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ABSTRACT

MCNELLIE, JAMES PATRICK. Mapping QTLs for Fruit Quality and Horticultural Traits in Fresh Market Tomato (Solanumlycopersicum) Using SNP Markers. (Under the direction of Dr. Dilip R. Panthee.)

Quantitative trait loci (QTL) mapping has been performed extensively in tomato, but

a majority of mapping populations have been inter-specific. The use of inter-specific

populations has been a necessity due to low genetic diversity within cultivated germplasm,

which limits the availability of polymorphic molecular markers. This research used the

Solanaceae Coordinated Agricultural Project (SolCAP) SNP array to genotype an

intraspecific F2 population for QTL mapping of fruit quality and horticultural traits.

NC 10204 is an intraspecific mapping population derived from two inbred breeding

lines: NC 30P (a plum breeding line) and NC 22-L(2008) (a grape breeding line). In 2013, F2

plants were grown at the Mountain Horticultural Crops Research and Extension Center in

Mills River, NC. F2 plants were individually harvested to create F2:3 families. In 2014, F2:3

families were grown at the Mountain Horticultural Crops Research and Extension Center in

two replications of six plants (when possible) and individual lines were bulk harvested to

obtain F2:4 seed. F2:4 lines were evaluated the following summer at the Mountain Horticultural

Crops Research and Extension Center and at the Mountain Research Station at Waynesville,

NC.

Plant growth habit and inflorescence branching were phenotyped in all three

generations. The following traits were phenotyped in two generations: jointless pedicles (F2

and F3), total soluble solids (F2 and F3), fruit shape (F2 and F3), and days to 50% fruit ripe (F2

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Analyzer quantitatively measures forty-four fruit quality traits related to size, shape,

uniformity and color.

Genomic DNA was extracted from F2 plants using a modified cetyltrimethyl

ammonium bromide method and used to genotype 190 lines with the SolCAP Illumina

Infinium Assay. The linkage map for NC 10204 was constructed using ‘R/qtl’, 187 plants,

and contained 415 polymorphic SNPs across 858.9cM with an average spacing of 2.13cM.

Segregation distortion favoring NC 22L-1(2008) was present on chromosome 2, and to a

lesser degree on chromosome 4.

Twenty-six QTLs were mapped for twenty-two fruit quality traits measured by

Tomato Analyzer. Size traits mapped QTLs to chromosome 2 (four QTLs between 78.10cM

and 80.10cM), and chromosome 9 (three QTLs at 55.66cM). Fruit shape traits mapped to

chromosome 3 (two QTL between 3.10cM and 6.10cM), chromosome 9 (four QTLs between

29.10cM and 41.19cM), and chromosome 12 (three QTLs between 5.10cM and 6.10cM).

Uniformity traits were mapped to chromosome 3 (one QTL at 7.10cM), chromosome 9 (three

QTLs between 27.10cM and 41.19cM), chromosome 11 (one QTL at 52.9cM) and

chromosome 12 (one QTL at 6.10cM). Color traits had three QTLs mapped to chromosome 2

between 78.1cM and 81.10cM. Visually phenotyping fruit shape mapped QTLs to roughly

the same location on chromosome 9 (56.10cM) and 12 (5.10cM) as the fruit shape traits

measured with Tomato Analyzer.

Total soluble solids mapped two QTLs to chromosome 2 (78.10cM) and chromosome

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17cM away from the QTL for growth habit. Inflorescence branching mapped two QTLs to

chromosome 6 (50.43cM) and 12 (29.21cM).

This research suggests that NC 10204 is segregating for genes controlling fruit size

on chromosome 2 and 9, fruit shape on chromosome 3, 9, and 12, fruit uniformity on

chromosome 3, 9, 11 and 12, and fruit color on chromosome 2. The fruit quality traits on

chromosome 9 and 12, if validated, may have value in marker assisted selection in tomato

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© Copyright 2016 James Patrick McNellie

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Mapping QTLs for Fruit Quality and Horticultural Traits in Fresh Market Tomato (Solanum lycopersicum) Using SNP Markers

by

James Patrick McNellie

A thesis submitted to the Graduate Faculty of North Carolina State University

in partial fulfillment of the requirements for the degree of

Master of Science

Horticulture Science

Raleigh, North Carolina

2016

APPROVED BY:

_______________________________ _______________________________ Dr. Dilip R. Panthee Dr. Peter Balint-Kurti

Committee Chair

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DEDICATION

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BIOGRAPHY

James Patrick McNellie was born in Fairfax, Virginia and raised in nearby McLean, VA.

After high school he attended Indiana University and earned a Bachelor of Arts in Economics

and History in 2003. James returned to northern Virginia and worked as a Financial Analyst

and Government Contractor in the Washington, D.C. metropolitan area before return to

Indiana. After working for three years as a Government Contractor in Indianapolis, IN, James

returned to school and in 2013 earned a Bachelor of Science in Plant Genetics and Breeding

from Purdue University. He started his Master of Science research at North Carolina State

University the following fall. His research focused on QTL mapping fruit quality and

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ACKNOWLEDGMENTS

First and foremost, Dr. Dilip R. Panthee for giving me the opportunity to earn my Master of

Science in his program. Secondly my committee members, Dr. Todd Wehner and Dr. Peter

Balint-Kurti. Other noteworthy faculty members at North Carolina State University: Dr.

Ralph Dewey, Dr. Christopher Gunter, Dr. Sergei Krasnyanski, Dr. Jeanine Davis, Dr.

Thomas Ranney, and Dr. Randy Gardner.

Everyone at the Mountain Horticultural Crops Research and Extension Center, Mills River,

NC; especially Ann Piotrowski, Jeremy Smith, Ragy Ibrahem, Nathan Lynch, Dr.

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TABLE OF CONTENTS

TABLE OF CONTENTS ... V LIST OF TABLES ... VII LIST OF FIGURES ... IX

CHAPTER 1 FRUIT QUALITY AND HORTICULTURAL TRAITS IN TOMATO .... 1

INTRODUCTION ... 1

FRUITQUALITYTRAITS ... 3

HORTICULTURALTRAITS ... 4

RESEARCHOBJECTIVES ... 6

REFERENCES ... 8

CHAPTER 2 QTL ANALYSIS FOR FRUIT TRAITS PHENOTYPED USING TOMATO ANALYZER IN THE INTRA-SPECIFIC TOMATO POPULATION NC 10204... 16

INTRODUCTION ... 16

MATERIALSANDMETHODS ... 18

Population Development ... 18

DNA Extraction and Genotyping ... 19

Phenotyping ... 19

Data Analysis and QTL Mapping ... 20

RESULTSANDDISCUSSION ... 23

Segregation Distortion ... 25

QTLs on Chromosome 2 Associated with Multiple Fruit Quality Traits ... 27

QTLs on Chromosome 9 Associated with Multiple Fruit Quality Traits ... 27

QTLs on Chromosome 12 Associated with Multiple Fruit Quality Traits ... 28

QTLs on Chromosome 3 & 11 Associated with Multiple Fruit Quality Traits ... 29

Total soluble solids ... 30

Fruit Shape... 31

Comparison of Phenotypic Data Obtain using Tomato Analyzer and Phenotyping Fruit Shape Visually ... 31

CONCLUSION ... 32

REFERENCES ... 33

CHAPTER 3 QTL ANALYSIS FOR TOMATO HORTICULTURAL TRAITS IN THE INTRA-SPECIFIC POPULATION NC 10204 ... 70

INTRODUCTION ... 70

MATERIALSANDMETHODS ... 73

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DNA Extraction and Genotyping ... 73

Phenotyping ... 74

Data Analysis and QTL Mapping ... 74

RESULTSANDDISCUSSION ... 78

Growth Habit ... 78

Jointless Pedicel... 78

Inflorescence branching... 79

Days to 50% Fruit Ripe ... 81

CONCLUSION ... 81

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LIST OF TABLES

TABLE 2.0.1NUMBER OF LINES PHENOTYPED IN THE INTRA-SPECIFIC TOMATO POPULATION NC 10204 FOR FRUIT QUALITY TRAITS BY GENERATION AND NUMBER OF LINES WITH SNP MARKER DATA ... 38 TABLE 2.0.2.DESCRIPTION OF TOMATO ANALYZER TRAITS WITH SIGNIFICANT QTLS REPORTED IN THE INTRA-SPECIFIC TOMATO POPULATION NC10204(RODRIGUEZ ET AL.2010) ... 39 TABLE 2.0.3.SUMMARY OF NC10204 LINKAGE MAP BY CHROMOSOME.FOR EACH

CHROMOSOME THE NUMBER OF POLYMORPHIC SNP MARKERS, LENGTH, AVERAGE

MARKER SPACING AND MAXIMUM SPACING IS SHOWN ... 41 TABLE 2.0.4.SOLCAP MARKERS IN THE NC10204 LINKAGE MAP WITH CHROMOSOME NUMBER

AND POSITION IN CENTIMORGAN.MARKER ORDER WAS DETERMINED USING THE PHYSICAL MAP (SL2.40) AND THE DISTANCE BETWEEN MARKERS WAS DETERMINED USING THE KOSAMBI MAPPING FUNCTION IN ‘R/QTL’(KOSAMBI 1943)... 42 TABLE 2.0.5.PEARSON’S CORRELATION COEFFICIENT AMONG FRUIT QUALITY TRAITS IN THE

INTRA-SPECIFIC TOMATO POPULATION NC10204 ... 53 TABLE 2.0.6.CORRELATION BETWEEN REPLICATIONS FOR FRUIT QUALITY TRAITS IN THE INTRA

-SPECIFIC TOMATO POPULATION NC10204 MEASURED WITH TOMATO ANALYZER ... 55 TABLE 2.0.7.ANALYSIS OF VARIANCE FOR TOTAL SOLUBLE SOLIDS AND FRUIT SHAPE USING F2

AND F2:3PHENOTYPIC DATA.MEAN SQUARED IS DERIVED FROM TYPE III SUM OF SQUARES USING PROC GLM IN SAS VERSION 9.4 FOR WINDOWS (SASINSTITUTE INC,CARY,NC) ... 56 TABLE 2.0.8.HERITABILITY ESTIMATES FOR FRUIT QUALITY TRAITS IN THE INTRA-SPECIFIC

TOMATO POPULATION NC10204.THE NARROW-SENSE HERITABILITY IS SHOWN FOR TOTAL SOLUBLE SOLIDS, FRUIT SHAPE AND GROWTH HABIT.THE BOARD-SENSE

HERITABILITY IS SHOWN FOR THE TRAITS MEASURED WITH TOMATO ANALYZER ... 57 TABLE 2.0.9.SUMMARY OF QTL ANALYSIS OF FRUIT QUALITY TRAITS IN THE INTRA-SPECIFIC

TOMATO POPULATION NC10204 USING HALEY-KNOTT REGRESSION.THE QTLS FOR THE

TRAITS MEASURED WITH TOMATO ANALYZER WERE MAPPED USING PHENOTYPIC DATA

FROM THE F3 GENERATION.THE QTLS FOR TOTAL SOLUBLE SOLIDS AND FRUIT SHAPE WERE MAPPED USING THE LEAST SQUARED MEANS OF THE F2 AND F3 GENERATIONS ... 58 TABLE 2.0.10.COMPARISON OF THREE NC10204 LINKAGE MAPS DIFFERING FOR SNP

MARKERS WITH SEGREGATION DISTORTION.MAP 1 IS THE LINKAGE MAP USED FOR QTL MAPPING IN THE INTRA-SPECIFIC TOMATO POPULATION NC10204.MAP 2 HAD SNP MARKERS REMOVED THAT DEVIATED SIGNIFICANTLY (Α =0.05) FROM EXPECTED

MENDELIAN SEGREGATION RATIOS AFTER BONFERRONI CORRECTION FOR MULTIPLE

TESTS.MAP 3 HAD MARKERS REMOVED THAT SIGNIFICANTLY DEVIATED (Α =0.05) FROM

MENDELIAN SEGREGATION RATIOS WITHOUT THE BONFERRONI CORRECTION FOR

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TABLE 2.0.11.COMPARISON OF LOD THRESHOLD, MAXIMUM LOD SCORE AND SIGNIFICANCE

OF TRAITS MEASURED WITH TOMATO ANALYZER USING THE THREE LINKAGE MAPS

DESCRIBED IN TABLE 2.10 ... 62 TABLE 2.0.12.COMPARISON OF QTLS MAPPED IN THE INTRA-SPECIFIC TOMATO POPULATION

NC10204 USING PHENOTYPIC DATA OBTAINED USING TOMATO ANALYZER AND QTLS

MAPPED IN FOUR POPULATIONS PREVIOUSLY PHENOTYPED WITH TOMATO ANALYZER.

ONLY CHROMOSOME NUMBER IS COMPARED.THE POPULATION HOWARD GERMAN X

LA1589 WAS PHENOTYPED IN TWO GENERATIONS (F2 AND BC1) ... 64 TABLE 3.0.1.NUMBER OF LINES IN EACH GENERATION PHENOTYPED FOR HORTICULTURAL

TRAITS IN THE INTRA-SPECIFIC TOMATO POPULATION NC10204 ... 87 TABLE 3.0.2.PEARSON’S CORRELATION COEFFICIENT BETWEEN HORTICULTURAL TRAITS IN THE INTRA-SPECIFIC TOMATO POPULATION NC10204 ... 88 TABLE 3.0.3.NARROW SENSE HERITABILITY FOR HORTICULTURAL TRAITS IN THE INTRA

-SPECIFIC TOMATO POPULATION NC10204.THE HERITABILITY FOR GROWTH HABIT,

INFLORESCENCE BRANCHING AND DAYS TO 50% RIPE WAS CALCULATED USING PARENT –

OFFSPRING REGRESSION.THE HERITABILITY FOR JOINTLESS PEDICELS WAS CALCULATED ON A LOGISTIC SCALE ... 89 TABLE 3.0.4.ANALYSIS OF VARIANCE FOR HORTICULTURAL TRAITS IN THE INTRA-SPECIFIC

TOMATO POPULATION NC10204.GROWTH HABIT AND INFLORESCENCE BRANCHING USED

PHENOTYPIC DATA FROM THE F2,F3, AND F4GENERATIONS.JOINTLESS PEDICELS USED PHENOTYPIC DATA FROM THE F2 AND F3 GENERATIONS, AND DAYS TO 50% FRUIT RIPE USED PHENOTYPIC DATA FROM THE F2 AND F4 GENERATIONS.MEAN SQUARED IS DERIVED FROM TYPE III SUM OF SQUARES USING PROC GLM IN SAS VERSION 9.4 FOR WINDOWS (SASINSTITUTE INC,CARY,NC) ... 90 TABLE 3.5.SUMMARY OF QTL ANALYSIS FOR HORTICULTURAL TRAITS IN THE INTRA-SPECIFIC

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LIST OF FIGURES

FIGURE 1.1.EXAMPLE OF TOMATO ANALYZER PHENOTYPING SOFTWARE ... 13 FIGURE 1.2.EXAMPLE OF A JOINTED TOMATO PEDICEL ... 14 FIGURE 1.3.EXAMPLE OF A JOINTLESS TOMATO PEDICEL ... 15 FIGURE 2.0.1.THE LINKAGE MAP FOR THE INTRA-SPECIFIC TOMATO POPULATION NC10204

SHOWING SNP MARKER DENSITY ON INDIVIDUAL CHROMOSOMES.CREATED USING THE

KOSAMBI MAPPING FUNCTION AND MARKER ORDER WAS DETERMINED USING PHYSICAL

MAP POSITION (SL2.40)(KOSAMBI 1943) ... 66 FIGURE 2.0.2.SEGREGATION DISTORTION ON CHROMOSOMES 2 AND 4 IN THE INTRA-SPECIFIC

TOMATO POPULATION NC10204.THE TOP PANEL SHOWS THE NEGATIVE LOG OF P-VALUES FROM CHI-SQUARE TESTS FOR MENDELIAN SEGREGATION.THE BOTTOM PANEL

SHOWS ALLELE FREQUENCIES.THE BLUE LINE REPRESENTS THE FREQUENCY OF

HETEROZYGOUS SNPS (AB), THE BLACK LINE HOMOZYGOUS SNPS FOR NC22L-1(2008) (AA) AND THE RED LINE HOMOZYGOUS SNPS FOR NC30P(BB).THE DASHED

HORIZONTAL LINES INDICATE THE PREDICTED MENDELIAN SEGREGATION FREQUENCIES OF

50% FOR HETEROZYGOTES AND 25% HOMOZYGOTES. ... 67 FIGURE 2.0.3.THE GUIDE USED TO PHENOTYPE PREDOMINANT FRUIT SHAPE IN THE INTRA

-SPECIFIC TOMATO POPULATION NC10204 IN THE F2 AND F2:3 GENERATIONS ... 68 FIGURE 2.0.4.EXAMPLE OF FRUIT SHAPE SEGREGATION IN THE INTRA-SPECIFIC TOMATO

POPULATION NC10204.ALL FRUIT ARE FROM A SINGLE F2:3FAMILY.THE FRUIT WITHIN THE YELLOW BOX IS PEAR SHAPED (OBOVOID) ... 69

FIGURE 3.0.1.AVERAGE MONTHLY PRODUCER PRICE INDEX FOR FRESH MARKET TOMATOES,

1947–2010. ... 92 FIGURE 3.0.2.PLOT OF SNP MARKER EFFECT ON GROWTH TYPE FOR THE SNP CLOSEST TO THE

QTL FOR GROWTH TYPE ON CHROMOSOME 1.THE TOP PANEL (A) IS THE F2 GENERATION, MIDDLE PANEL (B) THE F3 GENERATION AND THE BOTTOM PANEL (C) THE F4 GENERATION. THE GENOTYPE EFFECT APPEARS TO BE RANDOM IN A AND C, BUT NOT IN B.THIS IS LIKELY DUE TO THE LOWER NUMBER OF PLANTS PHENOTYPED IN THE F3 GENERATION (TABLE 3.1). ... 93 FIGURE 3.0.3.INTERACTION BETWEEN SNPS ASSOCIATED WITH JOINTLESS PEDICELS AND

GROWTH TYPE ON INFLORESCENCE BRANCHING USING DATA FROM ALL GENERATIONS.

THE X AXIS SHOWS THE SNP GENOTYPES FOR GROWTH TYPE WHERE A/- IS INDETERMINATE AND BB DETERMINATE.THE THREE LINES REPRESENT THE SNP

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FIGURE 3.0.4.EXAMPLE OF INFLORESCENCE REVERTING TO VEGETATIVE GROWTH (WHITE ARROW) IN AN INDETERMINATE, JOINTLESS PEDICEL CULTIVAR ... 97 FIGURE 3.5.INFLORESCENCE BRANCHING IN THE INTRA-SPECIFIC TOMATO POPULATION NC

10204.THE INFLORESCENCES LABELED P1 AND P2 ARE REPRESENTATIVE INFLORESCENCE FROM THE TWO INBRED PARENTS USED TO CREATED NC10204, WITH P1 CORRESPONDING TO NC30P,P2 TO NC22L-1(2008) AND F1 TO THE NC10204 HYBRID.THE THREE INFLORESCENCES TO THE RIGHT OF F1 ARE REPRESENTATIVE OF THE MULTIPAROUS

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CHAPTER 1 FRUIT QUALITY AND HORTICULTURAL TRAITS IN TOMATO

INTRODUCTION

The tomato, Solanum lycopersicum, is considered the model organism for fleshy fruit development and is an economically important crop with the 2014 United States of America

fresh market tomato harvest valued at $1.3 billion (USDA National Agricultural Statistics

Service 2015). A small diploid genome and autogamous reproductive system makes tomato

ideal for genetic studies. Quantitative trait loci (QTL) mapping has been performed

extensively in tomato. To give an idea of just how extensively, ‘Genetics, Genomics and

Breeding of Tomato’ lists 102 mapping studies reporting 5,675 QTLs (Grandillo, Termolino,

van der Knaap 2013). Unfortunately, the words of Rex Bernardo from his 2008 review of

QTLs is true that “the vast majority of the favorable alleles at these identified QTL reside in

journals on library shelves rather than in cultivars that have been improved through the

introgression or selection of these favorable QTL alleles” (Bernardo 2008). For tomato, this

can partly be attributed to the use of inter-specific mapping populations.

Inter-specific populations are derived from crossing two different species, S. lycopersicum

crossed to a wild relative. An intra-specific population is a cross between organisms of the

same species, S. lycopersicum crossed with another S. lycopersicum or S. lycopersicum var.

cerasiforme (cherry or grape tomatoes). A majority of S. lycopersicum linkage maps are inter-specific and are “of low-to-moderate density, having an average inter-marker spacing of

around 5cM and each includes between 70 and 400 markers” (Foolad 2007). During

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(Bai and Lindhout 2007). Inter-specific mapping populations were a necessity because the

low genetic diversity within cultivated germplasm hindered the detection of polymorphic

molecular markers (Foolad, Jones, Rodriguez 1993; García-Martínez et al. 2006; Joshi,

Gardner, Panthee 2012; Miller and Tanksley 1990; Park, West, St. Clair 2004; van Berloo et

al. 2008; Williams and Clair 1993). Twelve years after the first molecular linkage map by

Paterson et al. (1988), the first dense intra-specific linkage map was created in a cross

between a cultivated cherry line (Cervil) and a large fruited variety of (Levovil)

(Saliba-Colombani et al. 2000). The linkage map created by Saliba-(Saliba-Colombani et al. (2000)

contained 376 molecular markers, an improvement over two previous attempts at creating

whole genome intra-specific linkage maps which had at most 67 markers (Danesh et al.

1994; Lindhout et al. 1994). Problems obtaining sufficient polymorphic markers in

intra-specific populations persisted into the 2000s. Of the two intra-intra-specific populations in the

literature created after Saliba-Colombani et al. (2000), one had 37 polymorphic markers

(Yang et al. 2005) and the other 44 polymorphic markers (Wang et al. 2011).

The Solanaceae Coordinated Agricultural Project (SolCAP) 7,720 SNP array is the first

publically available tomato SNP array and was created by sequencing six tomato accessions

generating 62,576 non-redundant SNPs, of which 7,720 became the SolCAP array (Hamilton

et al. 2012; Sim et al. 2012a). Rodriguez et al. (2013) used the SolCAP array to genotype

three intra-specific bi-parental populations and found an average of 625 polymorphic

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parental QTL mapping (Rodríguez, Kim, van der Knaap 2013). Now that molecular markers

can readily be obtained in intra-specific populations, existing genetic variation within

breeding germplasm can be utilized in mapping experiments.

FRUIT QUALITY TRAITS

The most important traits for fresh market tomatoes are fruit quality traits. Tomato taste and

other organoleptic traits like scent, are complex quantitatively controlled traits. The

intra-specific population developed by Saliba-Colombani et al. (2000) has been used to map at

least 130 organoleptic related QTLs (Causse et al. 2001; Causse et al. 2002;

Saliba-Colombani et al. 2001). Total soluble solids is an estimation of how much total sugar a

tomato contains without measuring the exact amount of glucose, fructose or sucrose

(Grandillo, Termolino, van der Knaap 2013). Increasing total soluble solids in large fruit

varieties is difficult because it is negatively correlated with fruit size (Panthee et al. 2013b).

By 2007 there were 95 QTLs reported for total soluble solids, fructose, glucose and sucrose

(Grandillo, Termolino, van der Knaap 2013).

Fruit color is a polygenically-controlled trait; however, most discussions on the subject focus

on recessive mutations in the carotenoid pathway. For example, plants containing the ogc

gene have a mutation in a -cyclase protein preventing the conversion of lycopene to 

-carotene, leading to an accumulation of lycopene which gives the fruit a deep red color

(Ronen et al. 2000). Fruit color can be measured using a spectrophotometer (Ashrafi et al.

2012), colorimeter (Sacks and Francis 2001) or a flatbed scanner (Darrigues et al. 2008).

The fruit phenotyping program Tomato Analyzer was released in 2006 and, at the time, could

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phenotypes the scanned image of sliced tomato fruit (Figure 1.1). Subsequent versions of

Tomato Analyzer added the ability to phenotype color and additional morphological traits

(Gonzalo et al. 2009). Brewer et al. 2007 used Tomato Analyzer to map 95 QTLs for 15 fruit

traits in two inter-specific populations. A similar study by Gonzalo et al. 2009 mapped an

additional 63 QTLs and identified chromosomal regions that contain genes influencing fruit

shape. Tomato Analyzer has also been used to fine map genes controlling fruit shape

(Rodríguez, Kim, van der Knaap 2013; Sun et al. 2015), estimate fruit lycopene content

based upon color parameters (Panthee et al. 2013a) and to calculate the inheritance of fruit

color (Panthee et al. 2015).

Bauchet et al. (2014) used both the SolCAP array and Tomato Analyzer in a genome wide

association study for fruit size; but only one trait was phenotyped using Tomato Analyzer.

Rodriguez et al. (2013) used Tomato Analyzer to phenotype three intra-specific populations

for three fruit traits, followed by analysis of variance to identify SolCAP markers associated

with fruit traits. Phenotypic data obtained with Tomato Analyzer was used to map loci

influencing fruit color in a bi-parental inter-specific population (Darrigues 2007). To the

authors knowledge, Tomato Analyzer has not been used to map color traits in an

intra-specific bi-parental population.

HORTICULTURAL TRAITS

In tomato, polygenic horticultural traits have not been studied in as much depth as disease

resistance and fruit traits, even though their importance to growers is great. The production of

fresh market tomatoes is labor intensive; requiring hand pruning, stringing and multiple

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were estimated at roughly $7,000 per hectare, with harvest labor accounting for about half

the cost (Sydorovych et al. 2008). Tomato breeders can help growers by developing cultivars

with reduced production costs and increased marketable yield.

There are two widely deployed genes that tomato breeders use to improve horticultural traits:

the self-pruning and jointless-2 genes. When the self-pruning gene is homozygous recessive, plants have a determinant growth habit (Carmel-Goren et al. 2003). Determinant growth is a

requirement for processing tomatoes and is desirable for fresh market tomatoes as

determinant plants have a shorter harvest window. When the jointless-2 gene is homozygous recessive, inflorescences do not form an abscission zone on the pedicel (Figure 1.2 - Figure

1.3). The lack of an abscission zone makes it easier to separate fruit from stem and is a

required trait for mechanically harvesting processing tomatoes. Fresh market varieties that

are jointed (as most are), have stems that have to be manually removed from fruit. Picking

times can be reduced by 17 to 33% by using jointless varieties (Zahara and Scheuerman

1988). The acceptance of jointless fresh market hybrids has been slow due to undesirable

pleotropic effect on taste (Scott et al. 2013; Scott, Bryan, Ramos 1997).

Much of the research on horticultural traits has focused on finding genes with an

over-dominant effect. For example, one paper by Semel et al. 2006 reported 841 overover-dominant

QTLs for 35 traits. Perhaps the most significant horticultural trait discovery since the self-pruning gene has been the over-dominant effect of the single flower truss gene. When

heterozygous and in a determinant genetic background, single flower truss alters the hormone florigen leading to significant increase in yield (Krieger, Lippman, Zamir 2010). The

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Determinant plants have a gradual decrease in the number of leafs between inflorescences

before the apical meristem terminates in two inflorescences. The single flower truss gene interacts with the self-pruning gene to delay the termination of the apical meristem (Krieger, Lippman, Zamir 2010; Pnueli et al. 1998). The growth habit of determinant, single flower truss heterozygotes is called semi-indeterminate. The single flower truss increases yield by increasing the number of inflorescences, roughly 20% more per plant with an average of 1

extra flower per inflorescence (Krieger, Lippman, Zamir 2010).

The single flower truss and the jointless-2 genes also effect the differentiation of the inflorescence meristem, though in different ways. When single flower truss is homozygous recessive, many inflorescences have a single flower. When jointless and in an indeterminate

genetic background, many inflorescences revert to vegetative growth (Emergy and Munger

1970; Rick and Sawant 1955; Szymkowiak and Irish 1999). Double mutant studies have

hypothesized that jointless-2 and single flower truss are involved in a regulatory pathway for inflorescence meristem, but the pathway is far from resolved (Périlleux, Lobet, Tocquin

2014; Thouet et al. 2012). QTL mapping studies have not been conducted to identify

additional loci involved in regulating the inflorescence meristem in addition to the jointless-2

and self-pruning genes.

RESEARCH OBJECTIVES

The major objectives of this research were:

1. Perform QTL mapping in the intra-specific tomato population NC 10204 for fruit

size, shape, uniformity and color using traits using phenotypic data obtained with

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REFERENCES

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Blanca J, Montero-Pau J, Sauvage C, Bauchet G, Illa E, Díez MJ, Francis D, Causse M, van der Knaap E, Cañizares J. 2015. Genomic variation in tomato, from wild ancestors to contemporary breeding accessions. BMC Genomics 16(1):257.

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Darrigues A. 2007. Dissecting variation in tomato fruit color quality through digital phenotyping and genetic mapping. PhD Dissertation. The Ohio State University.

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CHAPTER 2 QTL ANALYSIS FOR FRUIT TRAITS PHENOTYPED USING TOMATO ANALYZER IN THE INTRA-SPECIFIC TOMATO POPULATION

NC 10204

INTRODUCTION

Plant breeders working on fresh market tomato put significant effort into the development of

cultivars with high yield, earliness, and disease resistance needed by growers. Consumers

demand that tomato fruit be large, red color, firm texture, sweet taste, and have no visual

defects (Oltman, Jervis, Drake 2014; Piombino et al. 2013; Stommel et al. 2005). Fruit shape

and size is largely controlled by five genes: ovate, sun, lc, fs8.1,fw2.2 and fas (Grandillo, Termolino, van der Knaap 2013; Tanksley 2004). The improvement of fruit quality is a

priority for breeders. Our research made use of the Solanaceae Coordinated Agricultural

Project (SolCAP) 7720 SNP array and the precision phenotyping software Tomato Analyzer

to quantify and map quantitative trait loci (QTLs) for fruit quality traits in an intra-specific

bi-parental F2 derived population.

Inter-specific mapping populations have been used for the majority of QTL mapping studies

in tomato due to the lack of polymorphic markers in elite populations (Joshi, Gardner,

Panthee 2012; Miller and Tanksley 1990; Park, West, St. Clair 2004). The confounding

effect of different genetic backgrounds and deleterious linkage drag have made the

introgression of potentially beneficial alleles mapped in inter-specific populations difficult

(Lecomte et al. 2004; Robert et al. 2001). The first dense intra-specific linkage map was

created in 2000 using 376 markers (Saliba-Colombani et al. 2000). That linkage map was the

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populations remained problematic until the release of the SolCAP array (Rodríguez, Kim,

van der Knaap 2013; Wang et al. 2011; Yang et al. 2005). The current research used to the

SolCAP 7,720 SNP array for genotyping to solve the problem of obtaining polymorphic

markers in intra-specific populations.

Mapping of QTLs hinges upon the identification of significant correlations between variation

at the phenotypic and genotypic levels. The SolCAP array increases the quantity of genetic

data, and Tomato Analyzer improves the quality of the measurement of the phenotypic data.

Tomato Analyzer was released in 2006 and with subsequent upgrades can now quantitatively

measure forty-seven fruit shape and color traits (Darrigues et al. 2008; Rodriguez et al.

2010). Data derived from the SolCAP array and Tomato Analyzer were used to map

inhibitors of the ovate gene (Rodríguez, Kim, van der Knaap 2013) and, in a separate study, to map fruit size QTL in a genome wide association study (Bauchet et al. 2014). Tomato

Analyzer has also been used to estimate the amount of lycopene in fruit, which largely

influences how red fruit are (Panthee et al. 2013; Panthee et al. 2015). The improvement of

fruit color is important for consumer preference and health benefits (Oltman, Jervis, Drake

2014; Perveen et al. 2015). A better understanding of the loci influencing fruit color and

nutritional value will help increase those qualities in released hybrids.

The fresh market tomato breeding program at North Carolina State University is an applied

breeding program, and has two overarching objectives: to elucidate the genetic control of

economically important traits and to release improved cultivars. These two goals can be

addressed simultaneously by using intra-specific mapping populations. In the present study

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data obtained from Tomato Analyzer in the F2:3 generation. Visual fruit shape and total

soluble solids was also phenotyped in two generations (F2 and F3). The main objective of this

research was to identify QTLs segregating within NC 10204 for fruit quality. It is unknown if

using Tomato Analyzer improves QTL mapping over traditional fruit phenotyping methods,

i.e. visual phenotyping.

MATERIALS AND METHODS

Population Development

The hybrid NC 10204 was created in 2010 by crossing the plum tomato breeding line NC

30P to the grape tomato breeding line NC 22L-1(2008). The F1 hybrid was allowed to

self-pollinate to create a segregating F2 population and in the summer of 2013, 284 F2 plants were

grown at the Mountain Horticultural Crops Research and Extension Center in Mills River,

NC. F2 plants were individually harvested to create F2:3 families. During seed cleaning in the

fall of 2013 F2:3 seeds were exposed to deleterious amounts of bleach and 111 lines were

eliminated leaving 173 F2:3 families. Problem with germination vigor reduced the number of

lines phenotyped in the F3 generation (Table 2.1).

Seeds were germinated in 72 cell trays (56 x 28 cm2) in potting mix and grown for 6 weeks

before hand transplanting in the field. Individual plants were grown 45 cm apart within rows,

150 cm apart between rows, grown in plastic mulch, with drip irrigation. Plants were hand

strung and sprayed according to the recommended schedule (Cooperative Extension Services

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Genomic DNA was obtained from F2 plants and parental lines in the summer of 2013 using a

modified cetyltrimethyl ammonium bromide method, and stored at -20C in 10 mM Tris–

HCl pH 8.0, and 1 mM EDTA (Kabelka, Franchino, Francis 2002). Prior to genotyping,

DNA was quantified using a NanoDrop 2000 Spectrophotometer (Thermo Scientific,

Wilmington, Delaware, USA). 190 F2 plants (94 in 2013 and 96 in 2015) were genotyped

using the SolCAP Illumina Infinium Assay. SNP genotypes were determined using

GenomeStudio version 1.0 (Illumina Inc, San Diego, California, USA).

Phenotyping

Fruit shape was visually phenotyped in the F2 and F3 generations using a 1-8 scale according

to the International Plant Genetic Resources Institute’s ‘Descriptors for Tomato’

(International Plant Genetic Resources Institute 1996). Total soluble solids was measured

using an ATAGO Co. Ltd PR-32 refractometer (ATAGO U.S.A., Inc. Bellevue, Washington

USA). In the F2 generation total soluble solids was measured from five to six ripe fruit for

280 plants and in the F3 generation total soluble solids was measured from two repetitions of

twelve fruits from 126 families (Table 2.1).

In 2014, 129 F2:3 lines were phenotyped using Tomato Analyzer version 3.0, but SNP data

exists for only 106 families (Darrigues et al. 2008; Gonzalo et al. 2009a). Twenty fruit were

cut proximal to distal (stem end to blossom end), scanned using a flatbed scanner (CanoScan

8800F, Canon U.S.A. Inc, Melville, NY, USA) and images saved in JPEG format. Images

were analyzed as described by Rodriguez et al. (2010) and the results saved in an Excel

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Tomato Analyzer with QTLs reported in this study can be found in Table 2.2; see the Tomato

Analyzer Version 3 User Manual for a full description of all traits

(http://oardc.osu.edu/vanderknaap/tomato_analyzer.php).

Data Analysis and QTL Mapping

Data analysis was performed in R 3.2.2 (R Core Team 2015) and SAS software, version 9.4

for Windows (SAS Institute Inc, Cary, NC). Pearson correlation coefficients were calculated

in R using the package ‘Hmisc’ (Table 2.5 - Table 2.6) (Harrell 2015). The broad-sense

heritability (H2) for fruit quality traits measured by Tomato Analyzer were calculated on a

plot basis by using the following formula:

𝐻2 = 𝜎𝑓 2

𝜎𝑓2+ 𝜎𝑒2

Where σ2

f is the family variance and σ2e is the residual variance. The results are shown in

Table 2.8.

The narrow-sense for total soluble solids, fruit shape and growth habit were calculated via

parent-offspring regression (Table 2.8):

ℎ2 = 𝐶𝑜𝑣𝐹2,𝐹3 𝜎𝐹22

Where 𝐶𝑜𝑣𝐹2,𝐹3is the covariance between the F2 and F3 generations, and 𝜎𝐹2

2 is the variance in

the F2 generation (Holland, Nyquist, Cervantes-Martínez 2003). Analysis of variance was

performed in SAS using ‘Proc GLM’ (Table 2.7). The least squared mean for individual

families (lines) in NC 10204 was calculated in R using the packages: ‘lsmeans’, ‘lme4’ and

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estimate variance and least squared means are the same. Traits measured with Tomato

Analyzer the equation was:

𝐿𝑒𝑎𝑠𝑡 𝑠𝑞𝑢𝑎𝑟𝑒𝑑 𝑚𝑒𝑎𝑛𝑠 = 𝜇 + 𝑙𝑖𝑛𝑒 + 𝑓𝑟𝑢𝑖𝑡(𝑏𝑙𝑜𝑐𝑘) + 𝑒𝑟𝑟𝑜𝑟

where fruits from each line (or family) are nested within blocks and 𝜇 is the population mean.

The equation for total soluble solids and fruit shape was:

𝐿𝑒𝑎𝑠𝑡 𝑠𝑞𝑢𝑎𝑟𝑒𝑑 𝑚𝑒𝑎𝑛𝑠 = 𝜇 + 𝑙𝑖𝑛𝑒 + 𝑦𝑒𝑎𝑟 + 𝑙𝑖𝑛𝑒(𝑦𝑒𝑎𝑟) + 𝑓𝑟𝑢𝑖𝑡(𝑏𝑙𝑜𝑐𝑘 ∗ 𝑦𝑒𝑎𝑟) + 𝑒𝑟𝑟𝑜𝑟

where fruits from each line (or family) are nested within year, and 𝜇 is the population mean.

The NC 10204 linkage map was created using the R package ‘R/qtl’. R/qtl was also used for

QTL mapping and analysis (Broman et al. 2003). The Kosambi mapping function and a

genotyping error probability of 0.001 was used (Kosambi 1943). The final linkage map was

composed of 415 polymorphic SNPs, with an average of 2.1cM between markers across

858.9cM (Table 2.3 – Table 2.4, Figure 2.1). Marker order was determined using the physical

map position of each SNP marker according to SL2.40

(http://solgenomics.net/organism/Solanum_lycopersicum/genome). The final linkage map

contained markers with segregation distortion for twelve of the twenty-six SNPs on

chromosome 2 and for three of the forty-six SNPs on chromosome 4.

Maximum likelihood methods of QTL mapping assume phenotypic data has a normal

distribution, and it is recommend that non-normal phenotypic data be transformed prior to

QTL mapping (Yang, Yi, Xu 2006). Phenotypic data was tested for normality using the

Shapiro-Wilk test and traits with a p-value greater than 0.05 were considered normal. Traits

with a p-value less than 0.05 were transformed using Box-Cox power transformation (Box

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𝑦𝑡 =(𝑦

𝜆 − 1)

𝜆

where yt is the transformed variable, y is the untransformed variable, and λ is the

transformation exponent determined using the ‘forecast’ package in R (Hyndman and

Khandakar 2008). When λ = 0 the transformation becomes a log transformation.

Traits were initially tested for the presence of a single QTL using the ‘scanone’ function in

R/qtl before a multiple QTL model was explored using the ‘stepwiseqtl’ command (Broman

and Speed 2002). No epistatic interactions were sought for fruit quality traits measured with

Tomato Analyzer because only one generation was phenotyped and marker data was

available for only 106 lines (Table 2.1). The same procedure was used to map QTLs for total

soluble solids and fruit shape but interactions were allowed during ‘stepwiseqtl’

(Manichaikul et al. 2009). Haley-Knott regression was used for QTL mapping (Haley and

Knott 1992) and the step width was set at 1cM. The LOD threshold for declaring a QTL was

determined via 5000 permutations at a significance level of α = 0.05. The 95% Bayesian

confidence intervals for the QTLs was determined in the R/qtl package. The LOD threshold

for epistatic interactions was determined for total soluble solids and fruit shape using the

‘scantwo’ function, but due to computational requirements was calculated at step width of

2.5cM and 1000 permutations.

QTL effects were calculated using ‘R/qtl’. The percent variance explained by the QTL, as

well as the additive and dominance effects were determined using the command ‘fitqtl’. The

additive effects were calculated using the formula:

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where 𝜇𝐵𝐵 is the average value of the allele from NC30P and 𝜇𝐴𝐴 is the average of allele

from NC 22L-1(2008). Dominance effects were calculated using the formula:

𝐷𝑜𝑚𝑖𝑛𝑎𝑛𝑐𝑒 𝑒𝑓𝑓𝑒𝑐𝑡 = 𝜇𝐴𝐵 −

𝜇𝐴𝐴 + 𝜇𝐵𝐵 2

where 𝜇𝐴𝐵 is the average value of the heterozygote allele. The 95% Bayesian confidence

interval was calculated using the command ‘bayesint’ and the confidence interval was

expanded to the nearest marker (Broman et al. 2003).

RESULTS AND DISCUSSION

The fruit quality traits that Tomato Analyzer measures can be placed into four categories:

shape, size, uniformity and color traits. Only traits with QTLs reported in NC 10204 will be

discussed (Table 2.1). Size traits that will be discussed are: area, curved height, height

mid-width, maximum mid-width, perimeter, and width mid-height. Shape traits with QTLs in NC

10204 were distal fruit blockiness, ovoid, proximal fruit blockiness, proximal indentation

area, rectangular, shoulder height and width widest position. Uniformity traits with QTLs in

NC 10204 were eccentricity, eccentricity area index, and horizontal asymmetry ovoid. Only

three color traits mapped QTLs in NC 10204, average a* (colors ranging from red to green),

average chroma (color saturation) and average green (the green value in the RGB color

system).

A total of twenty-six QTLs were mapped for fruit quality traits (Table 2.9). Twenty-two fruit

quality traits had at least one QTL above the LOD threshold. Those twenty-two traits were

then analyzed using ‘stepwiseqtl’ giving twenty-seven QTLs. Roughly 80% were mapped to

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mapped to chromosome 3 and one QTL to chromosome 11. Five fruit quality traits had two

QTLs: eccentricity, proximal angle micro, rectangular, shoulder height and horizontal

asymmetry ovoid.

Size traits mapped to chromosome 2 (four QTLs between 78.10cM and 80.10cM), and

chromosome 9 (three QTLs at 55.66cM). Fruit shape QTLs mapped to chromosome 3 (two

QTL between 3.10cM and 6.10cM), chromosome 9 (four QTLs between 29.10cM and

41.19cM), and chromosome 12 (three QTLs between 5.10cM and 6.10cM). Uniformity traits

were mapped to chromosome 3 (one QTL at 7.10cM), chromosome 9 (three QTLs between

27.10cM and 41.19cM), chromosome 11 (one QTL at 52.90cM) and chromosome 12 (one

QTL at 6.10cM). Color traits three QTLs mapped to chromosome 2 between 78.1cM and

81.10cM.

One trait, proximal angel micro, had a different number of significant QTLs when

transformed. The untransformed data for proximal angel micro mapped a single QTL to

chromosome 9 at 31.07cM. After Box-Cox transformation, proximal angel micro mapped

two QTLs to chromosome 9 (29.10cM) and 12 (6.10cM) (Table 2.9). Two traits, curved

height and maximum height, had no significant QTLs when untransformed but a single QTL

when transformed at 55.66cM on chromosome 9.

The correlation between blocks for all fruit quality traits were significant at p < 0.001 (Table

2.6). The broad-sense heritability for fruit quality traits measured with Tomato Analyzer

ranged from 0.22 (rectangular) to 0.52 (area) (Table 2.8). Overall fruit size traits (area,

perimeter, and width mid-height) had a higher heritability than fruit shape traits (rectangular,

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Two QTLs were mapped for both total soluble solids and fruit shape. There were no

significant interactions for either total soluble solids or fruit shape. Fruit shape mapped

QTLs to roughly the same location on chromosome 9 (56.10cM) and 12 (5.10cM) as the

shape traits measured by Tomato Analyzer.

Segregation Distortion

There was extensive segregation distortion on chromosome 2, and to a lesser degree on

chromosome 4, with segregation distortion favoring NC 22L-1(2008) alleles over NC 30P

(Figure 2.2). On chromosome 2, segregation distortion extended for 52cM and included

twelve of twenty-six SNPs. On chromosome 4, three of the forty-six SNPs had segregation

distortion and two SNPs were adjacent with segregation distortion extending 3cM.

Villalta et al. (2005) reported extensive segregation distortion in two inter-specific tomato

populations. The populations had the same S. lycopersicum cv. cerasiforme accession crossed to different wild relatives (S. pimpinellifolium and S. cheesmanii). Segregation distortion generally favored heterozygotes, but on chromosome 2 S. pimpinellifolium markers were favored in one population, and S. lycopersicum cv. cerasiforme in the other (Villalta et al. 2005). Another study used restriction fragment length polymorphic markers to genotype a S. lycopersicum x S. cheesmanii population and all three markers on chromosome 2 had

segregation distortion favoring the S. lycopersicum allele (Paran et al. 1995).

Since marker order was determined using the physical map positions and not recombination

frequencies, any detrimental effects of segregation distortion on linkage map construction

was limited. Studies have suggested segregation distortion has little impact on the ability to

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in segregation distortion (Xu 2008; Zhang et al. 2010). In an attempt to estimate the effects of

segregation distortion on QTL detection in NC 10204, the LOD threshold and maximum

LOD score for fruit quality traits was calculated using three different linkage maps.

Map 1 is the NC 10204 linkage map used for QTL mapping, Map 2 had twelve SNPs

showing segregation distortion after Bonferroni correction removed, and Map 3 had an

additional sixty-eight SNPs with segregation distortion without Bonferroni correction

removed (Table 2.10). There is little variation in LOD thresholds between the three linkage

maps, as the greatest difference is only 0.18. Similarly, the maximum LOD scores do not

vary by more than 1.00 for any linkage map. Map 1 had twenty-two fruit quality traits with a

maximum LOD score greater than the threshold, Map 2 had twenty-one, and Map 3 had

eighteen (Table 2.11). Two traits (fruit shape index external II and fruit shape index internal

I) had significant QTLs with Map 3 but not with Map 1 or 2.

The markers with segregation distortion were kept in the final linkage map because of the

effect on chromosome 2 when removed. Chromosome 2 in Map 1 has twenty-six markers

covering 85.26cM. When twelve markers with segregation distortion are removed to create

Map 2, the maximum spacing between SNPs increases from 41.09cM to 109.06cM (Table

2.10). Chromosome 2 in Map 3 only has four markers covering 4.45cM (Table 2.10). The

minimal difference between the three maps to detect QTLs, plus the detrimental effect on

chromosome 2 when segregation distortion SNPs are removed justifies the decision to not

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QTLs on Chromosome 2 Associated with Multiple Fruit Quality Traits

Four fruit size traits had QTLs on chromosome 2: area, perimeter, maximum width, and

width mid-height. All mapped to a 2cM region between 78.1cM and 80.1cM (Table 2.9). The

NC 30P markers were associated with greater fruit size, as seen by the positive additive

values in Table 2.9. Three color traits (average a*, average chroma and average green)

mapped to a 7.26cM region (78.10cM to 85.36cM) but their 95% confidence intervals range

from 19cM to 66cM.

QTLs for fruit size and color have previously been mapped to chromosome 2. Three genes

important for fruit size are located on chromosome 2 (ovate, fw2.2 and lcn2.1), as well as at least three other putative QTLs (Lin et al. 2014). An intra-specific population mapped QTLs

using the CIELAB color space for a* (where a* represents colors ranging from red to green),

b* (representing blue to yellow) and L* (color lightness ranging from white to black) to

chromosomes 2, 4 and 9 (Saliba-Colombani et al. 2001). (Yang et al. 2004) mapped a QTL

for L* and (Darrigues 2007) a QTL for color intensity (a function of a*, b* and chroma) to

chromosome 2.

QTLs on Chromosome 9 Associated with Multiple Fruit Quality Traits

QTLs for curved height, eccentricity area index, eccentricity, height mid-width, maximum

height, proximal angle micro, proximal fruit blockiness, proximal indentation area,

rectangular, and shoulder height mapped to chromosome 9 (Table 2.9). The allele from NC

22L-1(2008) conferred a higher value for all traits except eccentricity, cured height,

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and 55.66cM; though confidence intervals vary from 7.44cM (rectangular) to nearly the

entire chromosome 87.17cM (maximum height).

(Chen et al. 1999) mapped QTLs for fruit shape, polar diameter and total soluble solids to

chromosome 9 using an inter-specific (S. lycopersicum x S. pimpinellifolium) BC1S1 population. The S. lycopersicum parent in the aforementioned study was NC 84173 which has common ancestry with NC 30P and NC 22L-1(2008). Researchers have used Tomato

Analyzer to phenotype four populations derived from crossing the same S. pimpinellifolium

accession to different S. lycopersicum accessions. Table 2.12 compares the QTLs mapped using those four inter-specific populations, to NC 10204 (Brewer et al. 2007; Gonzalo and

van der Knaap 2008; Gonzalo et al. 2009b). Two traits in NC 10204 with QTLs on

chromosome 9 mapped to chromosome 9 in other populations: proximal fruit blockiness (one

population) and proximal indentation area (two populations) (Table 2.12).

QTLs on Chromosome 12 Associated with Multiple Fruit Quality Traits

Six fruit quality traits had QTLs mapped to chromosome 12: eccentricity, horizontal

asymmetry ovate, ovoid, shoulder height, width widest position, and proximal angle micro.

All six QTLs were located between 4.10-6.10cM and confidence intervals ranged

from10.65cM (ovoid and width widest position) to 75cM (horizontal asymmetry ovate)

(Table 2.9). The allele from NC 22L-1(2008) conferred a higher phenotypic value for

shoulder height, ovoid and horizontal asymmetry ovate) and the allele from NC 30P

conferred a higher phenotypic value for eccentricity and width widest position.

There are no similarities between traits with QTLs on chromosome 12 in NC 10204 and

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diameter on chromosome 12 located between 10.00cM and 38.00cM. A QTL for fruit weight

was also mapped to a similar region (0.00-22.00cM) using an inter-specific recombinant

inbred line population (S. lycopersicum x S. pimpinellifolium) (Ashrafi et al. 2012). The S. lycopersicum parent in RIL population described in Ashrafi et al. (2012), NC EBR-1, has common ancestry with both NC 30P and NC 22L-1(2008).

Ovoid is measured when fruit mass is greater in upper half of the fruit. If fruit mass is greater

in the lower half, then the fruit is considered obovoid and can be pear shaped. There are no

significant QTLs at α = 0.05 for obovoid, but after Box-Cox transformation there is a

significant QTL at α = 0.10 on chromosome 12 at 4.1cM. The ovate gene is responsible for

fruit elongation, and fruit are more rounded when ovate is not homozygous recessive (Rodríguez, Kim, van der Knaap 2013). Previous studies have identified putative QTLs on

chromosome 12 that influence ovate (Gonzalo and van der Knaap 2008; Rodríguez, Kim, van der Knaap 2013). The loci identified by Gonzalo and van der Knaap 2008 corresponds the

same end of chromosome 12 as the QTL for obovoid in NC 10204 (α = 0.10). The loci

identified by Rodriguez et al. 2013 is on the other end of chromosome 12.

QTLs on Chromosome 3 & 11 Associated with Multiple Fruit Quality Traits

One QTL for distal fruit blockiness, rectangular, and fruit shape triangle mapped to

chromosome 3. All three QTLs mapped to a 4cM region (3.10cM to 7.10cM) and the

confidence intervals ranged from 14.82cM to 35.16cM. NC 22L-1(2008) conferred a higher

phenotypic value for distal fruit blockiness and rectangular. One population in Table 2.12

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marker associated with distal fruit blockiness on chromosome 3, but the LOD score was not

above the significance threshold (Table 2.12).

One QTL for horizontal asymmetry ovate and ovoid mapped to the same location (52.90 cM)

on chromosome 11. The four populations in Table 2.12 mapped many fruit quality traits on

chromosome 11, but none reported QTLs for horizontal asymmetry ovate or ovoid.

Total soluble solids

QTLs for total soluble solids were mapped to chromosome 2 (78.1cM) and 9 (19.35cM)

when the least squared means of the F2 and F3 generations was analyzed (Table 2.1). For both

QTLs the allele from the small fruited NC 22L-1(2008) increased total soluble solids. The

position of the QTL on chromosome 2 corresponded to the QTLs for fruit size (area,

perimeter, width mid-height, maximum width) (Table 2.9). The QTL on chromosome 9 is

located between 7.75cM and 36.31cM from the QTLs for fruit quality traits measured with

Tomato Analyzer and the 95% confidence interval extended 46.01cM.

Total soluble solids and fruit size traits (perimeter, maximum width, and area) are negatively

correlated, r ≈ -0.50, p < 0.001 (Table 2.5). When phenotypic data from only the F2

generation was analyzed, two QTLs were mapped to the same chromosome and position as

the QTLs when the least squared means of both generations was analyzed (Table 2.9). No

significant QTLs were mapped in the F3 generation. The F2 and F3 generations are correlated,

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31 Fruit Shape

Fruit shape measured visually mapped two QTLs to chromosome 9 (56.10cM) and 12

(5.10cM) when the least squared means from the F2 and F3 generations was used (Table 2.9).

When the generations were analyzed separately, the F2 generation mapped a single QTL to

chromosome 9 (46.10cM) and the F3 generation mapped no QTLs. The correlation between

generations is, r = 0.38, p < 0.001.

Comparison of Phenotypic Data Obtain using Tomato Analyzer and Phenotyping Fruit Shape

Visually

It took the combined phenotypic data from two generations of visual fruit phenotyping to

map QTLs to chromosome 9 and 12. Fruit shape traits measured with Tomato Analyzer

mapped QTLs to the same regions of chromosome 9 and 12 using one generation of

phenotypic data. The 95% confidence interval were generally smaller with data obtained with

Tomato Analzyer. The confidence interval for fruit shape when phenotyped visually was

25.77cM (chromosome 12) to 35.60cM (chromosome 9), while six of the nine fruit quality

traits measured using Tomato Analyzer that mapped to chromosome 9 and 12 had shorter

confidence intervals (Table 2.9). This is not surprising as Tomato Analyzer quantitatively

measures fruit traits and visual fruit shape phenotyping is a categorical scale. The quality of

phenotypic data is only one factor to consider when comparing Tomato Analyzer to visually

phenotyping fruit shape.

Visually phenotyping fruit shape requires one to two people to phenotype ripe fruit in the

field. The speed and accuracy of fruit shape phenotyping depends largely upon the

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is undoubtedly more accurate than visually phenotyping fruit shape, but requires more labor.

The time required to scan fruit and manually adjust the images prior to analysis cannot be

understated. Fruit scanning occurs as summer field trials are being harvested and the fall

greenhouse season is starting. This is the busiest times of the year for breeding programs and

it is difficult to allocate the labor required to scan fruit.

The multiple generations of visually phenotyping fruit shape that were required equal one

generation of phenotyping with Tomato Analyzer should not be a limitation in most

scenarios since most populations are evaluated in multiple generations and locations anyway.

The amount of time and labor required to use Tomato Analyzer make it impractical for

regular use in an applied breeding program.

CONCLUSION

QTL analysis suggests that NC 10204 is segregating for genes controlling fruit shape on

chromosomes 9 and 12, and for fruit size on chromosome 2. Fruit shape QTLs were detected

at the same locations using both Tomato Analyzer and visual fruit phenotyping. Future

research is needed to validate these QTLs. Even if QTLs are never validated, the loci

identified in this research are still of value to the fresh market tomato breeding program at

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REFERENCES

Ashrafi H, Kinkade MP, Merk HL, Foolad MR. 2012. Identification of novel quantitative trait loci for increased lycopene content and other fruit quality traits in a tomato recombinant inbred line population. Molecular Breeding 30(1):549-67.

Bates D, Mächler M, Bolker B, Walker S. 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67(1):1-48.

Bauchet G, Munos S, Sauvage C, Bonnet J, Grivet L, Causse M. 2014. Genes involved in floral meristem in tomato exhibit drastically reduced genetic diversity and signature of selection. BMC Plant Biology 14:279,014-0279-2.

Box GE and Cox DR. 1964. An analysis of transformations. Journal of the Royal Statistical Society.Series B (Methodological) :211-52.

Brewer MT, Moyseenko JB, Monforte AJ, van der Knaap E. 2007. Morphological variation in tomato: A comprehensive study of quantitative trait loci controlling fruit shape and development. Journal of Experimental Botany 58(6):1339-49.

Broman KW and Speed TP. 2002. A model selection approach for the identification of quantitative trait loci in experimental crosses. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4):641-56.

Broman KW, Wu H, Sen S, Churchill GA. 2003. R/qtl: QTL mapping in experimental crosses. Bioinformatics 19(7):889-90.

Causse M, Saliba-Colombani V, Lecomte L, Duffe P, Rousselle P, Buret M. 2002. QTL analysis of fruit quality in fresh market tomato: A few chromosome regions control the variation of sensory and instrumental traits. Journal of Experimental Botany

53(377):2089-98.

Chen F, Foolad M, Hyman J, Clair DS, Beelaman R. 1999. Mapping of QTLs for lycopene and other fruit traits in a lycopersicon esculentum× L. pimpinellifolium cross and comparison of QTLs across tomato species. Molecular Breeding 5(3):283-99.

Cooperative Extension Services. 2015. X. disease control. In: 2015 north carolina agricultural chemical manual. 1st ed. Raleigh, North Carolina: North Carolina State University. 525 p.

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Darrigues A, Hall J, van der Knaap E, Francis DM, Dujmovic N, Gray S. 2008. Tomato analyzer-color test: A new tool for efficient digital phenotyping. Journal of the American Society for Horticultural Science 133(4):579-86.

Gonzalo MJ and van der Knaap E. 2008. A comparative analysis into the genetic bases of morphology in tomato varieties exhibiting elongated fruit shape. Theoretical and Applied Genetics 116(5):647-56.

Gonzalo MJ, Brewer MT, Anderson C, Sullivan D, Gray S, van der Knaap E. 2009a. Tomato fruit shape analysis using morphometric and morphology attributes implemented in tomato analyzer software program. Journal of the American Society for Horticultural Science 134(1):77-87.

Gonzalo MJ, Brewer MT, Anderson C, Sullivan D, Gray S, van der Knaap E. 2009b. Tomato fruit shape analysis using morphometric and morphology attributes implemented in tomato analyzer software program. Journal of the American Society for Horticultural Science 134(1):77-87.

Grandillo S, Termolino P, van der Knaap E. 2013. Molecular mapping of complex traits in tomato. In: Genetics, genomics and breeding of tomato. B. Liedl, J. Labate, J. Stommel, A. Slade & C. Kole, editor. 1st ed. Boca Raton, FL: CRC Press. 150 p.

Halekoh U and Højsgaard S. 2014. A kenward-roger approximation and parametric bootstrap methods for tests in linear mixed models–the R package pbkrtest. Journal of Statistical Software 59(9):1-32.

Haley CS and Knott SA. 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69(4):315-24.

Harrell FE. 2015. Hmisc: Harrell Miscellaneous.[computer program]. Version 3.16. http://CRAN.R-project.org/package=Hmisc.

Holland JB, Nyquist WE, Cervantes-Martínez CT. 2003. Estimating and interpreting heritability for plant breeding: An update. Plant Breed Reviews 22:9-112.

Hyndman R and Khandakar Y. 2008. Automatic time series forecasting: The forecast package for R. Journal of Statistical Software 26(3):1-22.

Figure

Figure 1.1. Example of Tomato Analyzer phenotyping software
Figure 1.2. Example of a jointed tomato pedicel
Figure 1.3. Example of a jointless tomato pedicel
Table 2.0.1 Number of lines phenotyped in the intra-specific tomato population NC 10204
+7

References

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