Call: FP7-KBBE-2012-6 EU IN ECONOMY GREEN THE ...For 27 of the 32 traits measured, a total of 90...

Work Programme 2012 “COOPERATION”

Theme 2: FOOD, AGRICULTURE AND FISHERIES, AND BIOTECHNOLOGY

Activity 2.3: Life sciences, biotechnology and biochemistry for sustainable non-food products and processes

Area 2.3.5: Environmental biotechnology KBBE.2012.3.5-03

Biotechnological waste water treatments and reuse in agronomical system Call: FP7-KBBE-2012-6

Integrating biotreated wastewater reuse and valorization with enhanced water use efficiency to

support the Green Economy in EU and India

Grant agreement no.: 311933 Funding scheme: Collaborative Project

Coordinator: Dr Antonio Lopez, IRSA-CNR (Italy)

Work Package n°: 4 Improving WUE and drought tolerance of maize, sorghum, millet and

tomato via genomics approaches and modelling Deliverable 4.1

Molecular markers (SNPs) for marker-assisted selection for root architecture traits in maize and tomato

Due date: month 48

Actual submission date: month 48

Start of project: 1/08/2012

Deliverable Lead contractor: UNIBO

Participants: UNIBO & HORTA

Contact for queries: [email protected]

Dissemination level: PU

Water4Crops‐EU

INTEGRATINGBIOTREATEDWASTEW

ATERREUSEANDVALORIZATIONWITHENHANCEDWATER

USEEFFICIENCYTOSUPPORTTHEGREENECONOMYINEUANDINDIA

Water4Crops EU‐FP7Projectn°311933

Pag.2

CONTENTS

Paragraphs

1. Introduction……………………………………………………………….…….. pag. 4

2. Maize…………………………………………………………………………..... pag. 4

2.1 Materials and Method…………………………………………………….. pag 4

2.2 Results………………………………………………………………….. pag 6

2.3 Discussion………………………………………………………………. pag 9

3 Tomato…………………………………………………………………………. pag 10

3.1 Materials and Method…………………………………………………….. pag 10

3.2 Results………………………………………………………………….. pag 11

3.3 Discussion………………………………………………………………. pag 18

REFERENCES………………………………………………………………………..… pag 19


Pag.3

1. INTRODUCTION

Conventional breeding has successfully improved plant performance under a broad range of

environments, including those characterized by drought conditions, mainly focusing on traits related

to plant habitus, flowering and grain quality (Tuberosa et al., 2007; Abendroth et al. 2011). In order

to maintain the linear increase that was observed in the last 70 years in maize yield (Duvick, 2005),

breeding should also address new morpho-physiological traits, often neglected because of the low

hereditability and difficult in phenotyping. In this context, the study of root architectural system

(RSA) features/QTLs is becoming increasingly important (Bacon et al. 2003; de Dorlodot, S. et al,

2007; Yu et al. 2007; Courtois et al., 2009; Rebouillat, J et al., 2009; Hochholdinger and Tuberosa

2009; Tuberosa 2012; Cobb J.N. et al., 2013; Lynch 2013; Uga et al. 2013; Meister et al. 2014).

The maize root system is composed of embryonic primary and seminal roots (Abbe and Stein, 1954)

and postembryonic shoot-borne and lateral roots. Shoot-borne roots formed at underground nodes are

called crown roots, while those formed at above-ground nodes of the shoot are called brace roots

(Hochholdinger et al., 2004). Embryonic roots are relevant for early establishment of the plant while

postembryonic roots are important toward flowering and can have a significant influence on grain

yield in water-limited conditions (Hochholdinger and Tuberosa 2009).

The tomato root system grown from seed is composed of a central root known as taproot with smaller

lateral root branching off from it. Other secondary lateral root may subsequently extend from the first

lateral roots. The development of tomato roots is characterized by linear growth during the vegetative

phase upon flowering (Gasim and Hurd, 1986). The length of the primary root and the density of

lateral roots determine the architecture of the root system, and this, in turn plays a major role in

determining whether a plant will secede in a particular environment (Malamy and Benfey 1997).

Despite of the agronomic importance of roots little is known about root system genetics and root

analysis has only recently became a phenotyping target of top interest. Roots are intrinsically complex

to study for several reasons. First, lack of data is mainly due to the difficulty of measuring root

characteristics in their underground habitat (i.e. the soil) and thus to obtain phenotypic data. Secondly,

the root system of an adult plant has a considerable size and complexity. Thirdly, genetic studies are

complicated by the strong influence of the environment on the variability of root structure and

formation. Root architecture is very susceptible to factors such as structure, composition, temperature

and humidity of the soil and the availability of nutrients (e.g. phosphorus and nitrate).


Pag.4

An alternative to root studies conducted in the natural habitat would be the use of hydroponic

cultivation, such as the “paper roll” (Hetz et al., 1996) and rhizotron boxes (James et al., 1985)

methods. All these methods are extremely useful in the screening of root system architecture at

seedling stage enabling a good control of environmental effects and offering the possibility of

phenotyping roots different times during growth of the plants.

General objective of this investigation is the identification of genomic regions involved in the control

of root traits in maize and tomato.

2. MAIZE

2.1 Materials and Method

The plant material consisted of the B73 x Gaspé Flint introgression library (Salvi et al., 2011). B73

is an elite inbred line of the Iowa Stiff Stalk Synthetic heterotic group and is reference genome for

maize (Schnable et al., 2009). Gaspé Flint is a Canadian landrace belonging to the Northern Flint

maize race group (Vigouroux et al., 2008) with a phenotype very different to B73. The IL population

includes 73 lines originated by the cross B73 x Gaspé Flint followed by five cycles of marker-assisted

backcross using B73 as recurrent parent, and two cycles of selfing. During backcross and selfing

cycles, SSR markers were used to introgress in each line, and to fix as homozygous, a different portion

of the Gaspé Flint donor genome (Salvi et al. 2011). The population has already been characterized

phenotypically for flowering time, number of leaves, plant height and root characteristics at early

stage (Salvi et al., 2011 and 2016).

DNA of the 73 ILs and the two parents (B73 and Gaspé Flint) was prepared using standard methods

from leaf tissue, quantified, quality-checked and analyzed using a SNP array based on the

ILLUMINA Infinium technology. Samples were analyzed in duplicate. Row SNP data produced by

the Infinium analysis were treated at UNIBO with the ILLUMINA software GenomeStudioTM. By

comparing B73 and Gaspè Flint, approx. 39% of the SNP resulted polymorphic and used for the

analysis of results.

The 73 IL lines were evaluated in replicated (3 reps) field trials at two water regimes (well watered -

WW, and rain-fed or water-stressed - WS) by Horta (Ravenna, Italy) during summer 2013. Thirty-


Pag.5

two traits covering phenology, plant architecture, yield, and root architecture were analyzed (Table

1).

Table 1- list of phenotypic traits evaluated by Horta on the collection of IL lines

For underground root analysis (Shovelomics. Trachsel et al. 2011), the root apparatus of three

representative plants per plot were considered by manually uprooting each plant, and subsequent

washing and phenotypic analysis. A total of 1350 samples (= plants) were evaluated. For each root,

digital photos and dry matter weight were assessed. The 18 available photos for each genotype have

been cleaned and analyzed with the software REST (Colombi et al. 2015) and brace root number at

each whorl counted and multiply by two. Moreover, traits related to root system architecture (mass


Pag.6

and depth) have been measured (visual score) in order to validate software results. Currently, the

shovelomics protocol (manually uprooting each plant and subsequent washing) is followed by field

score of selected traits. We would like to integrate the shovelomics protocol with new imaging

methods suitable for mature root system analysis that are now emerging (DIRT, Bucksch et al., 2014;

REST, Colombi et al., 2015).

QTL analysis was done using the program QTL IciMapping, version 4.0.6.0 (Meng et al., 2015)

developed for segment substitution (CSS) lines using the method “RSTEP-LRT-ADD” (Stepwise

regresson based likelihood ratio tests of additive QTL, (Wang et al., 2006).

2.2 Results

The pattern of Gaspé Flint introgressions known for this population based on SSR markers was fully

confirmed after this SNP analysis. However, based on SNP analysis, the coverage of the Gaspé Flint

genome provided by the IL collection is higher than initially estimated with a mean value of 3.3

introgressions per IL line with an average length of 39 Mb (corresponding to approx. 25 cM of Gaspé

Flint genome introgressed into B73). It can now be estimated that 80% of Gaspé Flint genome is

represented within the collection. Bin map was constructed for the 73 IL lines based on individual

SNPs. Consecutive SNP sites with the same genotype were lumped into blocks and a breakpoint was

assumed at the transition between two different genotype blocks (Figure 1).

1 2 3 4 5 6 7 8 9 10

Figure 1: Bin map representation for the 73 IL lines and the two parental lines B73 and Gaspé Flint based on SNPs analysis

For 27 of the 32 traits measured, a total of 90 putative QTLs were identified across the two water

ragimes analyzed. Of those 90, 38 in WD, 41 in WW (6 overlapping) and 18 in the combined analysis

only. Of those 90, 25 refers to root traits, 26 to phenological and morphological traits, 6 to stress


Pag.7

related traits and 33 to ear morphology and yield related traits. For most of the 25 QTLs identified

for root traits Gaspé provided the minus allele and they span six out of ten chromosomes.

The following region deserve special attention (figure 2):

- Chr 2 bin 2.04: this region is involved in the control of root angle measure by REST software,

number of brace root, ear per plant, thouthand kernel weight and NDRE. This region was already

indicated as involved in the control of both root angle and yield for the Root_ABA1 locus (Giuliani et

al., 2005) and collocate with the MQTL Rt6 for root traits (Hund et al., 2011) for Root pulling Force

(Lebreton et al., 1995).

- Chr 1 bin 1.03-1.04: this region is involved in the control of root dry weight, root area and number

of ear per plant in WS condition with Gaspé conferring the increasing allele. Interestingly, upstream

to those QTLs, a second non-overlapping region harbor QTLs for number of brace root (LOD 6),

SPAD and thousand kernel weight. This region is located very close (10 cM) after the rtcs locus even

if it is not included in the confidence interval based on this analysis.

- Chr 8 harbored the highest number of root QTLs. Chr. 8 bin 8.02-8.03: harbored two blocks of

averlapping QTLs. They are involved in the constitutive control of number of brace root nodes, root

depth scored, and plant height, in WW conditions root mass, plant node number and root dry weight

and in WD conditions number of brace root and female flowering. Interestingly this region was not

previously described in the control of root architecture.

- Chr 8 bin 8.05: involved in the control of male, female flowering time and plant node number as

correspond to the vgt1 locus but also in harvest root area root density and root dry weight.

- Chr 4 bin 4.10 Putative QTLs for constitutive control of ear traits (kernel row number, ear diameter,

ear weight, kernel number per ear) and root traits (root mass and root density).

For brace root, in addition to the QTLs already described on chr. 1 bin 1.02 and chr. 8 bin 8.03 we

have identified a second region on chr. 6 bin 6.05 involved in the control of brace root number and

brace root node number that collocate with ear length and kernel row number QTLs.


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PZA01022.1 5.0PZE-101009670 6.0

SYN14147 9.0PZE-101018057 11.0PZE-101021997 13.0PZE-101024424 15.0PZE-101029689 18.0PZE-101032246 20.0PZE-101037493 25.0

PZA00017.1 36.0PZE-101056681 40.0

SYN11249 43.0PZE-101060930 45.0PZE-101081964 67.0PZE-101081841 69.0

PZE-101084888 PZE-101085005PZE-101085026 74.0

SYN3057 85.0PZE-101093764 87.0

SYN25478 88.0PZE-101096451 95.0PZE-101101071 98.0

SYN382 162.0PZE-101129563 165.0

SYN38813 170.0PZE-101135107 175.0

SYN1740 PZA03531.1 184.0PZE-101148225 192.0PZE-101155962 199.0PZE-101158364 201.0

PZA03218.2 207.0PZE-101166237 209.0

SYN20167 211.0PZE-101173830 218.0

PZE-101177893 PZE-101178411 223.0PZE-101190874 237.0

SYN21006 240.0PUT-163a-148951459-517 254.0

PZE-101208921 258.0SYN19653 275.0

PZE-101225822 276.0PZE-101229026 279.0PZE-101231613 280.0PZE-101232381 281.0

SYN34903 PZE-101235852 283.0PUT-163a-71332426-3197 SYN20060 285.0

PZE-101239336 SYN38914SYN3018 PZE-101240454 287.0

PZE-101242552 289.0PZE-101243254 290.0

PZE-101246140 PZE-101246274 292.0PZE-101249154 294.0

SYN12422 PZE-101251928 296.0PZE-101252431 297.0

PZE-101257177 PZE-101257510 301.0

NB

RR

DW

RD

W

AR

EA

MW

MF

D

TK

WE

PP

SP

AD

FF

FF

MF

FF

1

PHM13440.11 PZE-102006513 3.0SYN27836 5.0

PZE-102013873 6.0SYN1141 9.0

SYN30601 11.0PZE-102030702 14.0

SYN9947 17.0PZE-102040639 20.0PZE-102040935 21.0

SYN25976 22.0PZE-102044897 23.0PZE-102050267 28.0PZE-102052836 30.0

PUT-163a-78083518-4162 PZE-102053630 31.0PZE-102056209 34.0

PZB00257.1 35.0SYN394 52.0

PZE-102104531 130.0

PZE-102125367 174.0

PZE-102147862 195.0SYN10567 204.0

PZE-102159395 PZE-102160379 206.0PZE-102164011 209.0PZE-102165681 210.0

PZE-102178234 PZE-102178542 221.0

PZE-102194304 237.0

TK

W

TK

W EP

P

EP

P

ND

RE R

AR

NB

RE

L

EL

EW

EW

KN

PE

KN

PE

2

PZE-104000008 0.0PZE-104001238 1.0

PZE-104002805 PZE-104003003PZE-104003099 PZE-104003437

PUT-163a-148946720-4943.0

PZE-104008924 6.0PZE-104012818 11.0PZE-104014780 14.0PZE-104020500 22.0PZE-104026198 31.0

PZE-104042855 61.0PZE-104044698 66.0

PZE-104052472 83.0

PZE-104063568 125.0PZE-104065696 130.0

SYN13516 135.0

PZE-104072760 144.0

PZE-104078327 153.0PZE-104079887 154.0

PZE-104095135 PZE-104095248 172.0PZE-104100589 177.0

PZE-104103558 PZE-104103955 180.0PZE-104109358 186.0PZE-104110323 187.0PZE-104116713 193.0

PZE-104137686 SYN2779 225.0PZE-104145006 234.0

SYN19392 235.0PZE-104150993 237.0PZE-104153707 238.0

SYN39223 239.0PZE-104158108 241.0

AS

I

TK

W

TK

WE

DK

NP

E

CD

CD

AS

I

FF

A

RM

S

EW E

W

EW

ED E

D

ED

KR

KR

KR

KN

PE

KN

PE

4


Pag.9

Figure 2: QTLs identified on chromosome 1,2,4 and 8. Red refers to WS conditions, Blu to WW and

Green to the combined analysis of the two treatment. Distance is expressed in Mbp.

2.3 Discussion

The high-density genotyping of the Gaspé Flint-B73 introgression library have been crucial for the

identification of genomic regions involved in the control of root morphology as give us an exhaustive

information of the real introgressions of each IL line. In fact, even if the Gaspé Flint introgressions

known for this population based on SSR markers was fully confirmed after this SNP analysis, new

introgressions have been identified and this information have been used for a more precise QTL

analysis .

A positive high correlation was observed between visually scored root traits and corresponding traits

based on software-based analysis done with the software REST and collocation among REST QTLs

and scored QTLs have been identified thus indicating that REST software is suitable for shovelomics

image analysis. Moreover, for both REST QTLs and scored and measured root traits QTLs it was

possible to identify QTLs or MQTLs for the corresponding traits reported in literature.

We identified six main root-QTLs region as described in the results. Interestingly, same regions

(except chromosome 8) are also involved in the control of yield or yield related traits. Whether this

relation is due to linkage of different genes at the same region or pleiotropy remains to be clarified.

Introgression Library

PZE-108000028 0.0PZE-108002113 2.0

SYNGENTA15947 SYN17933PZE-108003557 3.0

SYN12530 PZE-108005381 5.0PZE-108005605 6.0

SYN9898 8.0PZE-108009251 PZE-108009277 10.0

PZE-108011210 12.0PZE-108012841 SYN14462 13.0

PZE-108013757 14.0PZE-108017749 17.0PZE-108019899 18.0

SYN16954 PZE-108021947 21.0SYN19605 24.0

PZE-108027940 30.0PZE-108045054 48.0PZE-108035009 49.0PZE-108047603 80.0

PZE-108058577 105.0PZE-108061188 110.0

SYN27931 118.0PZE-108067299 119.0

PZE-108081297 138.0PZE-108082144 139.0PZE-108085450 142.0PZE-108092139 149.0PZE-108099415 156.0

PZA03592.3 163.0PZE-108111467 164.0

PHM3213.11 166.0SYN11251 PZE-108130293 172.0

PZE-108135936 176.0

FF

PN PH

PH

PH

RD

W

RD

W MW

DS

DS

DS

RM

S

RM

S

NB

R NB

R

NB

RN

NB

RN

NB

RN

RD

W

AR

EA

AR

EA

FF

A

PN

PN

MF

MF

MF

FF

FF

EL

8


Pag.10

The next step would be to confirm the QTLs identified and eventually proceed with the cloning. Our

introgression library would enable to make available a large collection of nearly isogenic pair of lines

(each introgression line vs B73). By crossing the IL line showing a putative effect with B73 a new

experimental population will be developed in order to proceed for QTL fine mapping and thus

improve the resolution of map position the of QTL (Salvi and Tuberosa, 2005).

3.Tomato

3.1 Materials and Method

The plant material consisted of a collection of 119 cultivated tomato (Solanum lycopersicum L.) and

wild-related tomato species. The cultivated tomato accessions included 60 tomato landraces from

Sardinia (L-SAR), 7 landraces from other regions of Italy (L-IT), 40 landraces from different

countries around the world (L-EXOT), and 10 vintage cultivars (C) and 2 wild type (W). Seeds of the

collection were kindly provided by “Centro Interdipartimentale per la Conservazione e

Valorizzazione della Biodiversità Vegetale” (CBV), University of Sassari, Italy.

The entire collection have been previously genotype with SSR markers and SNP data obtained from

RNAseq analysis on mature fruit have been kindly provider from Prof. Giovanna Attene from

University of Sassari, Italy.

The collection was evaluated in controlled environment using a plant root observation chamber

(rhizotron, figure 3).

Figure 3: rhizotron box used for tomato seedling growth


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Each rhizotron is 1 x 20 x 30 cm and has one transparent glass panel that allows for non-invasive

viewing of roots during growing and was positioned at 45° let most of the roots grow along the glass

surface. For each genotype 6 plants were analyzed (3 plants per rhizotron x 2 rep) at 21 days after

planting. Images obtained from the scanning of the ryzotron were analyzed with 2 dedicated software:

giaroots (Galkovskyi et al., 2012) and root system analyzer (Leitner et al., 2013). While the first

software provides information related to the global root shape (area, perimeter), Root System

Analyser measures root architectural traits related to single root such as root length, radius,

connectivity, inter-root distances, or branching angles. Moreover, after scanning, roots were cleaned

from soil and root dry weight measured.

In addition to root data al 21 days after germination also data related to shoot were recorded: leaf

number, shoot length and shoot dry weigh. The analysis of variance (ANOVA) was performed over

all the accessions to test the presence of significant differences among genotypes. To detect possible

marker-trait associations, the MLM (Mixed Linear Model) test (TASSEL 5) that accounts for the

genetic structure of the collection using kinship matrix was performed.

3.2 Results

A strong positive correlation (0.9, table2) was found between primary root length measured manually

and obtained from the analysis with the software Root System analyser thus validating the software

results. Moreover, positive correlation was observed between lateral root number and primary root

length as in most cases lateral roots are uniformly distributed along primary root. Interestingly the

correlation between shoot parameters (above all length) and root traits was always very low.

Table 2: correlation among traits measured on tomato seedling at 21 days after planting


Pag.12

The ANOVA (table 3) showed highly significant difference between genotypes for all traits with the

exception of length of 2nd order of lateral root that shows also very low hereditability. For all other

traits hereditability range from 37 % (Ellipse axis ratio) to 89 % (shoot length).

Table 3: tomato seedling evaluated in rhizotron: min, max and mean value; comparison between

genotypes : * and **, significant at P < 0.05 and P < 0.01, respectively; ns = not significant;

coefficient of variation c.v. and hereditability.

Figure 4 reports some example of the most interesting genotype for some of the root traits described.

The accession Cocktail that is close to the wild species S. pimpinellifolium and, for this reason, it is

classified as wild, is an example of genotype characterized by a very open root angle and with lateral

root localized only in the upper part of the primary root while most of the genotype presents lateral

root uniformly distributed along the primary root. High variability was also identified for root mass

with two extreme phenotype showed in figure 4


Pag.13

Figure 4 Example of phenotypic variability identified among the tomato genotyped

P40- cocktail-ITA-Ortisei (C/W) Primary root lenght Lateral root 1° and 2° order number,lenght and angle

P63-S. lycopersicum var. cerasiforme (W) Lateral root 1°order number Lateral root 2° order mean lenght R l 2° d

E17 – Local - Spain (L-EXOT) (+/+) Lateral root 1° order number Area, Volume, root density

P26 - Tramatta tunda – ITA- Scano montiferro - sardinia (-/-) Primary root length Lateral root 1° order lenght Area, Volume, root density


Pag.14

Analysis of molecular variance (AMOVA) showed that maximum of molecular variance was among

individuals (91%) while only 7% was within individuals and 2% among populations. Genetic distance

analyses have shown that Sardinian landraces are more genetically similar to the exotic landraces

than to cultivars while the cultivars are more genetically similar to the wild relatives of tomato than

Sardinian and exotic landraces. As tomato is a crop usually harboring a very low genetic diversity

when compared to other species, the collection used in this study composed mainly from landraces

maximize the probability to unravel high levels of diversity.

The mean r2 between all possible SNP pairs within each chromosome was plotted against physical

distance and curve LD decay was fitted using non-linear regression (Figure 5). Similar pattern of LD

decay was identified for all chromosome with the exception of chromosome 5 where all markers

showed very high LD (noise up to 50 Mb in figure 5).

Figure 5. Pattern of LD decay considering all chromosomes.

The possible associations between molecular markers and morphological traits were investigated by

factoring out the genetic structure using MLM analysis in Taassel 5. Figure 6 reports QQ plot for the

traits analyzed showing that MLM analysis. Even if for some traits the results are partially under or

over-estimated, in general the MLM analysis resulted suitable for this study.


Pag.15

Figure 6: QQ plot of traits analysed for tomato seedling.

Tassel analysis highlighted 70 significant associations between molecular markers and phenotypic

traits (Table 4).

In particular, 16 regions located on 10 chromosome have been identified as involved in the control of

root traits:

- chromosome 1 between 72 and 87 Mbp involved in the control of the general root shape (root

volume, area, perimeter), length of 1st order lateral root and root angle of 2nd order of lateral root.

This region is also involved in the control of both root and shoot dry weight;

- chromosome 2 between 34 and 47 Mbp involved in the control of 2nd order lateral root (angle and

length) and thus network solidity. The same region resulted involved in the control of shoot length;

- chromosome 3 between 52 and 60 Mbp involved in the control of primary root length, root angle

2nd order lateral root and also shoot length;

- chromosome 4 between 51 and 59 Mbp involved in the control of primary and lateral root length,

root angle of 2nd order and shoot length;

-chromosome 5 at 41Mbp involved in the control of root angle 2nd order of lateral root and at 64 Mbp

primary root length and leaf number;

- chromosome 6 between 29 and 44 Mbp involved in the control of network perimeter and root ellipse

axis ration but also leaf number;


Pag.16

- chromosome 8 beween 0.9 and 3 Mb involved in the control of mean length of lateral root (1st and

2nd order) and root angle of 1st order of lateral root and a second region between 51 and 61 Mbp

involved in the control of shoot length, shoot dry weight and root angle of 1st order of lateral root;

- chromosome 9 between 11 and 24 Mbp involved in the control of primary root length, angle and

number of lateral root 1st order and thus root area and perimeter and a second region between 58 and

90 Mbp involved in the control of primary root length and network solidity;

- chromosome 10 at 0.9 Mbp involved in the control of angle of 2nd order of lateral root, a second

region between 62 and 86 Mbp involved in the control of length of lateral root (1st and 2nd order) and

angle of 2nd order of lateral root and a third region between 134 and 148 Mbp involved in the control

of root dry weight and length of 2nd order of lateral root;

- chromosome 11 between 49 and 56 Mbp involved in the control of mean leght of 1st order of lateral

root, root dry weight and 2nd order of lateral root and a second region between 50 and 52 Mbp involved

in the control of mean length of 2nd order of lateral root and thus root solidity.


Pag.17

Table 4: Statistically significant SNPs (-Log10P > 2.5) identified with the MLM analysis


Pag.18

3.1 Discussion

The analyses carried out showed that the current collection, mainly consisting of landraces, is

characterized by a remarkable level of phenotypic diversity. Additionally, the high heritability

observed for most of the recorded traits indicate that the root phenotyping protocol is suitable for

identifying QTLs and genes of relevant interest.

In total, we have identified 16 different regions involved in the control of root morphology; 9 of them

resulted specific for root traits and thus not involved in the general vigor of the plant.

Presently, very few publications related to the tomato root morphology are available and, even if

further analyses will be necessary in order to confirm the importance of the identified regions also at

adult stage and their relevance, the identified regions appear an important first source of information

of interest for the tomato breeding.


Pag.19

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