94 -0.017 0.222 0.680 Population 3 EAR MAS 2.08 2.15 2.13 2.05 -0.011 0.100 0.975 1.66 1.59 1.41 1.68 -0.011 0.013 0.577 PHE 2.08 1.81 1.87 1.61 -0.135 0.809 0.002 1.66 1.30 1.40 1.54 -0.028 0.051 0.168 RAN 2.08 2.16 2.16 2.02 -0.017 0.105 0.939 1.66 1.54 1.50 1.15 -0.157 0.858 0.001 GYN MAS 95.6 84.0 64.5 75.4 -7.983 0.613 <0.001 91.5 80.5 56.6 63.8 -10.690 0.761 <0.001 PHE 95.6 81.2 85.7 90.1 -1.176 0.061 0.024 91.5 72.6 80.5 88.9 0.003 0.000 0.078 RAN 95.6 89.9 85.9 85.3 -3.476 0.904 0.001 91.5 88.4 80.4 66.7 -8.229 0.924 <0.001 L:D MAS 2.78 2.86 2.97 2.72 -0.006 0.006 0.518 2.83 2.79 2.81 2.74 -0.024 0.642 0.121 PHE 2.78 2.82 2.92 3.12 0.112 0.906 <0.001 2.83 2.85 2.91 3.14 0.099 0.818 <0.001 RAN 2.78 2.78 2.85 3.01 0.076 0.809 <0.001 2.83 2.74 2.84 3.07 0.083 0.589 <0.001 MLB MAS 1.67 1.69 2.07 2.02 0.142 0.755 0.007 2.99 2.80 3.11 3.12 0.070 0.369 0.347 PHE 1.67 1.39 1.92 1.93 0.130 0.443 0.060 2.99 2.88 3.13 3.13 0.066 0.498 0.316 RAN 1.67 1.59 1.55 1.88 0.058 0.270 0.474 2.99 2.55 2.86 2.89 0.000 0.000 0.415 Yield MAS 1.95 2.00 2.21 2.08 0.062 0.500 0.082 1.78 1.75 1.81 1.89 0.039 0.736 0.402 PHE 1.95 1.89 1.94 1.73 -0.061 0.602 0.148 1.78 1.76 1.64 1.71 -0.033 0.476 0.358 RAN 1.95 1.90 2.02 2.07 0.049 0.688 0.302 1.78 1.64 1.80 1.66 -0.021 0.113 0.464

151 First Planting Second planting

Traita Methodb C0 C1 C2 C3 bc R2 Pd C0 C1 C2 C3 bc R2 Pd

Population 4 EAR MAS 2.19 1.75 1.83 1.90 -0.081 0.296 0.011 1.34 1.34 1.10 1.28 -0.043 0.240 0.329 PHE 2.19 1.81 2.13 2.29 0.061 0.146 0.721 1.34 1.40 1.76 1.43 0.062 0.177 0.108 RAN 2.19 1.62 1.84 1.93 -0.057 0.096 0.018 1.34 1.36 1.20 1.25 -0.043 0.527 0.400 GYN MAS 58.5 61.6 56.4 74.9 4.389 0.465 0.001 40.8 61.0 52.5 61.4 5.322 0.506 <0.001 PHE 58.5 65.2 90.9 87.8 11.343 0.824 <0.001 40.8 69.4 84.5 81.6 13.753 0.791 <0.001 RAN 58.5 59.5 43.7 55.8 -2.411 0.182 0.026 40.8 53.6 37.3 49.3 0.897 0.024 0.146 L:D MAS 3.02 2.70 2.91 2.68 -0.079 0.392 <0.001 3.01 2.79 2.93 2.82 -0.041 0.276 <0.001 PHE 3.02 2.78 2.71 2.77 -0.080 0.590 <0.001 3.01 2.70 2.69 2.74 -0.082 0.501 <0.001 RAN 3.02 2.90 3.05 2.96 -0.002 0.001 0.503 3.01 2.85 2.92 2.92 -0.019 0.148 0.032 MLB MAS 2.47 2.15 2.25 2.24 -0.058 0.308 0.097 3.53 3.28 3.39 3.34 -0.048 0.311 0.185 PHE 2.47 2.16 1.83 2.21 -0.111 0.299 0.005 3.53 3.09 3.24 2.99 -0.149 0.649 0.001 RAN 2.47 2.06 2.54 2.60 0.086 0.206 0.434 3.53 3.58 3.53 3.38 -0.053 0.604 0.422 Yield MAS 2.40 1.82 2.12 2.19 -0.035 0.035 0.020 2.30 1.96 1.97 2.17 -0.039 0.089 0.052 PHE 2.40 2.01 2.18 2.13 -0.065 0.256 0.012 2.30 1.87 2.07 1.80 -0.129 0.551 <0.001 RAN 2.40 2.00 2.18 2.02 -0.096 0.442 0.001 2.30 1.98 1.92 1.86 -0.138 0.815 <0.001

a Traits are EAR = earliness measured as the number of fruits per plant in first harvest, GYN = gynoecy measured as the percent female flowers in the first ten nodes, L:D = fruit length to diameter ratio measured as the mean length to diameter ratio of 5-10 randomly selected fruit averaged over three harvests, MLB = multiple lateral branching measured as the number of lateral branches (at least three internodes long) on the mainstem in the first 10 nodes, and Yield measured as the number of fruits per plant averaged over four harvests b Methods are MAS =selection by marker, PHE = phenotypic selection, and RAN = random mating (no selection) c Slope of linear regression of means over cycles d P-values from F-tests of linear response to selection e Populations were created by intermating four inbred lines, and then bulking by the maternal parent (Figure 1.1)

152

Appendix B. The linear response of selection for five traits in four cucumber populations by marker (MAS), phenotype (PHE), and random mating (no selection; RAN) over three cycles. The five traits are earliness (measured as the number of fruits per plant in first harvest), gynoecy (measured as the percent female flowers in the first ten nodes), fruit length to diameter ratio (measured as the mean length to diameter ratio of 5-10 randomly selected fruit averaged over three harvests), multiple lateral branching (measured as the number of lateral branches of at least three internodes long on the mainstem in the first 10 nodes), and yield (measured as the number of fruits per plant averaged over four harvests; see Chapter 1).

Earliness (EAR)

Population 1

0.40

0.90

1.40

1.90

2.40

0 1 2 3

Frui

t per

pla

nt (1

st h

arve

st)

Population 2

0.40

0.90

1.40

1.90

2.40

0 1 2 3

Population 3

0.40

0.90

1.40

1.90

2.40

0 1 2 3

Selection cycle

Frui

t/pla

nt (1

st h

arve

st)

Population 4

0.40

0.90

1.40

1.90

2.40

0 1 2 3

Selection cycle

MAS PHE RAN G421 H19Parent Vlassett Linear (MAS) Linear (PHE) Linear (RAN)

153

Gynoecy (GYN)

Population 1

0.0010.00

20.0030.0040.0050.00

60.0070.0080.00

90.00100.00

0 1 2 3

Perc

ent f

emal

e flo

wer

s

Population 2

0.0010.00

20.0030.0040.0050.00

60.0070.0080.00

90.00100.00

0 1 2 3

Population 3

0.0010.00

20.0030.0040.0050.00

60.0070.0080.00

90.00100.00

0 1 2 3

Selection cycle

Perc

ent f

emal

e flo

wer

s

Population 4

0.0010.00

20.0030.0040.0050.00

60.0070.0080.00

90.00100.00

0 1 2 3

Selection cycle

MAS PHE RAN G421 H19Parent Vlassett Linear (MAS) Linear (PHE) Linear (RAN)

154

Mean fruit length to diameter ratio (L:D)

Population 1

2.50

2.70

2.90

3.10

3.30

3.50

0 1 2 3

Mea

n L:

D

Population 2

2.50

2.70

2.90

3.10

3.30

3.50

0 1 2 3

Population 3

2.50

2.70

2.90

3.10

3.30

3.50

0 1 2 3

Selection cycle

Mea

n L:

D

Population 4

2.50

2.70

2.90

3.10

3.30

3.50

0 1 2 3

Selection cycle

MAS PHE RAN G421 H19Parent Vlassett Linear (MAS) Linear (PHE) Linear (RAN)

155

Multiple lateral branching (MLB)

Population 2

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0 1 2 3

Population 3

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0 1 2 3

Selection cycle

Num

ber o

f lat

eral

bra

nche

s

Population 1

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0 1 2 3

Num

ber o

f lat

eral

bra

nche

s

Population 4

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0 1 2 3

Selection cycle

MAS PHE RAN G421 H19Parent Vlassett Linear (MAS) Linear (PHE) Linear (RAN)

156

Yield

Population 1

1.401.50

1.601.701.801.90

2.002.102.20

2.302.40

0 1 2 3

Frui

t/pla

nt (m

ean

of fo

ur h

arve

sts)

Population 2

1.401.50

1.601.701.801.90

2.002.102.20

2.302.40

0 1 2 3

Population 3

1.401.50

1.601.70

1.801.902.00

2.102.20

2.302.40

0 1 2 3

Selection cycle

Frui

t/pla

nt (m

ean

of fo

ur h

arve

sts)

Population 4

1.401.50

1.601.70

1.801.902.00

2.102.20

2.302.40

0 1 2 3

Selection cycle

MAS PHE RAN G421 H19Parent Vlassett Linear (MAS) Linear (PHE) Linear (RAN)

157Appendix C. Sequences of RAPD bands used to make SCAR primers. Sequences are presented in FASTA format with the name of the RAPD marker, and the parental line (Gy-7 or H-19) from which the band was produced in parentheses. Incomplete sequences are indicated by “FORWARD ONLY” or “REVERSE ONLY”. Sequences that correspond to a band other than the polymorphic RAPD band (as determined by the segregation pattern of the SCAR created from the sequence and the original RAPD marker) are indicated by “DOES NOT MATCH RAPD” (Table 2.1; see Chapter 2). >BC231 (H-19) agggagttccaaacttttcagtacagtaaggggtattaaaatacaaaggaaggttgttgctgtcaaaaac atatgttctactacccacaatcatgcatacctatcatgattttttccttggaggacattcagggttccta agaacgtacaagaggatgaccggagagatttactaggaaggaatgaagaaagacataaagaaatactatg aagaatgtcttgtatgccaaaggaataaaacacttgcattatcacctgccggattgctgctgccacttga gattcctaacacagtgtggtcggatatttccatggattttatagatggacttccgaaatcagctggaaaa gaagtgattgttgtggttgtagatcgtctgagtaagtatgcacattttatagctataaatcatccttaca tgggcagcttcgtagcagaagtttttgttaaagaagtggtgagattgcacgggtatccctagtcaattat atcggatcgagacaaaatctttattagtcatttttgaaaggaaatgttcaggttagtgggaactaaaatg aatagaaggaaatgttcaggttagtgggaactaaaatgaatagaaggaaatgttcaggttagtgggaact aagcccatatgcaccaagttcgaatatagaacttgcttcttactgaaagaataatgattatgggacttgc tataagggtatccaatattcgaagtaaatgaatgattacgaaatgattgtttcctcagtgtgaagaacaa caaaaaagaaaaaaggataaaaataaaaaaacaaactttctttttgaaattctaaaagaatccaaaaaga ctttaaaaggtgcaaccaagaaaggaggtcaaagaagggctctgatagatccccctatcatacgtcaata tagttgaaagaaaaagagagagatcacaggggaactccct >BC388 (Gy-7) cggtcgcgtccttagaccaaccacccaagttgatgcgctgtgcatggcagcagacctgagtttgtatgag aggacgatgttggccaaggctgtagagaaggggccaacttcgggacataaacggaagactgagcagcagc ctatagtagcaccacaaaggaatttgagatcaatggtttgttccaacggcactgacaacaagctactcag acaggccaaaccttgaaggtactattcatgactcttaattatggaagacctcactcgggtcgttacctag caggaagtggagtatgttacaaatgcagacatagaggttgacttcgccatagagcttctagggcttccgc gctgcagagaggttgacttcgccatagagctcgaaccaggtattggtctcatatcgagagcctcatacag aatgacgcgaccg >BC403 (Gy-7) ggaaggctgtcttccttatgtcttcatctcgtacccggatttgatggtaactcgatttcaaatctctctt ggagaaaacacttgagccattcaactcatccaacagctcatcaatcattggaattgggaatttgtcaggt atcgtagctcgattcaaagccctataatcgacgcaaaatctccaaccgccatccttcttttttactagaa tcaccgggctggaaaaggggctgatgctcggtcgtatgatacctgaagtcaacatttttctgtgcgtgtg ggtaacggtatggtcttacattaatggggtcggtaccttccttcagttggattctatgatcaatctgtcc cattggaggcagctcgtttggcataacgaatacatcttcgaattcattttggagttgttcaatttcgggt tgtatttcttctattgattctgttgccatcaacatccgattggtttcgggaattcccatagctctgaagt caactagaaatccttgatcatctgattgccacgttttaaccagcatcttcaaagatatttccattctggt cagtgagggatctcttttcaagatgacgttagtgcccccgacaaagaatgtcatggttaacgctttccaa tcaacagtcatcaccccttgctttcgaagccactacattcctagtaccatatctaagttgcctaattcca gcgataagaaatcctcaatgatggtcagtactggcagcccaactatgatgtccttgcacatccctcgacc ttgcacagccttcc >BC469 (H-19) ctccagcaaactaacaattgagggaaaaatacatacctgtactaggaatacatccaaaacaagaacaggg ctattcccaggggaggcagcctggtaatatggaattgctgaggaagtaagagccttagcaactaacaatc ccaccgttgcttgcatccctagaatggcagcacccatgcctaacaggtttactactattccatttctgag

158gctgttcacaacatcggctcgaggaggagcctgaaattgggttcaataaataagaagaggaagcttgagt tcagtcaagttgctactttattttgcacgcgctcatatttagcagataaaattatatcaaaataggtact ccaattagaaatagtgcaaaaataaatgaggagaagaaagatgctaaaagctggatgtataaacaagtta cgtttctcaagttaaaaaaaaaggatttgtaacataaaggaaatccgctggagatgaagtgccctgccca tatagataccaatctctcggcaaggtgccaatttaatgtcagcctaaagtttgtcatgtttatgtataga tagaagaatggtaagaaaattgataaagccttttgaaatctttgctggag >BC515 (H-19) gggggcctcattatgaggaatgaatgcaaggctggagtactaaaggtaatgttctacaagccaagagaat gcgaaagtcgttttccgatgttctacaagacatggttggaggagaagagcttttgttgtatggttgagct tttctcaggagataaagtgagacatttggccaaagattcaccatttccctgtgtagtcacaaggaaatta gtagctcttttgtggagttctttaataatttaggaggccttttcagtggatggtccgtcgttcaattttt tagtggtagaaaccttaaaggatttgggctttcgtcagtgtggttatatatgtaaaacgaagtttcagtg gtatgtttggtcctctttgtcagattattgtgttgcatattggtagagctgtctttttctctcactgcac aattgcgcattctccactggaaagctttcgaaagtaatgattttttaatttgttattgtgattctcattt gagtcttcgattattgggctgcatactttttctaagttctgtattggttatgtttgtttttgtttttttg tttcattattctttttgagcattagcctctgttcatttttatccatgaaaagttttcttttagaaaaatg attgttttcacttgaggccccc >BC523 (Gy-7) acaggcagacccgacgaggggcaggaaggaagcgtgattgccttagggaatattttaatttgcttccgtt gtttcttatttcgtttaagtacttgagactgtttagaaaattttgtggtttaactgaattcatgaactga taatttgtaatttgtaagcatttctttgaaagtttttttttttgttatttgaaatagggcccgaacttaa gtttgtttcctttgatcacatgatttcaaatgagttttaatatattttgtgccaatcaagatctctttga caaaattaatgacttcgacttagttataaaagttgggtcgttataaaacctccattcacaatctcgatgt actctggagtaacattagctcacttttaaggaagcggactaagaatgtatctcacttttaagccggtttc ttatcaccatgtagtcccgtacaccccatagtggccttaactctttatgtgcacataaaagttagctcat taccggaagcaaccaatggtttgaaatccattttataatcaaatcatgctttacaaaaaaataagctttc catataaatctttattttctttttttggaaactttcataattaaaaatcatcatttcaagaacttataag gaaacttttaacataaacattttaaagaaagcacttataaacattaacagaaaacacactcacaacactt agccttaaatccagaaatgctccttctttcttcttctcgagtaagtttttcgacaataattcacacttaa actttttctcttcaaaacaatagagtaaccactcttacaaatgactgagttcctctatttatacttctct taaaccagtttagtcatcctcaaactcttgtctgcctgt >BC526 (H-19) aacgggcacccgtctcactggaaaaattggacatgtctagaaatatttaaagcatatctcaaagtttacg gtcattggtattctctctatgaagaccttcaaaatattatttaacacgggcacaattaaatatttgagag agaaacaacgtaagtatttcaaaatatgtatcaataaattttgtaggtatttccatatttatgtagatta ttgtgaatcaacctttgtatcatatgattaaaaatatatatatgaaacaacaaaatgtactaatatgtaa atctaatataatataaacaatatgggatattttctattgattcctttaataagaaaatgttttctataat tttttttaaaaaaatatcaatccacatagaaaattcatatccattggcggctcattcaataatttaatat attcttttcgaaaactagaagccaaaattaaaaaaaaaaagaaattacattcaatagagaatatttggtg ttatggccatggaaagctcaaaaagaaagacctgtcaatgaaagtctttctttactcttaagctaaaggc ccccaattatggaattatatctcttcatccctccattttcgtttctccattccccaactctccaattttg cactacactgttctctactgccttctgcatcctcttttcatgaatcaatctgcttggtattcacctaact ttttcttccattgttgagaatagatggactattgatgtgtttttctttttatattgtaaagctattcttc tttctttgtgcttcttcatctgggttcattttttatcatgttttttcccatttctttttgttcccctgta ttttctttgtatttagcaacgtatcctcttctgctctctctgtagattcttactgcttctggggctgttt atgatctggggttgtttcttgtcttcaaattttagttttcactatgtgggtgcccgtt >BC592 (H-19) gggcgagtgcaatatctaaaatggtgaggcttagtttgataaagaagatgttccgttaactagtcgttca

159acgtttatcacgctgtgtgtttgcattgaaggtctaggcagcatgcctccacttagtcgcaaaaagtgac tttggggcatggtgtgacagtgtatccatcatttgagtttgaacccgacaaagaagttcaatagcgataa catgtgtgcaaatttcatgctagcctcaaaagaatgctaggagcttgtattttatgtattattggaacta gttgccgcgtttctcaagagtgtaagaatatctgacaaaagtgggttcgtcgaggtgggcccgtgtacat tatgggggaatgtgatatgatcaaatgatgaccaatatcgtggaatgcgtcaacacggttttgaaggaca caggggcattgccgataaccactttctttgaccacataaaatctatgcactaggtgtggttcgtcgaacg tagggagatagcgttgagtcgagaaactatattatcttaccacaatgagtccctactgaagttggctgat ctgaggtccagaaggtacgttgtgtgtcgaatagatcataatgatttggagattgtggatggtcaattgt aatcctcatgccaatttagccgataaaacatgcatgcgcgacgcgttcagttactacaaaattttgtgtt cgcatgcactcgccc >OP-AB14 (Gy-7) aagtgcgaccgggtcagtaaattataacaaattcatggagttaatttgtgcccacaaactgatttaaaga caagatttgaactttttgatccgcttttctttgaaaattatgtagtcttgcatgaattgtatgctgacga gtgagttgttctacttcttcttcttattatttttgctttagggagcttattaattggtagaacacctgat gagcttgacatgttaaagaggaaggtaatcaattttctggagctggactggaagctgaatacctgtctag tgttgatttgctttccatggaaccagctccgctgatcggggacagctgtggggctgcctttctccccaat gactgtcaactggatgcatatagtactgcagcattcatccaaaaggttttggctgaaagtaatcaaatat tgatttttttctttctacctatccatccatttatgtccgctttccaagtgttgatatcaagctaatacac gccttctgttactgatagatatgataaccatgtaggctaacaggcattttaaaggaagatatgcagagtt ttttcatgaccccgttactggcttgttaaggtctgtttttcttaggcaattgcacttagatgttggcaac aagaacaagaaatatcagtagcactgatagatcaccacatagttgatgtttggctacaatattaaatatg gatatcataaatggtcacactttcttttttcttatcgcttttttgttccccactccccattggtatcatt gtaattttccttcaatagatggggtcgcactt >OP-AC9 (Gy-7) agagcgtaccactatgagtgagaagatggccgagcctagtgtgcgagagacaatttcgatgatagcgaat aggagagaaggcccgacaaacaagggccatgaaaacaaaacgaaggacgggaaagtcgaaggagaagatg gaacgaatgagaggaataggttcatgaaggtcgagatgccggtattcaacggggaagatccctattcttg gcttttccgagcaaatagatattttcaaatacataaattgattgatgctgagaaagtgttagttgcgacc actagttttgacggtccgacattgaactggtatagggcgcaggaagaacgcgataagtttacaggttggt cgaatctcaaggacagattactcacctgttttcgattcgtaagagaaggatcaatatgcggacaattctt gagaatcatgcaacaatctatggtggaagaatattgtaatcaattctataaactgatggcaccattatcc gattcacaggacagagtggtggaagaaacatttatgaatggtttatttccttggattaaagcggaagttg atttttgccgtccggtcagcctagctcaaatgatacaagcggctcaacttgtggaaaaccgggagatcat tcgcaatgaggctaaccttaaggggtacgctct >OP-AC17 (Gy-7) cctggagcttcaacattttattttttatactatgtcaaatagaaaaaactagcttttcctacatcttatt taattgtgaaagtctcaattagaatatttaatttaaatatcgtgaaaatagtttacttaacaaatacgaa aattgtcactaaattatctcttacgcgaaggatttacatacgtgctaaaatgttatttaagatttataaa ctcggttacctgtagtcttgaacaaaataatacattaatcaaacaaaaaaaaaattaatatggttttgat taccaacctattccacgatgacagaacaggaaaaaattaatcaaacaaatgcttcgtacaatttacaatt aatatcgacttatgcatatgagacatccttattcttaaaacattttgcaatgattgactctacttcaatt gaaccctttgtgttttgaactactgccactcaattggcaaatatacttttaagaaaaagttgaaaattta ataaagcgcattttaattttaaaaaaaatacaagggaaagttttcaaacttaccatagttctcttaatta agacattgtttactaaagtggagttaaacaattttctcatcaaagctccagg >OP-AD12 (H-19) aagagggcgtgtgaaaatttgatataaaaactgaatggacaagattggttaaaagaaaatttaagtttaa atatatttaagaaactatcctataagtttaagactaacaatgaatgtttctttttttaagtttaaagaaa ctttcgaaattttagaaacatttttataatttagacaaaaaaaaaagaaaaaaaaagaaagaaagaaaga

160aagaaagaaaagaaatgatccaacgtgggacccaggacatgagaagtaaagttaagataattttttttct aaaaaaagcgaaagaacgaaagtgccctccactctcgaagcatcgttataaattctatcccagaatcgtt ctctctctcattccacttcccatttctgcttccattttcattaatacatatatacttttatttatttgaa tttaaatttctgagatcggggcaaatatgttgggcatatttagcagttcgatcatgtcaccgccggacga actggtagccgccgggtgccggactccgtcgccaaagatctcgtcaacggcactcgcaaaacggttcgct gattccaactctgccgccgtttctctccagataggcgaccatgtccaccttgccttcactcaccacaatg aatctcccttgcgccccaggtacgggagattctcccatcaatacgccctctt >OP-AD14 (Gy-7) gaacgagggttgagaacctaaaagcaaaccctatcaggttaggtagcttcgagctagggaatagcttaac aggctcaatggcccccattgaagagcatgtcacaacagtggacaactcacagaaaacgaaaatggtgaac gaatttccaaaagacaataaagtaattgtcgatgtcgtcaagacaaagattgcagatgtcaacacaagag taaatctcaccatgagagcggtgggaaaccagaccctagttgaaggcgcagtacaattcaatcgagtgaa agtgcccaaacccaagcccttctgtggggctcttgatgcaaaagctctggaaaactctctttgatttgga gcaatatttcaaagctacaaatacagtagccgaagagtgagaaatattggcagagagagtgttgtattac aagaccaagacacgacctagaggaactcttggtgaagtgaaaagacctacctgacgaagaaatcagttag gagtgcgctgaatacatccattgtgtcatcgtctatttggatttagttttgctgacttgttctacaaaac tgcaattgtgacacgattttgtaaacatctcgaagtgatcgtgcgtcacttgttctaactagagcacctg ctcgtccatctcgaccaaccggtcaagtgggacttgcccaattgtttcacaatttgacatgattatagct tataacttcacaagccgacttggctctgataccaactgtcacagtcactttcgaaagcgtcacaaggcga tcgtgcgacacatgtaccccgctcgagaacgagtcagcatatccaaatccaaatagttggtggcacaatg aatgtttcgcaacattcaccctcgttc >OP-AF7 (Gy-7) ggaaagcgtccctattgtttaacgacagacacatcttttgagatactgagaagcaattagaatacactaa ttgaaagaaattggtatatcaccgtcatagaggtcagataatactatgggctataactaactacaaactt tgtcttcctattttattgtacttccttacatagttaacaagacacggacaaacaaaagataaaacatttt cagtaagcgagaaatgactcaaagcagcaagagacctagcaaagaataatgcttgcgtcacgtacaataa cttttttagttttaccttagtgctgctaatcatgaaaattgttaatgggaagccaattaccttttgtcaa aaagtaaataataaaaaaatgaaaaggaaaaaagcaaggtgaaactttaattgctttgaataaagtagtt ctcgaggtgtcaatattgactctgtacccaagacctatcaacactagatccaaacccgtcgatctttatt atgctaacagacaaagataatatgataggatcgtctaattggtatcacgtagtttaaagtcaatacaaag ggtccaaattgttacaaatgaggccaacagtttatgcaaaggcactcgaatcaattatttggggttgaag aacgagtcaggtcgtatgtagcagagacaattgtaaacaattagttagttggttgtgtcccccgttgaaa atgagagatttaaattggtcgagatgaagagtaacgtcgtttgttgcctagatgtggttcaattttgtct ggcatgtgggtaaaccagtgagtaaagtagaggcttggtggtaaatctcaattagcagaagacaaatcca cgagaattcagggtaggcgtgaagaattcttgaaaagaaagagaggagacttcccccgacacgacgacat actgggaataaagcttcccc >OP-AG1 (H-19) DOES NOT MATCH RAPD ctacggcttcctggatattcacaaggcagaaaccccatgaaactaggctgccaaactaattgaaacagtg tattggcatataagatagtttctttatgtactatactcttagtttcttcacttaatatttagttattttc ttacttggtttttctcattgttggttttgtgataaattaatccttcacagagatgtatattgtaataatc catcttcttttatggataacaccattgacgtttcatgtacagtgagtaggatacattacttagttgttag aatggttgatggtcctgagaatttctttttctatagaagatccactgccttttaatccttgacactgttg tttgactaatggatcaggaaaaagaaagaaggaagttaatacatctggtgatattctgacgggagaaaga cagaaacaaggtcaaccaaatgcagttgcagaagactacaagtttcctcttgtttggattgacttggaaa tgactggtacttaacaactattgctaatcgctaagctttatacttgtataacatggattcaggagtgaga ggggattataagaaactcacttttcctgttttctttgctaggttgcagcctccttttattaatttttatt ttttatgccttgtgttcttcattttttttttcaatataaggtcagttattcatcatgtttctaaattcta cactgtgataatgcctaagcctatactcgatggcactgtttatcaggggaggcttttggagttttatgat tacatttatcttcacccagggttgaacagagttgatattcaacttatcgtttataagtttttattactct

161ttttttgtgcaaaatttttctccatacatttcttttagttttcctttgtgccaatgacattgacttcatt ttttttttctttttggtaatttcatacttcaatgaagccgtag >OP-AI4 (Gy-7) ctatcctgccattaatagatatcgatagaagtccgtgtctattattgatataatttaagaatatgatatg tttttggaaatattttcctcaatcttaccatttactctttaaaaacaatcctattatctatttttaatat ttatacgtttatgaaagtttgaagagttgaattaatagttactaaagagttcattgaaggtctaattttc aaatttaagaaaaaagtaggtcaaaaataaaaaattacaacttcatagtcaaaatcggtccaagtcattg tcattcacatttcagctcagcttcacagtctccaaagcttctcccaagctctccattccattcttcaatt ccactcgaaaccatctctttgctcctcgctgcatttgatggtgagttcagattgaatttctttgccaaca tttttcttttgagttcaatgattttcccatttcgcattttgtaacttcgttgaccatggcttttgcaggc gcccaaacctgttaccagcaccttctcaactcttccttcccaactggggtctcaatgatcgcctctatgg cctcattgttgccgcttcctcttctatgtaagttcgatcggaattttcgctttttagcttaatgaaatct gggttatattagtttttcagtgtcaaatgatgaatttgcattccttctcgttttattcaagttgatcaga gtctgtatgcttttactatttactttctggcgtttttttctgccttaattccctttgattactgtgatac caattagaggataacccattgcttggtttgggaggagattatatgcaactttattcattcgatcaaatat catcctccttttcgtttttttcacacgttttgtgttactacattaagaattgattgaagagatgttggag tgtggtcgaagtaccacgttggctagaacaaggatgatatatggtatgtaagtgcagagaactattttca atggcacgaggtcctttgggtgagacaaaaaacatgtcgggagttgtgcccaaaatggatgaggagatac attggaaagactgccacaatgtaattaatgttattagggcatgaagattcaaatctatatttaatgatta ttaagaggcaggatag >OP-AJ6 (Gy-7) gtcggagtggctttccactaagataggcagattcatgtatagagctttttttggtccgaaggtgaaaaga atattgacaagacaggtccgtccggaggactcaaggattccgctaaagtttagggcgaggcgcaaagatc gtggtccacgtagcgtagggggatattgatttgaggagcgcaactcaagaaatcccgggttggatagtca tctggacctttctttgagtattgaaataagcgggcgagtgcattcgagatctgaaagagagaattaggag ttcgtcctaagccgaagagatcaagctcttaaacgacctgaattgaatcattatattattaatgataaat gccatttcgaaagaatcttttattgtgaaagatctccttctgacggagcagatgttgatcgtatgatctc ttctttttgaatatccatgctttacataaaaaagaactagccgaatcggcgagcagagggaatcaatagg ctttcggccgaggcccacgtagcggtattcagtcttcacccccactccgac >OP-AJ18 (Gy-7) ggctaggtggtatggggatgacatgtcacacaacattgttttagtaaaggctttgtacgatgaaggttgg atgccatgcgatcaaggaggatcctaggtgtgctgtcatgactctgatgcagtccagtgacgtgttcaca atgaagcccaatctagctacctctgacactgtggtcagtatattgtgtaccagatgcaccccaacaaacg atagcgggtgattgtgggatttattgttgtaaatttttttaatacaatgcaacccactcctccttcccaa ccttaaattcaaaaaatcaacctcctatttcgtcaacaactaacaacacaattatgggccaatagggtct ttttgtaatttaagatacagttaactgatttttgtatagatgcttatctttgtgtggtctgtatagagaa attgtaataagactcaggtttgtattttgatatattgaagtcatgtaagtttgatggcttagtaacggaa gtcgtttaggctaaatgatttacatattgtggcatgcttaaaaaaaacttctaatgagattgacgaaaat catttatattttgaaatcatgtaacttaaatgatttagaaattgaggataatttttctcaggccaaattt gcaacaagagtcatttaggctaattgtttccaagccatcgtggccacgagatgcaccgagctgaaaaaac aaaagcacttatgtgaatgaccatggggcaaaattgcctcgagatggcccaagctaaataacataagcat tatgtgtaaaaggatcttacactatatttggcttgagaacccctaagtttaaaaacaaaaacttttggat taaaattctttatccacagatggctcgagatatccatttgttcaaaaatagaagcatttgggttaaaatc tcgatcaacgaatggctcgagatatctgtgagttcaaaaacagaaggatggtgtgaaagaacttaagccc acctagcc >OP-AK5 (Gy-7) gatggcagtctgataactatgtgatatcactaattttttcatattcacaatatgaaaaaaataggtatca tggactgtatttttctaaatatttttgtcatgtgatgcaacttttcttacaataaatacttaaaacttaa

162cttacagaaaaattattttttagtgtttaatggttaatctcttccattttgtatgataaatttcagagac tactgatagcactgaaaatttgctatataatctcgcttaatttgagatccttcgatgattttacgttttt atttgactaatttgtaggtctatacatgagaattgtatgactttcactttcattaaatctataattcatg atattccatcatatgttagcttgttattctagatgagtatattcctctaactaacgtagaacatttaata ttatgattagatgagtacattactctatctaacctagaacatttaatattacgattagatgagtatatta ctctatctaacctagaacatttaatattacgattagatgagtatattactctatctaacctagaacattt aataatcattccactaaaggatctctaaaacttaaatgtgattaggaaaattccaatgtgtatgtttttc gtcttcttgaattagtatagttaggaatctagggcaatttagttagattctctaaataatttagtcgggt gtaaacgatagatcgagttggataataatcacacaatttgatgaattaatattttaattagggaaaactt gtttcacaccaagctaagctcttttgattattgaatccaatcaatttacttttcttgcaattttacttta ttcataaccaaaaccaattacaatctcaaccctcccggttactctcaggttggaaattgtttgctagaaa aattaatgtgcttcctatactttgactagatcttgtcgctaagactagctcttgtggatagtttggtaaa ttatttggtctaatttaggtgacgaaatccatttgatcaaccatctgccacgatgctcatcgaccaacct tcgacaacaactgaccttcccgactgccatc >OP-AK16 (H-19) ctgcgtgctctgtcattatttgggagagactaagtacagtgtcaaacttaaaattcaataatgttacccc ttttactttttcggtgctacctttgtatttttttcattttcttctcaatgaaagttcagttattcactta aaaaacatattcacgttttgacatccaaatatggggaagaaactattgagtgattgatttgaaacttttc aagtgtgcagagactgtatcgatgtaaccatcaaagactcaggattaaatttaatttaatctgacttttg ttgcacaaactttatgttttgtattttcatttgcaggggtaaggtgatagttgtcacttttagatttaaa ggaaagaggaaagttaaagaatataatgggttcaacacccaaacagcctttcttttgcttcaaatggcca tgggacgtagacccaaaaaatcgttcagactgttcgtttgagagtccttggttgttcaaatcattgcaaa atgtgggtggctttgctttcgattttgtaaataaagcttcaaaatcatcgcctccatggatgacttttaa gtcattgcaattcaaccccttaactggtggaaataagatatctcagtctagaaagatgttaactcctgaa gagcagggggaagcagaaaatagagcattggcagcagcattagccagtgggaaagaagccaccataatcg agttttactcacccaaatgtctcctttgcaattcctgcttaatattgtacggagatggaagcaaggaatt cagatgggcttaatattgtgatggcagatgcagagaatgataaatggctgctgaggtgtcaactcttctc ttacctcacatttatatgtcaactgtggctttaggaattctagttaattctaaaaattgcgtttttagtg aatatattatcatggtttatcgttttcagatgagttgataaagtattctttagccataatttagtagggg ttaatttatggtatagaattagtggtattaactcgatatcttgcagtttgaattaatgcacatttgattg aaaattgctgaatgcaactatgaaacgttcaatcgttttcgacattcatttcgatagttttgtttttttt cttcttgggaattgaatgtataaactgaaatatttcccgtttggttgaatgttgggttagcactgctcct gatgatcttgcttgcatctttgtgcagcttcttcattatgacattacatatgttccttgctttgtgttgc tggacaaacacggcaaggcgctggcaaagacgagtcttcctagtagtcggcttcatgtaattgcagggct ttctcatcttatcaaaatgaaatcgcccaagagcacgcag >OP-AM2 FORWARD ONLY (Gy-7) gattacttgacgggctagtgagttggaaattgttttgtgtcaagtccaatgctgattatggtggatttca cttactcttgtacattaattatccacttctagtctgcactgaactttattatgacaactgtttttttttt ttaagtgaaagataagcccccaagagaaaataagctaaacacctcaaacatgcatcgttgctttttaatt gcgcttcctgaagtatttaaaatctcatcacggtttattgtttgcaacgttttatccagggttctccttt ataaaacttctatgacctgctattcagttggccatcctttagtttttcatatgttcaaggtccattggac ataatttcttgatatcgttgcactcaaactgcaataagttgcattttctactttccacctttgaagattt gtactgagagttaattgtgcttgtcgttttaatgggtttaggttgtgaagtcaaatgctgcctctcgcca atctgaaaag >OP-AM2 REVERSE ONLY (Gy-7) acttgacgggggatataccaagcctaaaatctgtgatgaagggcataaatcaactcataggaacttggct aagagcaaggattcgcagctttccttctggagagatcagaactcttgatgtgcataagctttctaaccct cccccacttaaaatatagtgtttcttccctagatttaatatatctgtagtaattaaatgaactgctcata atgtaatttaagagggaacctgcccaattcaccaatgtctgcaaatactatttggaataaattaaatcat

163gtacgggtttagactaaggaggtgttaggggtaaggaaggcaatagtgaggagtagggaattatgaagtc ttgggagttgtgaattccttgacccacaaggtaaacagttgatcagatataaaatggctgtttttagtag tgggactcactcaactccttggtctaaacaaattggagttcccaactcctactttgcaagccatggggcc aaacacaccctaagagactacaatttcacaagaaaggcctggaagtacacacctgttttgttcttggacg gaaaccaaagacaataattctcatgtcttttcctgcaatagacaaaggaggaagaaaacttttaactgtt gcattcagctagtatgctac >OP-AO8 (Gy-7) actggctctcccattaatcagaagaacttccgtatttggaaaggtaattcttgttgttttagcctttaac aatttttacatgagcacatcagtataaatatgaagaaatatggaagttaactgtaggagagatttgaaga aaaaaaggaatgaaagaatgagagagagctgcagggtgtttctcattttggctggtcaaagatgggggag ccaagaaggtttgttgggctttgatggaatggttttataaaaccacgcgagtcattatagcacaagaaca accatgagccaaaccctctttagtttcaaacctcttcctttgttagttttgtcatcttcaaattgtgctc aatgcctcaatctgataggaatggtatttgggtattgatggtatatatttgggatcctcgtgaaggaaaa gatagtagtttggtatcttgtagatttcacatgtgcctatacactctttataagcttacacacagacgca catatgaaagtagtgtttataggtttaattttttatcttataaaaaagtaagcttaattttagaaaacaa aagataaaatggtgtaatcatttgagtccgagggctatttatttatttatttatttttgaggtggagctc ccctcacaatatgtagagagccagt >OP-AO8 (H-19) actggctctctacatattgtgaggggagctccacctcaaaagtaagaatttaatataagtgctcaaagat atcaaaaaacaaaaataaataaataaatagccctcggactcaaatgattacaccattttatcttttgttt tctaaaattaagcttacttttttataagataaaaaattaaacctataaacactactttcatatgtgcgtc tgtgtgtaagcttataaagagtgtataggcacatgtgaaatctacaagataccaaaccactatcttttcc ttcacgaggatcccaaatatataccatcaatacccaaataccattcctatcagattgaggcattgagcac aatttgaagatgacaaaactaacaaaggaagaggtttgaaactaaagagggtttggctcatggttgttct tgtgctataatgactcgcgtggttttataaaaccattccatcaaagcccaacaaaccttcttggctcccc catctttgaccagccaaaatgagaaacaccctgcagctctctctcattctttcattcctttttttcttca aatctctcctacagttaacttccatatttcttcatatttatactgatgtgctcatgtaaaaattgttaaa ggctaaaacaacaagaattacctttccaaatacggaagttcttctgattaatgggagagccagt >OP-AO12 (Gy-7) tcccggtctcagtcaaaaccatttatcaaccaaacaagttgataagatcaataaccatttatcgataaaa tgacttttatcgaaggctggttatcaataactcaaattaataggagcaatgaatgatatgtgtgagtgca tataatcttgaaaattattctaaagtactagtgggtcacatccaatttgaattgagcacccttacatcta taaacacagattgtgttttacaattaattaagatttggtataaaatttaaaaagattatatatattgata aataaaaaaaactagtagaatgtcatatgttaatactaccccaagatttgatcaagaaagccaaaaacaa ttgataacattgaaaatgttgagtttttgagataatatcagtggttggcttggagcaagaaacacccaga aaaactagtttgacgacttttcaagaaaagccttattttgataatgtgaactttgagagagtagtttggg gaatttgccttcatgttagataacttgggtatgtttaggtagataaattaatgttagaattaaattcagc attgatgaataaagggccaaaatagcttttttattataggcgaagcatatgaatgctttgcaaacgactt tctatttaattcacgcatcagcatgtcagctcgtctcctagggggaaatacaagaaatgtccaatcacaa tgttgttctaagagaccacacaccatacacaaaatgatatcattaaacaatcatctgcctataacatgca aataaataaataaatagttctccaaccacttcacttcacttctttcgagcctttctaacaatgttactta tatccaattgaattctcttatgggcagccaaaatggaaaagacagaagacatacaatgaagccaaagttc atatcttcgcaaatctaaaagtacgtactgtgagcataaatgtagaatgaaccaacctgcaacagacgat ctgaaacgctgtgggttgtaggtctctgatcaaattcatcaccagactgccttcaatactttactgataa actgggctgtccgtcaaaacactaaatgcaatacaaaagttagaactaatagaatatcagtaataaggtt gagttctagtactgagtgtaattgcctaaaatgtttgttaagttgctagattaatctttcataccactcc ataacgaacatgagtaagttttagaagtgaaatcattatgcatcaataagccctccaaagcttatactac ttttcatctcactaggaagagaccggga

164>OP-AO14 ctactggggtatgattaagcatttgttgtcagatgcaccaactagtaggaacgatcagacatctttgttt tgcttcgtcatcagcgacccaacttctactaagggacgattgagcatcttccgtcagatccaccttctag taggattgatcggacttcttcgttctacttctagacatatttcaaaaggtgagctcccttttttctattt atcattttttttgctatcatttgcatttctaaacttgtttgaactttttttttaatctcaagaaacatca acaaagcttcataaattgattcggaagaacttataaacttgtttgaacttgaatttcaagaaacatcaac aaagcctcgtaaataaattctgaggaacttccaaacttgtctgaacttgaattccaagaaacatcaataa atcctcaaaattgattctgaagaacttttaaacttatttgaacttgaacttcaagaaacatcaacaaagt ctcaaattttgattttgatgaacttgtaatcttgaagatggagtgttgaacttctgaggaatgatgcctc ttataagcatctaactttagagtattttgttcctagtgcaacacattttttatttgttatattaccccag tag >OP-AS5 (Gy-7) gtcacctgctatatttatggatttgatgaacatggtgtttaaggatttcttggacacttttgtgatagtc ttcattgatgatatttaggtttattccaagactgaagccgaacatgaggaacacttacataaggtgctag agactcttcgaggcaataaactgtatgctaagttctctaagagactacccagatgaatttccaaaagatc ttccaggactaccgccacatagagaggttgattttgccattgagttggagccaaacactactcctatttc tagagccccttataggatggctcctgctgagttgaaagaactgaaggtacagttacagaaattgcttgac aaaggctttattcgacctagtgtgtcaccttggggtgcaccagtattgtttgtgaagaagaaggatgggt cgatgcgtctttgcattgactatagagagttgaataaagtaacagttaagaacaggtatcctttacccag aatcgatgatttgttcgatcagttacagggagccacggtgttctctaagattgacttacagtcaggttat caccagttgaggattagagaccgtgatattcctaagactgcttttcgttcgaggtatggacattatgaat tcatagtaatgtcttttggtttgactaatgcacctgctgtatttatggatttgatgaacagggtgtttaa ggatttcttggacacttttgtgatagtcttcattgatgatattttggtttattccaagactgaagccgaa catgaggaacacttacataaggtgttagagactcttcgagtcaataaactgtatgctaagttctctaagt gtgaattttggttgaagcaggtgac >OP-AT1 (Gy-7) DOES NOT MATCH RAPD cagtggttcctatgtttcattcctgtaatttttttgatgaatcatttgtcggatatacagaaatgaacga agaatcgcgattattatgattacctactggtttgagttaaaaacggctcaattgcatgacaaattgccaa ttgctaattcgacttggttccctggctttgactgtttaatatatcgattgaggcttggacctgtaggttt tagcattgtacttatctgtgttattggtggtgttattataattgagctaggcttcttcctgtgtatgaaa aacaactggaaatatgaatgatgagatataacggcttctttgagaccactgcactgattttcatccagtc atgagaatttatgatttggggactcctttcttattgttgggaaattgtaaattgacaactggctcttggt tgttattcttttaaatacagttattttatcctcttcatatgtactgagtgtataactcctctttctttgt aatgtagtattatttagcttgaaagagagttatgaacaggctttgcaaagagaagatgtgaaggcaattg ttgttacaggttagggtgaaacggtcttcgtacatgagctattagaaatgttggagatgtaaaagctaac tatgtatgtcatttgagcaggtgccagggggaagttctctggtggctttgatataacagccttcggtgga ctgcaaggtggaaaaggtgggtttacatttctctgtgtccgattgtcattgtctcataatttagtctaga cacttttgttgacatcttttaaatgatcttttagctgaggaaccactg >OP-C1 (Gy-7) ttcgagccagatgagaaagggcaaggattctcaaattgtttgtttgatgtatctcattttcctgtaggtt gttatagaatcaaatggtatagctgttgtgttgatagtgaggggtgtttttggaacctccttcctttgaa ttctggaccattacttactatccatcaactttcatcagctgggtgatttttcatcaaacaatgaagtttt acaggcgatggtagtcttttctcccgtaggtaagagaggtttgtatatagaaaacatatatcaatgcatc ttagattatattaatctttgtgtatcttattccgtgaagaactattttttcttgttaaaatatttttatt ttcttttatgcgctggctcgaa >OP-C10 ctgggtgtgtgggctgcccatggtgaggggagagcatacttccctgatgatggcgttcttgatcgtcttc tccactctaacttagctccactaagatactgtgatgatgatgggaatccaactgaagtttaccctttcaa

165tgtaaatgggtctcctctgggagttgcagcaatttgttctccagatggtaggcaccttgccatgatgcct catccagaacgttgcttcttgatgtggcagtttccctggtatccgaagcagtggaacgtgagcaaagaag gtcctagcccatggttgcggatgtttcaaaatgctcgagagtggtgctctgaagaggtttaattttttaa tgtattggacaataaatttttgggacttgtggattgccctttctaccaataatatttggatgcttggcat ttcatatgccaaaccgaaaattgtcatttctctaattttagtgaattctgttaatggtgacaattttgtg atcaatataccaaatatcatttttttaagcgaaaccaaagcttccttttattgacaaactgatggacaat gtgatgaaaccaatcacacacacaagaataaaccagtactatatagacattgagaggatatcaaacaaag aataattgtttgttgagaaactgtcccccctcctccctttcaaattctcatatgtatgttgttgattggt ctctgtttcaatgcacccagaca >OP-D11 FORWARD ONLY (Gy-7) agcgccattgtagttcaaccagtaagaacttgattctgtacgaacctacttaattagctttgtttacatg aataatgaatgcatgcattagaacataatagaatatgcgttgaaaaaaaaacaccctagtgcattggtga aataatgcgttctatgtgtcgaacatgtatttccatgtttatggtaggttaaatcaatttactaaatgta taatttgaatatttttttaaacataaaacacaataaccgtgaccatttattaaaaaaaaattaatagaaa gtagggcgatagatagtgcagtcaagtttgtgtggataatgtttttttaaagcatatgtgtaatcttgta cacaaatttttttaaaaataatcatggtgttttttttttaaagtgttgatcatatttgagaaaagaaatt gtgaatttttttaaaagtgaccgttattttattttaaaagagagagtaatggttcgaaaccctaaactct attttttttttcttctgaaattcatgattgtcgtttttcaaagccatagcaattgggattgccaaagtag gaagagtttttaaaaaatgcacgttcaaattaaagtgtgacagtatagcgattaccgtgtatgcattgga aatttaaaaaataagttcgcacatttgaaacaataataagaaatgattaggaacgacatatgctttccaa taatatgaaccaa >OP-D11 REVERSE ONLY (Gy-7) agcgccattgacattgtgtatgccaaaagacatactctagtcttatttggatcgcatgcttctgccttcg tcaattgagcttcttttacaaatgccaccaacaccaacgtcaaaacgaaaaaacttgtcattgagaagaa cgttcttcattttaggggattattttgagagaacagtaatatgtgtgagctgagaaagacaattggattg tattgtattttctaaatgattcaaccattattttatagacttgtatcatcaaatattttttttctttttt aagaaaaacattagttaatgttgttttgttaattcatgtgttacatgtcgttggaatgtttgttgatagt taatcagcttcttttagtaatttaattgtaaggctggttaaaagattgttggtttagtaatcgttaaaag tatggtttttaaggcatactatgcttcatgatgtgtgttgctgcaaatggttttttgtatgcatgatttc catgcccatacatttctggacttctatgttatgcgtattaaatattgatgtgtccactactaaaatcact tcatcgtattgaacagtttcatatcctctaaggctatttttttaaataaaaaaaggtcacattattcttt atattaatgagttttcatttccattttaacattgggcaaccctcgaaggtacatagtacgtacttatatg atcgtactggcaactattttttttcaataattttgttta >OP-J5-1 (H-19) ctccatggggtgcacgttaacgttaaaggtagcttttttagggagcaaaaccactcgaaatcattctctc attatgtctcacaccacttagcacgtaattagaaagggcacaaagcatattgcgtgaagagaagatttcc gctttctttctcgcttctaatggatagtccggaggaccggatacgggtggaagaactcgacttaagagga gaggggcggaaggagttgaacctctcctattagtggcctgaccacgccctgttcatacctcgtattcaga gagctcatctctttcattcgttattcgtaaatgcgggagcgtagcgtagcctttaaagaagcacttgatc ctgtatttctccggaccacctcctgtatttcactactttctcctagagaaaaggtcaagcaaattctttg cctctgtctttcaccatattgtattttaagaaagaaaatcatatttcccttttattacttgataaataat ttagtttgaagatagtattataatctagaattcagagaagtatatttttattcatattgagtcaaacgaa tgttacatacttatataaagaataaactaaaccccacaaactgtatatttcatacgatataaatatagat atacgtatttagtctcttacaatataaatatagacatacatattgtttaaatgtttaaattataataaga gattacaaaatttgatattagatttattaatacattgtaaatatttttaaaagtgaatttgaatagtagg tttatttcgttactatatgtaaaacactgacaataatatagatttgcatactaaacactataatcaactt ttcctaaactaaagttaaaatctatccaacactaacttgtttcatactatggttgattttttaaaaccgc cgttcttagcaattatttgaaataaacttaaatttatacatcttttggttttcaaaaatctgaggcccac acaaccttcttcctctgtttagcggaccaatgggccatatcaataacagttggagaatgaattgggttaa

166gttatggaaattcatggcccattaagattccacttattgtcctattactagtaggccaaagagcccatag atcaacagaagctaaagacataatcggacggggctgtggaaattcaggcccaatatgaaaagaaatcttg gaattcatcttttggttaaaaacctcagacccacacaaccttcttccgctgtttagctgccccatggag >OP-L19-2 (H-19) gagtggtgaccatatattaaagtgaacctcaggcttaagttcaaaatattgtttgaggtggttcacaaaa tttaaattaaataatagatttaaatttatagattggtatctaaactttattctattttagtttttctatt attttaattatcgttccgatcaccattatttattggtcatgatggaaaattgtatggagcagcaatcttt tataactaatggaatataaagaacatattggtcatgatggaaaattgtatggagcagcaatcttttataa ctaatggaatataaagaacatccgtgtggatacaacccaaccaaaatcaaaacaaaaagaatgagtcatg agtaattgttgttacttaaagtgatcatactatttctatttagtattatcaccataaagtttgaattaag agttattcaaaagttcaggatgtacatttctaatctcatctaagaccacaagtgtttaaagtcgaaaaaa gggaatgtgttttctaataatcaccaaacttccgaacttcatattagatagaaaccaaggcataccatac atcactaattagtctgcaccgacattttctttcaaactctcatatgcagaatcgaatgtccaatcacatt tttaaccacttgaatgagttttttcgaagctaaattactatgacaaaactccaatgaatacacttgaaaa tcgaaaaacccttcactatttctagccaattttatcatataaaatgtttgatctttggaatatgagacat acaaagaccgttcaatcattttgaaacagaagattaaaactagccaacatattcaaaagagagtcaataa ctcgagccgatcataattacagattgaaaattcccaaatttttgtgctactaacttaaatggagaaatca catcatcatttaaaaatcacataacttacaggacaatcaattttctacatgccaatggcctgaatggtta tgaattttgtgatattacagtcaccactc >OP-M8 REVERSE ONLY (H-19) tctgttccccatgatgtagacttccctccaatgcaactctcatactttattattattttttgtatgatta atcgaattatgtaaggaatcatttttaagattcttttgattacctcaaattctcatatttactccaaact ttagtaaaattgattaacattttcaactaaattagagaaaaataaatgttatgaagtgtgttcacggatc aacctactagtttcaaagttgtcaagaccaaattgtaattaagttgcgtgaataattaaaagtgtaatta cctaaaaaaagttattatgcaactttttgagaattgtttaattagatggttgttgtttaggagcatgggt taatgatgaaactttttatttggtatgagcaaaatttggctatgacattgatatatatcattgctaggga ccataactaaaaactctata >OP-N8 (H19) acctcagctcccaaacattaaaataactcatttaagaagtcacctcatcatatataacacaatggagaga aggagaagaaaagagatagaactttaactacatcaatggcgtcaaactccaaataaaaactcacaagcca taactccccaactaattgtatatcatataacaaaagcaaaaactccatcagcttcagccacagaagagct cttctagaatatagtaccaactctaacatcaccatccatatttctcacaatcgccccaaggcaagctgga ggctgggtgtgaaaatttcaaaggtcacaacttcaaaaaaattgcatagaaattgccaaattttggaggg acttgaaccacatacgctggaggtttttaacacatctagatgcaaaaattgagttaaatttatgttggtt tttaactccaagtcaataatgattccgttcgaagtatttcaattgctcagcttccaatttttcttgctaa acttcagttagagtgtgtttgacacgccaacatgagatgagttgagttggagtatagcagtgaataatcc tattgacatttggaagtctattgatttaaaaagtgtcaacatttgaatcctattgtttgattataaagtt gaagtctttcaacaaatcatggaaaatcaagcgatgggtgatcatataagaaaataaatttttattctac gaatagcattaagcccaccaaatgacaatggatatccattcaaagttcaagataaaacaatttcgcattc gattatgctttgcaatgacaattaacaaagcacaaggacaaataattcttaatgttggaatatatatttc ctgaaattgtgttttctcatggacaactttatgttgcactatctaaagaaatttcaatgaaaacaacaaa agtttggatcaagatcataggtgacaataagttaccttcaaattgtacaaaaaaatattttttgatatat gcaagtcttacattcaaattcttatattttaatctaattgttgtaacttttgtatgtgtacatctaattc tattaaattttaaattttctacttttaaatttagcacaacttaagcatgtacatgcaatgtttaatatag tctaaagctacacattgtacgtgtcatgcacgtgtcttttactagtatgtatgtatatatatataaacac acatggatatgtatatttaattggaagaaattaaaattttggtggaaaagtaccattaattatatatatt tgtatataattagtggaaaaatatatttaataaggaatgagaggttttgtaggggttcaaaaaatcggta aacccgaccaatccggattacccatcccaaaccgtaaatgttgagctgaggt

167>OP-P13 (H-19) ggagtgcctcctgtggtaatacgaaatgacgattggacatgggaagatttatgtcattacttgtatgaag agaaggcatttgataatattgttatatctcctgggccgggttctccagcatgtgccaatgatataggtag gcataaacaatgatataatgtgggatttttacctttctgtaatctatttcaatcaagggtcattttgaaa tgttgactggtataatatggcaatatcactttgttacatacattcctatccatcatcatatttccatcat catcctagtctcattcagaataccaactttttctgttttgttttgagctttaacttatttctcccaaaaa caaaatttagtacttgttgtcgtcagaacattgttcttcaaatttaatttggtttgcttgaaaattttca gctgtttatccctgtatctttcctgcttattaaacttcactttcatcgtggaacatttaatcttttanga gtatgtctacgtctcctccatgagtgcaaggatattcccattttangtgtttgtcttgggcatcaggtat gttctcctcttctcaaggacagtttgcactttttttaaatgatattcttatgcatatgatttttgcaggc tttaggttatgtgcatggagctaaagttgttcatgcaaatgagccagtccatggacgcctaaggtaagct tggtgttacagagaaattctcatgggtatattttctgtccgcttggtgaacaggtccatttgtttggagt acgaattataaaataaaatattgtgactgaatcacggacctttctcataaatgatatgaattggttttaa tttttgaagtattcttattggctctcttaacattggttagtataacatgttcgtccttgtacttatttct ccagtttctaactcttgcacagtgaaattgagcacaatggctgtagtcttttcaatggcataccatctgg gagaaattctggttccaaggtcagttttataaaatgctgttatatttgtttcttctttaagttttactaa gtgatatattgtgtatcattccttgcttattgttttatgaggtcaattgcaatgatgaacaatggcaaag acatcacaattttcttactcaaatgaagttaaaaaaaaaaaaatacagcaggatccaaacttcagtttat aacaaaaagttacttgtatcatcaaaatcagtaatgatatgacttattattttatgttcattatagaggc actcc >OP-T2 (Gy-7) ggagagactcagattaaactggcggcgacgcattgatcttgcgatcgatgtggcacgggcattggttttt ctacaccatgagtgtttcccctcagttgtgcatcgtgatgtcaaggccagtaatgttttgctcgacaaag acggacgaggacgggtgacagacttcggcttggctagaattatggatgtaggagacagccatgtgagtac catggtggctggaaccattggttatgtagcacccgaatatggacaaacatggaaagctactacaaagggt gatgtgtatagttttggagttttagccatggaacttgctacagcaagacgagcacttgacggaggggaag agtgtctagttgaatgggccaaaagggtgatgggaaatggaagacatgggttgagtagagcagtaatacc ggttgcagttttgggatcaggccttgtcgagggggctgatgagatgtgcgagctgctcaagattggggta aggtgcacaaacgaagcaccatcagcaagaccgaatatgaaggaagttctagctatgttgatcgacatca taggcttaagaggggggggtgaattcaaacacatcttctcccctccatccttgtgatcaagattttgatt gaagaaatgtgcatacaattaggttgttagatagtttcatagacataagatacttatacctacacagttt catttattaccatt >OP-W7-1 (H-19) ctggacgtcaactaaaaggtaattcctgctctgaatgtctcacatataataaaaaatttagctaagtcca ataaatcatattcggtatttacagctaattttcttcaaatagttgtgctacaaacatgagaatgttacca aaagcataaacgcttaactaagaacattagttcctccaacaataatgaccctctctactagtgtgtaaga tgctcctttacaagaagctacttagctctagtaaaaacaaaaattatacaatttagagagtgagtttacc tgactgagcaatttcggcttgtcaattgttgatattgtaatttcatgcattggcctgaaagtattgggat ggtgcttgagaaccaagggcatacataaatctttcattgttaaccaatccacatgcaacatatgttaaat aattccaagaaaaaaaagaacgaaaccttttccgatttaccgagaaaaagtttggggggggggggaggat ggactaagaacagaaggggccattgaactcttattaaagtgacatggaggaatcattgattgctgtcata accagaaaaccgctcctggtccactttttccttcaagctccaattaactgacttgccaaagttcctgcca ggagatgaatgaacctgcaagttgaccaatttttacatttgcattttgtcggcaagaactagatagaatt cttatatagaatgtataaaggttgacttttagaacactgggaaaattaaatgaaaaatactgttcagaca aaataaaacaacaaaattacactaaaaaactacttaatcacatatatagcgagagatgtaacatcaaatt ccattacttatgtaatatgaaaatctagtgcaataatcaatagttcgtgatagacattacctgtacgaga cggacgtcaattgcaggtcgagtagcaggatcatgcatgagaacatagcatacctttgacagtatatttc cacaatatactgtcaattgcacatatgtgccatatagagaagatttttgtgcatgagaacatcttcagcc ctctctacattgacgtccag

168>OP-W7-2 (H-19) ctggacgtcacatatcagtaagtagggttattttgtattagtttgatgtaatttatttttatttagatta caagttatgatttaaattagaaattatgttttgaattagaaatttggcttttgttttaggaataggattt ggaattatgttttaggaataatattatgttttatgaggggatgattagaatcgttgttgaaaattattgg ggttaggattggactagaatttttaaggaggggggtgttgaattaataattaaaaaggggtgaataaaaa attaatgggagggggtggatacaagaaatgaatgtntataccctccatttaaaatggaaggtgttttttt taaaaaaaaaaaatagaggaactcccaacgcacaacattatgcgtcgagagttttgagacatctcccgac actatatgacattgtttatgacgtccag

169Appendix D. SCAR marker database. An html (web-based) database of the SCAR markers created in Chapter 2, as well as additional SCAR markers, is available that contains primer sequences, annealing temperature gradient PCR (ATG-PCR) profiles, and spreadsheets with information on each of the SCAR markers. The structure and contents of this database are explained in the following screenshots and can be accessed at: http://www.vcru.wisc.edu/staublab/Matt/SCAR%20web%20page2/Scar%20database.htm Database homepage:

Clicking on the lin ll mark m S ct” on t

wink “Info on a ers created fro CAR proje he left will

open the follo g page:

170he na ar ill TG-P R gel photo er seq at mark

Clicking on t me of a SCAR m ker on the left w display an A Cand the prim uences of th er:

Information can be found in the database for the following SCAR markers: AA9SCAR AJ2SCAR AT1SCAR BC551SCAR N8SCAR AA9bSCAR AJ6SCAR AT2SCAR BC592SCAR P13SCAR AB14SCAR AJ18SCAR AT15SCAR BC600SCAR P14SCAR AC9SCAR AK5SCAR AW14SCAR C1SCAR R13SCAR AC17SCAR AK16SCAR B12-1SCAR C7SCAR T2SCAR AD12SCAR AM2SCAR BC231SCAR C10SCAR U15-2SCAR AD14SCAR AN5SCAR BC388SCAR D11SCAR W7SCAR AF7SCAR AO7SCAR BC403SCAR F4SCAR W7-2 SCAR AF15SCAR AO8SCAR BC413SCAR H13SCAR W7aSCAR AG1SCAR AO12SCAR BC450SCAR I1SCAR W7bSCAR AG1-1SCAR AO14SCAR BC469SCAR I20SCAR Y10SCAR AG17SCAR AQ18SCAR BC515SCAR J5SCAR

AI4SCAR AR13-1SCAR BC519SCAR K7SCAR

AI10SCAR AR13-2SCAR BC523SCAR L19SCAR

AJ1SCAR AS5SCAR BC526SCAR M8SCAR

171Appendix E. BAC clone end sequences used to create SCAR markers in Table 2.2 (Chapter 2). Sequences are presented in FASTA format with the sequence name followed by the number of bases in parentheses. Sequences are named with a one or two letter designation for the marker that hybridized to the BAC clone (AJ = AJ6SCAR, B = BC523SCAR, C = CSWCTT14, L = L19SCAR, M = M8SCAR, and W = OP-W7-1), the number of the clone that was sequenced, BE for “BAC end”, and L or R to signify the left or right end of the BAC clone, respectively (L2 or R2 indicates the second attempt to obtain the sequence). AJ-1-BE-L, for example, is the sequence of the left end of the first clone sequenced that hybridized to AJ6SCAR. >AJ-1-BE-L (861) CCTGTACAAGGAAGGTCAAGGCGCCAGGTTAGAAGTTAGAAGGAAGCGCAGTAAGCAGCTCCCTACTAGTTGGAGAGGCATCCCATCCCTTGAGCTTGAAAGTTCCTTGTCTATTATGATTCTGCTCTGTAGCACTTGTCCGCCAGAGCGGTCCAAGCTTCGCCTCGTGGGATTATTCCTTATTCTTTCATTTCATTCTCTCTTTCGGATCGGAATAAAGGATCGGAAGAAAGGGAAGAGAAAGAGGCAGAAAGGCGAGATGGAATTGAGTCGAGACCACCCACGCTTGATTTTGACTGTATCAACTCTGAATCACTATCCTTAATTGTAAGAAGGCAAGGGCCTTTGACGAAGTTGACTTTCGATCAGAAGCGAGACCAGAGCGGAGAGGAGTTCAACTGCGAGACTGTAACTTCAAAAAGGTGAATTTCCTCGATTCCGGAAGACACTCCCGGGCTCACTCGCGAAAACAAGGTCCTGTGAGGACTGATTCCGTAATTGTGCACTCATGGTCGATCGCTTAGCTTTACTACCTTTTTCTTGAAATAGGGAGCATCTAAAAATGCACTTGCACTCCTATTAGATTAAATCGCTTCTCCGCACTCTTCCCCTGAAAAAGTAAGTCGAAGATATGAAGAAGAAAAGATGAAAAGATATAATAAGATAATGATATTAGAACTCAAATCCCCCCTCCCCCAGTGCAAAGAATATATAGAAGGCCCATATGCGAGGCGGAAAGAGCCGTAAACTACTTTATTTATCCTTTCCGCGTAGCGACTCTGCTCTTGAAACTTCTTATTAAGCTACTTAGGACCGGAGCGACTCTATCTTTATTGAGAGCGTAGCTACTTAATCTGCCCG >AJ1-BE-R2 (822) ATTCGTCTGAGAAAGCAATTCCATTAGAATGCAGCGGAGTCCTTTCCGCAAGGGGAATATATGCGAACTGCAACACATTCATGTCCATTTTCCTCAATCGGGAGGCAGGAATTCTTCTTATATCTATTCTTGGATTTTCGTCGTTATGTAATAAATAGAGGTCGCTCTCTTGGGTTCCCTCAATGAAAAGGACGGATTTCAGTTTCCGCTTCCTAAAAAGAGGTTTTTCTATAAAAGGGAGTTCTAGTTTCGTTTTGGATTGGGCCGGCGGCCCTGTCTCCACGAATAATAGGCTGCCCTTCGGCACATGCAACCATACATGGTTGCGGCTATTCTGTCCTCTTGTAGGTCTTCGCAGTGGGGGGAGTCGAATGCTAAAGGCATAATTCTTGTACCCCTTTCTTTCTAAAGCTTTTGGGCAAGAGTTTTCCCATCAAAAGGTACGTTAGCCGTTAAAAGTCTTTTCATATTTGGCATCTAGCCTGCACTCCTGCCCATCAAGAAGAGGGAGAAGGTCCCTCGCGGAGGAGGGCTGCTCGCCTTCATCATATTCGTTTTCTTCGATGAACTCGTCGAAGACCCAAAAATCAAAGAAATCATGGGCTCCGGAGTAGTCATTCCTTCTTTTCCCGACTCCTTAAGCCAATCTTTTTAAGAAGACTTATAAGGAGGGTTTTCCGCGTATTTGATCTTCCGTAGAGAAAGAGCAGCCTTGTCTTTATTGGCCGCTCGAAGAGAAGGTTGGTTGCTATCCAATTCAGCAGGAGATACTTGTCTTGCAAGAATTCGTCAGTGTATTCTTGGGGCCGGCCTTTCCTTTCT >AJ-2-BE-L (824) ACCCTTCCTTTCAAGTCTCCCTTCCTTTCTTCCCGAAAGCAATGAAGGTATAAGATTCACCCAAAGGGCTAACTTCACTAGTTCCCTGGGATCACAGAACTAGCGTGAAAAGACCGGGAGTCCTAGTCCACCAAAGCGTGCAGATCCATCATGGCAAAGACCGGATCTGATCTAACTCTAAGATAAGAGCGGATTGGCCGACTGAAAAAGTATGAAAATCAGTTCCACAGGTCCGAAGGACTATCCATATTAACCCGTCTGTCCGAAGGACCTGTGACGCCTTGTTCATTGATTCTTTTTTCTTTCAGTGAATTAGAATGTGGCTCCTCGACCGAAAGGTTTATGACAAGTCTTCTTTCCTAAGAGTGAGTCAGTCGCTGTGGCAGGCACTAGTCAAATAGGGGAAGTCATATGCTCGGGAGAGGGACAAAGGAAAGCGGGAGAGCAAGCCTTGCTCTTTCAGTTGCCGTTGAAGAAGAAGGCAAAAACAAAGGCGCCATAAGACAGGTCCGAAGGACTATCCATTAGGGGATGAGAGGCCTATCCATTAGAGAATTTGACTTGCGACGTCCGCTTTAAGAGCCTAGAGGTCAATTTTTCTTAGGGGTGCTTTCGTGGAATCATTTCAGAGAAAAATCACTGTCTTTCTCTTTCTCACAAAGGACCGGAGAAGGGCCTGACCTCGATCCGACCCGAATGAGTTACCGGTG

172GAGTTCAGAGGCCCGTGCCCAAGTGAAAGTGAAGATCAAGCCGCCTATGGGCGGTTGTCCGTCTACATAAGAGGAAGGAGGATCCCTCGTCAATTCTAACCACCAGAGGTTTGA >AJ-2-BE-R (742) GCCTCTATCTAATTGAGAATGAAAGATCTCATAGCTGCTGGGTCGAGGCCCTTGCTTTCAATGCGCCAGTGGGAGTGATGAATATACGCATGGGGAATGAATCTTATTCAATGGAAAAATCTTCCTGTGCCGCACTGCGTAGTGGGGAAGAGTGGAGATCCCACTCTGAATCTTGAGTGATAGCTTTACTTCACACCCCCGACCGGTCGGTTTCGGCTGATCTTCCGAAAGAGAGGATTCGTGGACTCACACGATTACAAGGATTACGTGGTACGATTAGCCGATCGCCCTTGACAGCTTTTCATCATTGGACTCAAATAGAATATAATATTACAAGAAATCGTACCGGGCTCGCAAAGAATTCTAATAAGCAGTCGATAACTCTCCCTCAAATTCTGATCGCCTTGTTCATTGACTTTTCTCTATCGATAACTCTTCTCTTTCTCTTAGGNTGGAGAAGTCTTAGTTAAGATATGNAGTTGAGGACAAATAACCTATCGCCCGCTGTCTTCCTTTCATGACTGAATTAGCCTAGCCGCAGATGATGATCCTGGGATCGGAGCTCGAGTGGAGTTAGGGCGAAGGACCCAGCCGGGTGCAAGGTTTTCCCATTCAAGTCTTGTGNTCAAATCACCGCTACGTGGGCCCTACCTGGCTTCGCCCCTCTGAAATATGAGAAAGAGCTTCCGGACAAGGGTTCGCTCGCGCGCCAATCCTGAGTTCTAGAACCGAGAGGGAAGAG >AJ-3-BE-L (760) ATCTCATCGCGCAATTCGCAGTTGAGGATGAGAGAAGGAACCTAAATTTCACGTCCATCTCTGTAGAAGGAAGGATTCCGCCTTCCTTCCCGGATGCAAACGACCCCTACCAATATTCTCACATTGAAAGAAGAAATCGGCCACAAGTACGAAGCTCGACCGGACGGAGGAAGAGGAGAAGTACCATTATGTGAAGGCAGTAGCCTTTCTTGTCCCAGTCCCAGTGGGAGGAGGAGTTGCTGGTCTTTCTTGCAAAGCGTAGATGATTAATATCCTTTATGAACTTCATGAATTTGAGATTCCCACAATGCGAAAGGTAGGAGGTCCGCGTCCCATGAGAAGTAGAGTGAACTCGGAAAGATTTCCTTTTTTGATATTAGGTCGATCCCTCTTTTCAATGAGTTCGGGAACCCTCTCGGAGATTGAAAATCAAGAAGAAGAGAAAAACCCACTTCCACTGAAAGAAGAAGAACTGGGACGGTGGATCTGCCCTTTTTCCTCGCGCCTTTACTGGTAAGCTCATTCTTTGTCCGATCGGTCGTTCACGACTGGTCCCAGAATGAATGCGGCGGCGGTACCAAGGACAGCATGACCTAACCGCTCACTTGAATCTCAAATTCATTTCGCGCGCGCACCGCTTCGCCCATGCCCCTCACACTCGAGTCCTACCTCTCCCTCTCGTAAACTTATCGCACCTTGTAAACTCGTGGGTTCAAGGTATTTTTCTACGATCAGCCTATCTTAAGCTAATGCAAAGCTC >AJ-3-BE-R (768) ACTACATCGTAAAGCTAGTAAATGTCGGTTGTACTACTTGACGTACGATTTTATAAACATGAAATGTCTTACTAGAGATTTATAAACATACTTGTTGAGTATTTGAAAATTTCTTTACATTAAAATCACGAGGTTGCTTTGCTTTACAGATTTATCTATATGTTTGATAAGTTATCACTTACTAATTTCTAAGAATATCAATTTTATAAATTGGTCACTCTTTCAAAAATGTTTTTCCACTTTCCAGGTAAAGATCGAGCTTCCGGTATCTGACACTGTCAAACATTTATTTACAGTATATGAGTGAAGTTGTACATCGAGTCTTGCTTCTAAACATTGCCATGCGACACCGAAAATGTTACTTTGTCCTTTTCGCTAACAAGGTCACCAACTCGCTTGTCTTATAGTGATTCATATTGCCAAGTATCCTTTGTATATCGCCTAATTTGTTGACGTGAGCAATGACCCCCTAAGATAGTTTCTTCATTTCGCACCTCACCTCTTTTCACTATAATCTTGCGTTAAGACATGAAAAACAATGATAAACCGGTGTTTTGCCTTTTGCAACTTGTAAATCGCATAGTTCTTATTTTTACATGCTTTGTTACGCTTTATTCTCCTTTATATAATAATACTACTTCATTATTCTATTTAAAAGACTATAATAATTTGTACAAGTTATCATCTCATTATTCAATTTATTTGTACAAGTTATCCTCTCATTATTCTATCTAAAGGACTATAATAAATTGTACAAGTTATCACCCA >B-1-BE-L (836) CAGTGACCCATCAAACGTTTGAGGGTCATATTTCCTAAAGTCCCTCAAATGTTTCGCCTCAGCAGAAAGTTGGTTCGGTAATATCTGCGGCTGTTGTGCAGCCAATCCTTGTGCAGGAGGGGTTGTTGGTGCAGGTGGAGCTGCTGGTACCGGGGGAATCACAGGTGCAGGTGGAACTGCAGGTGCCTGCTGGTTCTGAGCTATAGCCGTCATAAGCTCCGTGAACCTCTGCTCCATGTGAGCAGACAGTGCAGCAAACTCAACGTGAGTCACAGGTGCAGTAGGAACTCGCTGTTCAGCTTGACCCTCAGTAGGTTGATTACGACCTGCTCCTTTACCCCGACCTCTACGACCTCCTCTACGTACACCTCTCTTTGGCGGCATAATTCCTATTATCCACCAACAACACTCTTTAAAAAGTCTTAATCATAAGCATAAGCAAGTCTTTATCGTCATGCAATCTAGTTTAATTTAGGCAAAGTCATGCAAATAAACCATACATATAAGCATAAAGCATACCTGACGAGAGACGAAGGGTCGCGTTAGCCATAGGGACATAAAACATAGACTTACATCGTAAGTCAGTCTACAGCCTAAAACTTAGGCTCTTATACCAATTGTAACGACCCAACTTTTCTA

173AGTAAGTCGAGGTCATTAATTTTTGACAAAAGACATTGGTTGATACAAAATGAAACGTAAGCCAATTGAAAAACAACTTTAAGTCAGGCCTGATTTTCAAATATTCAAAAACTTAAAAAAAATACTATTTACAATTAAAAGTTTGTAAGCTAAGTTTCAATACAACGTCTTTAAACCCTCAAAGAAACAATAAGCGG >B-2-BE-L (860) ATCTTATAATAGGGTACACCATTTTAGGCCATAGTAGTTAAAACCCCTAATTAAAATTGTGTGATTGTATTTAAGATACTTGTAGGATTGTTATTTAACACAAATTATGATCAAATTGGTATGTTATATTGATTATTGTTTGGTTGATTTTTGATATTAAATATTATTATTTAATATAATTGTGGGTGAAGTAGATAGTATCAATATTATGATTTAATTAAGTTAGTATTAATTTGCATAATTAATGATGAACATTCGTCTTAAGCAGCGGTTGGTAAATGAGTTGAGGAGGAAGAGAATGCCCGGATGGCCCACTAAATCTGCCACGTCCGTGACATTCCAAAATGAAAGCATATGATGTGGAAAGTGGACGATGTTGTTTTATTACAAAATATGCTTTTAACTTCGGATGTCCACATTCAATGAATCCAATCATTTCTTCTATTTAACTCGTGCCCATGCCAATGCATCCATGCACGCACGCACGTGCGCTTAATTGCATTTACATCAATCCTCATTTTCTTTCAAATATTAAAACCAGCCCTATTAAAATATGGTAATAATATTTGTGTCCTACCCAAAATAACTTCATAATTAATTACACACCTCTCTATGCAATCATAATATACACCCCACCCACGTTTTTGTATTTTAAAAACAATCCTATTTTAATATTAATTTTCAACTCTCACCTTTAATTTAACTAAATTCTAGTTTTCGTCTACTGAACCTGCCCCTTCAAAACCACGACCTTCTAAATTCGATTCTTGCCTATTACAATTATTAAAATTAAGATAATTTGATTTTTCTTCCTAAATTGAATCATTTTCTAAACCGAATATAATTTGATTTTTTTTTAA >B-2-BE-R (446) AAAAGTGCCTTTTGTCCATACTTTTATATAAATATATTTACAAAATAACAACAATAATACGACGATGGTGTAAATTTATGTCTTGAATTTTCTACTAGTGAAATTTGATTTTCTCCTCGTCTCCAAATACTGAGTTACTTGTAAAATAACTACTAGAGAATTGAAGGGAGAGATTTCCACATTCATTTTGGTGCTATTCCCATTGAAAAATCTTTAATTTTCCTCCATCAAAGAATTTTTATCCCTGATGTAAACTGAAGTAAGGAGAGAATTGAAGGGGGACATTTCCAAATCATTTTGGGTTTCATTTCCATGGAGAATTTTAATACTCTTCTGGAAGAATTTCTCCCCTCCCTCCCCCTTTGAGTTTCATCTTCTTTAGGAGAAAAAAAAAATGCTTCTTCTCTCCTTGTGCTCCCCTCCAGAGTCCCAAAAAAATATTTCTT >B-3-BE-L (873) CTCTAAAATGCTATATTAACTGAGAACAACCTCTTGTAGATACCGTATTTTGTCATGCTTTTTTTATTTTTTTCACTTATTTATTATTTTTTAAAAGAATTTGAATTTGAATTTTAAATCTTAATTTTTAAATTTATGTTTTAAAACAAAAATAAAAATAAAAACATATTTAAAATTCAAATTCAAATTAAAAGTAAAGGTTTTTTTCCTTCTCCCTATTTTTCTCAACCATCCTGTTTTCCTTGCTCACCATGCTCCCACTTTCTCCCCATCTCTCACGTTCTCCCACTTTTTCACCTTCTTCCCCTTTCTCTGCTATACTCCACTTCACTATCTAAAACTACTCCACTTCTAACTAAAAAATGAGACATTGTCCTATAAAAAGAGAAGGAGAAGATTCAATTGGGGGAGAGATTTTTTTTTTTTGGCTAGGAAGATTATTTGATAGAAAAGGGGGACACGTAAAAGAAATTTTTTTGGGAAGAGGTGAAGAGAATCAACACAGAATCTTTTGCATTTAAGGCATGCTTGATGATTGTGTTAATCATTTGACACTCATTTGACCTCTTTTAATTATTTGCATCTTTTAATTATTTGACACCCATTTCACATGTAAATTACTTGCACTTATTTGTGGGCATGATTATTTAATTAGCAATACTCATTTTCCATTTGGTCTATTAATTAATTAGGACCTGTCATATTTGACGTTTTTAATTTTTGGCATCTGGAGTTAATTATTTGACATCCATTTGACTACTGTGTTAATTAATTAGGGTGTTCCGTATTAATTATTTGACACCTTTTTGGGTGCTATGTTAATTATTTCATTTTGGTAATTAATTAAATGATGTGTTAATTATTTGGTTGCTG >B-3-BE-R (842) ACGCCCCTCGGTTTCAGCCCATTCGTCACTCCATCCATGGGCGTCGTTAAGTTCACATCCAAAACATACGACCCATCCTCCCTTTGTCCAACATCCAACACAGATCCATCTCCACATGGTGCATTGTCCACAAGAACAATCTTTTGGTTCTCTTTAGTTTCCCGGTTCACGTGAGCCGGTTCGATTCCACTCATGTCGGACCGTTTCTTCTTCACAGAAATTAGGTCAGCTTTTGCAGCCGCCAAACCGTTTCTTCTTCACAGAAAATAGGTCAGCTTCACAGATTCCACTCATGTCGGACCGATTCCCTCCGGTCGTTCGCCGCCAAATCGTGCACGCGTTGAAGACCTGACATGGCACGTTTCTTCACCTCTTCAGCGTCACTGGAGCCTTTCTTCATCATTTCATGCTGTGAACCAAACACCCGTGATGGATGCGTTCAGAAAGAAATTGAAATGAAACCCAGATATGATAATTAGAGCCCTCCAGCTAATGAAATTGAATGAACTGGAGATTATTAACGTAAATCACTATCTTCAAACCCAGATCTGATACTCAATAGTTCTAA

174ATTCGACCACACGAAGTTCTTTCACAGATTAGCTCAAATCAATTGTAAATTCGAACATGGAAACCGATTGAACTCAAATTTTGATTGGGCAATAAATCTCCCAAATCATGATGAAAATCACCGCGTATGGATTCAAATAATCACCGAATTCACTCCACGCTGCCACAATTCACCGAAGACAACCAAATCACTCGTCTGCAGCTCCCACCACCAGCCGTGCGGAAGACAAATTTGCAATCCTAGAGAGGCGCGCGTGAAGTGCACTCGCCCGCGC >B-4-BE-L (856) ACCAAAATTGGTAGAATATTAAATTTTAAGGTCTTTAACTTATATAGCCTAAATTGATATATGATATTACATTAAAGATTCAAATTACTAAAAAATTTCACAAAAGTCCTAAGTGGACCAATATATTTCACAAATGTAGGACCCGAATTCCATTTTTTCTCCTTGTATATTATTTTATTAATTTATAAATAAAAAGAAAAGTAAATGAATAGTCAAACATGGGTAAGACAGTTTTGGATAGTAGAGGAAATGTCTTATTACTTTCCATATACAAAAATTACCTCCATTATACGTGCCGAAGACAAATACAAATTGGAGAAATTGCAGAGCCTTCGCCATTGAAGAAGAAGAAACTAGACGGACGACGACTTGTTTAGTTTTTCGACAGAAACTGTGAAATTTTTTGAGGTTATTACATTGTGTAATTTAGGTATATTGAAGATATTCAATTAAATATGAACAAAAACGAAATTGGAGAATTGGAAGTGGTAGATTCTAGGGTTTTTGAGATTTCGGACATGAATAAAATGACTTCCGATGGCCGTGACATTTTGATATTTTGAAACTGAAGGATTGAAAGAGGTATATTTTAGATGTTTGATTCTTGTACCAATATCGAAAATGTAATTCTGTTTGTGTCATATTCTAGTGTTTATTGAGAATCTATGTTATAAAAACGAATACTCTGCAAATGAAATTATGTTTTTATGTCATTAGGTTCATTGTGTATGTTCGGAATGAAAACTGATATTTAACATCGTTGTCATTCTTTATTGTCATATCTTTCTTAAGACATTCTATAGGACCTAAGTGAAGGTATATATCAAAGTTGTTCGGATAAAGAAACTACCTAACT >B4-BE-R2 (832) AACCATTACAATATGTTGAAGTTTTTAAATCACACAAAAGAAAACTCTCTAAAAGAGTGAGTATACAATTTGAAGCTCACAAATTTAACAGATTTAATGATCTTGTCATTTAAAATTTGGGAAAAGTGAAGTATTTAAAAAGAGAGAGATAAATTTAAAATCATTTTTTGTCATTAGATATAAGTAAAAGAAAAATGAATGGTTGAGATTTAAATTTAATTAGACGTTAGACACAATATAAATAATAATATATTAATATGTCTTTTCAAAATATTTTGTTTAAAAAGAAACCAATAAAGCAAATTCACCATCTTTTTAAAAAAACTAGTCGTTAGATACAAGAAAAAATAATAATATTAAAATCATTTTCTTTTTAAATTCATTTTCTTTATAAAAGCCAATAAAACATAACCAAAAACCACATTTGTTACTTTTGTTACTCCTTCATTGCTCCAAGTGGCTTTCTCTAATTGGTTCAAATTAAATGCTTGGTTGCCACGTCACCATCTCGGGTATTTTTCTGTTAAGCTTCTTTTAGGAATATTTTCTTCATTTGAACTTCGATTTGGGTGATTCAAGAGGCGTTGGAATCATTTTTCCAAGCTCTACGATATGGTCTATTCAAATTAATAAAATTGTCAATAAAAAAATCAGATTTGTTATCATCAAAACATAAATTAATTGAATAATTAAAATTAATTAATTTGAGATTAAGGGCCAAAAAGCCAACATTTTCAGTGGTGTAACTAAGAGAGATGTAATATGACCATTTGGTTTATTTCAGGACTCTAACGAGAGACTAATAGACACATAGTGAGACAAATAGTGAGCC >B-5-BE-L (874) CAATGGAATTATGGATAGATACCGGTTTGGCTGTTGTTGCGAGTGCTATAGGAAAGCCCCTTTCATTAAATCTTGCTACCAAAGAGAGACGTAGACTGTCATATGCCCGAGTTTATGTGGAAATGAATGTAACAACTTTATGACTGCTGATATAACTGTAAACCTTAGGGAAGAGGAGTTCATAGTGACCGTAACGTACAAAGAAGACGCCACAACTTTTTTCCCACCCCCTTTCATATGCCCCGGTGGCAAATCCAAGACAACTCAACCCCCAAATATCCAATCCTATGAGAAGAGGACATGAACGTAAGGATCCTAGACCAAATTTCCCACCCAAAAGGACAAGAGAAAACCAAATGATCACGAGACTCATAGTTGTTGGATCGGTATGGCAACCCTAGAGGGGGGTGAATAGAGTTTAAATAAACTTTTTGACAAATAAAAAGTAAAATCAACTAATTAGATATTTTCTAAAATTTAAATAAAGAACTTTGTAAACTTTGTTAAATAACAAGATTGATAAAAAAATATGAATGGAAGTATGCAATCAAACTCAACCAATAAGAATATGTAACTAAAATTAAATTAAAAAATAATTATAAAAGAGTTAGAGAAGAAGACACCGTAATTTTATAGTGGTTCGGTTAAACTCAACCTACATCCACTTTCCCAAGCACCTCTTGGGATTTTGATTGAAAATCTTCTTTGGACTCTTTCCACGGATTTGAGCCGAACCGATTCTTGCTCCTTTTTCGGGTTCAAGAGCAAACCCGATCCTTTCCACGGGTTAGGATCAACCGTTACAATATGTTGAAGTTTTTAAATCAGCAAAAGAAACTCTCTAAAAGAGTGAGTATGCATTTTGAAGCTCACA

175>C-1-BE-R (855) AAATGTCAAATTTTAACTCTTAAATATTGGCTTGGTAGTTTCTTGGGCTTATCTCGAGCCCTTCTTTTTTTTTTTACTTCACTACGTGGTTCTTCATTCTTATCAAATATATTTTGAAGTAGGGATGATAAGACAGTTGTTAAGAGGGATTTGGTGGATGAAAACATGGTGGGATTTGGAAACAGTTGCCCAATTGAGTACAATACTTTGTAAATATAATGTATAGTGGATTGATTTTAGTTAAGAAATAAATAACTTGTGCCTAAATGATAAAATTATAAAAATATAGAACACATATATATTTCAAAAGAAAGATGTGATTAAAGTACATTCCAAAAGATAGACAATGACCAATTGGTCTAAAGATTTACTAATGAGGCTTCAGTGAACTATACCTTGAGGATAAATCCAATGAAAAACGAATCAAAGTATATGGCAAATCTCAACAGAAAATCAACAAATGATAAAAGAAAATGTATATTTTTTCTCCTAGCAGTAAGAACACTAAAATCATGAAATAATTGGCATGTAATTTGGCAATTTGGAGATCATAATGTAATATTTTGCCTAGATCAATTAAACATGAGTCATATATGTGAAATTTGTATAATATTGGCAACTGACCTTAGAGATTGAGAACTAGCAAGGTCACCTCAATGATTGTTACTAAACCAAACTGTGCTATCAACATCAAAGCATACTGCATTTGCACTTCTCCACCGTTCCAACACCTTCAATATTTTAAGAGTAAAGTTTAACATCACTAACCAAAAAAGAGATATGAAAGAATATTTACCAACATTGGGTTAATGAACTCAATGACAATCTAAGAAAAGATATCAACCAAATAATACT >C-2-BE-L (459) ATTCATTTGATTAACCAAACACCAACATCGACACCAACACCAACACCAACACCAACACCGACACCCCAGCACGATGTTCATACCTTGAACAGAAAGGGGATATCGGAACCCCATTTCTGAACGACCTTATCACCGAGAACATTGGGATTTTCCAACACCCATGACTTCAAAGAAGTTGAATAGGAGTCAATCCGGCCGCCATTCTCAACAGCAGGAGGAATTAAGAAGGAGGGTCCGGAATCGTGGGTTCCCATCCAGAATTCAGCGTAGGGTTTATGAGGATCGATAAGGGATCCAGAGTTCAAAGCGAAGAGCCTAGCAACAAGAGAATCTTGCCCTCTGATTCCCCAATCGTAAGTCTGAACAGAGCACTTCAATCGGAATACATTGCTCTGTTTCTTGGTGGGAAAAGCGTCAGACTCCATGAAAATTGGAGAGAGAGAGAAAGAGAAGATGCTA >C-2-BE-R (841) TAATCTCCGACATCGTCTCTCACAAGGTGGCATTTCTTTGAGTGGCTGCTGTTGCAAGAGGCGGAGGCAGCAGTGTCATTAACGGTGAAGCCGGAAGGAATGAGGAGGAGAAGGAGAATGATGGTCCCGATGGATGGTGTGGACAGTGCAGCCATGGTGTTTGAAGAAGAAAGTCCAATGGAGAGGTGAAAGAGTGATGAAATGGTGAAGAGATAACAATGAAGATTAATGAAACTATGGAAGATATTGAATTTTGGGGGGCCGTTGGTAGTTGGGGGACTTTGATGCTTGGATCCAATTTTATTTATTGATTCTGGAATATTAGATTGAATAGGAGGTGATTTTTATTTCTCATTTTCCTTTTCAAGTTAAAAGGAAAAAGAGTCACGTGATGATGATAATTCAATATTCAAGAGTTTATGTTGTGTGCCTTATTTATGTTATAAATGTTAGAATAAGTAATTATACGTCTATTAAAATAAGAAATTTTAAGATCAATCATAAACTTTCACGTCATTTACTAAATTATTGATGAAAAATACAAATAATTGAATATGAGATTAATTAAAAAAATTAGCATGTGTTCCAAATTAAAATTAGAGACATAAATTGGTCATTTTTGTTGATGTCACGTATTTTCATTATCACAGTTGGATCAATTTAAATATTTTAGTTCAATTAAATATTATTTACTTGGACTAAAGTTCAATTGAACCAAAAGAAAAACAAGTCTAATTGAGTCTAAAGACTAAATATGACTCAAATCCATGTGGTTGAGCTCATGGTTTGGTCCATGGACTAACCAGACTCAAGCCCAAAAAGCCAAGCATAGAACTCTATA >C-3-BE-L (888) CATCTTGTGTAATTATGTTTCCCCCTTTCGCCTCGATCTTGTCCCAAAATTATAAGTTTATTGACCGTCAATTTGACCACTCTCACCTGTAAAAATCAAAAGACAATCCCTTGTGAACATGAATTCATAATACACTCAGGATTAAGACTAAGTTACCTAGGTCATCCTAATGAAATAGAAACCTAATTAGTTAACGAAGTTACATCTAGTGGTTACTATTTTGCGATCCGATCTTATACAAACTCAATGCATAGGATACCTTCCCTCGTATGTAAAACTACACGATTGCATTGGATCATTGCGTTTGTATCAAATACAAAGTGAGTCATATCCATAGTGTTACCAGGATAAGGTACCCAACCTTATCTTTATGCTGTAGACTCTTTAAGTTGATTTCGAACATTGATCCCTGTATGTCTTTACATACTGTTCAAGACTTATTAAACAACTTAAGATGTTAGTTTATTGGATTTGAGTTACTAAGACAAAACTAATAATATGATCAATAACAATTACTACAATAATAACACTTTAACAATGGTCAATAGATTATATTTACAATCTATGAGTTTTAGCATATAAATCCCAACAACCTCCCACTTGGACTAAAACTCTAGTCTTTATGAGATGTACGTAATAAAGTAATCCTCAAGTATTGGAAATGGTAAAATGTACAAGTATATTACATAACTAGTATATATACAAATACAATAAACTAGGACATCCTCATACCTATTACACATCTCCCACTTGCCCTAGATCACCTAATGTACATAACTCGTAGACCTAGAATTTCTAGAAGACCCTCAAACACTATAGCTGTGAGAGTCTTTATAAATGGATCAACAATATGTCTCACTATTATCTCAATATAAAGTGATGATCATG

176 >C3-BE-R2 (768) TCGTTGGCAGCTCCTCTGCTAAGGATAATTGGTAAGGATGATAGCTGCGTTAAGGTGTTTGGACACTATTTAGCAGTACCTTTGGGTTTAAGTTTAGCCTTCAACGGAAATGTTGGATAACATGTTCCAACAGAAACATTGATTCTTGTTCATTAAAAAAAAAAAAAAAAAGAAGTAATCTAATGCATGCTACAAATGTTTACCGCGCCAGCTTGTTAGGTATTTTGTGTGATATTTTGTCCCTTGTTAGTTTCTATATTTCTCTGTGGGCATTGGCAACAAAGCTTTTTGTCATTACCGACCCATTAGGTCTCATTTTTTCTTGATTGGAGTCCTTTTATGTTGTCTGGATCCTTCTTTGTGGACTTTGTTTATTGAATGCCGTAGTATTTTTCTCAATTAAAGGTTGGTTTCTCATAAAAACAAATGCTTTCTGACAACACTGGCAAAACCATATTGGTGAAATAATTTGTGCTTGCATGTGCTTCTGTGTCACCATTGAACTTTTTTTAAAAAAAATGGTATCATGATTAAACTTGTACAAGTTAGTAATGTTCTTATTATTCGTTGTTACTTTTTACATTAGAAACTTTTATTTGATTTACAAATTACAATGCCTTTGTGCATCTCCTGTATTCACATATGCAAACCAAAGTGGACTTGACATGCTAGAAACTACCCTTGTTTCTCTTCAAGATAAATGACAATGGCTTTGTGGAAAAATAGACCAAGTATTCCCGGGGGTTTACTTCTTCTTTTGTAGTTATT >C-4-BE-L (910) CCTGTGTCAGCACATATATGTAAAACACAAGTAAACAACATCAAATGTCAATTATCATTAGACTACAGTAAATGTGGCTGAAAATTGTATTTTAGATATATGTTTGGCGTAATTGGTTCATAAATTAAATTAAAATTTAAACAAATTTACAAAATGTGTTCGGTACTGTATATTCAAAACATAAAATTTAAGAGAAAAGTACATTTCTAGTCCATTATGTTTCGATGAACAATGTGTGTTTGATCCTTGAATTTTAAAAATAGCTAGATATTTTTAATTAATATGTTTATAAGAATAGACTCGTTTGGTCGAGCATTTTGAGTTTTTAAAAATAGATTTTAAAGGTAATTTTTTTAAAAAAAACAAGGAGACAAAATATAATGTAACATTTTTATTTTAAGATGTCATTTGCATCGAACACCCTTTTTTTAGTTTGGTATACAAATTTTCAAAAGTTAATGAAAAAGGAAAACGTCCGTCTCAACTTGTACTAATAATTTTTTGTGCATTTCACTTAAATTGTTCAATTATTATTTCTTACATGGTAAAAATTTACTTTACATAATTTTAAAATCTTATGAAAAATACGTTTTCCATTCCTAAACTTTGGTTTAGTTTCTAATTCTTATGTTTCAAATATTACACCATTTAATTTGTAAATTTTAATTTATTCATGGAGTATCAAAATATTAGAATTTGATCTTTAAGATTTTAATTTTTCATTACAGTTTGTTCTCTAATATTAATTAATTTAAAGTAATCATGAAGTAAAATTTATCATTAATTTAAAAAGCGATAAAAAAATAGTAAAATTTAATTAATTACACAATTCATTTCATTATTTTAAATTTATTAATATACATAAACATGGAAGAATGTAGAAAATAAGTATTGATTGAAAATTTGAAGT >C-5-BE-L (895) CTTTTTATTGTAGCACATTGAGATATGGCACCAAAAATACAGTTATACTCAAGGAGCCTCAGTACCAGTGCTATGAAGTTAAATGGCAAAAGAACATATCATTGTCCTTGCAAATACATATACCCAATACAAAATTTTCATAGAAATCCTCAAATTTCACCATAAAAAACAATCCTATACGTAAAATTTCGTGAATTAGAATCTTAATGGTTGCGAATAAAGAATTCAGATATCAACATCAAGTATCCAAATAAAAAAATAAAAAAAAGAATGCAACAAATGGATATGGTTAGCTTAACCATTTACAAGCAAAACAAGAAAGGAAAAGAAAATGGCCTGATCAATGATCATAATAGGTTGAAGCTTTCTACTACGGAAGCATTCTCAAACCTTGTTTCAGCTATTTAGGTACAGAACTGTTGAGCCAGTGGATGTGAGACGGCTGAGAAGGAACATTGGCATTGCCATTTAAATACAGCAATCTTGAAATTAGGGTATTGCAAGGGAACCTCCCGCCGAACCCTAATCGTATAGAGTAATCAGAAGTTAGAGATCAACGGCCCTTAGTGGAAAGGAAGAATCTAAGCTCTTTGAAATCAACGGTGGTGATGAAATGTTTAGTACAGCCTGCAATCTAATACTTATAATTTAAAATTAAAATCAATATAAATTATTTTAAATCTCAAATTTGTATCAAATAATTTTATAAAATTAATAAAAAAGAAACAAATTATTTCCAAAATTTATTAGTTGTTTTTGTTTTTAAAGTGTAAATATTTTATCTATTTTACTGATTTTTGACAAATTTTTATTACTTTAAATATATAAAATGAAAAAGAAAATCTTAAAAATTGAAATTATTTCAAGCAACTTTATTTTTCTTTTAGGTGAAATT >C6-BE-L (709) TTCTCACTGTAGATATATATTTCTGTGTCCACGGATGTAGACCAATAACAGTAAGTTAATCCTTCACTAGCGTTCTTAACATCAGTTGGGTCAAATTTACCATTTTACTCCTAGGTTACTTCTAGTCCTTAAATACTAGTGCTTCTCTAATGAACAACCTGTTTAGGGTCCATCCAATAAGCAAAAACCCCTCGCGTGTCGTAGAGAGGGTAGGACCCTTTGTTTAAGTTCTGGAGACACTATTTAAGTGAACACTTATCTATTTACCCTAAAGTCGAGAATGAGTGAATTCCATCTTATGTGATTATGTTTCCAGCTCCCCACTCAATCTTGTCCCCAAAATGATAAGCATAGTGAGTCGACAATCTTGTCACTCTCACTTGTACAAATCAAAGGACTATCCCTCGAGAACAAGAGTTCATAATATACTCACGATTAAGACTAAGTTACCTAGGTCATCTTAACGAAATAGCATAAACCTAACTAGTTAACGGAAT

177TACATCTAGTGGTTACTATTTTGTGGTCCGGTCTTATGCTAACTCTTTGCATATGATACTCTCACTCGCATGTCATCTACATGAAGGTGTTCGATCATTGCATTTGTATCAAATAGGAAGTATGTTGCATCCATAGTGTTACCAAGATGAAGTACTCAACTTTATCCCCTATACTATAGACCCTTTAAGTTATTTCTTGAACATTGATCCTT >C6-BE-R (782) TAATACGAATTTGAATTTAAACTTAATTGAATACGAATGTGAATTTTATGATTATCTTGCAGTTATGGTAGCCTATTTCACTACTACAAAAACAACATTACTTGTCACCTGCATTTTTTATATACTTGACAAGTTAAAACCTTGTATCAGTTTGTATGTATATTTTTACGTTGAAGATCCAAGTGATGCTAGATGATAAATTGTGCTTACTCTACCACAGAGAGATTGTGAAGATCAATCAAATGACGAGGAACTTGGAGATATCATGTTACATAGTCAAGGAGTACCTAGTGATATGCCAAATATAGATGATAGTATTGATTTAGATGAGAATATCTCAACATATGTACGATCAGACTGTGAAGGTACATGGGTTCGTAAATAACACAATGTGGGTAGCTTTCTACTTGTTATATATGTACTTATAATATGATATGTTTTATTTTGAATATGCCTAAACCCTAAACCCTAATATAATCTACACTAACAAATTTCTTTTTAATGTTGCTTTGACAGGATATTCTCCTTACACATGGAGCTAAATATAGTAAGTGATGATCAAGACAAGGAGGTTAGTAGATTAGTGGTTGGAGGAGTTGATGACCAAATTATAGAGAAGCCCAAGAAACAAGGAAGTGGGAACCTACTATTATGTTTGATGTGACTCGGGTTAGAAGTGAAGGTAAGAGGAAAGTGTAGTGGAATATAATCAAGATGGTGTACATATTGGAGAATACGAAACTCATTTATTGGATCGTGTGTACACTACCACATCCCCATTA >C7-BE-R (607) ATGATGAAAATGAAATGGATTAAAATAAACAACATGTCAACGTGAGCTTAGCTCAATTGGCACCTATATGTACTTGTGGACAAGAGGTCTCAAGTTCAACTCCGAGTCAATTGGTTAAAATCCCGTTTGATAAATTTTACCTTTTTGTTTTAAAATCTAAAAAATCCACAAGTTGGACACTGTTGCAAATCTCCGAACTCCAGACAATACACATCCGTACTTGCAAGCAATTTTTGTTTGGCTTCATAAAATGAACTAGAGATATTGGTATCACACATTGGAAACACACATTTTATTATCTCTAACATCATGTTGAAGGGTTTGTTGCTCCAACCATTACCTTCACATGCATTATCGTAACCAAAAAGTTCAGGAACAAAAATTCTAAATAGTCATTTTTCATTCGTAATTATTAACGTGATGAAGAAACTAGCAAGAGATGTATTATTCTAAAAATTCAAATTTAATAAACAACCTCTCAATTCAATATAACCTATAAATCTCAAAACATTCAAGTTATATATAAATCTCAAAATTCAATCTCAACCTCAATTTTAAATCCTAAAAACAACTCAAATTCAATACTAAAATCTAAAACTAATTCAAT >C8-BE-R (596) CACGTGTTTGGTTTAATTCTATCATCCATGGTCTCATGTTGATAAGGCAATTATTTCATACGAAAAGTATAACAAGATGACATTTTAGAAATAACAATGTATTAATATTATTTTTTAAAAATGTAAATATAGTAAAATTTATGAACAATAAATTTCTATTATCGATAGACTATTACTAAAATTTGCAATATAGCTTATCATAGATAGACATTAAAAAATTTGGGTATATTTGTAGTGGTTTTAAAAATGTTGTTATATATATAAGTTAAAGTTAGGTTATTTTCATCCTTTTTACATAAATTGTACAAATATATTCATATTATGTGTTTTCTTTAATAAAAAATATTCATACCTCTATCCCCTATAACCATGCACTTTATTAGGGTATGGTTATAAGTACATTAATTAAGCTTCCAAATTGGACAAATCAAAAAAGAAAAAAGATTGTTAGTCTGAATGTCTATTTAAAATTGTGTATCTTGTAAACATAACAATAGAAGAATGGAAGAGTATTGATATTTGGTTGAATGTTTATTTAAAAAGTAGAGGATGTTTGATGGGAGAAGTAGCCAAATAAAATTGAATTTAGAATCCAT >L-1-BE-L (843) GAAGCCAAAGAGATGCATTTGGATTTTGGGAGTCTTAGAACTGAAAATTTAGCAGTTGGAGAGTTGTTTATCATCTTTGTCTTTTATCTGCACTGTACATGGAGAGAAGAACAGCACCCGTATTTTTCCTGTCTAGGCGTGGGTTGGTTAAGTTTCATTATGTATATGGTTTTTTCTCTTAGGTTTTCCACGATGTTGAAATGGAAGCGGATTGGTCAGACTACGACAATTATTGCAGGTGCATTAAATTTTCCATTATTTAGGTCACCAATTAATTTTATGTTTCCTACTGTACATATATGCATTCGAGAATTGATGATTTATTGGAGTCAGTACAGTTATGACTGCAATTTAGAGTATCTGAAGAGAAAGCAAAAGATTTTCTTGTCTTTAAGGCTTAATGATGTGATTGATTTGTGATATATGGGCCAGCCCCATTGTGGTTCACTAACCAAGCCATCGACAAAATCTCTTTGGTATTTAGTTTTATAGGCTTCACTTTCCAGCTCTAGTGTTTAGTTGTCTGTTTTCCTTTTTTCCCCCTCTTGGGTAATTTTCTTCTGTCTTCTATTCTGTATTTTCCCATGGATAAGAACTGCTGCGTTAAGAACAAATCTTAGTTTAATGGCTGAAAATAAGCAATGTCGTTGGGAAATGATTATTTCTGAAGGGAACTCAATTCTTCGTTGTCAATCTACTGTCATTAGTAT

178TTTTTGGTGCCTTCATGGAATAACTTTGTCATCAGTGTTTTTCTTTATCCCATTTTTTCTCCTCTCTTTTAACCTTATTCAAGATTTTTAGGCAATAGAACACCTTATATAAAGTGATAACTTTTTTAATCTA >L1-BE-R2 (854) TCTCCACATATTTTAAAAGTAATATTAGAAAATATATATATAAAAAAAGAAACTTCATAAGTTTAGTTTTTCAAAATTAAATGGTTGTCGATGATTAAGCTTATAAACAATAATTCAGCTTGATCATTTTTTCTGCCAACATGTGAAGTGGAAAAAATTGAATCTCTTACTTTGAAATTGATAGCACGAGTGGAACCACTTTATCGATTGAATTATGCTTATTGTGACACACTTCAACGTATTCATTTTTGTTTGTTTTGTTATATACATTTTCTGATTTTTTAAAAAGACTAATTAAATTTTTTTTTAACTAAATAAAGAATGTTTCACCGTTTTTCATAATTTGTTAGGGATTAAGTTCATTGATAGTTTATTTAAAATTAATGGATAGACTAAAAGTATATTAAAGCACCAAACTGAAATAAAAAAATCAAACCCAAGATATAGAAATTAAATGCACCAAATAAAACCGAGATCTAAAATGTTAGAATAAAAAAGAAAAGTAATTTTCCAACCATATATAAAAATTTATTGAACACATGTAATATTAAATGATAATCTACAAGAAAGAGATTCTAAAATCTAAAAAGAGGGTTTTGATAACGTGGAGATTATATATTATTAGAAAAAAAACTCAGAAGAAACAAAAGAAAAATGAATTTGTAATTTAGCTATGTAAATAATTTCATTTATATCATGTTTCATGTATTAATGATTAGTCACTGTTCATATTGATTCTTATATTAGATTCGAAAATGACTTTGGTTGATCATTCCAACCATATTTTGAAAAATATAAAAATCTCAATAGATGTGGCTGATAAATTAAATTAAAGTTGTAAACATTTCAACATC >M-1-BE-L (803) CAGGACCATTTTGCTCGAAAAGCCACATTAGGGAGTATCAAATTCTCGATACCTAACCCTCCCAAGGGTTATGGTTTTGAAACCACATCTCACCTAATAGATGGAGCCCTTCTCCCCATCGACCCTACCAACAAAAAATACCTTGTCATTTTCTCAATGGCCTTGCTCGCCAAAACAACATCCTAAACAAAAACAAGAAATAACTAGGGATGCTACCAAAATCGATTGGACAAGAGTTAATCTACTCTCGTTGGAAAAGAAGGATCTTCCTAAAGAAATGAACACTTTTGCACCTTTTACACCTCATAATTCCAAAAAGACCTATACTTTGAGTTACCACCCAAGGAGAACCTAAAACTGGCATCCTTAAAAGGAAAAGAGGTAAGGGATGCAAGTTCTCGACTGGATAAGATTTAATCAACCCCTTTGGAAAAGAAGGCTCTTCATCGAGAACCCAAACACTTTTGCAGCTTTTCCACAACTTGGTTTTAGAAAAAGACAGATACTTTTGAATTACCGGTATCACCCAAGGAGGGCCCCAGATAAGAAGGCAGTCAGTTTACCTTATAGTCAACCATGGAGACTCACACACTTTTTCCAAGGCACCAGTTTGACCCTATGGTGGAGCTTTTACACGTTAATCTCGAGGATCAAAATTATTGAAGGTTTTAAGAACTCCTTTAATGTAAAGAAAAAGTACTTGGGCAACATTTTTTTTTGTGATTATTCCTACACAGAATCTTACTTAGTTGGAAACCCTTTCTCTAGGGGTTTCTTCTATGGGTTGGTTTTTTTGTATGCTC >M-1-BE-R (632) TTAAATCCATTAAACTTGAACTAAAATAAAACCAATATAGAAAACAGCACTCTCATACATCCTTCAATGGCAAGCTAAAAACAGGACTAACATTGGAGTTGGAACTTCTAGTAATTTTAATTCACGCAATCCAACATCGCGTTGCTGATGAAGGGGAAGTAAGAATTAGAAAAGAACCACAACCAGAGCAATGCCGAGGCTCAAAAACGAGGTTTCGAACGAGCTGAAATGAAACTTACCTCAGTACTATGAACCAGGCCGAGGTTATGATAATGCTTAGCAAGGGCGTAAATGGCATTGAGAGTAGCGGCTCCAGAGCCAATCCGCTGGCCATCAGGATCGGGCACAGCGAGAGTAATAGTCGAGTGAGCAATTCGACCAATACGCTTCGCCCGATTGAGCTGCCACTCGTAAAGCTGAGCTTGCTCAGGGCTAGCAGCGGTGAGAACAATTGCGTCCCAAGTAGGAACTCTGGAGGGATGGCGCACAGAGAGACGTAAATGGTACCATGATTTTCTTAGGATAGAGTGAAGATCGGCCTTCTTCTGTCTGGTTCTCGACACCCTGGATTCCATCATCGACTTCCTGAGAATTTGGGAAGAAACCTCAAGAGCTTTAAAGGAGTGTTGAAA >M-2-BE-L (883) ATTTACTTTTTTGTGCTTCACACTATACACTTTTGAACTCTTCGGTAATGATGCGTTCTTTATTTATGTTAAATAGGGTTAAGATTCATGAGTCAATGTCTATTACATTTTCTCCAACTCTTTTTTTGAAGTAGTCACTAGCTAGTGTACTAGGTTATTGATTCTCTTCCCTGATATCTACAGACAAAACAAGGAATATAAAACGTGATGACATCAATGACTTTGGAACAATCGCAACTGATGGATGGGCATCCTCTATGTTGGGAAATAATGACAATAATTTGGAAGACATTTTTCTTAAAATTGAAGCTGCACAGTCAAAAGTTCATGAGTTGAAGAACAGAATTGACAAGGTGGTGAATGAAAATCCCATGAAGTTCTCTGTAATCAACCAGCTATACTCTCTTGCATCATCAAGTGATGATCCTGCTTCACCTGGAGATGGAAATGATGAGTTAGTAAGGTCTTTGCATGAAGCATCACAACACATGTCTGAGCATGCATTAGATGTACTAATGCCTGAAACTGCAATCAAAACTCATGGAGAGGTCATGCTACTTCCTGATAT

179GATGCGGAGCACGGATTGTGGAACGGTAAGTTTTGTCTAATGCTATTTGCAACTTTTTTTTTTTCTTGACAAGAAAAGATAAGATCTTTGAATTAATGAAATGGAAAAACAGCAGCCAACTCCTATGGGAGTGAAAAGGAATAGAAACCATAAAGCACTATATTTCTGGCAAAGACCTCCCAATTCATTATATTTCTTGCAAAGAAAAGCTTAGAAAAGTTCAGAAAGAGAACCACCATTGAGAATTTTTTCATCTAGCTGCCTCAGACTGATCAACCATTTAGTTTCCGGGTCTCAAAACCCCTTGATTTTGTC >M-3-BE-L (523) ATGTGGTTGGAGAATGATTTTTGCAATTTTTATTTATGTATACACACACACACACAAACACGAGCCTCACGGTTGTTGGCTACAAAACTTTGGAATATATTTATTCTATCAATTATTTATTAAAGTTTTATATATTCTATAATGTCTTCCTATAGATTCTATAAAACACAGGGATATATTTCAAGTCTAGATATTAATATTTGTTCTCAAATTGTGATAAGATATAGATAATAAAACATGAACAAAAATTGAGGGGAAAAAGATAGATGTTCTGATGCCAATTTCCTTTTGAAAGTATAAGCACAACGAAGATGAACTAAAAATTGAAATATGATTATATATATATATATAGAGAGAGAGAGAGAGATTGAAGTTTTGTTAGGATTACAGAGTGCAATATATTCCCAAAAAAGACCTAAAATAAAGAGAGAAATATACATAATTTTATTATTAATCGTGTAGGTTGATGATTAGAAGGAAACCCGAAAGCAACCGAGGTGGTGGTGGTGGTGTTGTTGTTGTT >M-3-BE-R (492) ATCTTGTAGGCTGTTCCCGCCATGAAATTGAGTCTGATAGGAGAAGTGGAAGAATTATAGTGACCGACGACGGAAACTCATCAGTGGGACAGTTGAATTTGGGAAGTAAAGGAAATTCCAATCTAGCTTCTTCTTCTTCTACTTCTACTTCTTCTTCCTGACGTGATTATCATCTCTATTCGAGTAATGGAAGCTTAAGTACGATATCAACTCACTCGATAACTGCCACAATAAGCCTAAGGTCTGATTCTCTCATCTCACGAAATTAAAATTAAGATAATATAACAAAAGAAAAATCACCTCTATTTTATTTTTCAACTTTAATCAACATCAATTACCAAAATTTATGATGTTTAATTGATGTTAGCCTATGATTCATTTTTTAAATATTAAACTCATGAAGATGTGTTAGTAATTCATTGAATTTAAGTGTATCAATCAAATAATGTATATGAATGATATAAATTTTCTTTTTTGTAAAACTATGTCTAG >M-4-BE-L (866) GAAGCCATTACTGCAACAATCAAGTGCAAACAAAAATGAAGCTCCAATTCAAAACTGATTGAAAATACAGAAGATGGGTTTCTTACAGATTAAAGTTTGAAAGAAAATGAAGAAAGGAGAAAATCAAATGAGCAAGGTATGTTGTCTTCTATATTTGACTAGAAGCACCACCGGCCAAGCCATTTCTTGCGACTCTTATTTGGCAGGTTACGCTTGAAGACAACTCAGATGAATTTGTTGTTACAAACTATTTCATTTCAGTCCTTGTTTTCCCTCTCTTTTACAAACTCCCCCCCAAATCTTCCTTTATTTTCCCATCCCCACGTGATTCTTCATTTCAACGTTTTTTCTTTTTCTATTACATTTCTTTCCAATTTTTTCATATTTTCAAAAACAAAATTAAGACGTAAATCCATATTTTCATTTTGGTAAATATTTTAATTTTTTTTCATAATATTTCACATTTTAGGTGTTAAATGGTATAATTAAAAAATGATTTAAAGGTAAGAGGGTAGATTGTAAAATACCAAAACATATATTTGGTAGTTAGTATTTAATGAAATAACATATATTTACCAATCTATTTAATTGTCTTTATGCTTAATTATTAGCCCTTCCCCACTCTAATGAAAACCTACATGGTAATAAGTATAATATTTTACCATTATATCAATTTTTATTACGAATTGATAAATGGAAGATTTTACTTAGTTATAGGTCAGTAAGCTAATTAGAATCCAATATTTTCATAAGCTCTCTTTTAATTTTGATGAATATTTTTCTAAAAGTTAAAAAGAACTAAAAATTACTGTCTCTTGAAAGCTTATTTTTAATTTAATTAATGGATTGATCTATAAAAAGACAGT >M-4-BE-R (570) TAAAAGAGGGAGAGGGAGACGAGAGATAGGATCCATCTTGGAGGCTGTTCCCGCCATGAAATTGAGTCTGATAGGAGAAGTGGAAGAATTATAGTGACCGACGACGGAAACTCATCAGTGGGACAGTTGAATTTGGGAAGTAAAGGAAATTCCAATCTAGCTTCTTCTTCTTCTACTTCTACTTCTTCTTCCTGACGTGATTATCATCTCTATTCGAGTAATGGAAGCTTAAGTACGATATCAACTCACTCGATAACTGCCACAATAAGCCTAAGGTCTGATTCTCTCATCTCACGAAATTAAAATTAAGATAATATAACAAAAGAAAAATCACCTCTATTTTATTTTTCAACTTTAATCAACATCAATTACCAAAATTTATGATGTTTAATTGATGTTAGCCTATGATTCATTTTTTAAATATTAAACTCATGAAGATGTGTTAGTAATTCATTGAATTTAAGTGTATCAATCAAATAATGTATATGAATGATATAAATTTTCTTTTTTGTAAAACTATGTCTAGTTCTTGTATAAAAGTTAGTAAATGAATTTGGTTTATAACTATTT

180>M-5-BE-L (873) TCGTACAATACAATCTGCACAAGTTATGACCCACTTACTAACATCTTTGTTAAAAATGGCATACTACATTAAAGAAATTGCAATATACCAGATTTCATGTCGATCCTTTCAACTCTCCCATATTTAGAGAATAAGCGCTCCAGCTCAGATTGCCGGGTGTCATACCCAAAGTTCCCAACAAATATCGGCCTCATCGTTGCTACAAAAGATCGTACTGTAGAAAATACGAAACCGAAACAAAGCGAAATCAATAAGCTCGAACGTTAATCAGACGAAAAGTGAAAAAAAGATAACAGCTTCCTCCATATATGTGTGTGTATATATATATATAATAAACAACTTCATCGACATGAATCGACCTATGTTCTTTCGAATCAAACGATGAAAGAAAGGAAATAACTTAGTTTGGGGGTTTGACCAGAAACGAAAAGTAAAACGATCGATTGGATCAATTACCAAAGGATTGAAGAGAACAAACCTTGGAGATTCTCGGATTCGAACAGAACAGAGAAACCCTAGGAGAGAGAGGAAGAGAAAGGCTGTGTCTTTTCTTTTTTCAAAGAAGCCCTAATGACGCAAATCGATTCATTTTAAACCGCAAAGCAAAACCGGTACTCTTGATGTTCTTCCTTTCACACCGTCCGATTATCTGACTTTCCTATGCAAGACTACCGTCGACCCAGATTTCACCCTTACTCCCTAAAGGGTATTTTGGAGATTTGGAATTTTTAGAAGGATGTTTGGGCTTTGTGTGGAAGAAGGCCCAATTCTCGGTTCCTTTCACTTTTTGATCCCTAATTTCTCGATTTTAGAAAAAATAACTATGGATACTAACATTTTTAAAAAAATTCAATTACCCACATCAAATCTATA >M6-BE-L (796) AGTTTACTCTATGCTAAGGAAGGCAGACTCTTATCTCATTTCATTTTAGTTTCTTAAACTATCCTTGACTGAACTACCTCCAGATTCTCTCTTTAATCATCGTGCTATGCTATGAAAGGTGTTTAGGTCAAGAAGTGAGTTATTATAATCTTTTAGGTTATTTTAGTTTGAGTTATAGTAATGTGTTTGGTATGTATGGTTTTAGTTTGATAAAAAATAGTAAACTCTGCCTAATAAAAAAAATTTGAAGAATGTTAAATAGTAAATACTATAACAAATTGAGAGTTTTGAAATAGTGTTTAATGTAGTTAATTGAAAGCTTTTAAATAGTATTTATTATACTAAGGATGATTACCTCAAAGGTTCCTATATTTTTGTATGCTTTCTTTTCCTATTATTCTTAAACTTCTAGACTCCTCCATTCATCCCTGAATTTTGAATCCATATTTTCAGGTAAAATAGTAGTGAATGTGAACAAATAAATACTTTACAGATGTGCAGTAGAAACCTTAATGGAGTGCTATTAATATTGGTTATTGTTAATTAATTACTATTAAAGAGATGTAATTAATTATAGTATTAAGTGACCCAACTTGTCTAATTTGACCTTGACGAAGGAATTAATGCGACATCTTTTGCACATAAAGTTAATGGGAGAAAATTCCTTACTTGTAAGTTTGGTAAATACTTGAAGACCTGGGCTTTAAATGAGCAAATTTAGTGCATGTTTTGATTGATTTTTAATGTGTCTAGAAGTAGAAAAAAAAGTTTATAAACACTTGAAAAGTAAATCAAA >M6-BE-R (837) TTCGTACAATACAATCTGCACAAGTTATGACCCACTTACTAACATCTTTGTTAAAAATGGCATACTACATTAAAGAAATTGCAATATACCAGATTTCATGTCGATCCTTTCAACTCTCCCATATTTAGAGAATAAGCGCTCCAGCTCAGATTGCCGGGTGTCATACCCAAAGTTCCCAACAAATATCGGCCTCATCGTTGCTACAAAAGATCGTACTGTAGAAAATACGAAACCGAAACAAAGCGAAATCAATAAGCTCGAACGTTAATCAGACGAAAAGTGAAAAAAAGATAACAGCTTCCTCCATATATGTGTGTGTATATATATATATAATAAACAACTTCATCGACATGAATCGACCTATGTTCTTTCGAATCAAACGATGAAAGAAAGGAAATAACTTAGTTTGGGGGTTTGACCAGAAACGAAAAGTAAAACGATCGATTGGATCAATTACCAAAGGATTGAAGAGAACAAACCTTGGAGATTCTCGGATTCGAACAGAACAGAGAAACCCTAGGAGAGAGAGGAAGAGAAAGGCTGTGTCTTTTCTTTTTTCAAAGAAGCCCTAATGACGCAAATCGATTCATTTTAAACCGCAAAGCAAAACCGGTACTCTTGATGTTCTTCCTTTCACACCGTCCGATTATCTGACTTTCCTATGCAAGACTACCGTCGACCCAGATTTCACCCTTACTCCCTAAAGGGTATTTTGGAGATTTGGAATTTTTAGAAGGATGTTTGGGCTTTGTGTGGAAGAAGGCCCAATTCTCGGTTCCTTTCACTTTTTGATCCCTAATTTCTCGATTTTAGAAAAAATAACTATGGATACTAACA >M7-BE-L (472) ATCAAGCATTGTGATGAAAAAAAACTTATATGGAAGACTTTCCATTACAAATAGAAAAATATAACGATACAGAAACCTTAGCAAATAAATCGACACTTCACACTAGAAAATATTATTCTAACAGAAACGCTAAAACAAGTGGATTACAGGGCTTGGAAATTATGTTCCACATGAAGAATATCGAATTTCGAAAGGGGGAGAAAAGTACCGTGATGGGAGTAAGCATAGGCAGCTCTGTAGCTGTAAATGTGGGCACCTGGCTTGATCTTGCATTTGTCCACTCGATTACTCCAGATACCCATCTGCACCCTCCGCCTATCTGGCTCTACTTCTTCCAATGGCCACTTACTGTCAGTAATGCAACTCACTAGCACCCAACAGTCACCGTACCAAACACCCGACTGCATTATATTTATGATCATTTTATACACAAATAAACCAAGCATAATAAACGAAAGGGTGTGGTCTGGTT

181>W-1-BE-L (827) ATTTACTTTATTTCTCATGATTGAATACATATTCAAAAATCAGAATCTCTATTAAACAGAATTTCATCAAAACAGGAACCTCTTTATTACCTGTACTGGTAAAGAAACGCCGAGTTTAGTTTTGATGTCAAGAACAATGTTTGGATTACCATCCCATTGCATCTCCAACTCCAAAGTTATTCCACCGGTATCTGGCTCATCTTCAAGTACAGAAATTCCTACAGAACAGAACGCCTATCTTGCATTGGTATAAATGAATTCACATTGTTTTGATTTAGAAGTTCGTGATAACTAGTAGTGAATATGGTTTATATCTGCAATGACTTGATTATCAAACCAATTTAATACCTGTAAAACTTGGAGCCACAGTACCCAAGGTCAACTTTGAGAATTTCAAAGAAGATAATATCACCGGTCGAAATTCTTCTAGAACCGGCTCCACATTGCTCCTTATCAGCTCCGATGCTGCCTAAATAAATCGCCATCCCATAGAATGCCAGAACATAATAACAAAAGGAAAAAAAAAAAAAAAAAAGACGAATCGACTCGAGAATCAACACCATAGACTAATGAGTTAAGCCATCAAAGAAGTGCAGATTTGGATTACCGCATCAACATATGGCCAGATTTTATCAAGCTGAAGATTAAGCCAAGTTAACTGCAAATTACATATTACAAAGAAATTACAACTCAAAATCCTGAAATCAATCCTTCTCCACGTCCAGACACTAGCATCAAATGAGATCTTCTAATCTATCAAGTAAAAAACAGAAAACGAACGAACCTTCTGTCGCTGCGTAAATACAACCCACGATGGGTAAAATTCC >W-1-BE-R (346) CCAACAATACATCGTGATCCTGTGTTTACTGTAACATTCTATTTAAATCCATAATATATATACATAACATTTAAGAAAAGAAGTTTGACGAATATGTAAATTACAGCTACCTCTTATTCTTTATCATATTCACTTTTCATTATATACTTTAGACATCAAATCAACCAATTTTTTTTATAAAAAAAAGTCGTAAAAGCCACTTTGAGGGCGCAAAGCGAATGCGACATTAAACAAACGCTGCCCATTTTCTTTCTTCAATTCTTAATCTTCATCTTCATCTTTTTCTTTAAATTAATTATATTTAAATGTCATTGTAATTGCAGATTTTTATATTTTATAGTTAAAT >W-2-BE-L (619) TTGATGTGAGAGATCACATCTTTTGGAGTCCAAGATTTGATTCCAATAAAAAATGATTTTAAAAAAATCTCTTTATTAGAAGGCTATTCCTACGAGGGATTTTGAGAAATTGTACTAATTTCTCACTCTTTTTAGGTGAGAAAATTTTCTTCAATATAGTTCAATTGATTAAATGCTCTAAACTTATTTTATGAGATCATTGCTTCAAATATTGGAGTAACGAGGGTAAAATAAGACATTATTTTTAAAGAATATTTTCAATTCAAAATTTAAAATTTTGCCACTATTTTCTAAACGGGTGTTGTGGGGTGCTAACACCTTTCTCACACATAAATGACTGCCGAACTCAACTTTAGTTTTCGCATACCATTTTTTAATGATTTTATTTAAAAATTGTTTACTTTATTTCGGTGTCCAATCACATCGTAAAAAAGATTGGTGACAACTCTTCTCTTAAAACTAATCCATTTGGAGGACGTTGGCCGCTTCGCATTGTCTCGAGCATGTGGTGACAATGTGGAATGACAGTGGCCACTACCAAAATGATGCGAGTACAAGGTATAAAAGACAGTCAAGACTGCAAGTTCAACATTGGATGTTCTATCTGGTGGGAGGGGGT >W-2-BE-R (834) ATTAAAATAAGTTTATTGAATCTTTCCTTTAAATAATTTTATTGAAAATATTTCATTAAATAAATTTATTAAAATATTTTATACAAAATTTATATATTTTAATGAAATCTTTCATTAAATAAATTTAGCAATGTATTTTGATGAAATGCTTATTTAATAAATATTAAAATATTTTAATGAAAACATTATTAAATGTATTTGTTAAAAACTTTAACGAAAATATTATGAAATAAATTTATTAAAAAAATTAATAAAAAATAATTAAATAAATTTATTGAAATATTTCATTAAAATAAATTTATTTAAAATTTTCCTTTAAATAAATTTATTGATAATATTTTTAAAAATAAATTTTATAAAATATTTTAATGAAAAGATTACTAAATAAATTGATTAAAATATTGTAATGAAAAGATTATTAAAGAAAATTTTAGAAATCTTTCACTAAAATAATTTATTGAAATCTTTTGTTAAAATAAATTTATTGAAATATTTTACCAAAACAGATTTGTTGAAGTGTTTCACTAAATAAATTTATTTAAATAGTTCATCAAAGAAATTCATTAAAATATTTTATTGAAATGATTATTAATGAAATTTATTGAAACATTTTAATAAATTTTAAATGGATCAACTTTTTCCATTGTTTAGTTTTTACTTTGTAGTGTAGATTAATAATAGATCCAATTTATTGGTATATCTCCATAATTATTTACTTTTACACATCATTTATCACTACTCACATAAAAAAATTACAAATTTTGTGACATGATTAGACAATTTGATAAACAAAAATAGATGATATCCTGGAGATGCATATCTAAATATTGTATT >W-4-BE-L (835) TATTTTCTATCACACTCCAACTTCCATGATTCACAATGAGATGGTGGATCGTTAATTCAACATTATGTGTCAACCCATAATTCATATTATTTCAGTAAGTATTTAAAAGGTTTAAATATTATTTTGGTTCCATATATTTGATTTAGTTTTTTTTTTTCTTTCTTTCTTTACTTTTAAAATATTCATTTTAGTTCTTATACTTTTAACTTTGGTTCACTTTGATCTTTACACCTATGCCACTTGAGATATAGTGGAGATATATCATCTTTAGTAATTTGGTTCACTTTGATCTTTACACCTATGCCACTTGAGATATATCCTCTTTAGTAAGTTAATTATAAGCCTTAAGCCTACG

182TGACAAATATTGTATCTTTTTAAGTATGCAAGAAGAATTCTTTTAAAAGTTAATTATAAGCCTTAAGTCTAGGTGACAGATATTGTATTTTTTTAAGTATGCAAGAAGAATTCTTTTAAGTATTAAAATTGTTTTCTTGCAGAGGTTTAATTTCTTATTTAATTTTGTAGCAGCTCATTAATGAATGGGGTAGAGGTCATGTAAGCTTATAATTATTAAGTTTAACATTTGCCTATGATAAGGGTAATTTTTTTTATTATTATTCTTTAAAAATGTCAAATCGTAAAAGCTTAAATTGAAACCTTTTAAAGCAATAGGGATAGGAACTAAGTTAGAAAAGCACATCAACATATGGGCTAAACATATATATCCTCAATTAAAGTCTTACTTTTTCATTTGAAGCAATTTGTTGTGTGTAGCTAATTCTATTAATGTGATGTGTAATAATTTGTCAACAACTCTTTCTCCATTTTACTCATT >W-4-BE-R (811) CTATTTAAGAAAATAAATTTTAGTTCATGAGAGTTAAATTTTACCTTTTTAGTGTTTTGTCAACATTTTTCTTTTATTTATTTATGGGAAAATAACTTCAGGTCCAGGTCCAGGTCCATTCTAAGATGAGGCCCAATACATTTTATTTGAGTTAATTGTGGAAATAACAAACTGAAAATGGCAAAAATTAAAAATAATAAAAATTATAGCAAAGTCGTTTTCTATCATTTTTATTGATAAATGTAAACCCGATTTTTCAAACAACATTCGAATTTGGTAAATCTTAACTAAATCGACTATTTTGCTGTCGGAGAATATTGCACCAACATCCCCTAAAATAATGTCAATATTTTCTACCAGATTTTCAAATTCTTTATATTTCAAAAAATTTATTTGGATAACTACCAAAAAAAAATTTATCAATAATTTATTAAATAATACATAATATATATAATTTTAACAAAAAATTAAATTTAAATTTTAGATTGTCCCCTAAAAAAATGCCAATTACGATTATCTACTTATTTTAGATACTCTTTAAAGATAAATACATCATTTTCGCATTTTGGAGACATTTCTAATAATAGTAAGATAAATTTAAATATTTACATGCTAGAGTGAAATTTTGGATTATATCAATGATAGATTTTGATAGACTTCTATCACTATCTATCAATGTTATTGATAGGAGCATATCAGTGTTTATCAATGTCTATTTTTGATAGAATCTGAAAATTTGCTATATGTTGTAAATATTTTATCAAATTTGCTATTTTGACAATTTTCCTTTGTTTTATTCATTATTTTTTCT >W-5-BE-L (897) AAGGACTCTTCCCATCGACACCGATAAAACTAAGGGACCGAGGCATACTTGATTGAGCAATGACTGCTAGAGTGGATGGTGGACTGAAACTGTTTTGGTTTACAGTAGGGCCAAATGGTTGAGCGGTTCTAGCATACTAACTTATATTAGCACACCCTGAATTATGCTTATCATTTGAGGAACGTTTCTTACCTCCTGGAGGACGACCATGAAGTTTCCAACACTGATCCTTAGTGTGTCATTTTTCTTGGAATGCTCATAGATAGGAATCGGTTTCTTGTTGTTCTTCTAACTATCATGGGTAGAGGATCTAATCTAGCATTGACGGCAACGAAGTCAGTAGTAGGAGTAGTCAGGACACTCATGGCACTCGTACGATTTTCTTCAAGACGAAATTCACAACGCTTCCATTAAAGAGGAAAGGGGTCTCTGGCCAAGTAACGACCACAAACAATGTCAAACTTGGGGTTCAGACCTGCAAGGAAATCATAAATCCGATCAATTTCTTCAAGTCTAGCATTCTGTGTGCCATCATTCGGAGTATTCTTTACTATCTCTCTACACAGGCCCATCTCTTGCCTAAGTTGGGAAAGTTCATTGAAGTAGGATGTTACATCCAGAGTTCCTTGCTTGCAATCATGAACTTGTTTCCTCAGTGTATACAGATGAGAGACGTTTCAAATAGAGTTTCTAATTAGTGTCCCATAGATCCTTTGCAGTTGTTGCATAGAGCATAGACTTGCCAATTTGTGGTCCCATACTATTAATCAACATGGACCGAAGAAGGGAGTCCTCCCCCTCTCCAGAAACGTCCTTGGGCATCATGGGTGGGGGACCAGAGTCTCCCCTGTTAGAAACTAAATTGCTCACGTCCCCTCATAACATTTTGACCGATTG >W5-BE-R2 (823) TTGTTGTGGGCCTTCGGGCCTTCTTCACTCACTCAGTCAGTATTTCTTTAACTTCTTTATTCTCTTTCTGACTGAGGTCGATGGTCAGTCGGACTATGACATTGCGTTGCATGTCATTGGAATCTTGATGGTAGGGCTTTAAGTTATTCACATGAATTACTGGGTGAATTTTCATCCATGTAGGCAACACCACTCTGTAAGATGTACTCCCTACCCTTTTCATCACTTTTACTGGTCCCTCGTATTTCCTCACGAGCATTGGTCTTTGCGTCTCCAGAATCAAACCTGTTCCGATCGCAGTTTGATGAGGACCTAATCTCTTGTCCAAAGCTCAAGGGGGCAGTGCTTCTTATCTACCCATTTCTTCATCCACTTCGAGGCTTTCTCCAGATAGGCTCGCACGATGTCCGTTGTTTGTTTCAATTTTCTGGTAAAATTGTGAGCCTGGGGATTCTTTCTTGCATAGGAATGATCAACAAGGTGTGGCAATATAAGTTGTCTACCATAAACAATCTCAAACGGGCTTTTCCCAGTTGACGAGCTCGTCTGAGCATTAAAACAAAATTAGGCTACCATCCAATAGTTAAACCCAATTCTTCTGCCTTGCATCGACAAAATGGTAGCTTAAGGATACATTTAGACTTGTCCCCAAGAAAGTGAATAGTTCTGTCCAAAAAGTGTCAATGAATCTACCATCTCGATCACTCGCAATGCTTGTCGGTACACCTCATAACTTAACAATATATTTGAAGAACAACTGGGTTGTCAATTCTGCTGTATACAACTTGGTAGTGGGAATGAAGGTGACATATTTTGAAAATCG

183Appendix F. SCAR primers from BAC clone end sequences as developed in cucumber (see Chapter 2). Sequence name Product lengtha Primer name Startb Lengthc Tmd GC (%)e Primer sequence (5' to 3') AJ-1-BE-L 458 AJ-1-BE-L_F 39 20 60.3 55.0 GAAGGAAGCGCAGTAAGCAG AJ-1-BE-L_R 496 20 60.1 55.0 ACGGAATCAGTCCTCACAGG AJ1-BE-R2 434 AJ1-BE-R2_F 61 20 59.7 45.0 ATGCGAACTGCAACACATTC AJ1-BE-R2_R 494 20 61.1 60.0 GCAGGAGTGCAGGCTAGATG AJ-2-BE-L 504 AJ-2-BE-L_F 21 20 60.3 50.0 CTTCCTTTCTTCCCGAAAGC AJ-2-BE-L_R 524 20 60.1 55.0 TGGATAGTCCTTCGGACCTG AJ-2-BE-R 332 AJ-2-BE-R_F 84 20 59.6 45.0 ATACGCATGGGGAATGAATC AJ-2-BE-R_R 415 20 59.8 45.0 TCAATGAACAAGGCGATCAG AJ-3-BE-L 592 AJ-3-BE-L_F 15 20 59.1 50.0 TTCGCAGTTGAGGATGAGAG AJ-3-BE-L_R 606 20 60.3 55.0 AGTGAGCGGTTAGGTCATGC AJ-3-BE-R 435 AJ-3-BE-R_F 123 20 60.3 45.0 AAATCACGAGGTTGCTTTGC AJ-3-BE-R_R 557 21 57.9 38.1 CAAAACACCGGTTTATCATTG B-1-BE-L 409 B-1-BE-L_F 54 19 60.3 52.6 TCGCCTCAGCAGAAAGTTG B-1-BE-L_R 462 23 58.8 39.1 GATTGCATGACGATAAAGACTTG B-2-BE-L 579 B-2-BE-L_F 13 22 57.6 45.5 GGTACACCATTTTAGGCCATAG B-2-BE-L_R 591 23 57.3 39.1 TGAAGTTATTTTGGGTAGGACAC B-2-BE-R 374 B-2-BE-R_F 49 22 58.0 40.9 CAACAATAATACGACGATGGTG B-2-BE-R_R 422 19 59.9 63.2 GAGGGGAGCACAAGGAGAG B-3-BE-L 506 B-3-BE-L_F 23 23 56.7 43.5 AGAACAACCTCTTGTAGATACCG B-3-BE-L_R 528 21 60.2 38.1 GCATGCCTTAAATGCAAAAGA B-3-BE-R 582 B-3-BE-R_F 59 20 60.1 50.0 CCAAAACATACGACCCATCC B-3-BE-R_R 640 20 58.4 40.0 TTCAATCGGTTTCCATGTTC B-4-BE-L 302 B-4-BE-L_F 282 20 58.3 50.0 CTCCATTATACGTGCCGAAG B-4-BE-L_R 583 22 57.1 40.9 CCTCTTTCAATCCTTCAGTTTC B4-BE-R2 413 B4-BE-R2_F 106 23 57.4 26.1 TCATTTAAAATTTGGGAAAAGTG B4-BE-R2_R

B-5-BE-L518 10

20 59.8 57.9

50.0 45

AAATACCCGAGATGGTGACG CCCGAGTTTATGTGGAAAB-5-BE-L 577 _F 6 20 .0 TG

B-5-BE-L_R 682 20 59.3 50.0 AAGAGGTGCTTGGGAAAGTG M-1-BE-L 579 M-1-BE-L_F 13 20 59.9 50.0 GCTCGAAAAGCCACATTAGG M-1-BE-L_R 591 20 58.0 50.0 TGTGTGAGTCTCCATGGTTG M-1-BE-R 419 M-1-BE-R_F 65 21 59.8 42.9 CAATGGCAAGCTAAAAACAGG M-1-BE-R_R 483 21 59.9 57.1 CCATCCCTCCAGAGTTCCTAC

184Sequence name Product lengtha Primer name Startb Lengthc Tmd GC (%)e Primer sequence (5' to 3') M-2-BE-L 516 M-2-BE-L_F 39 GTTC 20 45.0 TCTTCGGTAATGATGC58.7 M-2-BE-L_R 554 22 59.6 45.5 CATGACCTCTCCATGAGTTTTG M-3-BE-L 510 M-3-BE-L_F 4 21 59.9 38.1 TGGTTGGAGAATGATTTTTGC M-3-BE-L_R 513 19 61.8 63.2 ACACCACCACCACCACCTC M-3-BE-R 221 M-3-BE-R_F 17 20 61.6 50.0 CCGCCATGAAATTGAGTCTG M-3-BE-R_R 237 21 59.6 47.6 same as M-4-BE-R M-4-BE-L 518 M-4-BE-L_F 116 22 60.1 40.9 GGAGAAAATCAAATGAGCAAGG M-4-BE-L_R 633 21 58.2 42.9 TTTTCATTAGAGTGGGGAAGG M-4-BE-R 268 M-4-BE-R_F 4 20 60.0 60.0 AAGAGGGAGAGGGAGACGAG M-4-BE-R_R 271 21 59.6 47.6 GGCTTATTGTGGCAGTTATCG M-5-BE-L 520 M-5-BE-L_F 129 20 60.3 55.0 GAATAAGCGCTCCAGCTCAG M-5-BE-L_R 648 20 60.9 50.0 TAATCGGACGGTGTGAAAGG M6-BE-L 528 M6-BE-L_F 101 21 58.5 47.6 CGTGCTATGCTATGAAAGGTG M6-BE-L_R 628 21 59.7 42.9 CGCATTAATTCCTTCGTCAAG M6-BE-R 520 M6-BE-R_F 130 20 60.3 55.0 same as M-5-BE-L_F M6-BE-R_R 649 20 60.9 50.0 same as M-5-BE-L_R M7-BE-L 314 M7-BE-L_F 81 23 58.7 39.1 GCAAATAAATCGACACTTCACAC M7-BE-L_R 394 20 59.5 50.0 TTTGGTACGGTGACTGTTGG L-1-BE-L 501 L-1-BE-L_F 15 20 59.4 45.0 GCATTTGGATTTTGGGAGTC L-1-BE-L_R 515 23 59.3 47.8 ACACTAGAGCTGGAAAGTGAAGC L1-BE-R2 410 L1-BE-R2_F 71 23 58.9 30.4 TCAAAATTAAATGGTTGTCGATG L1-BE-R2_R 480 23 57.4 34.8 TTAGATCTCGGTTTTATTTGGTG C-1-BE-R 501 C-1-BE-R_F 45 20 62.4 60.0 GGGCTTATCTCGAGCCCTTC C-1-BE-R_R 545 21 60.2 38.1 CCAAATTGCCAAATTACATGC C-2-BE-L 405 C-2-BE-L_F 21 20 59.3 50.0 ACCAACATCGACACCAACAC C-2-BE-L_R 425 20 59.3 50.0 ATGGAGTCTGACGCTTTTCC C-2-BE-R 542 C-2-BE-R_F 92 19 59.7 57.9 CGGAAGGAATGAGGAGGAG C-2-BE-R_R 633 22 60.3 40.9 CGTGACATCAACAAAAATGACC C-3-BE-L 582 C-3-BE-L_F 25 20 62.2 55.0 CTTTCGCCTCGATCTTGTCC C-3-BE-L_R 606 20 60.4 55.0 GTCCAAGTGGGAGGTTGTTG C3-BE-R2 502 C3-BE-R2_F 1 18 61.3 61.1 TCGTTGGCAGCTCCTCTG C3-BE-R2_R 502 21 59.7 47.6 TCAATGGTGACACAGAAGCAC C-4-BE-L 510 C-4-BE-L_F 100 20 57.5 40.0 ATGTTTGGCGTAATTGGTTC C-4-BE-L_R 609 22 57.9 36.4 CAAAGTTTAGGAATGGAAAACG

185Sequence name Product lengtha Primer name Startb Lengthc Tmd GC (%)e Primer sequence (5' to 3') C-5-BE-L 512 C-5-BE-L_F 52 AGTGC 20 59.5 60.0 AGGAGCCTCAGTACC C-5-BE-L_R 563 21 60.1 52.4 GGGCCGTTGATCTCTAACTTC C6-BE-L 401 C6-BE-L_F 17 21 57.4 42.9 ATATTTCTGTGTCCACGGATG C6-BE-L_R 417 22 57.2 50.0 CTCTTGTTCTCGAGGGATAGTC C6-BE-R 468 C6-BE-R_F 59 23 57.1 43.5 GCAGTTATGGTAGCCTATTTCAC C6-BE-R_R 526 23 59.2 43.5 GGAGAATATCCTGTCAAAGCAAC C7-BE-R 524 C7-BE-R_F 28 20 57.1 45.0 AACAACATGTCAACGTGAGC C7-BE-R_R 551 23 58.4 34.8 TGAGGTTGAGATTGAATTTTGAG C8-BE-R 348 C8-BE-R_F 26 22 59.3 45.5 CCATGGTCTCATGTTGATAAGG C8-BE-R_R 373 22 58.9 50.0 GCATGGTTATAGGGGATAGAGG W-1-BE-L 505 W-1-BE-L_F 108 20 58.4 45.0 CGCCGAGTTTAGTTTTGATG W-1-BE-L_R 612 20 59.9 45.0 ATGCGGTAATCCAAATCTGC W-1-BE-R 230 W-1-BE-R_F 1 22 60.3 45.5 CCAACAATACATCGTGATCCTG W-1-BE-R_R 230 20 59.8 40.0 TTAATGTCGCATTCGCTTTG W-2-BE-L 535 W-2-BE-L_F 83 22 59.1 45.5 GCTATTCCTACGAGGGATTTTG W-2-BE-L_R 617 20 60.7 60.0 CCCCTCCCACCAGATAGAAC W-2-BE-R 387 W-2-BE-R_F 128 23 58.2 30.4 GCAATGTATTTTGATGAAATGCT W-2-BE-R_R 514 23 55.2 26.1 CAACAAATCTGTTTTGGTAAAAT W-4-BE-L 528 W-4-BE-L_F 32 20 59.9 50.0 CACAATGAGATGGTGGATCG W-4-BE-L_R 559 22 57.9 45.5 TTACATGACCTCTACCCCATTC W-4-BE-R 212 W-4-BE-R_F 103 19 60.9 57.9 TCCAGGTCCAGGTCCATTC W-4-BE-R_R 314 21 60.4 47.6 TCTCCGACAGCAAAATAGTCG W-5-BE-L 533 W-5-BE-L_F 11 20 60.2 50.0 CCCATCGACACCGATAAAAC W-5-BE-L_R 543 20 59.5 50.0 TACTCCGAATGATGGCACAC W5-BE-R2 513 W5-BE-R2_F 12 19 59.9 57.9 CTTCGGGCCTTCTTCACTC W5-BE-R2_R 524 21 59.5 42.9 GCCCGTTTGAGATTGTTTATG

P

a Length of the PCR product using the F (forward) and R (reverse) primer for each sequence P

b The number of base pairs from the beginning of the sequence to the start position of the primer (where the 5’ base anneals to the sequence) P

c Length of the primer in base pairs P

d Melting temperature as calculated by Primer 3 P

e GC base content of primer

186Appendix G. SNPs identified between Gy-7 and H-19 in markers from two sequencing sources [sequencing of SCAR fragments (SCAR) and utilizing markers to identify BAC clones for sequencing (BAC end); see Chapter 2]. Asterisks (*) in either the Gy-7 or H-19 sequence (cucumber parental lines used in map construction) indicate an insertion/deletion (indel). Marker Sequence source SNP SNP posa Gy-7 sequence H-19 sequence AB14SCAR SCAR 1 423 G A 2 451 T A 3 459 C T 4 491 C T 5 627 G A 6 738 G A 7 760 A T AC17SCAR SCAR 1 235 A G AC9SCAR SCAR 1 95 G A 2 135 A G 3 270 T A 4 274 A G 5 287 A G 6 322 G A 7 557 T C AD14SCAR SCAR 1 36 A C 2 108 G T 3 157 A T 4 403 G A 5 410 ** TC 6 497 G A 7 500 C T 8 578 A G 9 589 A G 10 602 C T 11 629 C T 12 648 C A 13 663 G A 14 784 A T 15 809 G A AI4SCAR SCAR 1 172 G T 2 460 G A 3 499 T C 4 655 A G 5 760 C T 6 893 A G 7 914 T C 8 985 C G 9 1067 A *

187Marker Sequence source SNP SNP posa Gy-7 sequence H-19 sequence AK16SCAR SCAR 1 153 C A 2 173 C G 3 841 C T 4 985 A T 5 1081 C G 6 1106 T C 7 1133 A G 8 1241 G C 9 1263 A G AT1SCAR SCAR 1 233 G A 2 283 G C 3 654 C G 4 658 G T 5 771 C A C10SCAR SCAR 1 255 G A 2 508 A T 3 643 T C C1SCAR SCAR 1 97 T G 2 247 A G D11SCAR (Forward) SCAR 1 270 A T 2 380 T * D11SCAR (Reverse) SCAR 1 116 C T 2 135 G A 3 263 A G M8SCAR (Reverse) SCAR 1 146 G A 2 183 * T 3 234 G A N8SCAR SCAR 1 398 * A 2 431 G A 3 497 A G 4 545 C T 5 597 T C 6 676 G A 7 685 C T 8 699 G A 9 843 A G 10 923 A G 11 926 T C W7SCAR SCAR 1 248 T G 2 296 A G 3 352 A T 4 364 TA AT 5 378 G A 6 405 C T B-1-BE-L BAC end 1 237 ACCTCTCT TTCTTCCC 2 287 AAAG CAAA

188Marker Sequence source SNP SNP posa Gy-7 sequence H-19 sequence C-1-BE-R BAC end 1 103 G A 2 131 G T 3 138 G A 4 152 C T 5 171 T A 6 180 A T C-3-BE-L BAC end 1 106 C T 2 127 G A 3 132 T C 4 154 T C 5 179 AG CN 6 187 A G 7 199 G A 8 212 A G 9 231 C T 10 249 G A 11 261 C T 12 270 CT TC 13 275 G A 14 279 TG CA 15 317 T C 16 320 AA GT 17 330 TTTT TTG 18 338 A G 19 343 G N 20 346 A G 21 360 TTG ACA 22 366 T N 23 381 TC CT 24 384 CA AG 25 410 T N 26 420 A G 27 470 G A 28 484 TTCA CTTC 29 493 A G 30 495 A G 31 498 G A C3-BE-R2 BAC end 1 124 A G C-4-BE-L BAC end 1 25 * T 2 46 TTAT AATAA 3 257 ** TT 4 286 T A 5 334 T A 6 352 T C 7 391 G A 8 439 G A 9 451 C T

189Marker Sequence source SNP SNP posa Gy-7 sequence H-19 sequence C-5-BE-L BAC end 1 174 T A 2 290 T A 3 318 T C 4 393 T A C6-BE-L BAC end 1 77 A * 2 86 A G 3 93 T C 4 97 A G 5 104 T C 6 109 G A 7 121 T C 8 128 T C 9 132 T C 10 150 G T 11 156 TA AT 12 165 A A 13 168 A G 14 178 G T 15 183 T C 16 185 G A 17 187 A G 18 209 T C 19 225 T C 20 233 T G 21 247 T C 22 262 A G 23 266 T G 24 283 A G L-1-BE-L BAC end 1 76 A G 2 195 A G 3 296 A G M-4-BE-L BAC end 1 152 * A 2 328 ** AT 1 134 T C 2 213 T C M7-BE-L BAC end 1 171 G A W-2-BE-R BAC end 1 264 TTAATTTTTTTA ************ 2 326 A C 3 343 A G 4 348 T C 5 356 T G a The number of base pairs from the beginning of the sequence to the SNP

190Appendix H. Characteristics of single nucleotide polymorphism (SNP) markers (comprising an allele-specific primer based on a SNP, and a non-specific primer) and their allele-specific primers as developed in cucumber (see Chapter 2). Allele-specific primer characteristics

Sequencea SNP Marker Alleleb Allele specificityc 3' mismatchdInternal mismatche

Mismatch positionf

AB14SCAR AB14SNPG1 Gy-7 allele specific A:A G:T 2 AB14SNPG2 Gy-7 allele specific A:A C:T 2 AB14SNPG3 Gy-7 allele specific A:A T:T 2 AB14SNPH1 H-19 allele specific A:C A:G 2 AB14SNPH2 H-19 allele specific A:C G:G 2 AB14SNPH3 H-19 allele specific A:C C:C 3 AC17SCAR AC17SNPG1 Gy-7 allele specific A:C T:T 2 AC17SNPG2 Gy-7 allele specific A:C T:G 3 AC17SNPG3 Gy-7 allele specific A:C G:G 3 AC17SNPH1 H-19 allele specific G:T T:T 2 AC17SNPH2 H-19 allele specific G:T G:T 2 AC17SNPH3 H-19 allele specific G:T C:T 4 AC9SCAR AC9SNPG1 Gy-7 allele specific A:C G:A 3 AC9SNPG2 Gy-7 allele specific A:C C:A 3 AC9SNPG3 Gy-7 allele specific A:C A:G 4 AC9SNPH1 H-19 not allele specific A:C A:A 2 AC9SNPH2 H-19 not allele specific A:C G:G 3 AC9SNPH3 H-19 allele specific A:C A:G 4 AD14SCAR AD14SNPG1 Gy-7 allele specific C:A A:A 2 AD14SNPG2 Gy-7 allele specific C:A T:T 3 AD14SNPG3 Gy-7 allele specific C:A C:T 3 AD14SNPH1 H-19 allele specific A:G C:T 3 AD14SNPH2 H-19 allele specific A:G T:T 3 AD14SNPH3 H-19 allele specific A:G C:C 4 AI4SCAR AI4SNPG1 Gy-7 allele specific A:C A:A 2 AI4SNPG2 Gy-7 allele specific A:C G:A 4 AI4SNPG3 Gy-7 allele specific A:C G:A 2 AI4SNPH1 H-19 allele specific T:G A:A 3 AI4SNPH2 H-19 not allele specific T:G G:A 3 AI4SNPH3 H-19 allele specific T:G C:A 2 AK16SCAR AK16SNPG1 Gy-7 allele specific A:A T:C 3 AK16SNPG2 Gy-7 no product A:A C:C 2 AK16SNPG3 Gy-7 allele specific A:A C:C 3 AK16SNPH1 H-19 allele specific C:A G:A 2 AK16SNPH2 H-19 allele specific C:A G:A 3 AK16SNPH3 H-19 allele specific C:A G:A 4 AT1SCAR AT1SNPG1 Gy-7 allele specific C:A A:A 2 AT1SNPG2 Gy-7 allele specific C:A G:A 2 AT1SNPG3 Gy-7 allele specific C:A C:T 3 AT1SNPH1 H-19 allele specific G:G G:A 3 AT1SNPH2 H-19 allele specific G:G A:A 3 AT1SNPH3 H-19 allele specific G:G C:A 3

191 Allele-specific primer characteristics

Sequencea SNP Marker Alleleb Allele specificityc 3' mismatchdInternal mismatche

Mismatch positionf

B-1-BE-L B1LG1 Gy-7 not allele specific T:T G:T 5 B1LH1 H-19 not allele specific A:G T:G 4 C10SCAR C10SNPG1 Gy-7 allele specific A:A G:G 2 C10SNPG2 Gy-7 allele specific A:A G:A 3 C10SNPG3 Gy-7 allele specific A:A A:A 3 C10SNPH1 H-19 allele specific C:A G:A 2 C10SNPH2 H-19 no product C:A G:A 3 C10SNPH3 H-19 no product C:A A:A 3 C-1-BE-R C1RG1 Gy-7 allele specific A:A G:T 2 C1RG2 Gy-7 allele specific A:A C:T 2 C1RG3 Gy-7 allele specific A:A T:T 2 C1RH1 H-19 not allele specific A:G C:T 4 C1RH2 H-19 not allele specific A:G A:A 3 C1RH3 H-19 not allele specific A:G T:T 4 C-3-BE-L C3LG1 Gy-7 not allele specific A:C G:T 3 C3LG2 Gy-7 no product A:A A:C 2 C3LG3 Gy-7 allele specific A:A A:C 2 C3LH1 H-19 no product *:T C:G 4 C3LH2 H-19 not allele specific G:T A:C 2 C3LH3 H-19 allele specific A:C C:A 2 C3-BE-R2 C3R2G1 Gy-7 allele specific A:C C:C 3 C3R2G2 Gy-7 not allele specific A:C A:C 2 C3R2G3 Gy-7 allele specific A:C C:C 2 C3R2H1 H-19 allele specific C:A C:C 2 C3R2H2 H-19 not allele specific C:A T:C 2 C3R2H3 H-19 allele specific C:A A:C 2 C-4-BE-L C4LG1 Gy-7 allele specific G:T G:G 2 C4LG2 Gy-7 allele specific G:T G:A 3 C4LG3 Gy-7 allele specific G:T A:A 3 C4LH1 H-19 allele specific A:A C:A 2 C4LH2 H-19 allele specific A:A A:A 2 C4LH3 H-19 allele specific A:A G:A 2 C-5-BE-L C5LG1 Gy-7 allele specific A:A G:T 2 C5LG2 Gy-7 no product A:A C:T 2 C5LG3 Gy-7 allele specific A:A T:T 2 C5LH1 H-19 allele specific G:T C:A 2 C5LH2 H-19 allele specific G:T A:A 2 C5LH3 H-19 allele specific G:T A:C 3 C-6-BE-L C6LG1 Gy-7 not allele specific A:C G:T 4 C6LG2 Gy-7 not allele specific T:G G:G 2 C6LG3 Gy-7 not allele specific T:G A:G 2 C6LH1 H-19 not allele specific A:C C:A 6 C6LH2 H-19 not allele specific C:A C:A 5 C6LH3 H-19 not allele specific C:A A:G 3

192 Allele-specific primer characteristics

Sequencea SNP Marker Alleleb Allele specificityc 3' mismatchdInternal mismatche

Mismatch positionf

D11SCAR D11SNPG1 Gy-7 allele specific C:A T:T 2 D11SNPG2 Gy-7 allele specific C:A C:T 3 D11SNPG3 Gy-7 allele specific C:A C:T 2 D11SNPH1 H-19 allele specific A:A C:A 2 D11SNPH2 H-19 allele specific A:A G:A 2 D11SNPH3 H-19 allele specific A:A A:A 2 L-1-BE-L L1LG1 Gy-7 allele specific A:C A:A 2 L1LG2 Gy-7 not allele specific A:C C:A 4 L1LG3 Gy-7 allele specific A:C G:A 2 L1LH1 H-19 not allele specific G:T A:G 2 L1LH2 H-19 allele specific G:T G:G 2 L1LH3 H-19 allele specific G:T A:C 3 M-4-BE-L M4LG1 Gy-7 allele specific A:A G:G 2 M4LG2 Gy-7 allele specific A:A T:T 3 M4LG3 Gy-7 allele specific A:A A:G 2 M4LH1 H-19 allele specific A:C A:A 2 M4LH2 H-19 allele specific A:C A:G 3 M4LH3 H-19 allele specific A:C A:G 2 M-4-BE-R M4RG2 Gy-7 not allele specific A:G A:A 3 M4RG3 Gy-7 not allele specific A:G G:A 3 M4RG8 Gy-7 not allele specific A:G C:C 2 M4RH1 H-19 not allele specific C:A A:G 2 M4RH2 H-19 not allele specific C:A A:G 3 M4RH5 H-19 not allele specific C:A A:G 4 M7-BE-L M7LG1 Gy-7 allele specific A:C C:C 2 M7LG2 Gy-7 no product A:C T:C 2 M7LG3 Gy-7 allele specific A:C A:C 2 M7LH1 H-19 allele specific A:C A:A 2 M7LH2 H-19 allele specific A:C G:A 2 M7LH3 H-19 allele specific A:C C:T 3 M8SCAR M8SNPG1 Gy-7 allele specific C:A G:A 3 M8SNPG2 Gy-7 allele specific C:A G:A 4 M8SNPG3 Gy-7 allele specific C:A A:A 3 M8SNPH1 H-19 allele specific T:G A:A 2 M8SNPH2 H-19 not allele specific T:G G:G 3 M8SNPH3 H-19 allele specific T:G G:A 4 N8SCAR N8SNPG1 Gy-7 allele specific G:T C:A 2 N8SNPG2 Gy-7 allele specific G:T A:G 3 N8SNPG3 Gy-7 allele specific G:T T:C 4 N8SNPH1 H-19 allele specific C:A T:T 2 N8SNPH2 H-19 allele specific C:A G:A 4 N8SNPH3 H-19 allele specific C:A C:T 2 W-2-BE-R W2RH1 H-19 no product C:A G:A 3 W2RH2 H-19 no product C:A G:T 4 W2RH3 H-19 no product C:A C:T 2 W2RH4 H-19 no product C:A C:T 4

193 Allele-specific primer characteristics

Sequencea SNP Marker Alleleb Allele specificityc 3' mismatchdInternal mismatche

Mismatch positionf

W7SCAR W7SNPG1 Gy-7 allele specific A:G G:G 4 W7SNPG2 Gy-7 not allele specific A:G C:T 3 W7SNPG3 Gy-7 not allele specific A:G T:T 3 W7SNPH1 H-19 allele specific TA:TA C:A 3 W7SNPH2 H-19 allele specific TA:TA G:G 4 W7SNPH3 H-19 allele specific TA:TA A:G 4 a Original sequence from which the SNP was identified b Each marker was designed to amplify either the Gy-7 or H-19 allele during PCR c Allele specific = the SNP marker amplified the expected allele, not allele specific = the SNP marker amplified both alleles, no product = no product was detected after PCR d The final base at the 3’ end of the allele specific primers matches only one of the alleles (line Gy-7 or H-19). The mismatch between the non-target allele is reported as the sequence of primer:template e The allele specific primer contains a mismatch to both alleles near the 3’ end of the primer. The mismatch is reported as the sequence of primer:template f The position of the internal mismatch as the number of base pairs from the 3’ end of the primer

Appendix I. Prim

SNP Ma

194er names and sequences of SNP markers as developed in cucumber (see Chapter 2).

rker Allele-specific primer name Allele-specific primer sequence (5’ to 3’)

Non-specific primer name Non-specific primer sequence (5’ to 3’)

AB14SNPG2 AB14SNP451G2 ACTTGGAAAGCGGACATACA AB14SCARF AAGTGCGACCGGGTCAGTAAATTA AB14SNPG3 AB14SNP451G3 ACACTTGGAAAGCGGACATATA AB14SCARF AAGTGCGACCGGGTCAGTAAATTA AB14SNPH1 AB14SNP491H1 GTTATCATATCTATCAGTAACAGAAGGAA AB14SCARF AAGTGCGACCGGGTCAGTAAATTA AB14SNPH2 AB14SNP491H3 TTATCATATCTATCAGTAACAGAAGGGA AB14SCARF AAGTGCGACCGGGTCAGTAAATTA AB14SNPH3 AB14SNP491H7 TATCATATCTATCAGTAACAGAAGCCA AB14SCARF AAGTGCGACCGGGTCAGTAAATTA AC17SNPG1 AC17SNPG1T GATACAGATACACTCGGTTACCTGTAGTCTTGAACTA AC17SNPUR TTTTTCCTGTTCTGTCATCGTG AC17SNPG2 AC17SNPG3T GATACAGATACACTCGGTTACCTGTAGTCTTGAATAA AC17SNPUR TTTTTCCTGTTCTGTCATCGTG AC17SNPG3 AC17SNPG6T GATACAGATACACGGTTACCTGTAGTCTTGAAGAA AC17SNPUR TTTTTCCTGTTCTGTCATCGTG AC17SNPH1 AC17SNPH3 CGGTTACCTGTAGTCTTGAACTG AC17SNPUR TTTTTCCTGTTCTGTCATCGTG AC17SNPH2 AC17SNPH4 GGTTACCTGTAGTCTTGAACGG AC17SNPUR TTTTTCCTGTTCTGTCATCGTG AC17SNPH3 AC17SNPH7 CGGTTACCTGTAGTCTTGACCAG AC17SNPUR TTTTTCCTGTTCTGTCATCGTG AC9SNPG1 AC9SNP274G3 CGCATATTGATCCTTCTCGTA AC9SCARF AGAGCGTACCACTATGAGTGAGAA AC9SNPG2 AC9SNP274G5 CGCATATTGATCCTTCTCCTA AC9SCARF AGAGCGTACCACTATGAGTGAGAA AC9SNPG3 AC9SNP274G6 TCCGCATATTGATCCTTCTATTA AC9SCARF AGAGCGTACCACTATGAGTGAGAA AC9SNPH1 AC9SNP322H1 CTTGAGATTCGACCAACCAA AC9SCARF AGAGCGTACCACTATGAGTGAGAA AC9SNPH2 AC9SNP322H4 CCTTGAGATTCGACCAACGTA AC9SCARF AGAGCGTACCACTATGAGTGAGAA AC9SNPH3 AC9SNP322H6 TCCTTGAGATTCGACCAAACTA AC9SCARF AGAGCGTACCACTATGAGTGAGAA AD14SNPG1 AD14SNP602G1 ATTTTGTAAACATCTCGAAGTGAAC AD14SCARR GAACGAGGGTGAATGTTGCGAAAC AD14SNPG2 AD14SNP602G3 ATTTTGTAAACATCTCGAAGTGTTC AD14SCARR GAACGAGGGTGAATGTTGCGAAAC AD14SNPG3 AD14SNP602G4 TTTGTAAACATCTCGAAGTGCTC AD14SCARR GAACGAGGGTGAATGTTGCGAAAC AD14SNPH1 AD14SNP648H2 TCTGCTCGTCCATCTCGCCA AD14SCARR GAACGAGGGTGAATGTTGCGAAAC AD14SNPH2 AD14SNP648H3 TCTGCTCGTCCATCTCGTCA AD14SCARR GAACGAGGGTGAATGTTGCGAAAC AD14SNPH3 AD14SNP648H4 TCTGCTCGTCCATCTCCACA AD14SCARR GAACGAGGGTGAATGTTGCGAAAC AI4SNPG1 AI4SNP499G1 TTAGCTTAATGAAATCTGGGTTAAA AI4SCARR CTATCCTGCCTCTTAATAATCATT AI4SNPG2 AI4SNP499G2 TTAGCTTAATGAAATCTGGGTGATA AI4SCARR CTATCCTGCCTCTTAATAATCATT AI4SNPG3 AI4SNP499G6 TTAGCTTAATGAAATCTGGGTTAGA AI4SCARR CTATCCTGCCTCTTAATAATCATT AI4SNPH1 AI4SNP460H2 ATGATGAATTTGCATTCCATT AI4SCARR CTATCCTGCCTCTTAATAATCATT AI4SNPH2 AI4SNP460H5 TGATGAATTTGCATTCCGTT AI4SCARR CTATCCTGCCTCTTAATAATCATT AI4SNPH3 AI4SNP460H8 ATGATGAATTTGCATTCCTCT AI4SCARR CTATCCTGCCTCTTAATAATCATT

AB14SNPG1 AB14SNP451G1 ACTTGGAAAGCGGACATAGA AB14SCARF AAGTGCGACCGGGTCAGTAAATTA

195

rker Allele-specific primer name Allele-specific primer sequence (5’ to 3’)

Non-specific primer name Non-specific primer sequence (5’ to 3’)

SNP Ma

AK16SNPG2 AK16SNP985G2 CTTTAGCCATAATTTAGTAGGGCA AK16SCARR CTGCGTGCTCTTGGGCGATTTCAT AK16SNPG3 AK16SNP985G3 CTTTAGCCATAATTTAGTAGGCGA AK16SCARR CTGCGTGCTCTTGGGCGATTTCAT AK16SNPH1 AK16SNP1106H2 TCGTTTTCGACATTCATTGC AK16SCARR CTGCGTGCTCTTGGGCGATTTCAT AK16SNPH2 AK16SNP1106H3 AATCGTTTTCGACATTCATGTC AK16SCARR CTGCGTGCTCTTGGGCGATTTCAT AK16SNPH3 AK16SNP1106H4 AATCGTTTTCGACATTCAGTTC AK16SCARR CTGCGTGCTCTTGGGCGATTTCAT AT1SNPG1 AT1SNP233G1 TACAGGTTAGGGTGAAACGAAC AT1SCARR CAGTGGTTCCTCAGCTAAAAGATC AT1SNPG2 AT1SNP233G2 CAGGTTAGGGTGAAACGAGC AT1SCARR CAGTGGTTCCTCAGCTAAAAGATC AT1SNPG3 AT1SNP233G4 CAGGTTAGGGTGAAACGCTC AT1SCARR CAGTGGTTCCTCAGCTAAAAGATC AT1SNPH1 AT1SNP283H1 AGAGTTATGAACAGGCTTGGG AT1SCARR CAGTGGTTCCTCAGCTAAAAGATC AT1SNPH2 AT1SNP283H2 AAAGAGAGTTATGAACAGGCTTAGG AT1SCARR CAGTGGTTCCTCAGCTAAAAGATC AT1SNPH3 AT1SNP283H3 AGAGTTATGAACAGGCTTCGG AT1SCARR CAGTGGTTCCTCAGCTAAAAGATC B1LG1 B1L237G1 TTATGCCGCCAAAGAGAGGT B-1-BE-L_F TCGCCTCAGCAGAAAGTTG B1LH1 B1L287H1 GCTTATGTTTATGATTAAGATTTTG B-1-BE-L_F TCGCCTCAGCAGAAAGTTG C10SNPG1 C10SNP508G1 AATTTTGTGATCAATATACCAAATATGA C10SCARR TGTCTGGGTGCATTGAAACAGAGA C10SNPG2 C10SNP508G2 TTTGTGATCAATATACCAAATAGCA C10SCARR TGTCTGGGTGCATTGAAACAGAGA C10SNPG3 C10SNP508G3 AATTTTGTGATCAATATACCAAATAACA C10SCARR TGTCTGGGTGCATTGAAACAGAGA C10SNPH1 C10SNP643H2 GGATATCAAACAAAGAATAATTGTGC C10SCARR TGTCTGGGTGCATTGAAACAGAGA C10SNPH2 C10SNP643H3 GGATATCAAACAAAGAATAATTGGTC C10SCARR TGTCTGGGTGCATTGAAACAGAGA C10SNPH3 C10SNP643H4 AGGATATCAAACAAAGAATAATTGATC C10SCARR TGTCTGGGTGCATTGAAACAGAGA C1RG1 C1R180G1 AGTTCACTGAAGCCTCTTTAGTAAGA C-1-BE-R_F GGGCTTATCTCGAGCCCTTC C1RG2 C1R180G2 GTTCACTGAAGCCTCTTTAGTAACA C-1-BE-R_F GGGCTTATCTCGAGCCCTTC C1RG3 C1R180G3 ATAGTTCACTGAAGCCTCTTTAGTAATA C-1-BE-R_F GGGCTTATCTCGAGCCCTTC C1RH1 C1R131H1 TATACCTTGAGGATAAATCCACTGA C-1-BE-R_R CCAAATTGCCAAATTACATGC C1RH2 C1R131H2 CTATACCTTGAGGATAAATCCAAAGA C-1-BE-R_R CCAAATTGCCAAATTACATGC C1RH3 C1R131H3 TATACCTTGAGGATAAATCCATTGA C-1-BE-R_R CCAAATTGCCAAATTACATGC C3LG1 C3L484G1 AAGACAATCCCTTGTGAACATGAA C-3-BE-L_R GTCCAAGTGGGAGGTTGTTG C3LG2 C3L360G1 GCGATCCGATCTTATACAAACTCAA C-3-BE-L_R GTCCAAGTGGGAGGTTGTTG C3LG3 C3L321G1 TACAAACGCAATGATCCAATGCAA C-3-BE-L_F CTTTCGCCTCGATCTTGTCC C3LH1 C3L329H1 TACCCTCACTCGCATGTCAAC C-3-BE-L_R GTCCAAGTGGGAGGTTGTTG C3LH2 C3L381H1 CATCTAGTGGTTACTATTTCTCAG C-3-BE-L_R GTCCAAGTGGGAGGTTGTTG C3LH3 C3L280H1 CTTATCTTGGTAACATCATGAATACA C-3-BE-L_F CTTTCGCCTCGATCTTGTCC

AK16SNPG1 AK16SNP985G1 TATTCTTTAGCCATAATTTAGTAGGTGA AK16SCARR CTGCGTGCTCTTGGGCGATTTCAT

196

rker Allele-specific primer name Allele-specific primer sequence (5’ to 3’)

Non-specific primer name Non-specific primer sequence (5’ to 3’)

SNP Ma

C3R2G2 C3R2G6 AAGGATCCAGACAACATAAAAGAA C3-BE-R2_F TCGTTGGCAGCTCCTCTG C3R2G3 C3R2G7 GGATCCAGACAACATAAAAGCA C3-BE-R2_F TCGTTGGCAGCTCCTCTG C3R2H1 C3R2H1 CTCATTTTTTCTTGATTGGACC C3-BE-R2_R TCAATGGTGACACAGAAGCAC C3R2H2 C3R2H2 GTCTCATTTTTTCTTGATTGGATC C3-BE-R2_R TCAATGGTGACACAGAAGCAC C3R2H3 C3R2H5 GTCTCATTTTTTCTTGATTGGAAC C3-BE-R2_R TCAATGGTGACACAGAAGCAC C4LG1 C4L391G2 ATCAAACACACATTGTTCATGG C-4-BE-L_F ATGTTTGGCGTAATTGGTTC C4LG2 C4L391G4 CAAACACACATTGTTCAGCG C-4-BE-L_F ATGTTTGGCGTAATTGGTTC C4LG3 C4L391G5 ATCAAACACACATTGTTCAACG C-4-BE-L_F ATGTTTGGCGTAATTGGTTC C4LH1 C4L334H1 CAAACGAGTCTATTCTTATAAACATACA C-4-BE-L_F ATGTTTGGCGTAATTGGTTC C4LH2 C4L334H2 CAAACGAGTCTATTCTTATAAACATAAA C-4-BE-L_F ATGTTTGGCGTAATTGGTTC C4LH3 C4L334H3 CAAACGAGTCTATTCTTATAAACATAGA C-4-BE-L_F ATGTTTGGCGTAATTGGTTC C5LG1 C5L174G1 GGATACTTGATGTTGATATCTGGA C-5-BE-L_F AGGAGCCTCAGTACCAGTGC C5LG2 C5L174G3 GGATACTTGATGTTGATATCTGCA C-5-BE-L_F AGGAGCCTCAGTACCAGTGC C5LG3 C5L174G4 TTTGGATACTTGATGTTGATATCTGTA C-5-BE-L_F AGGAGCCTCAGTACCAGTGC C5LH1 C5L318H1 TTGAGAATGCTTCCGTAGCG C-5-BE-L_F AGGAGCCTCAGTACCAGTGC C5LH2 C5L318H2 GTTTGAGAATGCTTCCGTAGAG C-5-BE-L_F AGGAGCCTCAGTACCAGTGC C5LH3 C5L318H5 GTTTGAGAATGCTTCCGTAATG C-5-BE-L_F AGGAGCCTCAGTACCAGTGC C6LG1 C6L168G1 GTTTAGGGTCCATCCAATAAGCAA C6-BE-L_R CTCTTGTTCTCGAGGGATAGTC C6LG2 C6L225G2 CCTTTGTTTAAGTTCTGGAGACAGT C6-BE-L_R CTCTTGTTCTCGAGGGATAGTC C6LG3 C6L225G4 CCTTTGTTTAAGTTCTGGAGACAAT C6-BE-L_R CTCTTGTTCTCGAGGGATAGTC C6LH1 C6L109H1 GTTTTACCCCTGGGTTACCTCTAA C6-BE-L_R CTCTTGTTCTCGAGGGATAGTC C6LH2 C6L132H1 ATCCTTAAATACCAGTGCTCCTCC C6-BE-L_R CTCTTGTTCTCGAGGGATAGTC C6LH3 C6L128H1 CTCTAATCCTTAAATACCAGTGATC C6-BE-L_R CTCTTGTTCTCGAGGGATAGTC C8RG C8RG_F TATGAGTTTGTGCGAAGGAGTG C8RG_R GACTGGGATTATCTCGTGAAGC C8RH C8RH_F TCACACAAAAAGTATAACAAGTAGACG C8RH_R TGCAAATTTTAGTAAGAGCCTATCG D11SNPG1 D11SNP116G1 ACCAACACCAACGTCAAATC D11SNP116GRU CAAACATTCCAACGACATGTAAC D11SNPG2 D11SNP116G4 ACCAACACCAACGTCAACAC D11SNP116GRU CAAACATTCCAACGACATGTAAC D11SNPG3 D11SNP116G5 ACCAACACCAACGTCAAACC D11SNP116GRU CAAACATTCCAACGACATGTAAC D11SNPH1 D11SNP270H1 ACTATCTATCGCCCTACTTTCTATTAGA D11SCARF AGCGCCATTGTAGTTCAACCAGTA D11SNPH2 D11SNP270H2 CTATCTATCGCCCTACTTTCTATTACA D11SCARF AGCGCCATTGTAGTTCAACCAGTA D11SNPH3 D11SNP270H3 CACTATCTATCGCCCTACTTTCTATTATA D11SCARF AGCGCCATTGTAGTTCAACCAGTA

C3R2G1 C3R2G5 GGATCCAGACAACATAAAACGA C3-BE-R2_F TCGTTGGCAGCTCCTCTG

197

rker Allele-specific primer name Allele-specific primer sequence (5’ to 3’)

Non-specific primer name Non-specific primer sequence (5’ to 3’)

SNP Ma

L1LG2 L1L76G5 GTTGTTTATCATCTTTGTCTTTTACCTA L-1-BE-L_R ACACTAGAGCTGGAAAGTGAAGC L1LG3 L1L76G9 TGTTTATCATCTTTGTCTTTTATCGA L-1-BE-L_R ACACTAGAGCTGGAAAGTGAAGC L1LH1 L1L195H1 CACGATGTTGAAATGGAAGAG L-1-BE-L_R ACACTAGAGCTGGAAAGTGAAGC L1LH2 L1L195H3 ACGATGTTGAAATGGAAGGG L-1-BE-L_R ACACTAGAGCTGGAAAGTGAAGC L1LH3 L1L195H6 ACGATGTTGAAATGGAAACG L-1-BE-L_R ACACTAGAGCTGGAAAGTGAAGC M4LG1 M4L152G1 GTTTGTAAAAGAGAGGGAAAAGA M-4-BE-L_F GGAGAAAATCAAATGAGCAAGG M4LG2 M4L152G2 GTTTGTAAAAGAGAGGGAAATCA M-4-BE-L_F GGAGAAAATCAAATGAGCAAGG M4LG3 M4L152G3 GTTTGTAAAAGAGAGGGAAAAAA M-4-BE-L_F GGAGAAAATCAAATGAGCAAGG M4LH1 M4L152H1 CAAACTATTTCATTTCAGTCCTAA M-4-BE-L_R TTTTCATTAGAGTGGGGAAGG M4LH2 M4L152H3 CAAACTATTTCATTTCAGTCCGTA M-4-BE-L_R TTTTCATTAGAGTGGGGAAGG M4LH3 M4L152H7 CAAACTATTTCATTTCAGTCCTGA M-4-BE-L_R TTTTCATTAGAGTGGGGAAGG M4RG2 M4R213G2 GGAGGCTGTTCCCGCCAAGA M-4-BE-R_F AAGAGGGAGAGGGAGACGAG M4RG3 M4R213G3 GGAGGCTGTTCCCGCCAGGA M-4-BE-R_F AAGAGGGAGAGGGAGACGAG M4RG8 M4R213G8 GGAGGCTGTTCCCGCCATCA M-4-BE-R_F AAGAGGGAGAGGGAGACGAG M4RH1 M4R134H1 CAGTGGGACAGTTGAATTTGGTG M-4-BE-R_F AAGAGGGAGAGGGAGACGAG M4RH2 M4R134H2 CAGTGGGACAGTTGAATTTGTGG M-4-BE-R_F AAGAGGGAGAGGGAGACGAG M4RH5 M4R134H5 AGTGGGACAGTTGAATTTTGGG M-4-BE-R_F AAGAGGGAGAGGGAGACGAG M7LG1 M7L171G1 GTACCGTGATGGGAGTAACC M7L171UR GGTGACTGTTGGGTGCTAGTG M7LG2 M7L171G2 GTACCGTGATGGGAGTAATC M7L171UR GGTGACTGTTGGGTGCTAGTG M7LG3 M7L171G6 GTACCGTGATGGGAGTAAAC M7L171UR GGTGACTGTTGGGTGCTAGTG M7LH1 M7L171H1 TTACAGCTACAGAGCTGCCTAAA M7-BE-L_F GCAAATAAATCGACACTTCACAC M7LH2 M7L171H6 TTACAGCTACAGAGCTGCCTAGA M7-BE-L_F GCAAATAAATCGACACTTCACAC M7LH3 M7L171H8 TTACAGCTACAGAGCTGCCTCTA M7-BE-L_F GCAAATAAATCGACACTTCACAC M8SNPG1 M8SNP146G3 TAATTTAGTTGAAAATGTTAATCAATGTC M8SCARR TCTGTTCCCCATGATGTAGACTTC M8SNPG2 M8SNP146G4 CTAATTTAGTTGAAAATGTTAATCAAGTTC M8SCARR TCTGTTCCCCATGATGTAGACTTC M8SNPG3 M8SNP146G5 TCTAATTTAGTTGAAAATGTTAATCAATATC M8SCARR TCTGTTCCCCATGATGTAGACTTC M8SNPH1 M8SNP234H2 GCAACTTAATTACAATTTGGTCAT M8SCARR TCTGTTCCCCATGATGTAGACTTC M8SNPH2 M8SNP234H4 GCAACTTAATTACAATTTGGTGTT M8SCARR TCTGTTCCCCATGATGTAGACTTC M8SNPH3 M8SNP234H5 CAACTTAATTACAATTTGGGCTT M8SCARR TCTGTTCCCCATGATGTAGACTTC N8SNPG1 N8SNP545G1 TCAATAGGATTATTCACTGCCG N8SCARF ACCTCAGCTCCCAAACATTAAAAT N8SNPG2 N8SNP545G4 AATGTCAATAGGATTATTCACTGATG N8SCARF ACCTCAGCTCCCAAACATTAAAAT

L1LG1 L1L76G1 TTGTTTATCATCTTTGTCTTTTATCAA L-1-BE-L_R ACACTAGAGCTGGAAAGTGAAGC

198

rker Allele-specific primer name Allele-specific primer sequence (5’ to 3’)

Non-specific primer name Non-specific primer sequence (5’ to 3’)

SNP Ma

N8SNPH1 N8SNP497H1 GGCGTGTCAAACACACTCTATC N8SCARF ACCTCAGCTCCCAAACATTAAAAT N8SNPH2 N8SNP497H2 CGTGTCAAACACACTCGAAC N8SCARF ACCTCAGCTCCCAAACATTAAAAT N8SNPH3 N8SNP497H5 GCGTGTCAAACACACTCTACC N8SCARF ACCTCAGCTCCCAAACATTAAAAT W2RH1 W2R343H2 AAATGCTTATTTAATCAATATTTGAAGAC W-2-BE-R_R CAACAAATCTGTTTTGGTAAAAT W2RH2 W2R343H3 GAAATGCTTATTTAATCAATATTTGAGTAC W-2-BE-R_R CAACAAATCTGTTTTGGTAAAAT W2RH3 W2R343H5 ATGCTTATTTAATCAATATTTGAATCC W-2-BE-R_R CAACAAATCTGTTTTGGTAAAAT W2RH4 W2R343H6 GAAATGCTTATTTAATCAATATTTGACTAC W-2-BE-R_R CAACAAATCTGTTTTGGTAAAAT W7SNPG1 W7SNP248G1 CACCCCTCTTTAATTATTAATTGAAA W7SCARF CTGGACGTCACATATCAGTAAGTA W7SNPG2 W7SNP248G3 ACCCCTCTTTAATTATTAATTCCAA W7SCARF CTGGACGTCACATATCAGTAAGTA W7SNPG3 W7SNP248G4 TCACCCCTCTTTAATTATTAATTCTAA W7SCARF CTGGACGTCACATATCAGTAAGTA W7SNPH1 W7SNP364H1 GAGCGTTGGGAGTTCCTCCAT W7SCARF CTGGACGTCACATATCAGTAAGTA W7SNPH2 W7SNP364H2 GAGCGTTGGGAGTTCCTGTAT W7SCARF CTGGACGTCACATATCAGTAAGTA W7SNPH3 W7SNP364H3 GAGCGTTGGGAGTTCCTATAT W7SCARF CTGGACGTCACATATCAGTAAGTA

N8SNPG3 N8SNP545G6 AAATGTCAATAGGATTATTCACTTCTG N8SCARF ACCTCAGCTCCCAAACATTAAAAT

199Appendix J. Sample alignment of sequences from the RAPD marker OP-C1 generated in cucumber by Horesji et al. (1999; silver staining sequencing) and Chapter 2. Asterisks highlight differences in sequences and undetermined bases (N) are indicated by ^. ....|....| ....|....| ....|....| ....|....| ....|....| ....|....| 5 15 25 35 45 55 OP-C1 HORESJI ---------- ATGAGAAAGG GCAAGGATTC TCAAATTGNT T--TGNATGT ATCTCATTTT OP-C1 Chapter 2 TTCGAGCCAG ATGAGAAAGG GCAAGGATTC TCAAATTGTT TGTTTGATGT ATCTCATTTT ^ ** *^ ....|....| ....|....| ....|....| ....|....| ....|....| ....|....| 65 75 85 95 105 115 OP-C1 HORESJI CCTGTAGGTT GTTATAGAAT CAAATGGTAT AGCTGT---G TTGATAGTGA GGGGTGTTTT OP-C1 Chapter 2 CCTGTAGGTT GTTATAGAAT CAAATGGTAT AGCTGTTGTG TTGATAGTGA GGGGTGTTTT *** ....|....| ....|....| ....|....| ....|....| ....|....| ....|....| 125 135 145 155 165 175 OP-C1 HORESJI TGGAACCTCC TTCCTTTGAA TTCTGGACCA TTACTTACTA TCCATCAACT TTCATCA--- OP-C1 Chapter 2 TGGAACCTCC TTCCTTTGAA TTCTGGACCA TTACTTACTA TCCATCAACT TTCATCAGC

200Appendix K. The effect of increasing the number of quantitative trait loci (QTL) on the number of lateral branches in two leaf types of cucumber in two locations as determined by near-isogenic lines (NIL; Table 3.2). Lines indicate the incremental addition of QTL, and the comparisons in the legends refer to means comparisons performed (Table 3.4; Chapter 3).

Little leaf NILHancock, Wisc.

NIL-146

NIL-1246

NIL-12456NIL-1236

NIL-136

NIL-1356 NIL-12356

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

2 3 4 5 6

Num

ber

of la

tera

l bra

nche

s

Standard leaf NILHancock, Wisc.

NIL-0

NIL-23

NIL-235

NIL-25

NIL-36

0.0

0.5

1.0

1.5

2.0

2.5

0 1 2 3 4

Little leaf NILArlington, Wisc.

NIL-12456

NIL-1246NIL-146

NIL-1236 NIL-12356

NIL-1356

NIL-1364.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

2 3 4 5 6

Number of QTL

Num

ber o

f lat

eral

bra

nche

s

Standard leaf NILArlington, Wisc.

NIL-235

NIL-23

NIL-0

NIL-25

NIL-36

0.0

0.5

1.0

1.5

2.0

2.5

0 1 2 3 4

Number of QTL

Comparisons A & B Comparisons C & D

Comparisons E & F

Comparisons G & H Comparisons I & J NIL-36

201

Source of variation DFa Mean square F value P value

Appendix L. Analysis of variance (ANOVA) table of a test for main effects and interactions on the number of lateral branches in cucumber (MLB; Chapter 3). Effects examined were location (Hancock, Wisc. and Arlignton, Wisc.), replication (reps), with-in row spacings (10, 15, and 20 cm between plants), leaf type (standard and little leaf) and genotype [near-isogenic lines (NIL) that vary in the number of QTL (Table 3.2) for MLB]. The coefficient of variation (CV) was 19.6%.

Location 1 9.992829 12.21 0.0006 Rep w/in location (rep/location) 6 15.509777 18.95 <0.0001 Spacing 2 40.731809 49.77 <0.0001 Location*spacing 2 0.058206 0.07 0.9314 Spacing* rep/location 12 1.882534 2.3 0.0095 Leaf type 1 2146.975812 2623.56 <0.0001 Location*leaf type 1 1.563275 1.91 0.1687 Spacing*leaf type 2 2.503242 3.06 0.0494 Location*spacing*leaf type 2 0.358645 0.44 0.6459 Entry w/in leaf type (entry/leaf type) 10 5.308937 6.49 <0.0001 Location*entry/leaf type 10 7.499191 9.16 <0.0001 Spacing*entry/leaf type 20 0.605907 0.74 0.7802 Location*spacing*entry/leaf type 20 0.39418 0.48 0.9708 Error 176 0.818345

a Degrees of freedom