Theor Appl Genet

DOI 10.1007/s00122-007-0599-9

ORIGINAL PAPER

A high-density, integrated genetic linkage map of lettuce (Lactuca spp.)

M. J. Truco · R. Antonise · D. Lavelle · O. Ochoa · A. Kozik · H. Witsenboer · S. B. Fort · M. J. W. Jeuken · R. V. Kesseli · P. Lindhout · R. W. Michelmore · J. Peleman

Received: 15 December 2006 / Accepted: 17 June 2007© Springer-Verlag 2007

Abstract An integrated map for lettuce comprising of2,744 markers was developed from seven intra- and inter-speciWc mapping populations. A total of 560 markers thatsegregated in two or more populations were used to alignthe individual maps. 2,073 AFLP, 152 RFLP, 130 SSR, and360 RAPD as well as 29 other markers were assigned tonine chromosomal linkage groups that spanned a total of1,505 cM and ranged from 136 to 238 cM. The maximuminterval between markers in the integrated map is 43 cMand the mean interval is 0.7 cM. The majority of markerssegregated close to Mendelian expectations in the intra-spe-ciWc crosses. In the two L. saligna £ L. sativa inter-speciWccrosses, a total of 155 and 116 markers in 13 regions exhib-ited signiWcant segregation distortion. Data visualizationtools were developed to curate, display and query the data.

The integrated map provides a framework for mappingESTs in one core mapping population relative to pheno-types that segregate in other populations. It also provideslarge numbers of markers for marker assisted selection,candidate gene identiWcation, and studies of genome evolu-tion in the Compositae.

Introduction

The development of several types of molecular markers inthe last 20 years (Peters et al. 2003; Gupta and Rustgi2004) has greatly facilitated the construction of geneticlinkage maps for a variety of crop species. These linkagemaps have been used for marker-assisted breeding(reviewed in Dekkers and Hospital 2002), map-based clon-ing strategies (Mayerhofer et al. 2005; van Os et al. 2006),comparative genomics (reviewed in Paterson et al. 2000)and dissection of quantitative traits (reviewed in Salvi andTuberosa 2005).

Genetic maps have been developed using intra- or inter-speciWc crosses, mostly predicated on whether suYcientpolymorphism exists within the species. Maps generatedfrom intra-speciWc crosses can be more readily used forbreeding purposes because they contain markers that areinformative within the closely-related gene pool of the cul-tivated species. However, in self-pollinated species like let-tuce, the development of comprehensive maps from intra-speciWc crosses has been obstructed by limited polymor-phism within the cultivated species. Inter-speciWc crosseshave a higher degree of polymorphism but segregation dis-tortion for some crosses may be frequent and hybrid steril-ity precludes the development of permanent populationssuch as recombinant inbred lines. Furthermore, reducedrecombination frequency or segregation distortion towards

Communicated by A. Kilian.

Electronic supplementary material The online version of this article (doi:10.1007/s00122-007-0599-9) contains supplementary material, which is available to authorized users.

M. J. Truco (&) · D. Lavelle · O. Ochoa · A. Kozik · H. Witsenboer · S. B. Fort · R. V. Kesseli · R. W. MichelmoreThe Genome Center and The Department of Plant Sciences, University of California, Davis, CA 95616, USAe-mail: mjtruco@ucdavis.edu

R. Antonise · H. Witsenboer · J. PelemanKeygene N.V, 6708 PW Wageningen, The Netherlands

M. J. W. Jeuken · P. LindhoutLaboratory Plant Breeding, Graduate School for Experimental Plant Sciences, Wageningen University, PO box 386, 6700 AJ Wageningen, The Netherlands

R. V. KesseliDepartment of Biology, University of Massachusetts, Boston, MA 02125, USA

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the cultivated allele in certain regions may limit access towild alleles in those regions of the genome.

Genetic maps developed using individual populationscan be of limited use for other populations if markers arenot frequently polymorphic among populations. Integratedmaps that combine information from multiple populationsincrease the number of markers in a given genomic regionand improve the chances of obtaining suYcient polymor-phic markers in a speciWc population as well as allowingcomparison of locations of genes of interest across maps.Integrated linkage maps have been developed for severalcrop species including pepper (Paran et al. 2004; Lefebvreet al. 2002), soybean (Song et al. 2004), melon (Périn et al.2002), sunXower (Gedil et al. 2001), rapeseed (Lombardand Delourne 2001), tomato (Haanstra et al. 1999) and bar-ley (Qi et al. 1996).

Lettuce (Lactuca sativa L.) is a diploid (2n = 18) specieswithin the Lactucae tribe of the Asteraceae. There are fourwell-established species within the subsection Lactuca: thecultivated L. sativa and three wild species, Lactuca serri-ola, Lactuca saligna, and Lactuca virosa (in order ofdecreasing sexual compatibility with L. sativa). L. serriolais probably the progenitor of and con-speciWc with L. sativa(Kesseli et al. 1991; de Vries 1997). Lettuce is a major hor-ticultural crop with a worldwide production of over21 million tons in 2004 and accounts for $2.06 billion infarm value within the USA (Anonymous 2004). Lettuce hasbeen the subject of classical and molecular studies. It is oneof the species studied in the Compositae Genome Project(CGP; http://compgenomics.ucdavis.edu/). An objective ofthis project is the characterization of genes underlying agri-culturally important and domestication traits. This includesgenetic analyses to dissect qualitative and quantitative traitsas well as the identiWcation of candidate genes from exten-sive expressed sequence tag (EST) databases and the studyof synteny among sunXower, lettuce and Arabidopsis(Timms et al. 2006; http://cgpdb.ucdavis.edu/).

Five genetic maps have been published previously for let-tuce. Kesseli et al. (1994) and Waycott et al. (1999) reportedthe construction of two linkage maps assembled on popula-tions derived from crosses within cultivated lettuce (L.sativa). These two maps are fragmented with multiple link-age groups exceeding the chromosomal number of Lactucaspecies. This is probably a consequence of the low intra-spe-ciWc polymorphism and a high frequency of monomorphicregions between the parental lines. Two other maps are fromcrosses between cultivated lettuce and its closest wild rela-tives L. serriola and L. saligna. Johnson et al. (2000)described a framework map of AFLP® markers (a registeredtrademark of Keygene N.V.) for a QTL analysis of a popula-tion derived from L. sativa and L. serriola. Jeuken et al.(2001) used AFLP markers to generate a consensus map ofnine chromosomes for two populations derived from crosses

between L. saligna and L. sativa. Because these four mapshave been constructed using diVerent sets of markers, align-ment of linkage groups across maps was not possible,excluding the possibility of integrating information betweenthem. Recently, a map from a cross between L. serriola DH-M21 and L. sativa cv. Dynamite has been published (Syedet al. 2006). This map was developed using retrotransposon-based markers as well as some AFLP markers in commonwith the map of Jeuken et al. (2001).

Here we report on the construction of a consensus map inlettuce that integrates four previously published maps of let-tuce with data from three new populations: two maps gener-ated from crosses between L. sativa and L. serriola and anadditional map derived from L. saligna £ L. sativa. Thismap integrates data from all seven individual maps, thusproviding for a better coverage of all genomic regions. It isalso more representative of the Lactuca genus. L. serriolaand L. saligna are the closest wild relatives of cultivated let-tuce (L. sativa) and their inclusion allows for more universalutility of markers across diVerent genetic backgrounds. Dis-play of the data in the public Compositae Genome Database(http://cgpdb.ucdavis.edu/) provides a current resource forexploiting lettuce genetic and genomic data.

Materials and methods

Plant material

Seven diVerent populations were used to develop an inte-grated map of lettuce (Table 1). Two were F2 populations ofintra-speciWc crosses between cultivars or breeding lines ofL. sativa: Population DB0D was a F2 of a cross between cv.Calmar (crisphead type) £ cv. Kordaat (butterhead type);The RYDER population was a cross between F4 breedinglines 87-25-1M £ 87-1090M (for more detailed pedigreessee Waycott et al. 1999). Three other populations were inter-speciWc crosses between L. sativa and L. serriola. Popula-tion DB0H was a F2 from a cross between L. sativa cv. Sali-nas (crisphead type) and L. serriola US96UC23; populationDA0F was a RIL7 of the same cross derived by single seeddescent. Population DB9X was a F2 from a cross between L.sativa cv. Salad Bowl (leaf type) and L. serriola CGN14263.The last two populations were inter-speciWc crosses betweenL. saligna and L. sativa. Population DB0F was a F2 fromL. saligna UC82US1 £ L. sativa cv. Vanguard 75 (crispheadtype). Population DB0T was a F2 from L. salignaCGN5271 £ L. sativa cv. Olof (butterhead type).

Construction of individual maps

Linkage maps for populations DB0D and RYDER utiliz-ing restriction fragment length polymorphism (RFLP),

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Theor Appl Genet

random ampliWed polymorphic DNA (RAPD), resistantgenes and morphological markers have been previouslypublished (Kesseli et al. 1994; Waycott et al. 1999). Addi-tional markers were run on these populations to assist inaligning and integrating the maps. AFLP markers wereanalyzed in all populations except RYDER using standardprocedures (Vos et al. 1995). A total of 53 primer combi-nations were assayed in these populations. Of those, 13were public primer combinations, the rest were propri-etary (Keygene). Images of AFLP gels were electronicallyscanned and AFLP markers were scored co-dominantlyby using the proprietary software developed for AFLPanalysis at KeyGene. The KeyGene software analysesband intensity and accurately assigns genotypes using amixture model of normal distributions as described byJansen (2001). Therefore the majority of markers used formap integration were co-dominant. A set of proprietarysimple sequence repeat (SSR) markers developed bySeminis Inc. (Woodland, CA, USA) were analyzed in allseven populations following standard procedures. Indi-viduals were genotyped as “A” when they were homozy-gous for the parental female allele, “B” when they werehomozygous for the parental male allele, “H” when theywere heterozygous, “C” (not A) for dominant markerswhere the B allele was dominant and “D” (not B) for dom-inant markers where the A allele was dominant. Informa-tion for each marker and segregation data for eachpopulation is provided at http://cgpdb.ucdavis.edu/mjt_2006.

Individual linkage maps were constructed using Joinmapv 2.0 (Stam 1993; Stam 1995) and are displayed at the samewebsite. Markers were assigned to linkage groups (LGs) byincreasing the LOD score for grouping in steps of 0.5 LOD.This iterative process for each population selected a maxi-mum LOD score for grouping on the basis of stability ofgroups over diVerent LOD values and suYciently largegroups approaching the number of chromosomes for lettuce.In some cases, additional steps were required to split upsome large groups at a higher LOD value. Mapping was car-ried out using the following thresholds for Joinmap; REC of0.49, LOD of 0.10, RIPPLE of 1, JUMP of 4 and TRIPLETof 7. No order was forced during the linkage analysis.Recombination frequencies were converted to map distancesin centimorgans (cM) using the Kosambi mapping function(Kosambi 1943). Using the proprietary Keygene softwareprogram “Genome Typer” graphical genotypes were gener-ated for each population. These graphical genotypes providean overview of the genotype conWgurations of all individu-als. In this way, possible inconsistencies in the map, like theoccurrence of frequent double crossovers in small cM inter-vals and “recombination hotspots”, are readily detected.Markers which resulted in frequent apparent double cross-overs (in at least 15% of the individuals) were removed fromthe map and the map was re-calculated and checked in asimilar way. On average, the number of markers removeddid not exceed 3%. The quality of the Wnal maps were visu-alized using the publicly available CheckMatrix program(see data visualization below).

Table 1 Generation, number of individuals and markers of the seven populations integrated on the consensus map of lettuce

a Parentals are ordered following standard procedures. First parental is the female parentalb RFLP restriction fragment length polymorphism, AFLP ampliWed fragment length polymorphism, SSR simple sequence repeats, RAPD, randomampliWcation polymorphic DNA, others: isozymes, morphological traits, resistant genes and SCAR sequence characterized ampliWed regionc Additional information for individual maps for populations DB0D, DB0H, DA0F, DB9X and DB0F can be found at http://cgpdb.ucdavis.edu/database/genome_viewer/viewer/

Code Parentalsa Generation No. of individuals

No. of mapped markersb Referencesc

RFLP AFLP SSR RAPD others

DB0D L. sativa cv.Calmar £ L. sativa cv. Kordaat

F2 66 129 411 39 223 9 Kesseli et al. (1994); Witsenboer et al. (1997)

RYDER L. sativa 87-25-1M £ L. sativa 87-1090M

F2 101 – – 28 69 7 Waycott et al. (1999)

DB0H L. sativa cv. Salinas £ L. serriola US96UC23

F2 92 – 643 70 – 2 Johnson et al. (2000)

DA0F L. sativa cv. Salinas £ L. serriola US96UC23

RILF7 119 – 729 18 – –

DB9X L. sativa cv. Salad Bowl £ L. serriola CGN14263

F2 90 – 166 19 – –

DB0F L. saligna UC82US1 £ L. sativa cv. Vanguard 75

F2 75 59 726 60 110 8

DB0T L. saligna CGN5271 £ L. sativa cv. Olof

F2 126 – 423 12 – – Jeuken et al. (2001)

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Theor Appl Genet

Marker distortion

Segregation ratios were calculated for all markers. Good-ness of Wt �2 tests were calculated with expected Mendelianratios of 1:2:1 (A:H:B; co-dominant marker) or 3:1 (C:A orD:B; dominant marker) for F2 populations and of 1:1 (A:B)with a residual heterozygosity of 1.5625% for the F7 RILpopulation. Markers were considered distorted at a level ofsigniWcance of P < 0.05. For co-dominant markers withindividuals that were scored as “C” or “D” because theycould not be unambiguously assigned a genotype, two �2

tests were calculated. One assumed a ratio of 1:2:1 (A:H:B)while not considering “C” or “D” scores. The otherassumed a 3:1 ratio (C:A or D:B), where “H” genotypeswere added to the dominant homozygous allele. Both �2

tests had to be signiWcant for the marker to be consideredskewed.

Recombination percentage

Recombination percentages were calculated for conservedmarker intervals between individual maps. Markers deWn-ing conserved intervals were chosen from framework mark-ers used for marker integration (see below). Conservedintervals among each pair of maps were identiWed and thenumber of recombinant individuals calculated as those indi-viduals where there was a change on the genotype at themarkers deWning the interval; for example, if markers mk1and mk2 deWned a conserved interval in two populations, arecombination event was considered as present in thoseindividuals where there was a change in the genotype atmk1 versus mk2 from A to H, B to H, A to C, B to D andvice versa and two recombination events were consideredas present when there was a change in genotype from A toB and vice versa. Therefore, some recombination valuesmay be slightly underestimated since in dominant markerschanges between A to C (H or B) and B to D (H or A) canresult from two recombination events; however, this situa-tion is not frequent because most markers used were co-dominant rather than dominant.

Integrated map construction

Individual maps were integrated using INT_MAP (Pelemanet al. 2000). This proprietary software integrates individualgenetic linkage maps into a single consensus map by usingcommon markers between populations as anchor markers.Map integration consisted of three separate steps. First,markers were identiWed that were common to at least twopopulations. These common markers were identiWeda priori as framework markers and then used to associatethe corresponding linkage groups in the individual maps.Second, the consensus order of framework markers within a

linkage group was calculated from the relative positions ofthe framework markers in each individual map. Startingsequentially with the most common marker, additionalmarkers were added one at the time following the rule ofmost proximate, most common marker. Therefore within aset of equally common markers, markers are added in orderdepending on their distance to the already placed markers.Subsequently, the next step is made with less commonmarkers. The consensus position of each framework markerwas calculated as a weighted average of the individual mapdistances between new markers and the previously posi-tioned markers. Two parameters controlled map integra-tion: (1) The minimum fraction of overlap between groups(the minimum fraction of markers that are common to atleast two groups, for example 3 out of 10 common markers)and (2) the maximum disagreement allowed in the distancebetween two adjacent framework markers on the individualmaps. We used 0.30 and 10.0 cM for these two parametersrespectively for the construction of the integrated lettucemap. Markers deviating from these criteria were not used asframework markers for map integration and were notincluded on the integrated map but remained on the individ-ual maps. Lastly, markers unique to individual populationswere positioned on the integrated map using interpolationand extrapolation to the framework markers. Markers thatwere unique to a map were placed in a “bin” betweenframework markers; the accuracy of their positiondepended on the size of the interval between the frameworkmarkers in the individual map.

Framework RFLP markers were identiWed by the use ofthe same probe name. Common RAPD markers were identi-Wed when the same size of polymorphic fragment wasampliWed with the same primer. Common AFLP markerswere identiWed when, using the same enzyme/primer combi-nation, bands co-migrated on reference gels including all theparents (Rouppe van der Voort et al. 1997). FrameworkSSRs were recognized by the use of the same set of primers.

Linkage groups in the integrated map were assignednumbers corresponding as close as possible to the linkagegroup designations in the previously published maps; prior-ity was given to the Wrst map published (Kesseli et al. 1994).

Nomenclature

Each of the populations and the maps derived from themwere identiWed by an alpha-numeric code in upper case (seesection “Plant Material” above). The same code in lowercase followed by a number was used to identify the linkagegroups within each of the maps. For example, the F2 popula-tion derived from L. sativa cv. Calmar £ L. sativa cv. Kor-daat was identiWed by the code DB0D and linkage group 3in the map derived from this population was designatedDb0d-3. The code for the integrated map was INT (Table 2).

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Theor Appl Genet

Data curation and visualization

In order to curate and query all the genetic data, we devel-oped several modules and utilities for the Compositae data-base (http://cgpdb.ucdavis.edu/). This website and databasewas created originally to display and query EST data forlettuce and sunXower generated by the CompositaeGenome Project. Segregation data is available for all loci athttp://cgpdb.ucdavis.edu/mjt_2006. Enabling informationand images are also available for most public markers in amySQL relational database.

Lettuce Genetic Map Viewer is a PHP program thatallows the visualization of markers on the integrated lettucemap as well as the individual maps (http://cgpdb.ucda-vis.edu/mjt_2006). It uses GD graphic libraries and thePHP scripts interact with the mySQL database dynamicallyto generate graphical images “on the Xy” upon a user’squery via the web interface. Each map can be displayedseparately allowing the search for speciWc markers within aspeciWc map or linkage groups can be compared betweenmaps to identify common markers and to align them to theintegrated map. The types of markers displayed and theappearance of the map can be customized depending on theneeds of the researcher.

A CheckMatrix plot is displayed for each linkage group.CheckMatrix (http://cgpdb.ucdavis.edu/XLinkage/) is aPython script designed to assess the quality of genetic mapsby generating 2D plots (heat maps) of the degree of linkagebetween all markers. Markers are ordered according to theirgenetic position. The colored pattern of the linkage rela-tionships indicates the quality of the map and helps identify

problematic markers and regions. Checkmatrix also dis-plays the segregation ratios for each marker, which facili-tates the analysis of segregation distortion.

Results

Individual genetic maps

Individual linkage maps for populations DB0D, RYDER,DB0H and DB0T have been previously published(Table 1). Additional AFLP and SSR markers were ana-lyzed on populations DB0D, RYDER and DB0H to providemarkers common to multiple maps. AFLP markers wereanalyzed for six of the seven populations (Table 1). RFLPmarkers were analyzed only for populations DB0D andDB0F and RAPD markers were analyzed for populationsDB0D, RYDER and DB0F. SSR markers were the onlymarker type assayed in all seven populations. Limited num-bers of isozymes, morphological markers, resistant genesand SCAR markers were also assayed in populationsDB0D, RYDER, DB0H and DB0F.

New individual maps were constructed for all popula-tions, except DB0T, integrating the old and new markerdata. Segregation data and individual maps for the DB0D,RYDER, DB0H, DA0F, DB9X and DB0F populations areavailable at http://cgpdb.ucdavis.edu/mjt_2006. The twomaps constructed on crosses between L. saligna £ L. sativa(DB0F and DB0T) and the RIL map (DA0F) comprisednine chromosomal linkage groups. Maps constructed oncrosses within L. sativa had higher numbers of linkage

TNI D0BD REDYR H0BD F0AD X9BD F0BD T0BDGL km# Mc GL km# Mc MP a GL km# Mc MP GL km# Mc MP GL km# Mc GL km# Mc GL km# Mc GL km# Mc MP

1 063 3.141

a1b1

83921761

4.24 5.75

9.99

11

1 61 2.95 ’a1‘ 1 75 3.121 01 1 18 5.131 1 42 3.411 1 49 801 1 73 9.99 1

2 492 4.631

a2b2

274269

9.45 8.05

601

22

2 41 4.15 ’a2‘ 2 57 1.341 6 2 46 311 2 02 421 2 101 031 2 05 1.39 2

3 662 391

a3b3

838468

5.85 8.63

3.59

33

a3b3

927164

5.58 8.33

3.911

89

3 06 8.571

a3b3

66223

5.46 6.691.161 3 101 5.19 3 32 77 8

4 705 832

a4b4c4

872551541

6.85 8.85 6.61

431

46

’71‘a4b4

61171

4.82711541

’a5‘ ’c5’+b2‘

4 631 9.702 4 4 241 6.791

a4b4

91872

49 2.452.841 4 971 861 4 48 361 4

5 963 1.491 5 601 421 ’61’+’51’+5 5 7 9.04 ’b4‘ 5 021 2.491 2 5 211 6.781 5 21 2.35 5 99 351 5 15 711 5

6 712 2.821

a6b6

411253

5.61445.06

2 ’31‘

6 07 9.731 3 6 17 3.151 6 51 1.99 6 97 701 6 94 1.89 3

7 402 5.521

a7b7c7

22112255

4.42 3.72 2.02

27

’11‘77

7 11 7.38 ’a8‘ 7 55 8.431 5 7 56 2.731 7 21 6.811 7 17 221 7 03 3.76 7

8 243 4.051

a8b8

533588

5.03 7.74 2.87

8 ’21‘

8 01 1.94 ’a6‘ 8 79 9.281 7 8 101 6.351 8 32 5.651 8 79 5.18 8 76 99 6

9 581 6.791

a9b9

429134

1.44 6.65

101

’01‘ ’9‘

9 7 9.34 ’61’+’b7‘ 9 06 9.621 1 9 15 4.89 9 02 1.931 9 98 211 9 44 201 9 TNI ni ton 1dda 5 6.21 ’41‘ 1dda

2dda3dda

796

7.34 6.55 6.44

’a3‘ ’a7‘ ’b3‘

9 4472 5.4051 02 328 388 31 401 816 01 617 8631 9 747 6431 11 581 4111 9 019 3701 9 534 719

Table 2 Number of markers and size in cM for linkage groups (LG) inall populations integrated on the lettuce map. For previously reportedmaps the correspondence among published linkage groups and thelinkage groups in the integrated map is given in the PM (previous map)column. In populations DB0D and RYDER, linkage groups designated

between quotations indicated those LG where numbers were not as-signed on the published map. For the purpose of this table numbershave been assigned corresponding to the order of the linkage groups inthe previously published map

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groups (20 linkage groups in DB0D and 11 on RYDER).Population DB0T was used for construction of the consen-sus map without any modiWcations (Jeuken et al. 2001).The seven maps ranged from 618 to 1368 cM and com-prised of 104 to 910 markers.

DiVerent types of markers were distributed uniformlythrough the linkage groups in the individual maps. Cluster-ing of AFLP markers was evident in most of the maps.RFLPs and RAPDs were also clustered in the map frompopulation DB0D.

Marker order of the individual maps was generally con-sistent between the maps as compared using LettuceGenetic Map Viewer. Markers showing inconsistent posi-tions were not subsequently used for map integration. Com-parisons of linkage groups among individual maps and withthe integrated map revealed that some individual mapswere missing large segments of the integrated map. Forexample, in map DB9X, linkage group Db9x-5 is only53.2 cM and corresponds to a terminal third of Int-5. Theother two thirds of Int-5 are missing from map DB9X (sup-plementary material S1). In map DB0D, linkage groupsDb0d-6a and Db0d-6b correspond to three quarters of Int-6(supplementary material S2); similarly, linkage groupsDb0d-8a and Db0d-8b correspond to two thirds of Int-8(supplementary material S3). However, linkage groupDb0d-add1 could not be integrated in the consensus INTmap for lack of markers in common with other maps andthis linkage group may correspond to the missing portionsof either Int-6 or Int-8.

Segregation distortion

All populations had some markers with segregationratios that deviated from Mendelian expectations. Popu-lations DB0F and DB0T had markers with distorted seg-regation in almost all the linkage groups (Table 3). On

the individual maps most of these markers were associ-ated in groups, indicating that the distortion was due tobiased transmission rather than mis-scoring of individualmarkers. Segregation distortion was highest in theL. saligna £ L. sativa crosses. Some regions showed seg-regation distortion in multiple populations with the samedirection of the distortion (Table 3). Linkage group Int-7was distorted in DB0D (linkage groups Db0d-7b andDb0d-7c), DB0F (linkage group Db0f-7) and in DB0T(linkage group Db0t-7) with a maximum number of dis-torted markers in Db0t-7 where the segregation of all butone of the markers was distorted (Table 3). In all thesepopulations, the distortion of linkage group 7 markerswas towards the L. sativa haplotype. Linkage groups Int-5, Int-6 and Int-9 were distorted only in populationsDB0F and DB0T; in these groups, even though the regionof distortion was similar, the distortion was towardsdiVerent parental haplotypes (Table 3) except for one ofthe regions in linkage group 9 distorted towards the culti-vated haplotype in both maps. Linkage group Int-3 hadgroups of distorted markers in populations DB0H, DA0F,DB9X, DB0F and DB0T. All the distorted regions werecoincident except the one in DB0F map (linkage groupDb0f-3) that did not overlap with the others (Table 3)Distortion was towards the L. sativa haplotype in DB0H,DA0F and DB9X and towards the heterozygous haplo-type in DB0F and DB0T.

Recombination percentage

We calculated recombination percentages in all the F2 pop-ulations except RYDER (that could not be included due tothe small number of conserved intervals between this mapand any of the others). Recombination percentages for con-served intervals between pairs of maps are presented inTable 6. In most of the comparisons when more than one

Table 3 Distribution of groups of distorted markers across linkage groups in the diVerent populations considered in this study. A group of skewedmarkers is deWned as a group of at least three linked distorted markers with gaps of non-distorted markers of three or less

LG linkage group, # m total number of markers on linkage group, s/g s number of distorted markers, g number of total markers on the distortedgroup, i interval in cM of the distorted group, d direction of the distortion. A towards the mother’s allele, B towards the father’s allele, H towardsthe heterozygous, D not B and C not A. Crosses are shown female £ male

TNI D0BD REDYR H0BD F0AD X9BD F0BD T0BDavitas .L x avitas .L avitas .L x avitas .L avitas .L x aloirres .L avitas .L x aloirres .L avitas .L x aloirres .L angilas .L x avitas .L angilas .L x avitas .L

)srekram detrotsid %23.6( detrotsid %98.7( )srekram otsid %12.3( )srekram detr detrotsid %82.11( )srekram %89.7( kram detrotsid )sre )srekram detrotsid %24.42( )srekram detrotsid %87.73( GL GL m# g/s i d GL m# g/s i d GL m# g/s i d GL m# g/s i d GL m# g/s i d GL m# g/s i d GL m# g/s i d

1

1 61 9/5 3.73 D

1 49 61/01 7/5

9.02 2.21

BB

2 2 101 21/7 31/8

9.21 5.8

AA

2 05 9/4 8.8 A3

a3b3

9271

6/4 11/8

6.91 1.22

AA

3 06 03/92 7.77 A b3 62 61/21 63 A3 101 31/5 6.72 D

3 32 7/5 7.03 H

4 4 241 11/11 3.01 B 4 971 11/5 1.3 B 4 48 11/9 22/51

6.2265

BA

5 5 99 44/24 8.07 B 5 15 22/91 7.54 A6 6 97 33/52 8.02 A 6 94 3/3 4.5 B7 c7

b72211

11/9 5/3

7.7 9.9

BB

7 17 62/41 1.54 B 7 03 03/92 3.76 B

88 101 31/01 1.61 A 8 79 5/3 2.4 A

8 76 81/31 6.23 B

9 9 98 62/91 61/21

4.44 3.51

AB

9 44 01/01 71/21

8.72 1.94

BB

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Theor Appl Genet

interval was considered, populations where L. saligna wasone of the parents had always a lower number of recombi-nation events. For the four conserved intervals betweenDB9X and DB0F in LG2 the same rate of recombinationwas observed (Table 6).

When comparing the maps from the two inter-speciWccrosses with L. saligna (DB0F and DB0T, Table 6), DB0Thad a more severe suppression of recombination. This ten-dency of suppressed recombination was also apparent in thesmaller sizes of the linkage groups in these two maps whencompared to the other individual maps or to the integratedmap (Table 2; http://cgpdb.ucdavis.edu/mjt_2006).

Map integration

A total of 695 markers common to two or more maps wereavailable for map integration. Of those, 62 (8.9%) were dis-carded as having inconsistent grouping (mapping in diVer-ent linkage groups in the individual maps). A total of 47markers were discarded for having contradictory geneticpositions (exceeding the 10 cM maximum distance dis-agreement allowed between two adjacent framework mark-ers). A total of 560 (80.6% of the original frameworkmarkers) were used for map integration. Of these 560 mark-ers, 474 were AFLP, 24 RFLP and 25 SSR markers(Table 4; http://cgpdb.ucdavis.edu/database/genome_viewer/viewer). Only 16 RAPD markers were used as frameworkmarkers, mostly to allow the integration of the RYDERlinkage groups. As expected, maps DB0H and DA0F hadthe most common framework markers between them (274)since these maps are from diVerent generations derivedfrom the same cross. Maps DB0F and DB0T derived fromcrosses between L. saligna and L. sativa had the secondmost common number of markers (136). The RYDER maphad the fewest common markers (18) with any of the othermaps (Table 5).

Most of the linkage groups from the individual mapswere successfully integrated into the consensus map. Thecorrespondence among linkage groups in the individualmaps and the linkage groups in the integrated map is shown

in Table 2. When possible, linkage groups were designatedto be consistent across maps with priority given to the earli-est report. Only linkage groups add1, add2 and add3 in theRYDER map and linkage group add1 in the DB0D mapwere not integrated on the consensus map for lack of com-mon framework markers (Table 2).

Integrated map characteristics

The integrated lettuce map was composed of 2,744 markersassembled into nine linkage groups, corresponding to thenine chromosomes of lettuce, with a total map length of1,505 cM (Table 2; Fig. 1). The integrated map was»150 cM larger than the largest individual maps from pop-ulations DB0H and DA0F (1,368 cM and 1,346 cM). Thiswas the result of the expansion of linkage groups 3 and 9 byintercalating new markers from the other maps (Table 2).Average interval size between markers was greatly reducedon the integrated map (Fig. 2). Less than 1% of the inter-vals were larger than 5 cM.

Marker distribution in the integrated map was a reXec-tion of the individual maps distributions (Fig. 1). AFLPmarkers were obviously clustered in multiple regions andfew regions exhibited clustering of RFLP and RAPDmarkers. Morphological markers that only segregated inpopulations DB0D and RYDER (DM resistance genes etc.)were integrated into the linkage groups using anonymousmarkers.

Discussion

Our integrated map of lettuce integrates four previouslypublished maps of lettuce with data from three previouslyunreported datasets. The seven maps were constructed fromcrosses within and between L. sativa (cultivated lettuce)and its closest wild relatives L. serriola and L. saligna.Consequently it provides markers for accessing andexploiting variability within the sexually-compatible genepool of cultivated lettuce.

Table 4 Number of common framework markers among populationsused for linkage group integration

RFLP AFLP SSR RAPD Morphological Total

All framework markers 24 474 45 16 1 560

Between 2 populations 24 349 24 16 1 414

Among 3 populations – 88 13 – – 101

Among 4 populations – 22 5 – – 27

Among 5 populations – 15 2 – – 17

Among 7 populations – – 1 – – 1

Table 5 Distribution of common framework markers between popu-lations

DB0D RYDER DBOH DA0F DB9X DB0F DB0T

RYDER 18

DB0H 68 12

DA0F 51 5 274

DB9X 15 4 47 55

DB0F 58 7 63 64 42

DB0T 22 1 44 56 28 136

Total 141 26 333 328 87 237 180

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Fig. 1 Integrated map of lettuce. AFLP and SSR markers are displayed below the linkage group line. RFLP, RAPD and morphological markers are displayed above the linkage group line. For details, see http://cgpdb.ucdavis.edu/mjt_2006

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Segregation distortion

We found two diVerent types of distortion: speciWc regionsdistorted in multiple populations and distortion of multiplelinkage groups in a single population suggesting multiplereasons for segregation distortion. Markers with segrega-tion distortion were found restricted to certain genomicpositions in maps within L. sativa and between L. sativaand L. serriola. Factors that aVect the segregation of allelesduring meiosis (e.g. direct gamete competition, male kill-ing) can produce distorted ratios at aVected loci (Moyle andGraham 2006). A region in Int-3 was consistently distortedin populations DB9X, DB0H and DA0F towards the L.sativa maternal allele. In tomato and rice, distortiontowards one of the parental alleles was the result of selec-tive abortion of gametes carrying a speciWc allele at a gam-ete eliminator locus (Rick 1966; Sano 1990).

In crosses between L. saligna and L. sativa, distortedmarkers were widely distributed through the genome pres-ent in almost all the linkage groups. In a comparison among58 intra and inter-speciWc crosses in diVerent agriculturalcrop species, Jenczewski et al. (1997) reported an increaseof distorted markers in inter-speciWc crosses versus intra-speciWc crosses in agreement with our results. The degreeof distortion in a genetic mapping population is thought tobe correlated with the level of genomic divergence betweenparental taxa (Taylor and Ingvarsson 2003). Hybrid invia-bility or sterility is known to occur by the accumulation ofinter-locus incompatibilities between divergent populations(Sweigart et al. 2006). F1 hybrids between L. saligna and L.sativa are partially sterile (de Vries 1990; Koopman et al.1998). Possibly disruptive genetic interactions amongdivergent loci between L. saligna and L. sativa are respon-sible for hybrid sterility and observed distorted ratios in theF2. Distorted segregation during the development of a set of29 lettuce backcross inbred lines that introgressed singlesegments from L. saligna into L. sativa over more than Wvegenerations resulted in six regions in which the L. salignasegment could not be obtained as homozygous (Jeuken and

Lindhout 2004). This extreme preference for L. sativa alle-les was observed for segments that mapped to Int-6, Int-8,Int-7 (two regions) and Int-9 (two regions). One segmentcorresponding to a region on Int-7 showed complete distor-tion as only the L. sativa haplotype was obtained. Thesesegments are similar to those that showed segregation dis-tortion in the L. saligna £ L. sativa F2 populations DBOTand DBOF (Table 3).

Wild ancestors of crop plants have been useful sourcesof genetic variation that has been lost during domesticationof crop plants (Zamir 2001). Wild species in the Lactucagenus have been successfully used in the introgression ofhorticulturally important traits in breeding programs, par-ticularly disease resistance (Crute and Dickinson 1976).Understanding the mechanisms responsible for segregationdistortion in a given cross is important since such distortioncan obstruct gene Xow between species and hamper theintrogression of horticulturally interesting alleles in certainregions of the genome. In contrast to the development ofBILs (Jeuken and Lindhout 2004), the distortion observedin our interspeciWc populations was never complete (i.e.only one of the parental haplotypes was recovered); all thepossible haplotypes were recovered in the progeny for allregions, albeit at diVerent frequencies. The consequence forselection of genes in these skewed regions is the increasedprogeny sizes required to recover wild alleles in regionsthat become distorted towards the cultivated allele. Horti-culturally, interesting genes can be located in regionsexhibiting segregation distortion; quantitative trait locus(QTL) analysis of numerous horticulturally important traitssegregating in population DA0F identiWed QTLs for leafshape and heading in a distorted region of Da0f-3 (MJTruco, unpublished data).

Recombination rates

Observed recombination frequencies are dependent on theaccuracy of the dataset as well as the actual rate of recombi-nation. Mis-scored markers are especially problematic ondense maps, where a few errors can greatly increase appar-ent marker distances. This type of error was reduced bysearching for and removing individual markers Xanked byrecombination events. The maps from L. sativa £ L. sativaand between L. sativa and L. serriola have similar rates ofrecombination supporting the idea that these taxa are con-speciWc (Kesseli et al. 1991) (Table 6).

Recombination was reduced in crosses between L. sal-igna and L. sativa resulting in more compressed linkagemaps (Table 2; Supplementary material S5). This wasprobably the consequence of the greater sequence diver-gence between these parents. The higher levels of diver-gence were reXected by the mean numbers of AFLPmarkers per primer combination; populations DB0T and

Fig. 2 Distribution of size intervals among populations. Comparisonwith INT map

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DB0F averaged 35 and 45 AFLP markers respectively,while DB0H and DB9X averaged 23 and DBOD only 12AFLP markers per primer combination. This is similar tointer-speciWc hybrids in tomato with a high degree ofmarker polymorphism that showed severe suppression ofrecombination between homeologous chromosomal seg-ments due to reduced chiasma frequency in F1 hybrids(Chetelat et al. 2000). Sequence divergence is known torepress homologous recombination. The recombinationrate in Arabidopsis was reduced threefold by singlenucleotide mismatches (Opperman et al. 2004). Exten-sive recombination studies in maize have documentedreduced recombination associated with sequence hetero-geneity (Schnable et al. 1998). Within the bronze gene, ahot spot for recombination in maize, recombinationoccurs randomly within the gene in the absence ofsequence divergence but is reduced by point mutations orsingle base pair mismatches (Dooner and Martínez-Fér-rez 1997).

Integrated map

Of the 695 potential framework markers available for mapintegration, over 90% were readily assembled into ninechromosomal linkage groups. Only 62 (8.9%) had to be dis-carded because of inconsistent grouping. This proportion isconsistent with map integration studies with other species(Keygene unpublished). The reason for inconsistent group-ing of markers has been studied in maize by re-sequencingbands that gave inconsistent grouping in diVerent maps;one third of these bands were the result of diVerentsequences with very close mobility being called the same.The remaining two thirds of the inconsistent markers hadthe same or similar sequence and were presumed to beduplicated sequences. The inconsistent positions may reX-ect the segregation of one or more polymorphisms in diVer-ent paralogs.

The integrated map of lettuce reported here is a moreinformative genetic resource than any of the individualmaps previously available. It combines 2,744 markers intonine linkage groups corresponding to the nine chromo-somes of Lactuca spp. with better marker coverage(1,505 cM) than any of the other individual maps with ninelinkage groups (DA0F, 1,346 cM; DB0F, 1,073 cM; DB0T,9,17 cM). Genomic regions that were either split into sepa-rated linkage groups or missing in the individual maps forlack of polymorphism are now combined into single link-age groups. For example, linkage group Int-3 is split intotwo linkage groups in DB0D (36.8 and 58.5 cM), DB0H(85.5 and 33.8 cM) and DB9X (96.6 and 64.5 cM) and hasmissing regions in DB0F (91.5 cM) and DB0T (77 cM).Int-3 has a total length of 193 cM and is the result of com-bining regions from all the populations (Int-3, supplemen-T

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Theor Appl Genet

tary material S4). Some of the maps, for example DB9X,contribute to this linkage group with unique regions notpresent in any of the other maps.

Even marker distributions through the linkage groupswere observed for most of the marker types except for clus-tering of AFLP markers. The same situation has beenreported previously in an ultradense map of potato (van Oset al. 2006). Clustering of AFLP markers may be due to amore even distribution of AFLPs over the physical map.Consequently, clusters of AFLPs may be indicative of het-erochromatic regions near centromeres (Haanstra et al.1999); conWrmation of this for lettuce awaits cytologicalanalysis.

The integrated map of lettuce combines diverse types ofmarkers into a single map. Combining information frommultiple crosses increases the chances of identifying puta-tive polymorphic markers in a given region. Dm13, a dis-ease resistance gene against lettuce downy mildew, waspreviously mapped in DB0D to a region of Db0d-3b withonly dominant RAPD markers linked to it (Kesseli et al.1994). In the integrated map this gene is linked to a suite ofAFLP, SSR and RAPD markers. To map new traits thatsegregate in previously unmapped populations, bulked seg-regant analysis (BSA, Michelmore et al. 1991) could beused to identify a few markers linked to the gene of interest.These markers would then be aligned to the integrated mapto identify other candidate markers in the region avoidingthe time-consuming construction of a detailed map in thenew population.

Comparisons among individual maps and the integratedmap of lettuce were greatly facilitated by Lettuce MapViewer. This program provided graphical displays of thecomparisons and allowed easy access of the raw databehind the construction of the individual and integratedmaps including genotypes and details of markers. Thesetools are publicly available for application to other species.These tools and the integrated map data will greatly facili-tate analyses of synteny with Arabidopsis (Timms et al.2006), and marker assisted selection as well as genetic dis-section (QTL analysis) and map-based cloning of agricul-turally important traits.

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