WHITE MOLD RESISTANCE - ASSOCIATION MAPPING AND QTL IDENTIFICATION

IN COMMON BEAN

By

ORITSESANINORMI BLESSING ORAGUZIE

A thesis submitted in partial fulfillment of

the requirements for the degree of

MASTER OF SCIENCE IN CROP SCIENCE

WASHINGTON STATE UNIVERSITY

Department of Crop and Soil Science

MAY 2015

ii

To the Faculty of Washington State University:

The members of the Committee appointed to examine the thesis of

ORITSESANINORMI BLESSING ORAGUZIE find it satisfactory and recommend that it be

accepted.

______________________________

Phil Miklas, Ph.D., Chair

______________________________

Lyndon Porter, Ph.D.

______________________________

Arron Carter, Ph.D.

______________________________

Kevin Murphy, Ph.D.

iii

ACKNOWLEDGEMENTS

I shall begin with God the almighty: without His love, mercy and directions I would not have

been able to complete this study. I am forever indebted to Him. I thank you Lord for your

wisdom and for your protection.

I owe a great deal of thanks to my advisor Dr Phil Miklas. Without him I would not have

had this opportunity. I would like to thank him for the guidance, endless ideas and the generosity

of his time and manual labor in helping me complete this project. I am appreciative for his

willingness to serve as my mentor, and hope to be a positive reflection of his skills in my future

endeavors. I am also very grateful to Dr(s). Lyndon Porter, Arron Carter, and Kevin Murphy for

serving on my committee and providing guidance.

The involvement of Samira Mafimoghaddam was paramount to the success of this

project. Her skills in advising me on some statistical analyses and GWAS study gave me the

toolset I needed and for that I am extremely grateful. I owe thanks to Dr. Perry Cregan, Samira

Mafimoghaddam and Sujan Mamidi for the training I received on GWAS at the NDSU. They

were very helpful in answering my questions regarding association mapping and its theory.

My fellow graduate students and coworkers were good friends as well as a resource to

troubleshoot new ideas and designs. I am thankful for the involvement of Marco Bello, Eninka

Mndolwa, Josephine Mgbechi, Sandya kesoju, Bhanu Donda,and Jati Adiputra on this project.

To Dr. Perry Cregan and the entire staff of the Soybean Genomics and Improvement Lab,

Beltsville Agricultural Research Center; Beltsville, MD, I say thank you for all your support in

genotyping and always willing and cheerful to take questions.

iv

Worth mentioning are Susan Swanson, Mike Nelson and Jeff Colson. I am particularly

grateful for the maintenance of the experiments in the glasshouse and field support.

To the amazing farm manager, Marc Seymour and the entire field crew, thank you for the timely

management of my field trials at Paterson.

I realize this entire endeavor would have only been a pipe dream without the involvement

and support of my family. To my mom and dad, Samuel and Alero Athanson, your love,

dedication and hardwork has brought me this far. You made me into who I am. Mom, I do not

know how to thank you enough for all the sacrifices you made. You are my root, my foundation.

You planted the seed that I base my life on; I love you so much and miss you every day. To my

mother-in-law, Alice Oraguzie, how fitting the Jewish Proverb that says “God could not be

everywhere, so therefore he made mothers”. Thank you mama for being there the first nine

months of Chiamaka’s life; while I went about the usual runs of graduate school, you were the

only one I depended on to care for my daughter, and I am eternally grateful to you. To my

brothers, Sunny and Paul, my sisters, Omawumi, Patience and Laju, I love you all and thank you

for your support.

I also dedicate this thesis to my husband, Nnadozie and our beautiful daughter Chiamaka

Joyce. Nnadozie, you have been a constant source of support and encouragement during the

challenges of graduate school and life. You were always there with me through the toughest

moments, you saw me laugh and you saw me cry in my misery. Your godly advice, soothing

words and your big heart helped me face all the obstacles and continue with my work. I am truly

thankful for having you in my life. Chiamaka, you who are the pride and joy of my life, I love

you more than anything, and I appreciate all your patience and support during mommy’s

graduate studies.

v

DEDICATION

In loving memory of my mother, Alero Joyce Athanson, whose great love for and humble

service to her creator, her family, and every person she encountered are a constant inspiration.

vi

WHITE MOLD RESISTANCE - ASSOCIATION MAPPING AND QTL IDENTIFICATION

IN COMMON BEAN

Abstract

by Oritsesaninormi Blessing Oraguzie, M.S.

Washington State University

May 2015

Chair: Phil Miklas

White mold, incited by Sclerotina sclerotiorum, Lib. de Bary, is a major disease, limiting

common bean (Phaseolus vulgaris L.) production around the world. The effect of white mold

disease on beans can be profound, causing a significant decrease in yield. There is little genetic

diversity for white mold resistance in the Phaseolus vulgaris gene pool and genetic control

appears to be complex, or quantitative with low to moderate heritability. Understanding the

genetic mechanisms underlying the partial resistance would facilitate the development of new

bean cultivars that are resistant to white mold (WM). The objectives of this research were to i)

use association mapping in the Middle American diversity panel (MDP) consisting of ~300

accessions to identify novel QTL for partial resistance to white mold, and ii) validate the

presence of QTL conferring partial resistance to white mold in a backcross RIL population

consisting of ~100 F5:7 derived lines (Orion//Orion/R31-83). In the MDP population, principal

component analysis was performed using the Tassel software to identify population structure and

along with the kinship matrix was used as covariates in multiple linear models (including Naïve,

GLM and MLM approaches) to identify marker-locus-trait associations. For all traits analyzed,

26 SNPs were significantly associated with 29 quantitative trait loci (QTL) spanning the eleven

bean linkage groups (chromosomes). These SNPs satisfied the 0.01 percentile (p-value ≤ 5.58E-

04) significance threshold. Although, association mapping in the MDP identified significant

vii

QTL conditioning resistance to white mold, further validation of the QTL may be warranted. For

the RIL population, monomorphic and low-quality SNPs were filtered out in Genome Studio

software leaving a total of 1,130 polymorphic SNPs. However, only 347 SNP were used for QTL

analysis and detection due to co-localization of SNPs on the linkage maps. The Icimapping

software was used to construct linkage maps while the WinQTLCartographer was used for QTL

analysis. A total of eight putative QTLs were detected corresponding to five genomic regions on

the eleven bean linkage groups (LG). The LOD values for the QTL ranged from 2.8 to 3.5,

explaining between 6.7 to 19.2% of the phenotypic variance of the traits.

viii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ........................................................................................................ iii

DEDICATION ..............................................................................................................................v

ABSTRACT ................................................................................................................................ vi

TABLE OF CONTENTS .......................................................................................................... viii

LIST OF TABLES ........................................................................................................................x

LIST OF FIGURES .................................................................................................................... xi

OUTLINE. ................................................................................................................................. xii

CHAPTER ONE. INTRODUCTION AND LITERATURE REVIEW .......................................1

References .......................................................................................................................15

CHAPTER TWO. ASSOCIATION MAPPING OF WHITE MOLD RESISTANCE IN A

PANEL OF NORTH AMERICAN BREEDING LINES AND CULTIVARS REPRESENTING

THE MIDDLE AMERICAN GENE ..........................................................................................25

Abstract ..........................................................................................................................25

Introduction .....................................................................................................................27

Materials and Methods ...................................................................................................30

Results and Discussion ...................................................................................................36

Conclusion ......................................................................................................................42

Acknowledgment ............................................................................................................43

References .......................................................................................................................44

CHAPTER THREE. QTL FOR WHITE MOLD RESISTANCE IN A BACKCROSS RIL

POPULATION ...........................................................................................................................64

ix

Abstract ...........................................................................................................................64

Introduction .....................................................................................................................65

Materials and Methods. ...................................................................................................67

Results and Discussion ..................................................................................................73

References .......................................................................................................................78

x

LIST OF TABLES

Table 1.1. Comprehensive list of quantitative trait loci (QTL) conditioning partial resistance

to white mold in common bean (Phaseolus vulgaris L.) from previous studies and identified

in Benton/VA19 (BV) and Raven/I9365-31 (R31) recombinant inbred line populations

(italic type). BJ, BAT 93/Jalo EEP558; DG, DOR364/G19833.

(Table adapted from Soule et al., 2011) ......................................................................................11

Table 2.1. Summary Mean square from analysis of variance (ANOVA) for tests conducted

on 274 dry bean lines and cultivars in the greenhouse and field in 2013 ...................................50

Table 2.2. Promising lines from the 2013 straw test evaluation for white mold at the

USDA-ARS greenhouses at Prosser, WA ..................................................................................51

Table 2.3. Promising lines from the 2013 field trial for white mold severity at USDA-ARS

Cropping Systems Research Farm at Paterson WA ....................................................................52

Table 2.4. Pearson correlation coefficients between white mold disease severity and

agronomic trait means for 274 dry bean lines and cultivars tested in Paterson, WA. in

2013.............................................................................................................................................54

Table 2.5: MLM output showing significant marker-trait associations in a panel of 274

Middle American lines and cultivars tested with 15,000 SNP markers .....................................55

Table 3.1. Mean, range, and coefficient of variation (CV) for traits measured in the

greenhouse and field for Orion//Orion/ R31-83 BC1F5:7 population and means for the parents,

tested across multiple environments ...........................................................................................83

Table 3.2. LS mean of the average score between 7 and 11d greenhouse straw test not

significantly different from R31-83 (p< 0.05) and significantly improved over the recurrent

parent Orion (p < 0.05) ...............................................................................................................84

Table 3.3. Analysis of variance for response of Orion//Orion/R31-83 BC1F5:7 RILs to

white mold evaluation in the greenhouse and field in 2014 .......................................................86

Table 3.4. Pearson correlation coefficients between white mold disease score means from

greenhouse straw tests and the field and agronomic trait means from the field in a population

of 104 BC1F5:7 RILs from Orion//Orion/R31-83 ........................................................................87

Table 3.5. Putative QTL positions, likelihood ratios (LR), percentage variance explained (PVE), and additive effects, for the white mold resistance and agronomic traits identified in

field and greenhouse environments in a BC1F5:7 population of Orion//Orion/R31-83 .............88

xi

LIST OF FIGURES

Figure 2.1. Response of the two races representing the MDP to a greenhouse straw test

(average score between 7- and 11- d ratings) conducted at the USDA-ARS greenhouses at

Prosser, WA in 2013. Vertical arrow bars showing USPT-WM-12, Bunsi and Beryl

representing indicate the resistant, intermediate and susceptible checks ...................................57

Figure 2.2. Response of the two races representing the MDP to field WM severity grown at

the USDA-ARS, Cropping Systems Research Farm near Paterson, WA in 2013. Vertical

arrow bars showing USPT-WM-12, Bunsi and Beryl representing indicate the resistant,

intermediate and susceptible checks ...........................................................................................58

Figure 2.3. Summary plot of estimates of Q. Each individual is represented by a single

vertical line broken into K colored segments, with lengths proportional to each of the K

inferred clusters. The number of segments correspond to the predefined populations of

K=6 .............................................................................................................................................59

Figure 2.4. Principal component analysis (PCA) matrix showing the first three PCs where

multiple clusters were observed ..................................................................................................60

Figure 2.5. LD decay plot showing LD measured as R2 between pairs of polymorphic

marker loci plotted against physical distance (Mbp) ..................................................................61

Figure 2.6. QQ Plot showing the four models tested. P-value observed is plotted on the

y-axis and P- expected is plotted on the x-axis. Each color represents the different traits

analyzed ......................................................................................................................................62

Figure 2.7. Manhattan plots showing significant QTL that are associated with white mold

resistance. Eleven Chromosomes ordered on x-axis and each chromosome is represented

by a different color. The –log10 (p-value) is presented on the y-axis. The cutoff horizontal

lines indicate 0.01 (black) and 0.1(blue) percentile tails of the empirical distribution obtained

using 10,000 bootstraps. Vertical grey blocks indicate QTL regions that have major effect on

the different trait measured .........................................................................................................63

Figure 3.1. Response of Orion//Orion/R31-83 BC1F5:7 populations to a greenhouse straw

test in 2014. Parents are indicated by arrows ..............................................................................89

Figure 3.2. Response of Orion//Orion/R31-83 BC1F5:7 populations to white mold and other

agronomic traits. Parents are indicated by arrows ......................................................................90

Figure 3.3. Linkage map for Orion//Orion/R31-83 showing previously QTL identified for

resistance to white mold..............................................................................................................91

xii

Outline

This thesis is a compilation of a literature review and two journal articles in lieu of chapters. The

articles were formatted for submission to Crop Science Society of America. Additional authors

were involved with regards to experimental design, statistical analysis, and editing.

1

CHAPTER ONE

INTRODUCTION AND LITERATURE REVIEW

Phaseolus vulgaris, or "common bean", including dry beans, green or "snap" beans,

"shell beans", and popping beans, is a member of the Fabaceae family. There are several other

domesticated crop species within the genus which includes lima bean (P. lunatus), tepary bean

(P. acutifolius), scarlet runner bean (P. coccineus), and year-long bean [P. dumosus formerly P.

polyanthus or P. coccineus subsp. darwinius (Freytag and Debouck 2002) (Hall, 1994)]

Phaseolus is most closely related to the group of "warm-season legumes" Vigna genus: cowpea,

Vigna unguiculata; uraddal, Vigna mungo; mung bean, Vigna radiate; ricebean, Vigna

umbellate; and bambara groundnut, Vigna subterranea. Other more distantly related warm-

season legumes include soybean, Glycine max; jicama, Pachyrrhizus erosus; pigeonpea, Cajanus

cajan; African yam bean, Sphenostylis stenocarpa; hyacinth bean, Dolichos lablab; and potato

bean, Apios americana. Phaseolus vulgaris is the most prominent cultivated species cultivated

worldwide in tropical, semitropical and temperate climates (Hall, 1994).

The common bean has preferred adaptation to highland areas of the tropics and the

temperate zones. They are also grown in the humid tropics and the semi-arid tropics, and even in

some cold climate regions (Schoonhoven and Voysest, 1991). Common bean thrives best in

well-drained sandy or silt loam soils with a pH range between 5.2 to 6.8 and soil temperatures

between 130C to 21

0C. The bean crop requires about 46-52 cm (18-20 inches) of water for

optimum growth. Beans are sensitive to early season freezing temperatures because they have

epigeal emergence; thus are unable to recover from a frost. Optimum growing temperatures

2

ranges between 240C to 29

0C, with the minimum temperature about 10

0C (Bassett, 1986; Gepts,

1988; Hall, 1994). The vegetative period varies from less than 70 days to more than 200. This

enables use as an excellent rotation crop where a crop with a short vegetative period is required

or as a continuous food source (Schoonhoven and Voysest, 1991).

Phaseolus vulgaris was domesticated independently in the Andean region of South

America and the Mexican-Guatemalan region of Central America (Mesoamerica) (Gepts, 1988;

Hall, 1994; Smartt and Simmonds, 1995; Schmutz et al., 2014). Harlan et al. (1971) classified

cultivated P. vulgaris as a noncentric crop with multiple centers of domestication and a wide

geographical distribution of its wild relatives in Middle and South America. Gepts et al. (1988)

characterized P. vulgaris into two main gene pools based on phaseolin (Phs) seed storage protein

variation and partial reproduction isolation using an extensive isozyme analysis. This includes

the Mesoamerican lines with ‘S’ phaseolin patterns and small seed (<25g/100 seeds) and the

Andean lines with “T”, “C” and “A” pattern and large seed (>40g/100 seeds). Similarly the

Mesoamerican wild populations had predominantly the “S” and “M” alleles, while the Andean

wild populations had “T”, “C”, “S”, “H” and “A” alleles (Gepts and Bliss, 1986; Gepts et al.,

1986). Phs has been used as a marker to identify QTL for white mold resistance derived from

G122 landrace cultivar ‘Jatu Rong’ from India (Miklas et al., 2001; Miklas, 2007; Chung et al.,

2008).

Both wild populations and the cultivated forms of P. vulgaris are self-pollinating and are

diploid species (2n = 2x = 22) with 0.66 picograms/DNA haploid genome (Arumuganathan and

Earle, 1991; McClean et al., 2004). They hybridize with each other, easily producing viable and

3

fertile individuals. The genome size of P. vulgaris (580Mbp/haploid genome) is comparable to

that of rice (490Mbp/haploid genome) (Bennet and Leitch, 2005). Dry bean yield has been

increasing 0.6% per year because of genetic improvement (Kelly et al., 1998; Vandemark et al.,

2014).

Phaseolus coccineus, a member of the secondary gene pool for P. vulgaris, is a tender

perennial with hypogeal emergence. It was domesticated in Mexico about 2,200 years before

present (Kaplan, 1965). The scarlet runner bean has very large, flat seeds, showy red flowers and

is cross-pollinated by carpenter bees in the wild (Freytag and Debouck, 2002). P. coccineus is

cultivated less frequently than P. vulgaris. It grows naturally in cool humid uplands of Chiapas

Mexico and Guatemala in oak-pine regions above 1800 m (Kaplan, 1965). P. coccineus is

resistant to a number of diseases and is a germplasm source for other members of the genus. It

has been the best source of resistance for white mold disease found within the genus to date

(Abawi et al., 1978; Adams et al., 1973; de Bary, 1887; Debouck, 1999; Gilmore and Myers,

2000; Gilmore et al., 2002; Lyons et al., 1987; Schwartz et al., 2006). Apart from an Italian

breeding program to develop bush varieties, very little breeding has been done on P. coccineus.

The common bean provides an important source of nutrition in many cultures, the leaf is

occasionally used as a vegetable, and the straw can be used for fodder. During the lean years of

the Great Depression, beans were tagged "poor man's meat" because of their protein power at

pennies per pound. Beans are a source of Niacin, Thiamin, Riboflavin, B6 vitamins and many

other nutrients as well (Aykroyd et al., 1982). Beans are an extremely beneficial component in

all diets because they are high in complex carbohydrates, protein and dietary fiber, low in fat,

calories and sodium, and completely cholesterol-free. As little as a half-cup of beans added to the

4

daily diet can be very helpful in reaching important nutrition goals. They are also important in

agronomic systems for their nitrogen-fixing capacity. In 2011, total world production of dry

beans was 3.9 billion tonnes, harvested from over 6 billion hectares (USDA -

www.MyPyramid.gov). It is a food of great nutritive value consumed by millions of people

living on five continents (Schoonhoven and Voysest, 1991).

Despite the nutritive value and popularity, bean has not been able to capture the

preference of medium and large scale farmers as does other crops. The reasons are multiple but

largely due to risk for low yield. Factors responsible for low bean yields can be grouped into

three main categories: biological (disease, insect, weed); edaphic (poor fertility, high aluminum

saturation etc); and climatic (drought, high temperature). Diseases however are one of the most

important factors associated with low bean yield in most bean producing regions (Singh and

Sharma, 1975).

White mold, caused by Sclerotinia sclerotiorum, Lib. de Bary, is a major yield-limiting

factor (Singh and Schwartz, 2010) in common bean production around the world. It is a

ubiquitous necrotrophic fungus causing disease in a wide range of plants and is widespread in

most bean production regions (Purdy, 1979). It is a “cosmopolitan pathogen” capable of

colonizing over 400 plant species found worldwide, primarily dicots (Bolton et al., 2006). Crop

losses due to white mold disease outbreaks in dry bean average 30% in the central high plains of

the United States with individual field losses as high as 92% (Kerr et al., 1978; Schwartz et al.,

1987). However, under favorable weather conditions 100% seed yield and quality losses occur

on susceptible common bean cultivars (e.g., Argentina in 2011) (Schwartz and Singh, 2013).

5

In 1837, Madame M.A Libert described white mold and called it Peziza sclerotiorum in

Plante Crytogamicae Arduennae (Exsiccati) No. 326. In 1870 Leopoldi Fuckel renamed it as

genus Sclerotinia libertiana Fuckel in honor of Madame Libert. Several other scientists have

used this name to describe the fungal disease but finally in order to make the name consistent

with the International Rules of Botanical Nomenclature Wakefield and Massee (1895) renamed it

Sclerotinia sclerotiorum (Lib) (Purdy, 1979).

Sclerotinia sclerotiorum persists in the bean fields over time by survival structures called

sclerotia. These are dark colored, circular to irregular in shape, and range in size from less than

1/8 inch in diameter up to the size of a large bean seed. A sclerotium contains food reserves and

functions much like a seed, surviving for years in the soil and eventually germinating. A

germinated sclerotium can produce millions of ascospores beneath the bean canopy that attack

and colonize senescing flowers before moving into the plant. Sclerotinia sclerotiorum can

survive from one season to the next as mycelium in infected bean straw and seeds left in the field

after harvest. Stem lesions develop and may eventually be overgrown with white mold (Schwartz

et al., 2011). This allows the disease to spread directly by contact from plant to plant. It grows at

temperatures from 40C to over 30

0C, with optimal range of 20

0C-25

0C C (Hall, 1994). Spread

and development of white mold is greatly influenced by the prevailing weather conditions and

certain agronomic practices (Purdy, 1979). The fungus can be transported within infected seeds

or in sclerotia-contaminated seed lots to be planted during the next growing season. Therefore, it

is important to plant only certified seeds that have had sclerotia and poor quality seeds removed

during threshing and cleaning operations (Schwartz et al., 2011). In green beans when pod

infection rates exceed 3-5%, processors reject the entire production field (Stivers, 2000).

6

White mold disease may occur on all aerial plant parts. Infected flowers may develop a

white, cottony appearance as mycelium grows on the surface. Lesions on pods, leaves, branches,

and stems are initially small, circular, dark green, and water-soaked but rapidly increase in size,

may become slimy, and may eventually encompass and kill the entire organ. Under moist

conditions, these lesions may also develop a white, cottony growth of external mycelium.

Affected tissues dry out and bleach to a pale brown or white coloration that contrasts with the

normal light tan color of senescent tissue. Colonies of white mycelium (immature sclerotia)

develop into hard, black sclerotia in and on infected tissue. Entire branches or plants may be

killed (Steadman and Boland, 2005).

While the white mold resistance mechanism is unknown, recent discoveries suggest a

mechanism the pathogen uses to attack the plant which includes releasing oxalic acid at a level

sufficient to suppress the plant’s defense mechanism early during infection (Williams et al.,

2011). Oxalate acid occurs in fungi and serves as an important virulence factor for S.

sclerotiorum. Godoy et al. (1990) carried out an experiment to test the importance of oxalic acid.

They found that oxalic acid deficient mutants were less virulent compared to the wild type.

Oxidative burst, the controlled release of O2 and H2O2 which is a plant defense response is

suppressed by oxalic acid (Cessna et al., 2000). Dutton and Evans (1996) reported that the

release of oxalic acid causes acidification of plant tissues. Pathogenic enzymes secreted by S.

sclerotiorum are more active in the lower acidic pH conditions facilitating degradation of cell

walls (Callahan and Rowe, 1991). Several different enzymes have similar activity, but

sometimes have different activities on different substrate, even at the same pH.

7

Biological control can be achieved by using the parasitic fungus Coniothyrium minitans.

However, the reduction in viable sclerotia does not necessarily result in lower disease pressures

in the field (McQuilken et al., 1995). Previously, the fungicide - Benomyl was useful in the

control of S. sclerotiorum but has long been removed from the market due to environment and

human health concerns. Newer fungicides such as Topsin M and Endura are currently available.

Most all available fungicides require multiple applications, are costly to use, and require precise

timing to maximize effectiveness (Bolton et al., 2006).

The evaluation and selection for resistance to white mold using phenotypic analysis is

tedious and environmentally sensitive, resulting in high costs for breeding using traditional

screening methods. At present, there is no source for complete resistance to S. sclerotiorum.

Resistance described has been partial. There is little genetic diversity for white mold resistance

in the Phaseolus vulgaris gene pool and genetic control has been reported as a complex, or

quantitative, trait with low to moderate heritability (Fuller et al. 1984; Kolkman and Kelly, 1999,

2002, 2003; Miklas et al., 1992a; Park et al., 2001). Genetic control of resistance factors in P.

coccineus has been reported as having major effect (Gilmore, 2007). Interspecific P. vulgaris x

P. coccineus populations have been reported to have a single dominant gene controlling white

mold resistance (Abawi et al., 1978; Schwartz et al., 2006) but also as quantitatively inherited

(Adams et al., 1973; Gilmore and Myers, 2004; Schwartz et al., 2004).

Varieties that have large dense canopies, holding moisture and creating a more favorable

environment for the pathogen are more likely to become infected. Bean cultivars with upright

architecture and porous canopy structure avoid disease by contributing to a less favorable

microclimate for the pathogen (Schwartz et al., 1978, 1987; Abawi and Hunter, 1979). Early

flowering and maturity may contribute to disease escape (Boland and Hall, 1987). Avoidance

8

will not be effective when the environment is highly conducive to disease, necessitating the use

of physiological resistance.

Physiological resistance results from some functioning mechanism of the plant that

excludes completely or in some degree, the effect of a pathogen (Agrios, 1997). Morphological

mechanisms in beans, such as thick cuticle, serve as a physical barrier to infection. Resistance to

S. sclerotiorum in P. vulgaris is partial and may be controlled by several genes (Grafton, 1998).

Avoidance may be necessary for expression of partial physiological resistance, because an

increase in relative humidity within the canopy is allowing the pathogen to overcome the

physiological resistance mechanism of the host. Even the most resistant genotypes will become

infected if wet conditions exist for prolonged periods. Partial physiological resistance may be

most valuable when combined with cultural controls and/or avoidance mechanisms that create

environmental conditions less favorable to the pathogen (Schwartz et al., 1987; Miklas et al.,

1992).

In summary, breeding for resistance has proven difficult, because of the complexity of

the trait. It is difficult to fully evaluate physiological resistance mechanisms, because of the

additional influence of physical avoidance traits and architecture.

The identification and use of QTL (quantitative trait loci) and marker assisted selection

(MAS) may enable breeders to effectively transfer physiological resistance through the use of

marker assisted breeding. A QTL refers to a genomic region on a linkage map that cosegregates

with the observed phenotype. The relative position of markers can be identified from a consensus

map covering a small region of a chromosome or linkage group. Genetic linkage mapping is a

tool that helps to characterize genetic control of traits, chromosomal rearrangements and linkage

of markers with specific traits (Gepts et al., 1993). QTL analysis associates genotypic markers

9

with phenotypic traits measured quantitatively. The first bean linkage maps were developed

using RFLP markers (Adam-Blondon et al., 1994; Nodari et al., 1993; Vallejos et al., 1992). The

Davis map (Nodari et al., 1993) was developed from a Bat93 x JaloEEP558 population

consisting of 70 F2-derived F3 families and 152 markers divided into 15 linkage groups and

covering 827 cM. The Florida map (Vallejos et al., 1992) consisted of a Mesoamerican (XR-235-

1-1) x Andean (Calima) population with 224 mapped RFLP markers representing 11

chromosomes and covering 960 cM. Freyre et al. (1998) used RAPD markers to increase the

density of and align the previous RFLP maps using common markers.

Park et al. (1999) reported six major QTL for resistance to white mold in common bean

in the PC-50 x Xan159 RIL population. Three were identified using the greenhouse straw test

(WM2.1, WM5.1, and WM8.2), two were detected with minor effect in both field and straw tests

(WM4.1, WM7.1) and the last was associated with avoidance traits (WM8.1).

WM 1.1 QTL linked to the fin locus for field resistance (18% Phenotypic Variation

Explained [PVE]) and associated with architectural trait of canopy porosity was detected in the A

55/G 122 RIL population (Miklas et al., 2001). Another QTL linked to the Phs locus for the

Phaseolin protein for physiological resistance (WM7.1) was detected with 38% and 26% of the

variance for straw test and field response, respectively in the same population.

In Bunsi/Newport and Huron/Newport RIL populations, Kolkman and Kelly (2003)

found WM2.2 QTL for the field trial (12%, 40% PVE) and WM7.2 QTL in Bunsi x Newport

(17% PVE). Indeterminate vs. determinate growth habit mapped to Pv07, which is a novel

source of indeterminancy in navy bean (the most widely used source of

determinancy/indeterminancy is fin on Pv01) (Kolkman and Kelly, 2003).

10

Two resistance QTL located on Pv06 and Pv08 were detected for both greenhouse straw

test (WM6.1, WM8.3) and field resistance (WM6.1, WM8.3) in a Benton/NY 6020-4 RIL

(Miklas and Delorme 2003).

Ender and Kelly (2005) detected QTL on (Pv) 02, 05, 07, and 08 that accounted for 9%,

11%, 15%, and 9 % of the variance in field disease reaction, respectively, in a Bunsi/Raven RIL

population. Miklas et al. (2007) found two QTL in an Aztec/ ND88-106-04 population located

on Pv02 for field resistance and on Pv03 for the green stem trait explaining 25% and 16%

phenotypic variation respectively.

Soule et al. (2011) identified eight QTL from two separate populations. WM2.2 and

WM8.3 were detected in the Benton/VA19 (BV) population for greenhouse straw test and field

resistance while WM 2.2, WM4.2, WM5.3, WM5.4, WM6.1, WM7.3 were detected in the

Raven/I9365-31 (R31) for greenhouse straw test and field resistance.

In another population involving G122 ( G122/CO72548 RIL population), five QTL were

located on Pv01, Pv02, two on Pv08 and Pv09 for partial physiological resistance (straw test)

and accounting for 20, 15, 7, 11 and 13% of PVE respectively, and a QTL on Pv08 accounting

for 12% of PVE in the field (Maxwell et al., 2007). G122 is a well-known source for partial

resistance in greenhouse straw test from the Andean gene pool. Below is a list of described QTL

conditioning partial resistance to S. sclerotiorum in P. vulgaris germplasm from previous studies.

11

Table 1.1. Comprehensive list of quantitative trait loci (QTL) conditioning partial resistance to

white mold in common bean (Phaseolus vulgaris L.) from previous studies and identified in

Benton/VA19 (BV) and Raven/I9365-31 (R31) recombinant inbred line populations (italic type).

BJ, BAT 93/Jalo EEP558; DG, DOR364/G19833.

(Table adapted from Soule et al., 2011)

QTL Population Traits R2 (%) §

WM1.1† AG Field (CP, avoidance) 18 (34)

WM1.2 GC ST 20

WM2.1 PX ST 7

WM2.2 BN Field 12

HN Field 40

BR Field 9

AN Field 25

BV Field, ST, NWT 13, 35, 36

R31 Field 32

WM2.3 BR Field 10

GC ST 15

WM3.1 AN Field (CP, avoidance) 16 (36)

WM4.1 PX ST, Field 5, 5

WM4.2 R31 Field 14

WM5.1 PX ST 11

WM5.2 BR Field 11

WM5.3 R31 Field, (PH, avoidance) 21 (14)

WM5.4 R31 ST, NWT 8, 5

WM6.1 B60 ST, Field 12, 10

R31 Field 12

WM7.1 AG ST, Field 38, 26

PX ST, Field 9, 16

WM7.2 BN Field 17

BR Field 15

12

QTL Population Traits R2 (%) §

WM7.3 R31 ST, NWT 51, 22

WM8.1 PX Field (PH, avoidance), ST 9 (15), 24

CG Field 12

WM8.2 PX ST 12

WM8.3 B60 Field, ST 26, 38,

GC ST 7

BV Field 11

WM8.4 BR Field 9

GC ST 11

R31 NWT 8

WM9.1 GC ST 13

†Pop, population. The populations in which the QTL have been identifi ed are abbreviated AG = A55/G122 (Miklas

et al., 2001), PX = PC-50/XAN-159 (Park et al., 2001), BN = Bunsi/Newport and HN = Huron/Newport (Kolkman

and Kelly, 2003), BR = Bunsi/Raven (Ender and Kelly, 2005), B60 = Bunsi/NY6020-4 (Miklas et al., 2003), AN =

Aztec/ND88-106-04 (Miklas et al., 2007), GC = G122/CO72548 (Maxwell et al., 2007), and BV = Benton/VA19

and R31 = Raven/I9365-31 (current study).

‡NWT = nonwounding test; ST = straw test; CP = canopy porosity; PH = canopy height.

§The R2 values were rounded to the nearest whole number, and most represent values obtained by regression

analysis (single-factor ANOVA) with significance levels ranging from P < 0.05 to P < 0.001. For studies that

reported R2 values for individual environments, the environment with the highest value is listed. For NWT, ST, and

Field, values represent amount of phenotypic variation explained for disease score. Values within parentheses

represent avoidance traits that co-located with fi eld resistance.

¶The QTL were named based on recent QTL nomenclature guidelines (Miklas and Porch, 2010). For example,

WM2.3 represents the third QTL for white mold resistance identified on linkage group 2. It was originally identified

in the BR mapping population, and the same QTL as determined by comparative mapping was subsequently

observed in GC population.

The majority of the WM QTL studies in common bean have targeted major QTL

segregating in wide crosses (Miklas et al., 2001, 2003, 2007; Kolkman and Kelly, 2003).

However, there may be important minor effect QTL (Atwell et al., 2010), that condition partial

13

resistance in advanced breeding lines and cultivars that may not be detected by bi-parental

approaches for white mold resistance gene mapping. These minor QTL may be be easier to

transfer into high yielding cultivars. For quantitative traits, exploration of historical and

evolutionary recombination events in establishing marker-trait associations (Nordborg and

Tavare, 2002) is very important.

Association mapping (AM), also known as "linkage disequilibrium mapping", is a

method of mapping QTL that takes advantage of historic linkage disequilibrium to link

phenotypes to genomic regions. AM is performed by scanning the entire genome represented by

single nucleotide polymorphisms or SNPs (in many cases are spotted onto glass slides to create

“SNP chips”) for a panel of genotypes (lines/cultivars) and then associating the markers with

phenotypic traits measured for the same panel. There are two primary approaches for AM. The

first approach uses candidate genes, often of known function, to test for significant marker-trait

associations. The second approach is similar to conventional QTL mapping, where a large set of

markers is used to screen the genome for statistically significant associations (Risch and

Merikangas, 1996). SNPs are recommended for association mapping due to their low cost in

high-throughput settings and relative abundance within the genome (Syvanen, 2005; Clark et al.,

2007). SNPs are also useful as markers in candidate gene studies.

In AM, it is important to account for underlying population structure, as the technique

tends to give false positives. Correction factors such as structured analysis (SA) and genomic

control (GC) and principal component (PC) axes are useful in accounting for structure and

reducing Type 1 errors (Yu et al., 2006; Zhao et al., 2007). AM is dependent upon the extent of

linkage disequilibrium (LD). Where LD extends for long distances, fewer markers are needed,

14

but the precision to detect the gene controlling polymorphism is lower. Inversely, if LD decays

rapidly, the power of resolution is high, but a larger numbers of markers must be screened.

Association mapping has the potential to circumvent some of the limitations of QTL

mapping. First, association mapping uses a large set of cultivars or elite breeding lines rather

than a set of progeny from one or a few limited crosses. This allows for a greater chance to

capture and analyze genetic variation. Second, because of the structure of an association

mapping population, presumably any significant marker trait association is immediately

transferable to a much wider collection of germplasm. This is not so say, however, that AM is

without its own drawbacks. Association mapping is dependent upon the extent of linkage

disequilibrium.

Wang et al., (2008) reported significant marker-trait associations in soybean for iron

deficiency chlorosis. The study used 139 soybean lines from maturity groups 00, 0, and 1, and

tested these lines with 84 SSR markers. Researchers reported two SSR markers significantly

associated with the resistant phenotype. Presumably, it would be possible to replicate this

success in the dry beans using a larger number of lines but with SNP markers. Such a study

could be used to confirm marker utility with known QTL and possibly identify other, previously

undiscovered, QTL for partial resistance to white mold. Rossi et al. (2009) suggested that

genome-wide LD extends up to 10-15 cM in domesticated bean, which is significantly larger

than in wild bean.

The objectives of this research are i) to use AM to identify novel QTL in the Middle

American diversity panel (MDP) conferring partial resistance to white mold based on natural

field and artificial inoculations, and ii) to validate the presence of QTL conferring partial

15

resistance to white mold in a backcross RIL population consisting of 104 F5:7 derived lines

(Orion//Orion/R31-83) phenotyped for disease reaction in the greenhouse and field and

genotyped with a 6K SNP chip.

16

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25

CHAPTER TWO

ASSOCIATION MAPPING OF WHITE MOLD RESISTANCE IN A PANEL OF NORTH

AMERICAN BREEDING LINES AND CULTIVARS REPRESENTING THE MIDDLE

AMERICAN GENE POOL

Abstract

White mold, caused by Sclerotina sclerotorum, Lib. de Bary, is a major disease, limiting

common bean (Phaseolus vulgaris L.) production around the world. Partial resistance exists and

may contribute to control of the disease. A better understanding of the genetic mechanisms

underlying the partial resistance would facilitate the development of new bean cultivars that are

resistant to white mold (WM). Our objective was to search for QTL conditioning white mold

resistance and disease avoidance traits in the Middle American Diversity Panel (MDP) using

single nucleotide polymorphism (SNP) markers. A panel of 300 accessions in the MDP

representing black, navy, pinto, great northern, pink, red, and other small and medium seed-sized

market classes grown in the US was phenotyped for reaction to WM in the greenhouse and field.

The panel was genotyped with ~35,000 SNPs generated from genotype by sequencing (GBS)

approach by the NDSU Molecular Bean Project. Principal component analysis was performed

using the Tassel software to identify population structure and along with the kinship matrix was

used as covariates in multiple linear models (including Naïve, GLM and MLM approaches) to

identify marker-locus-trait associations. Prior to association tests, monomorphic and low

frequency SNPs were filtered out reducing the SNP number to 15,033. For all traits analyzed, 26

SNPs were significantly associated with 29 quantitative trait loci (QTL) spanning the eleven

26

bean linkage groups (chromosomes). Some of the SNPs influenced multiple traits. These SNPs

satisfied the 0.01 percentile (p-value ≤ 5.58E-04) significance threshold. The QTL conditioning

field white mold resistance mapped near previously identified QTL on Pv02, Pv05 and Pv08.

QTL associated with greenhouse straw test were detected on Pv02, Pv03, Pv04, Pv05, Pv07,

Pv08 and Pv09. Avoidance traits, tall and open plant canopies and reduced lodging, were

correlated (26%, 48%, and 51%, respectively) with less disease in the field. Although,

association mapping in the MDP identified significant QTL conditioning resistance to white

mold, further validation of the QTL may be warranted.

27

INTRODUCTION

White mold (WM), caused by Sclerotina sclerotorum, Lib. de Bary, is a major disease (Singh

and Schwartz, 2010) in common bean (Phaseolus vulgaris L.). Crop losses due to WM mold

disease outbreaks in dry bean average 30% in the central high plains of the United States with

individual field losses as high as 92% (Kerr et al., 1978; Schwartz et al., 1987). However, under

favorable weather conditions 100% seed yield and quality losses occur on susceptible common

bean cultivars (Singh and Schwartz, 2010). Partial resistance can be used to control this disease

(Soule et al., 2011).

Genetic resistance in the host conditioned by avoidance (Miklas et al., 2013) and

physiological mechanisms provides an important component of an integrated control strategy.

But the evaluation and selection for resistance to WM using traditional screening methods is

tedious and environmentally sensitive. Moreover, few resistance sources exist within

commercially adapted common bean germplasm. Field resistance is comprised of both

avoidance and physiological mechanisms while the greenhouse straw test evaluates partial

physiological resistance in the absence of avoidance. Inheritance studies including QTL analysis

in bi-parental inbred populations have revealed numerous QTL affecting partial resistance to

WM in the field and greenhouse straw test (Soule et al., 2011; Miklas et al., 2013), but many of

these QTL derive from exotic sources that can be difficult to transfer into commercial cultivars

(Miklas, 2007).

Association mapping (AM) has emerged as a new tool to dissect complex trait variation

at the genomic sequence level by exploring historical and evolutionary recombination events

within a natural population (Nordborg and Tavare, 2002; Brachi et al., 2011). AM offers three

28

main advantages over conventional linkage analysis, i) increased mapping resolution, ii) reduced

research time, and iii) evaluation of greater allele number (Yu and Buckler, 2006). Since its

debut in plants, AM has continued to raise curiosity among genetic researchers owing to

advances in high throughput genomic technologies, interests in identifying novel and superior

alleles, and improvements in statistical methods (Thornsberry et al., 2001). AM provides an

opportunity to identify QTL (Atwell et al., 2010) present in groups of breeding materials.

Population structure, which is a division of the population into distinct subgroups related

by kinship (Pritchard and Rosenberg, 1999; Price et al., 2006), has long been a hurdle in

association genetic studies because it can create false positives. Complex population structure

could be expected in crop species that were subject to a severe domestication bottleneck

followed by breeders’ selection; example, the division of maize germplasm into heterotic groups

(Reif et al., 2005). An association mapping population panel can be assembled from a common

geographical origin or breeding programs, or from a diverse mix of accessions. These non-

independent samples usually contain both population structure and familial relatedness (Yu and

Buckler, 2006). However, several statistical methods have been proposed to account for

population structure and familial relatedness (Falush et al., 2003; Pritchard and Rosenberg, 1999;

Pritchard et al., 2000), including genomic control (GC) (Devlin and Roeder, 1999), mixed model

(Yu et al., 2006), and principal component approaches (Price et al., 2006). Principal component

approach (PCA) requires much less computing time compared to “Structure” analysis.

Many agriculturally important traits such as productivity and quality, tolerance to

environmental stresses, and some forms of disease resistance are controlled by polygenes and

greatly influenced by environmental effects. Deciphering the genetics of complex traits has long

been the focus of traditional quantitative genetics. Genetic mapping and molecular

29

characterization of these genes that contribute to the variation of complex traits has the potential

to facilitate genome assisted breeding for crop improvement (Holland, 2007).

Association mapping in the form of a genome-wide association study (GWAS) is

performed by scanning an entire genome for SNPs associated with a particular trait of interest. It

identifies QTLs by examining marker-trait associations that can be attributed to the strength of

linkage disequilibrium between markers and functional polymorphisms across a set of diverse

germplasm. AM holds great potential for the dissection of complex genetic traits (Oraguzie et al.,

2007; Yu and Buckler, 2006).

The overall goal of this project was to identify QTL conferring field and physiological

resistance to WM in a panel of commercial adapted materials (300 accessions) from the Middle

American gene pool. Major gene pools in common bean are the Andean and Middle American

(see review by Singh et al., 1991). Another sub-objective was to identify the most resistant

materials among the 300 accessions in the MDP for potential use in a WM resistance breeding

program.

30

MATERIALS AND METHODS

Plant Material

A panel of 300 Middle American cultivars/lines (MDP) including the small-seeded navy and

black bean market classes from Race Mesoamerica (108) and medium-seeded great northern,

pink, pinto, and red market classes from Race Durango (182), was developed by the Bean

Coordinated Agriculture Project (BeanCAP) for studying drought and nutrient content of the

grain. The Race designation for 10 lines was unknown. G122 ‘Jatu Rong’ landrace cultivar from

the Andean gene pool and USPT-WM-12 pinto bean germplasm line with documented resistance

to white mold in the field and greenhouse (Miklas et al., 2014; Miklas et al., 2001; Jhala et al.,

2014) were included as checks. G122, and members of the panel ‘ICA Bunsi’ navy bean (also

known as Ex Rico) and ‘Beryl’ great northern bean, represent the resistant, intermediate resistant

and susceptible checks for the Bean White Mold Nursery (BWMN) that is tested annually in

field trials and greenhouse straw tests across the US and internationally (Jhala et al., 2014).

Disease Screening

Straw test

The straw test (Petzoldt and Dickson, 1996) was performed in the USDA-ARS greenhouses at

Prosser, WA in six replications randomized in a complete block design. Greenhouse conditions

were maintained at approximately 18°C/night and 25°C/day schedule. Plants were grown under

artificial high-intensity discharge (HID lamps) lighting to maintain 12-hour day length and were

watered as needed. Two seeds per line were planted in four inch diameter square pots using

Sunshine® brand SB40 professional growing mix (Sun Gro Horticulture, Agawam,

31

Massachusetts) with 2.5 ml of Scott‟s Osmocote® 14-14-14 slow-release fertilizer (A. M.

Leonard, Inc Piqua, Ohio) applied as a top dressing. Following seedling emergence, each pot

was thinned to 1 plant.

Mycelium produced on potato dextrose agar (PDA) was used for inoculum. Sclerotia of

isolate T001.01 (collected from ‘Newport’ navy bean in Quincy, WA in 1996) was cultured onto

sterile 15 x 100 mm plates of PDA. Each plate contained an individual sclerotium placed in the

center of the plate. The plates were incubated at 20°C in the dark until the mycelium germinated

from the sclerotia reached the outer edge of the plate (3 to 4 days). Plants were inoculated 28 to

30 d after planting as described by Petzoldt and Dickson (1996) using a 100 ul pipette tip

containing two mycelia plugs from the actively growing mycelia toward the outer portion of the

plates. The open end of the pipette tip was used to bore out the plugs and was then placed over

the cut stems. Stems were cut ~ 2 cm above the 4th

or 5th

node .

Evaluation for disease severity was conducted at 7 and 11 d after inoculation. Disease

severity was rated based on a 1 to 9 scale (Petzoldt and Dickson, 1996) where 1 = no progression

of symptoms beyond the first node, 3 = some progression of symptoms beyond the first node, 6 =

progression of symptoms to the second node, 8 = progression of symptoms beyond the second

node, and 9 = complete susceptibility and death of the plant. An average score was obtained from

the two ratings, and analyzed separately from the 7 and 11 d ratings.

Field test

The panel was planted 20 June, 2013 in a replicated field trial at the USDA-ARS, Cropping

Systems Research Farm near Paterson, WA. This research farm has a history of uniform and

moderate to severe white mold infestation (Miklas, 2007; Miklas et al., 2001, 2003, 2004; Soule

32

et al., 2011). The soil is a Quincy sand (mixed, mesic Xeric Torripsamment). The 300 accessions

including two checks, G122 and USPT-WM-12, were arranged in a RCBD with two replications.

An individual plot consisted of three rows, 3 m in length and spaced 0.56 m apart. Excess

nitrogen and irrigation was applied to promote a full and wet canopy favorable for WM

epidemics. From flowering to physiological maturity excess water, 6.3 mm daily, was applied by

overhead center-pivot irrigation in the later afternoon. Six application of Nitrogen was foliar-

applied weekly by chemigation at a rate ~20 lbs from the early seedling growth stage (about 18

DAP) to mid pod fill (about 62 DAP).

Reaction to WM disease was measured at physiological maturity and was scored from 1

to 9 based on combined incidence and severity of infection, where 1 = no diseased plants and 9 =

80 to 100% diseased plants and/or 60 to 100% infected tissue (Miklas et al., 2001). Other traits

measured include lodging, recorded at R6 (see Schwartz et al. 2009 for explanation of growth

stages) using a scale from 1 to 9, where 1 = no lodging and 9 = completely lodged (Miklas et al.,

2001). Canopy porosity, using an expanded scale of 1 to 9, where 1 = an open canopy with the

soil surface between rows completely visible, 9 = completely closed canopy over the furrow with

no soil visible was measured at the R4 to R5 (Brick, 2005) mid pod fill growth stage. Flowering

date (DAP, number of days after planting) when 50% of the plants had at least one open

blossom, and harvest maturity was recorded as DAP. Plant stand was based on a scale of 1-9,

where 1 = full plant stand and 9 = no stand due to lack of germination or extremely poor seedling

emergence. Vigor score estimates the volume of foliage scored from 1 to 9 where 1 = best and 9

worst. Canopy height (cm) was measured from the soil surface to the top of the canopy at R5

before plants started to lodge.

33

Statistical Analysis

Data analysis was performed in SAS for Windows 9.4 (SAS Institute, Cary NC, 2013) using

ANOVA, GLM, and MIXED procedures. The WM mean disease score in the field was adjusted

using plant stand as a covariate due to the importance of a complete stand for the disease to

manifest. The average score of the 7d and 11d straw test scores was treated as a separate

variable. To determine associations among phenotypic traits, simple correlation coefficients

using means were calculated by PROC CORR.

Genome-wide association study (GWAS)

Single Nucleotide Polymorphism (SNP) genotyping

The ~30,000 SNPs from GBS (genotype-by-sequencing), generated by Dr. McClean in the ARS

laboratory, Fargo, North Dakota State University and the Illumina Infinium BeadChip

(BARCBEAN6K_3) containing 5,398 SNPs generated by Dr. Perry Cregan in Beltsville, MD,

with support from the BeanCAP AFRI project were used to genotype the Middle American

panel. Only 274 of the 300 accessions were genotyped using this set of 35,000 SNPs. SNP data

were not available for 26 accessions. Filtering for monomorphic SNPs and others with minor

allele frequencies (MAF) (i.e., <0.05) left 15,033 SNPs for use in the association tests.

34

Population structure

The panel was examined in STRUCTURE software version 2.3.4 (Pritchard et al., 2000) and in

the Trait Analysis by Association, Evolution and Linkage (TASSEL) software version 5.2.1

(Bradbury et al., 2007).

STRUCTURE was run under the “Admixture with allele frequencies correlated” model

with a ‘burn-in’ of 10,000 and 50,000 Markov Chain Monte Carlo (MCMC). Twenty

independent runs each were performed with the number of clusters (K) varying from 2 to 10. The

best value of k was determined by lnP(d) (log posterior probability) and ∆K, as described by

Evanno et al. (2005).

Principal Component Analysis (PCA) in TASSEL was used to estimate the structure of

the population and to avoid unlinked loci being in LD simply because of population structure

(Price et al., 2006; Mangin et al., 2011). Three PCs explaining 33%, 36% and 40% of the

cumulative genotypic variation was derived from the SNP markers to represent population

structure (Price et al., 2006; Zhao et al., 2007). Multiple clusters were observed in the PCA

matrix when the three PCs were plotted. This explains the level and structure of the genetic

diversity that characterizes the MDP. PCA was further used for GWAS.

Relative kinship

Pairwise kinship estimates were calculated by constructing relative kinship matrix using

TASSEL software. The kinship matrix according to Endelman and Jannink (2012) compared the

identity by SNP (IBS) among all pairs of the 274 MDP accessions genotyped using 15,033

markers. The kinship estimate was used as a covariate in the association test to control for

relatedness. Kinship provides a more subtle way to capture relationships.

35

Linkage disequilibrium

LD was estimated by calculating the square value of correlation coefficient (r2) between all pairs

of markers with the software package TASSEL. Only polymorphic marker loci with minor allele

frequency values above 0.05 were included further for LD analyses. P-values for each r2 estimate

were obtained with a two-sided Fisher's exact test as implemented in TASSEL. The decay of r2

with physical and/or genetic distance between loci is often used to determine the density of

markers to use in whole genome association scans (Stram, 2004) whereas local LD on

chromosomes is used to account for genes/QTL associated with trait variation. LD decay graphs

were plotted with physical distance (Mbp) versus r2 using nonlinear regression as described by

Remington et al. (2001).

Model Testing

We conducted association mapping to identify loci underlying the genetic control of the traits

mentioned above. Four different linear regression models including Naïve and General Linear

Model (GLM) that do not correct for population structure or kinship and another that corrects for

population structure and/or kinship (MLM) were fitted with 1) PCA, 2) k, and, 3) PCA + k

values as covariates. The mixed linear model (MLM) takes population structure into account and

therefore renders fewer false positives compared with a GLM (Larsson et al., 2013) approach.

Significant QTL

36

Significant QTL were determined using the tails of the empirical p-value distribution of 10,000

bootstraps in R software version 3.0.3 (Venables et al., 2014). As a population with unknown

distribution, the empirical distribution of data provides an efficient and precise estimation of

marker significance (Li et al., 2009; Hall and Miller, 2010; Mamidi et al., 2014). This approach

is based on choosing a predefined percentile tail from an empirical distribution.

RESULTS AND DISCUSSION

Greenhouse straw test

The straw test was able to differentiate between the resistant G 122 and USPT-WM-12 and

susceptible Beryl checks. Bunsi is known to have a susceptible reaction in the straw test as

observed here (Fig. 2.1). The MDP lines were mostly susceptible in the straw test regardless of

the sub-population origin ( Mesoamerican or Durango races); however, there was significant

(p<0.001) variation for WM reaction among accessions for the “7d”, “11d” and “average”

ratings (Table 2.1). A high correlation between 7 and 11 d ratings (r = 0.79, P <0.001) suggests

that either of the two ratings could be used to assess disease reaction in this test. However, the 11

d rating allowed greater separation among lines (data not shown). The average of the two ratings

helped to identify lines with consistent expression of partial resistance as the disease progressed

from 7 to 11 d post inoculation. ‘Laker’, ‘Fleetwood’ and ‘Seafarer’ from the Mesoamerican

race and ‘Stampede’, NDZ06249,‘Maverick’, NE2-09-4 and USPT-WM-1 from the Durango

race, exhibited similar resistance to the resistant checks G 122 and USPT-WM-12 (p > 0.05,

37

Table 2). Note USPT-WM-1 is a parent of the resistant check USPT-WM-12 (Miklas et al.,

2014). Overall, the results confirm that there is minimal physiological resistance to white mold

within the Middle American gene pool as detected by the straw test.

Field trial

There was a significant variation among lines (p<0.001, Table 2.1) for WM severity in the field

trial. Mean score ranged from 2.8 to 9.0 for the Durango race and 2.0 to 9.0 for the

Mesoamerican race (Fig. 2.2). G122 and USPT-WM-12 showed relatively high levels of

resistance, with a mean severity scores of 3.0 and 4.0, whereas Bunsi and Beryl were more

susceptible with scores of 5.5 and 6.0, respectively. Poor seedling emergence was observed for

some of the plots necessitating the need for measuring plant stand. Correlation analysis

determined stand density influenced disease severity (R = 0.85, p<0.001). Therefore, stand was

used as a covariate adjustment for disease score.

The results clearly show that the Mesoamerican race has more field resistance than the

Durango race. USPT-WM-1 showed partial resistance in both glasshouse and field evaluations

with a mean WM score of 4.8 and 3.4, respectively. Schwartz and Singh (2013) also observed

that USPT-WM-1 was as a useful source of partial resistance to white mold in common bean

(Table 2.3). The Mesoamerican race cultivars ‘Red-Ryder’, ‘INTA-Precoz’, ‘Rojo Chiquito’ and

‘Morales’ exhibited partial resistance to white mold in the field and warrant further testing.

38

Trait correlations

Reduced white mold severity was associated with disease avoidance traits: increased canopy

porosity and reduced lodging (Table 2.4). Weak correlation between field and straw test disease

ratings confirms the importance of using field screening to characterize partial resistance

segregating in breeding populations.

Coyne et al. (1974) and Miklas et al. (2013) reported a similar trend in correlation

between disease incidence, plant height and lodging in field screening trials as an interplay

between architectural traits and physiological resistance mechanisms. The association of reduced

canopy porosity with decreased disease severity has been consistent with other studies (Soule et

al., 2011)

Genome-wide association study (GWAS)

Population structure

The MDP panel was assigned to six sub-groups with significant admixture within groups

(Fig. 2.3). These results can further be used to interpret the geographic distribution of the MDP.

PCA matrix plotted with the three PCs explaining 33%, 36% and 40% of the cumulative

genotypic variation showed multiple clusters (Fig. 2.4). This explains the level and structure of

the genetic diversity that characterizes MDP. The principal components were further used for

GWAS.

39

LD decay

The LD values for pairs of markers were exported from TASSEL into SAS 9.4 (2014) to

construct the LD decay graph using a nonlinear regression model. The average decay of LD (r2)

in terms of physical distance declined to r2=0.7 at ~500kb. The physical distance at which r

2=0.2

is 3.2 Mbp and at r2=0.1 was 8.0 Mbp (Fig. 2.5).

Model Testing

The linearity of the diagonal line of the quantile–quantile (QQ) plots and the threshold at which

the expected values deviated from the linear line was used as the basis for selecting the best

model (Fig. 2.6).

For the naïve model, the upward deviation from the linear line occurred at around the

threshold of –log10 P>1.3. About 18K associations were detected for all the traits with p value

ranging from 1.67E-26 to 3.33E-06. These are spurious associations indicating false positives.

The upward deviation from the linear line for the K and PCA model occurred at the

threshold of –log10 P>3.23 and –log10 P>5.49 respectively with p values between 5.92E-04 to

8.25E-06 for the K model and 3.27E-06 to 1.78E-10 for the PCA model. One hundred and eighty

two and one hundred and twenty four associations were detected above this threshold for K and

PCA model. These two models show the importance of correcting for population structure for

this study. The p values indicate some important associations but there is a huge and early

deviation of expected values from the linear line for the PCA model. This reduces the confidence

in this model.

40

The last model incorporating PCA and K together showed the most linear QQ plot with

upward deviation from the diagonal line detected at the threshold of –log10 P>3.25. P values

ranged from 6.08E-04 to 1.17E-10. Twenty nine unique associations were detected. The results

from this model were reported for this study.

Significant QTL

A threshold of 0.01 percentile (p-value of ≤ 5.58E-04, –log10 P>3.25) tail of the empirical p-

value distribution of 10,000 bootstraps was defined as the cut off for significant associations. The

threshold of –log10 P>3.25 was also derived from the quantile–quantile (QQ) plots, since most of

the upward deviation from the linear line occurred at around –log10 P>3.25 (Fig. 2.6). Distinct

peaks were observed on the Manhattan plots at this threshold for all the traits (Fig. 2.7).

Marker-trait associations

For all traits analyzed, 26 SNPs were significantly associated with 29 quantitative trait loci

(QTL) spanning the eleven bean linkage groups (chromosome). Some of the SNPs influenced

multiple traits. These SNPs satisfied the 0.01 percentile (p-value ≤ 5.58E-04) significance

threshold. The phenotypic variation explained (PVE) by the QTL (R2) ranged between 5 and 7

percent (Table 2.5).

Twelve QTL were detected in the greenhouse straw test for “7d”, “11d” and “average”.

Three of which were detected on the same chromosomes as the QTL for field WM severity. Due

to proximity of these QTL, the same gene might be controlling both traits (greenhouse straw test

and field WM severity). These QTL mapped on (Pv) 02, 05 and 08. Kolkman and Kelly (2003)

41

detected QTL for field WM resistance on Pv02 (WM2.2) in Bunsi/Newport mapping population.

Ender and Kelly (2005) detected the same QTL on Pv02 and another on Pv05 (WM5.2) in the

Bunsi/Raven population. Miklas et al. (2007) detected QTL WM2.2 in the Aztec/ND88-106-04

mapping population. In the R31 (Raven/I9365-31) population, Soule et al. (2011) observed that

QTL WM2.2 and WM5.3 conditioned field resistance. Other QTL conditioning resistance in the

straw test were located on (Pv) 03, 04, 07 and 09. While the field tests may confound

physiological resistance with avoidance mechanisms and other environmental influences, the

straw test is believed to measure physiological resistance directly.

Our study revealed four QTL on (Pv) 02, 07, 08 and 10 associated with lodging, plant

height, days to flower and field WM severity. Another QTL detected on Pv11 conditioning field

resistance was influenced by disease avoidance traits (canopy porosity). Kolkman and Kelly

(2003) identified QTL WM7.2 for plant height, lodging and branching angle in the BN

(Bunsi/Newport) population and Ender and Kelly (2005) detected QTL WM7.2 associated with

lodging in BR (Bunsi/Raven) RIL population on Pv07. Plant canopy height and resistance to

lodging are important in disease avoidance in common bean. Tall plants with narrow profiles,

porous canopies, and resistance to lodging have much greater capacity to avoid white mold

disease. Lodged dry beans create denser and more compact canopies which result in cooler and

more humid microclimates favorable to the pathogen. In addition, plant organs in contact with

the ground are vulnerable to mycelia infections emanating from colonized senescent blossoms

and leaf litter on the soil surface.

Other QTL identified for days to flowering mapped on Pv 01 and 10. Blair et al. (2006), and

Perez-Vega et al. (2011) also identified several loci associated with flowering on (Pv) 01,02, 06, 09

and 11. The number of days to flowering is an important phenological trait in relation to disease

resistance. QTL for harvest maturity (HM) were detected on Pv 03 and 08. The use of avoidance

42

mechanisms, including late maturity enhanced disease avoidance. Miklas et al. (2007) detected

WM3.1AN

, a physiological resistance QTL associated with late maturity and the stay-green stem

characteristic, which were introgressed from ICA Bunsi into pinto USPT-WM-1. USPT-WM-1

is a parent of the resistant check, USPT-WM-12 used in this study. The genomic positions for all

of the previously detected QTL was based on recombination frequency (cM) which is imprecise

compared to actual physical positions which is now possible to discern due to availability of

SNPs (see Table 2.5).

Conclusion

Although most materials lacked resistance in the straw test, our study demonstrated substantial

genotypic variation for partial resistance to white mold within the MDP panel, which provides a

rich basis for breeding for white mold resistance within commercially adapted materials. At least

25 MDP lines showed significant improvement over the susceptible checks in the field. These

are promising lines that could be used as potential parents to breed for partial resistance to white

mold. GWAS provided a basis for comprehensive analysis of QTL resistance to white mold

disease in this population. Several existing significant QTL were confirmed and a few novel

QTL were detected. Finally, these results demonstrate the utility of association mapping for

detecting marker-trait associations which can potentially be used for developing marker assisted

breeding strategies in dry bean.

43

Acknowledgements

I am grateful to Susan Swanson and Mike Nielsen and Jeff Colson for supporting the

experiments in the glasshouse and field support. Funding for this project was provided by ARS

National Sclerotinia Initiative. We appreciate the many contributions of AgriNorthwest to the

Paterson research farm.

44

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49

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50

Table 2.1. Summary mean squares from analysis of variance

for tests conducted on 300 dry bean lines and cultivars in the

greenhouse and field in 2013.

Mean Square

Traits† Rep Line Error

Error

DF

Straw test: 7d (1-9) 41.39 2.02***

0.93 1299

Straw test: 11d (1-9) 46.86 2.46***

1.03 1296

Straw test: avg (1-9) 38.11 1.98***

0.82 1291

White mold (1-9) 10.25 5.77***

1.12 267

Plant height (cm) 1.23 24.32***

2.2 269

Canopy porosity (1-9) 346.88 10.77***

5.28 269

Vigor (1-9) 65.7 0.64NS

0.45 269

Lodging (1-9) 4.97 6.16***

0.63 267

Harvest maturity (DAP) 569.86 21.81***

4.4 269

Days to flower (DAP) 118.98 29.05***

3.2 269 Greenhouse straw test rated on 1 to 9 scale where 1 is no symptom and 9

completely diseased.

White mold, 1 = no symptoms and 9 = completely diseased; Canopy

porosity, 1 = open canopy and 9 = completely closed canopy; Vigor, 1 =

best and 9 = worst; Lodging, 1 = no lodging and 9 = completely lodged;

Harvest maturity and Days to flower = number of days after planting.

*** denotes significance at 0.1% probability level and NS denotes non

significance. †Straw test had 6 reps and field trials 2.

51

Table 2.2. Promising lines from the 2013 straw test evaluation for white

mold at the USDA-ARS greenhouses at Prosser, WA.

Genotype

Market

class Race‡ 7d 11d Average

Checks† (1-9) (1-9) (1-9)

USPT-WM-12R Pinto D 5.0 6.2 5.6

G-122R Cranberry A 5.1 6.0 5.5

BunsiI Navy M 6.1 7.8 6.9

BerylS GN

§ D 6.2 8.2 7.2

Promising lines Laker Navy M 4.5 5.7 5.1

Fleetwood Navy M 4.7 6.4 5.5

Seafarer Navy M 4.8 6.2 5.5

Stampede Pinto D 4.0 6.6 5.3

USPT WM-1 Pinto D 4.8 6.1 5.5

Maverick Pinto D 4.8 5.7 5.3

NE2-09-4 Pinto D 5.0 7.3 6.1

NDZ06249 small red D 4.7 6.2 5.4

Mean for Durango

6.2 8.0 7.1

Mean for Mesoamerica

6.4 8.0 7.2

Overall Mean

6.2 8.0 7.1

LSD (0.05)

1.1 1.2 1.0

CV %

15.4 12.7 12.7 Greenhouse straw test rated on 1 to 9 scale where 1 is no symptom and 9

completely diseased.

†R, I, S denote partial resistant, intermediate and susceptible check. ‡M, D, A denotes Mesoamerica, Durango and Andean race respectively.

§GN, Great Northern.

52

Tab

le 2

.3. P

rom

isin

g l

ines

fro

m t

he

2013 f

ield

tri

al

for

wh

ite

mold

sev

erit

y a

t U

SD

A-A

RS

Cro

pp

ing S

yst

ems

Res

earc

h F

arm

at

Pate

rson

WA

.

Mark

et

Mean

Gen

oty

pe

Cla

ss

Race

‡

Poro

sity

H

av.m

at§

L

od

ge

Wh

ite

mold

Ch

eck

s†

(1-9

) (D

AP

) (1

-9)

(1-9

)

US

PT

-WM

-12

R

Pin

to

D

7.0

105.5

5.0

4.0

G-1

22

R

Cra

nber

ry

A

7.5

105.0

3.3

3.0

Bunsi

I N

avy

M

6.0

96.5

5.8

5.5

Ber

ylS

G

N §

D

9.0

106.0

9.0

6.0

Pro

mis

ing l

ines

P

ueb

la-1

52

B

lack

M

6.0

109.0

5.8

2.0

115M

B

lack

M

5.0

108.5

6.5

2.7

I9365

-31

B

lack

M

9.0

110.0

7.0

2.8

CD

C-E

xpre

sso

B

lack

M

6.5

103.0

4.5

2.9

Jaguar

B

lack

M

2.0

106.5

3.0

3.1

A801

Car

ioca

M

9.0

107.5

6.0

3.8

A-2

85

C

ream

M

2.5

108.0

5.8

3.9

Des

ert-

Rose

F

DM

§

D

5.5

102.0

8.0

3.1

GN

-Sta

r G

N §

D

9.0

106.5

3.8

3.6

OA

C-L

aser

N

avy

M

4.5

105.5

3.5

2.8

Sea

haw

k

Nav

y

M

2.0

106.5

6.3

2.8

Sea

bis

kit

N

avy

M

2.0

108.0

6.0

3.0

Med

alis

t N

avy

M

2.0

109.0

4.5

3.2

S08418

P

ink

D

6.0

105.0

5.3

3.4

RO

G-3

12

Pin

k

D

2.0

104.5

6.3

3.5

53

Tab

le 2

.3.

Pro

mis

ing

lin

es f

rom

th

e 2013 f

ield

tri

al

for

wh

ite

mold

sev

erit

y a

t U

SD

A-A

RS

Cro

pp

ing S

yst

ems

Res

earc

h F

arm

at

Pate

rson

WA

.

M

ark

et

Mea

n

Gen

oty

pe

Cla

ss

Race

‡

Poro

sity

H

av.m

at§

L

od

ge

Wh

ite

mold

US

WA

-61

Pin

k

D

8.5

105.5

4.0

4.0

Mar

iah

P

into

D

4.5

101.5

2.8

3.3

US

PT

-WM

-1

Pin

to

D

2.0

107.5

4.5

3.4

Oura

y

Pin

to

D

6.0

97.5

2.0

3.9

ND

040494

-4

Pin

to

D

9.0

104.0

5.5

4.1

Red

-Ryd

er

Sm

all

red

D

5.5

102.0

7.0

2.7

INT

A-P

reco

z

Sm

all

red

M

9.0

107.5

6.8

3.6

Rojo

Chiq

uit

o

Sm

all

red

M

9.0

106.5

6.0

4.0

Mora

les

Sm

all

whit

e M

8.5

110.0

5.5

2.3

BA

T 4

77

T

an

M

8.0

106.0

7.5

2.5

Mea

n f

or

Dura

ngo

7.3

102.6

6.8

6.5

Mea

n f

or

Mes

oam

eric

a

5.4

105.9

5.6

4.8

Over

all

Mea

n

6.5

103.9

6.4

5.8

LS

D (

0.0

5)

4.6

5.0

1.6

2.1

CV

%

35.1

2.4

12.5

18

Po

rosi

ty,

1 =

open

can

op

y a

nd

9 =

co

mp

lete

ly c

lose

d c

ano

py;

Lo

dgin

g,

1 =

no

lod

gin

g a

nd

9 =

com

ple

tely

lo

dged

; H

av.

Mat

= n

um

ber

of

day

s af

ter

pla

nti

ng;

Whit

e m

old

, 1

= n

o s

ym

pto

ms

and

9 =

com

ple

tely

dis

ease

d.

†R

, I,

S d

eno

te p

arti

al r

esis

tan

t, i

nte

rmed

iate

an

d s

usc

epti

ble

ch

eck.

‡M

, D

, A

den

ote

s M

esoam

eric

a, D

ura

ngo

an

d A

nd

ean

rac

e re

spect

ivel

y.

§G

N,

Gre

at N

ort

her

n;

FD

M,

Flo

de

may

o;

Hav

. M

at,

Har

ves

t m

atu

rity

.

54

Table 2.4. Pearson correlation coefficients

between white mold disease severity and

agronomic trait means for 300 dry bean lines and

cultivars tested in Paterson, WA. in 2013.

Traits White mold (1-9)

Straw test: 7d (1-9) -0.18

**

Straw test: 11d (1-9) -0.07

Straw test: average (1-9) -0.13

*

Plant height (cm) -0.26

***

Canopy porosity (1-9) 0.48

***

Vigor (1-9) -0.34

***

Lodging (1-9) 0.51***

Days to flower (DAP) -0.38

***

Harvest maturity(DAP) -0.47***

Greenhouse straw test rated on 1 to 9 scale where 1 is

no symptom and 9 completely diseased;

White mold, 1 = no symptoms and 9 = completely

diseased; Canopy porosity, 1 = open canopy and 9 =

completely closed canopy; Vigor, 1 = best and 9 =

worst; Lodging, 1 = no lodging and 9 = completely

lodged; Harvest maturity and Days to flower =

number of days after planting.

*Significant at P < 0.05.

**Significant at P < 0.01.

***Significant at P < 0.001.

55

Tab

le 2

.5. M

LM

ou

tpu

t sh

ow

ing s

ign

ific

an

t m

ark

er-t

rait

ass

oci

ati

on

s in

a p

an

el o

f 274

Mid

dle

Am

eric

an

lin

es a

nd

cu

ltiv

ars

tes

ted

wit

h 1

5,0

00 S

NP

mark

ers

.

Chro

moso

me

Tra

it†

Mar

ker

Posi

tion

(Mb)

Pro

bab

ilit

y

val

ue

R2 (

%)

-log 1

0

(P v

alue)

Pv01

D

ays

to f

low

er (

DA

P)

m22589

6,8

35,7

59

3.7

1E

-04

4.6

7

3.4

3**

Pv02

S

traw

tes

t: a

ver

age

(1-9

) m

27159

3,5

70,0

57

5.3

9E

-04

4.5

1

3.2

7**

Pv02

W

hit

e m

old

(1-9

) m

23312

30,1

25,4

61

3.7

8E

-04

4.5

8

3.4

2*

Pv02

L

od

ge

(1-9

) m

23630

36,5

41,4

42

5.4

7E

-04

4.5

0

3.2

6**

Pv03

S

traw

tes

t: 1

1d (

1-9

) m

29255

3,6

61,5

27

1.1

9E

-04

5.6

2

3.9

2**

Pv03

H

arves

t M

aturi

ty (

DA

P)

m887

9,9

45,3

99

5.0

7E

-04

4.5

5

3.3

0**

Pv03

P

lant

hei

ght

(cm

) m

2260

44,2

17,6

85

2.7

6E

-04

4.9

3

3.5

6**

Pv04

S

traw

tes

t: a

ver

age

(1-9

) m

25574

2,7

58,0

52

2.5

3E

-04

5.0

6

3.6

0**

Pv04

S

traw

tes

t: 1

1d (

1-9

) m

25574

2,7

58,0

52

3.2

4E

-04

4.8

9

3.4

9**

Pv05

S

traw

tes

t: 7

d (

1-9

) m

5625

25,8

56,9

42

5.3

4E

-04

4.5

1

3.2

7**

Pv05

W

hit

e m

old

(1-9

) m

6347

39,2

32,0

04

1.1

4E

-04

5.4

2

3.9

4*

Pv05

S

traw

tes

t: 1

1d (

1-9

) m

6509

40,3

11,2

19

3.0

8E

-04

4.9

3

3.5

1**

Pv06

V

igor

(1-9

) m

7860

25,6

15,1

27

5.3

3E

-04

4.3

6

3.2

7**

Pv07

P

lant

hei

ght

(cm

) m

9327

6,0

46,7

55

5.5

5E

-05

6.0

9

4.2

6**

Pv07

S

traw

tes

t: 1

1d (

1-9

) m

10406

42,6

72,0

93

2.2

1E

-04

5.1

7

3.6

6**

Pv07

L

od

ge

(1-9

) m

10688

46,1

29,8

96

2.0

4E

-05

6.9

2

4.6

9**

Pv08

L

od

ge

(1-9

) m

25230

3,1

97,0

79

3.7

0E

-04

4.7

8

3.4

3**

Pv08

S

traw

tes

t: a

ver

age

(1-9

) m

13056

41,4

11,9

62

5.3

8E

-05

6.1

9

4.2

7**

Pv08

S

traw

tes

t: 1

1d (

1-9

) m

13056

41,4

11,9

62

4.1

3E

-05

6.4

1

4.3

8**

Pv08

H

arves

t M

aturi

ty (

DA

P)

m28230

56,1

76,7

61

3.0

0E

-04

4.9

3

3.5

2**

Tab

le 2

.5. M

LM

ou

tpu

t sh

ow

ing s

ign

ific

an

t m

ark

er-t

rait

ass

oci

ati

on

s in

a p

an

el o

f 274 M

idd

le

Am

eric

an

lin

es a

nd

cu

ltiv

ars

tes

ted

wit

h 1

5,0

00 S

NP

ma

rker

s.

56

Tab

le 2

.5. M

LM

ou

tpu

t sh

ow

ing s

ign

ific

an

t m

ark

er-t

rait

ass

oci

ati

on

s in

a p

an

el o

f 274 M

idd

le

Am

eric

an

lin

es a

nd

cu

ltiv

ars

tes

ted

wit

h 1

5,0

00 S

NP

ma

rker

s.

Chro

moso

me

Tra

it†

Mar

ker

Posi

tion

(Mb)

Pro

bab

ilit

y

val

ue

R2 (

%)

-log 1

0

(P v

alue)

Pv08

S

traw

tes

t: 7

d (

1-9

) m

14205

58,4

44,6

34

4.0

5E

-04

4.7

1

3.3

9**

Pv08

W

hit

e m

old

(1-9

) m

14314

58,8

42,9

50

3.5

8E

-04

4.6

2

3.4

5*

Pv09

D

ays

to f

low

er (

DA

P)

m26439

14,2

50,7

62

1.9

1E

-05

6.8

1

4.7

2**

Pv09

S

traw

tes

t: a

ver

age

(1-9

) m

34960

32,7

41,5

74

3.6

6E

-04

4.7

9

3.4

4**

Pv09

S

traw

tes

t: 1

1d (

1-9

) m

34960

32,7

41,5

74

2.4

8E

-04

5.0

8

3.6

1**

Pv09

V

igor

(1-9

) m

16778

37,3

52,8

02

3.6

3E

-05

6.2

6

4.4

4**

Pv10

L

od

ge

(1-9

) m

33200

10,1

73,5

02

2.7

0E

-04

5.0

1

3.5

7**

Pv10

D

ays

to f

low

er (

DA

P)

m25720

39,5

27,6

33

1.6

9E

-05

6.9

0

4.7

7**

Pv11

C

anop

y P

oro

sity

(1

-9)

m27093

41,3

38,4

38

4.8

9E

-05

6.1

7

4.3

1**

Gre

enhouse

str

aw t

est

rate

d o

n 1

to 9

sca

le w

her

e 1 i

s no s

ym

pto

m a

nd 9

com

ple

tely

dis

ease

d;

Whit

e m

old

, 1 =

no s

ym

pto

ms

and 9

= c

om

ple

tely

dis

ease

d;

Can

opy p

oro

sity

, 1 =

open

can

opy a

nd

9 =

com

ple

tely

clo

sed c

anopy;

Vig

or,

1 =

bes

t an

d 9

= w

ors

t; L

odgin

g, 1 =

no l

odgin

g a

nd

9 =

com

ple

tely

lodged

; H

arves

t m

aturi

ty a

nd D

ays

to f

low

er =

num

ber

of

day

s af

ter

pla

nti

ng.

†S

traw

tes

t had

6 r

eps

and f

ield

tri

als

2.

57

Figure 2.1. Response of the two races representing the MDP to a

greenhouse straw test (average score between 7- and 11- d ratings)

conducted at the USDA-ARS greenhouses at Prosser, WA in 2013.

Vertical arrow bars showing USPT-WM-12, Bunsi and Beryl

representing the resistant, intermediate and susceptible check means

Note: Ten lines from the 300 MDP were unclassified as either Durango

or Mesoamerican.

58

Figure 2.2. Response of the two races representing the MDP to field WM

severity grown at the USDA-ARS, Cropping Systems Research Farm near

Paterson, WA in 2013. Vertical arrow bars showing USPT-WM-12, Bunsi

and Beryl representing the resistant, intermediate and susceptible check

means.

Note: Ten lines from the 300 MDP were unclassified as either Durango or

Mesoamerican.

59

Figure 2.3. Summary plot of estimates of Q. Each individual is represented by a single

vertical line broken into K colored segments, with lengths proportional to each of the K

inferred clusters. The number of segments correspond to the predefined populations of

K=6.

60

Figure 2.4. Principal component analysis (PCA) matrix showing the first three PCs

where multiple clusters were observed.

61

Figure 2.5. LD decay plot showing LD measured as R2

between pairs of polymorphic marker loci plotted against

physical distance (Mbp).

62

Figure 2.6. QQ Plot showing the four models tested. P-value observed is plotted on the y-axis and P-

expected is plotted on the x-axis. Each color represents the different traits analyzed.

Naive PCA Kinship Kinship +PCA

63

Figure 2.7. Manhattan plots showing significant QTL associated with white mold resistance.

Eleven Chromosomes ordered on x-axis and each chromosome is represented by a different

color. The –log10 (p-value) is presented on the y-axis. The cutoff horizontal lines indicate 0.01

(black) and 0.1(blue) percentile tails of the empirical distribution obtained using 10,000

bootstraps. Vertical grey blocks indicate QTL regions that have major effect on the different trait

measured.

64

CHAPTER THREE

QTL VALIDATION FOR WHITE MOLD RESISTANCE IN A BACKCROSS RIL

POPULATION

Abstract

Sclerotinia sclerotiorum is a necrotrophic fungus that is widespread in all major snap and dry

edible bean (Phaseolus vulgaris L.) temperate production areas of the United States and

worldwide. There is little genetic diversity for white mold resistance in the Phaseolus vulgaris

gene pool and genetic control appears to be complex, with low to moderate heritability.

Increased attention has focused on using secondary gene pool as a source of resistance in

breeding programs. The objectives of this study were to: (1) validate previously identified QTL

for white mold resistance in the Raven/I9365-31 (R31) population (Soule et al., 2011), and (2)

identify lines with superior response to the recurrent parent. The mapping populations consisted

of backcross populations of Orion (P. vulgaris)// Orion/ R31-83 (P. vulgaris x P. coccineus

interpecific breeding line). A total of 104 BC1F5:7 RILs were developed. The RILs were

phenotyped for disease reaction in the greenhouse and field and were also genotyped using single

nucleotide polymorphism (SNP) markers from the BeanCAP BARCBEAN6K_3 6k Illumina-

Infinium SNP chip. Only 347 of 1130 polymorphic SNP were used for QTL analysis and

detection due to co-localization of SNPs on the linkage maps. A total of eight putative QTLs

were detected corresponding to five genomic regions on the eleven bean chromosomes (Pv). The

LOD values for the QTL ranged from 2.8 to 3.5, explaining between 6.7 to 19.2% of the

phenotypic variance of the traits. The WM2.2 and WM7.3 QTL derived from I9365-31 were

65

confirmed to influence partial resistance in the straw test and warrant further investigation for

marker-assisted breeding.

INTRODUCTION

Sclerotinia sclerotiorum (Lib. de Bary), the causal organism of white mold, Sclerotinia stem rot,

Sclerotinia wilt and stalk rot is one of the most devastating and ubiquitous fungi on cultivated

plants (Steadman, 1979; Tu, 1997). It is capable of infecting more than 400 species including the

common bean (Phaseolus vulgaris L.). Sclerotinia is responsible for losses up to 100% in some

crops (Steadman, 1979; Kerr et al., 1978).

Phaseolus coccineus (scarlet runner bean) is a member of the secondary gene pool for P.

vulgaris. P. coccineus is cultivated less frequently than P. vulgaris but it is resistant to many

diseases. It has been the best source of resistance for white mold disease found within the genus

to date (Abawi et al., 1978; Adams et al., 1973; De Bary, 1887; Debouck, 1999; Gilmore and

Myers, 2000; Gilmore et al., 2002; Lyons et al., 1987). Scarlet runner bean has been used as a

germplasm source for introgressing white mold resistance into common bean (see review by

Schwartz and Singh, 2013).

In the past, only limited levels of resistance to white mold that was quantitatively

inherited with low to moderate heritability was found in common bean. Miklas et al. (1998)

identified P. vulgaris accessions that had moderately high levels of resistance, but even those

accessions had much lower levels of resistance than that found in P. coccineus (Gilmore et al.,

2002)

66

P. vulgaris x P. coccineus interspecific populations has been reported as conditioned by a single

dominant gene (Abawi et al., 1978; Schwartz et al., 2006) or with quantitative inheritance

(Adams et al., 1973; Gilmore and Myers, 2004; Schwartz et al., 2004).

Tanksley and Nelson (1996) proposed advanced backcross QTL analysis (AB-QTL) as a

technique for integrating QTL discovery and the development of superior varieties. The lines

being evaluated are much more similar to their elite recurrent parent, allowing more accurate

assessment of traits. Our objective was to use an advanced backcross population,

Orion//Orion/R31-83 consisting of 104 BC1F5:7 RILs to dissect quantitative traits conferring

partial resistance to white mold in the field and greenhouse derived from dry bean breeding line

I9365-31 (Soule et al., 2011) with purported resistance to white mold obtained from P. coccineus

via interspecific hybridization. Note that R31-83 is a resistant RIL from the Raven/I9365-31

population. The lines were assayed with 5398 SNP markers to develop a linkage map that was

used for QTL analysis.

67

MATERIALS AND METHODS

Parental material

The BC1F5:7 mapping population, consisting of 104 recombinant inbred lines (RILs), was

developed by single seed decent (SSD) from a BC1F2 population (Orion//Orion/R31-83)

developed by crossing the R31-83 partial resistance donor to the susceptible great northern cv

Orion with the resulting F1 backcrossed to Orion. For each cross Orion was used as the maternal

parent.

Only BC1F1 plants that possessed SCAR markers linked with the WM2.2 and WM7.3

QTL (Soule et al., 2011) from R31-83 parent were selfed to produce BC1F2 populations for SSD.

The lines were advanced from BC1F2 to BC1F5 in the greenhouse by SSD. The BC1F5:6 progeny

from the greenhouse harvest were increased in the field in 2013 to obtain enough seed of BC1F5:7

bulk populations for subsequent phenotypic tests. The BC1F5:7 was chosen for QTL analysis

because analysis in later generations of inbreeding favor the detection of additive effects

(Tanksley and Nelson, 1996) and allows seed increase for replicated trials.

In total five SCAR markers have been developed from the Benton/VA19 (BV) and

Raven/I936531 (R31) RIL populations associated with WM2.2 and WM7.3 QTL (Soule et al.,

2011). SF13R15.290 was most closely associated amongst the four linked with WM2.2 and

SF18R7.410/415 linked with WM7.3 in the R31 population. These markers will be screened

against the BC1F5:7 for marker-assisted selection (MAS) of WM2.2 and WM7.3 allele derived

from I9365-31. R31-83 is a RIL from the Raven/I9365-31 population with partial resistance to

white mold (Soule et al., 2011). I9365-31 is a dry bean with partial resistance to white mold

derived from a P. vulgaris x P. coccineus interspecific hybridization (Miklas et al., 1998).

68

Greenhouse straw test

The 104 BC1F5:7 RILs and the two parents were evaluated for reaction to white mold in the

USDA-ARS greenhouses at Prosser, WA, using the straw test described by Petzoldt and Dickson

(1996). The experimental design was a randomized complete block (RCBD) with three

replications. Two seeds of each RIL were planted in a four inch diameter square plastic pot

containing Sunshine® brand SB40 professional growing mix (Sun Gro Horticulture, Agawam,

Massachusetts) and 2.5 ml of Scott’s Osmocote® 14-14-14 slow-release fertilizer (A. M.

Leonard, Inc Piqua, Ohio) applied at the time of planting. The experiment was conducted twice

during the winter months in 2013-2014. Temperatures were maintained at 21°C day and 16°C

night and artificial high-intensity discharge (HID lamps) lights were utilized to maintain a 12 h

day length. After emergence, pots were thinned to one plant and were watered as necessary for

vigorous growth.

Sclerotia of S. sclerotinia isolate T001.01 collected from ‘Newport’ navy bean in

Quincy, WA in 1996 was cultured onto sterile 15 x 100 mm plates containing potato dextrose

agar (PDA; Becton, Dickinson and Company, Franklin Lakes, NJ). One sclerotium was used per

plate. The plates were incubated at 200C in the dark. After about 3 to 5 days mycelium

germinated from the sclerotia covered the entire plate. A 100µl Eppendorf pipette tip

(Eppendorf, Hamburg, Germany) was used to extract two plugs colonized with mycelium from

the actively growing outer portion of the plates, and individual plugs were placed on the cut

stem. The growth terminal, about 28 d after planting was cut leaving ~ 2 cm of stem above the

4th

or 5th

node of the plant. The tip with the mycelia plug remained on the cut stem until disease

ratings. Evaluation for disease severity was conducted at 7 and 11 d after inoculation using the

69

modified straw test scale used to rate disease progression (original scale by Petzoldt and

Dickson, 1996).

Modified straw test scale used to rate disease progression in a straw test

Score Phenotype

1 no progression of symptoms beyond the first node

3 some progression of symptoms beyond the first node

6 progression of symptoms to the second node

8 progression of symptoms beyond the second node

9 complete susceptibility and death of the plant

Field test

The population was phenotyped for disease reaction in the field at the USDA-ARS Cropping

Systems Research Farm at Paterson, WA. The trial was planted June 20, and scored for white

mold response on September 11. The field used for this trial has a history of white mold infection

(Miklas et al., 2001, 2003, 2004; Miklas, 2007; Soule et al., 2011). Field design was in four row

plots with lines replicated twice and arranged in a randomized complete block design.

Experimental plots were 3 m in length and spaced 0.56 m apart. About 6.3 mm of water was

applied daily by overhead center-pivot irrigation in the mid-afternoon from the first appearance

of flowers until near physiological maturity. Six applications of nitrogen in the form of 20-0-0

NPK was foliar-applied weekly at a rate ~20 lbs to promote a full and wet canopy favorable for

WM epidemics. Normal cultural practices for optimum growth were practiced.

70

Reaction to white mold disease was measured at physiological maturity and was scored

from 1 to 9 as described by Miklas et al. (2001). Lodging was scored from 1 to 9 at R6 (see

Schwartz et al., 2009 for explanation of growth stages); where 1 = no lodging and 9 = completely

lodged (Miklas et al. 2001). Canopy porosity was measured at R5 (Brick 2005) using an

expanded scale of 1 to 9, where 1 = an open canopy with the soil surface between rows

completely visible, and 9 = completely closed canopy over the furrow with no soil visible.

Canopy height was measured in centimeters from the soil surface to the top of the canopy at R5

before plants lodged. Plant stand was estimated using a scale of 1-9, where 1 = complete

germination and seedling emergence with plants filling the entire plot and 9 = poor germination

with few emerged plants. Plant vigor was estimated from 1-9 and was based on volume of

foliage at V3-V4 growth stages where 1 = highest volume and 9 = poor growth with minimum

volume. Flowering date was measured as d after planting when 50% of the plants had at least one

open blossom. Harvest maturity was recorded as days after planting.

DNA extraction and Genotyping

DNA was extracted from the young leaves of 104 BC1F5:7 recombinant inbred lines (RILs) and

parents, collected from the green house using the extraction kit and protocol provided by Qiagen

® ( Qiagen, Hilden, Germany). DNA quality was checked on 1% agarose 1x TBE gel and

quantified using a NanoDrop ND-1000 UV-Vis spectrophotometer. The DNA was diluted to

100ng μl-1

and sent to Dr. Perry Cregan (USDA-ARS, Beltsville, MD) for SNP genotyping using

the Illumina Infinium BeadChip (BARCBEAN6K_3) containing 5,398 SNPs. Monomorphic

and low-quality SNPs were filtered out with Genome Studio software © 2014 Illumina, Inc. (San

Diego, CA). A total of 1130 SNP were found to be polymorphic in the population.

71

Phenotypic Data Analysis

For all traits recorded in the greenhouse and field, PROC GLM (SAS, 2014) was used to analyze

data, calculate least square (LS) means and calculate LSD tests for mean separation at P < 0.05.

The average score of the 7d and 11d straw test scores was treated as a separate variable.

Pearson’s correlation coefficient was calculated from the means to determine the degree of

association among all traits. The data was combined across runs as separate replications from 1

to 6.

SNP Analysis

Markers identified as polymorphic between the parents were surveyed across the population of

104 RILs. For each marker locus, chi-square analysis was conducted to determine significant

deviation of marker classes from the expected 3:1 Mendelian segregation ratios for BC1 RIL

populations. The presence of segregation distortion has been reported in linkage analyses for

several interspecific populations (Grant, 1975). This may affect the estimation of genetic

distance between two markers as well as the order of markers on a linkage group (Lorieux et al.,

1995a, 1995b).

Linkage Map

Linkage maps were constructed using Icimapping v4.0 (Wang et al., 2014). A pairwise linkage

analysis of the marker data, imposing a minimum LOD score of 3.0 and a maximum distance of

30 centimorgan (cM) was used to establish the linkage groups. Kosambi mapping function was

used to determine genetic linkage distances in cM. The polymorphic markers were arranged into

72

the eleven linkage groups representing the eleven chromosomes of P. vulgaris from Pv01

through Pv11. Marker order was confirmed from the known physical map position for the SNPs.

QTL Analysis

Composite interval mapping (CIM) was employed using WinQTLCartographer’s default

parameters with 1000 permutations to ascertain an empirical threshold for QTL significance.

CIM was executed using Model 6, 10 cM window size, and forward and backward stepwise

regression with genome scanning every cM (Churchill and Doerge, 1994; Wang et al., 2005).

Loci found significant at this threshold were considered the probable location of a QTL and the

percentage of variance for white mold and other traits explained by each locus was estimated

based upon the peak R2 value. A support interval of two LOD was calculated on both sides of

each QTL.

73

RESULTS AND DISCUSSION

Greenhouse Trial

Differential disease reaction was observed among the RILs and between the parents (P<0.05) in

the greenhouse straw test at 7d, 11d, and for the average. R31-83, as expected, exhibited a higher

level of resistance as indicated by a lower disease score (Table 3.1). The frequency plot of LS

means for disease score did exhibit a normal distribution (Fig. 3.1). A slight bimodal and

continuous distribution is consistent with previous reports of quantitative resistance to white

mold (Fuller et al., 1984; Kolkman and Kelly, 2003; Miklas and Grafton, 1992; Park et al.,

2001). Table 3.2 highlights the lines exhibiting a higher level of resistance, significantly

improved over the recurrent parent Orion (p < 0.05) but not significantly different from the

response of the resistant parent R31-83.

Field Trial

Significant variation was observed among lines and between the parents for white mold disease

severity in the field trial (Table 3.3). A normal continuous frequency distribution for field

reaction to white mold (Fig. 3.2) further supports quantitative inheritance of white mold

resistance in this population.

Disease score ranging from 2.5-6.8 in the field indicated that uniform white mold

pressure occurred across the trial. The field trial was able to differentiate between the resistant

R31-83 and susceptible Orion parents (Tables 3.1). However with canopy porosity ranging from

1-3 with a mean of 1.42 rated from a 1 to 9 scale and with most plants being tall (canopy height

ranged from 51-61 cm with a mean of 56.1 cm and CV being 4.6%), disease avoidance likely

74

confounded expression of physiological resistance in the field. Lodging and plant height are

usually associated as taller upright plants have stronger stems that resist lodging.

Trial correlation

Plant height was strongly correlated with the WM infection in the field as mentioned above

(Table 3.4). Similar results were observed by Soule et al. (2011) in the Raven/I9365-31 RIL

population. A more open or taller canopy was correlated with less white mold. A negative but

low correlation (0.28) between plant height and lodging was observed.

There was a significant correlation between WM disease score and lodging (0.48,

p<0.0001). Resistance to lodging was consistently correlated (r=0.45) with reduced disease in

previous studies (Miklas et al., 2013). Straw test and field trial scores were weakly correlated.

This lack of association indicates that greenhouse response to white mold cannot be used to

predict field performance. This is expected because field response to white mold is due to

expression of both avoidance mechanisms and physiological resistance. Harvest maturity showed

a negative correlation to WM. Late maturity associated with less disease severity is consistent

with previous studies (Kolkman and Kelly, 2003; Miklas et al., 2013).

QTL Analysis

The χ2 goodness of fit for markers showed a divergence from expected 3:1 Mendelian

segregation ratios. 347 SNPs (p<0.05) mapped to distinct loci and was used for linkage

mapping. The polymorphic markers were arranged to the eleven linkage groups representing the

eleven chromosomes of P. vulgaris from Pv01 through Pv11. Eight QTL were detected by CIM

on various chromosomes (Table 3.5, Fig. 3.3).

75

Pv01

A single QTL (11%) associated with SNP 47097 was detected on Pv01 for field white mold

resistance. Miklas et al. (2001) and Maxwell et al. (2007) both identified QTL WM1.1AG

(18%)

and WM1.2GC

(20%) on Pv01 for avoidance (canopy porosity) and straw test resistance,

respectively. White mold as reported in many QTL studies is shown to be quantitatively

inherited, but usually with only a few major QTL detected. The QTL detected on Pv01 for field

resistance to white mold may be related to harvest maturity due to the proximity of the QTL for

both traits. The location of the QTL, 42 to 46 Mb on Pv01, is in the general vicinity of the ppd

gene conditioning photoperiod response which may affect maturity.

Pv02

Two QTL for the greenhouse straw test were located on Pv02. They mapped near SNPs 46675

(2Mb) and 45827 (24Mb) and explained 8.5 and 10.3% of the phenotypic variation, respectively.

Park et al. (2001) detected WM2.1PX

in the straw test while Soule et al., (2011) discovered

WM2.2R31

in the field and WM 2.2BV

expressed in both greenhouse and field environments. The

resistance allele for R31is from I9365-31 and the VA19 resistance allele is of Andean origin.

One of the two major QTL identified on Pv02 in this study is likely the same WM2.2R31

QTL.

Pv03

Two QTL associated with disease avoidance traits were identified on Pv01 and Pv03 explaining

9% and 11%, respectively of phenotypic variation in harvest maturity. They locate near SNP

48652 and SNP 47689. Miklas et al. (2007) detected WM3.1AN

, a physiological resistance QTL

76

associated with late maturity and the stay-green stem characteristic, which were introgressed

from ICA Bunsi into pinto USPT-WM-1.

Pv06

A single QTL conditioning resistance in the greenhouse straw test was detected on Pv06. To

date only one QTL has been identified on Pv06. WM6.1B60,R31

QTL was first identified in the

B60 (Benton/NY6020-4) (Miklas et al., 2003) and then subsequently in the R31 population

(Soule et al., 2011). The resistance allele for the QTL derived from NY6020-4 conditioned

partial resistance in both the straw test (12%) and field (10%). The field resistance was

associated with QTL for disease avoidance traits lodging (15%) and canopy height (20%).

WM6.1 QTL in R31 (12%) was detected solely in the field and was not associated with any of

the disease avoidance traits measured. The QTL detected in this greenhouse study is most likely

the same WM6.1R31

QTL.

Pv07

The QTL responsible for canopy porosity (19%), a disease avoidance trait in the field co-

localized with the QTL expressed in the greenhouse straw test (10%) near SNP 40392. Soule et

al., (2011) discovered WM7.3R31

in the straw test but not in the field. This QTL on Pv07

confirms the previously identified QTL WM7.3R31

for white mold resistance in the straw test

discovered in the R31 population.

77

Conclusion

The QTL detected on Pv02, Pv06, and Pv07 in this study confirmed previously identified QTL

for partial resistance to white mold identified in the Raven/I9365-31 (R31) population. This

suggests that moderate levels of white mold resistance have been transferred from I9365-31 to a

susceptible great northern background. The lack of expression of these QTL in the field suggests

that they should be transferred to great northern beans with better disease avoidance traits than

those possessed by Orion which has unfavorable disease avoidance characteristics.

Factors such as extreme segregation distortion might have had a major effect upon our ability to

identify more QTL conditioning white mold resistance. To further investigate the trends in the

difficulties of mapping QTL for interspecific populations, more populations with different and

varied parents from both the P. vulgaris and P. coccineus background should be developed and

mapped. Nonetheless, the multiple QTL and continuous distributions exhibited in both the

greenhouse straw test and field experiments support quantitative inheritance of white mold

resistance in RIL R31-83 derived from I9365-31. Several lines were identified with superior

response to the recurrent parent Orion in both the straw test and field trial. These lines could

provide valuable germplasm for breeding common bean lines with superior resistance to white

mold in the susceptible great northern dry bean market class.

78

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Table 3.1. Mean, range, and coefficient of variation (CV) for traits measured in the

greenhouse and field for Orion//Orion/ R31-83 BC1F5:7 population and means for

the parents, tested across multiple environments.

Trait (measurement) Parent means Recombinant inbred lines CV

Orion R31-83 Mean (range)

Straw test: 7d (1-9) 6.0 3.2 4.7 (2.5-6.6) 13.2

Straw test: 11d (1-9) 8.0 5.2 6.7 (4.1-7.4) 11.6

Straw test: average (1-9) 7.0 4.2 5.7 (4.1-7.4) 10.6

White mold (1-9) 7.3 2.3 4.73 (2.5-6.8) 22.9

Lodging (1-9) 6.3 3.2 5.81 (3.3-7.8) 17.1

Plant height (cm) 52.0 60.0 56.1 (51.0-61.0) 4.6

Canopy porosity (1-9) 1.0 2.0 1.45 (1.0-3.0) 39.7

Days to flower (DAP) 52.0 50.5 50.6 (49.0-52.0) 1.6

Harvest maturity (DAP) 96.0 91.0 92.8 (78.5-106.0) 3.0 Greenhouse straw test rated on 1 to 9 scale where 1 is no symptom and 9 completely

diseased;

White mold, 1 = no symptoms and 9 = completely diseased; Canopy porosity, 1 = open

canopy and 9 = completely closed canopy; Vigor, 1 = best and 9 = worst; Lodging, 1 = no

lodging and 9 = completely lodged; Harvest maturity and Days to flower = number of days

after planting.

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Table 3.2. RILs from Orion//Orion/R31-83

populations with mean disease scores

significantly (p < 0.05) better than the

recurrent parent.

Line Straw Test Mean (1-9) †

Parent

Orion 7.0

R31-83 3.5

Recombinant inbred lines

302A01 5.3

302A02 4.8

302A04 4.9

302A05 4.6

302A06 4.9

302A07 4.9

302A08 5.1

302A09 4.5

302A10 4.3

302A11 4.8

302A12 4.7

302A13 4.3

302A14 5.3

302A15 5.1

302A16 4.9

302A17 4.7

302A18 4.6

302A19 4.6

302A20 4.5

302A21 4.3

302A22 5.7

302A23 4.9

302A24 4.8

302A25 5.0

302A26 5.1

302A27 5.5

302A29 5.6

302A31 4.8

302A32 5.3

302A34 4.6

302A35 4.7

302A38 4.6

85

Line Straw Test Mean (1-9) †

302A39 5.5

302A40 4.6

302A41 5.1

302A42 4.8

302A43 4.1

302A44 4.9

302A45 5.0

302A46 4.8

302A47 5.0

302C01 4.7

302C12 4.3

302C13 5.7

302C19 5.4

302C20 5.3

302C26 4.8

302C27 5.6

302C28 5.2

302C33 5.3

302C34 4.5

302C36 5.7

302C37 5.3

302C41 5.1

302C44 5.8

302C49 5.2

302C50 5.0

302C51 5.3

302C54 4.8

302C59 5.3

Overall Mean 5.7

LSD=0.98 CV=10.64

† White mold, 1 = no symptoms and 9 =

completely diseased

86

Table 3.3. Analysis of variance for response of

Orion//Orion/R31-83 BC1F5:7 RILs to white mold evaluation

in the greenhouse and field in 2014.

Mean Square

Traits† DF‡ Rep Line Error

Straw test: 7d (1-9) 104,198 0.29 3.20**

0.61

Straw test: 11d (1-9) 104,198 1.94 2.84**

0.37

Straw test: avg (1-9) 104,198 0.37 2.84**

0.37

White mold (1-9) 102,102 1.57 2.40**

1.20

Lodging (1-9) 102,102 10.63 1.51* 0.98

Plant height (cm) 102,102 0.01 13.53**

6.80

Canopy porosity (1-9) 102,102 6.29 0.40 0.30

Days to flower (DAP) 102,102 6.65 0.89 0.63

Harvest maturity (DAP) 102,102 77.1 48.7**

7.90

Greenhouse straw test rated on 1 to 9 scale where 1 is no symptom

and 9 completely diseased;

White mold, 1 = no symptoms and 9 = completely diseased;

Canopy porosity, 1 = open canopy and 9 = completely closed

canopy; Vigor, 1 = best and 9 = worst; Lodging, 1 = no lodging

and 9 = completely lodged; Harvest maturity and Days to flower

= number of days after planting.

*, ** denotes significance at 0.01% and 0.001 probability level. †Straw test had 3 reps and field trials 2. ‡First number DF line, while number after comma is DF error.

87

Table 3.4. Pearson correlation coefficients between white mold disease score means from

greenhouse straw tests and the field and agronomic trait means from the field in a population

of 104 BC1F5:7 RILs from Orion//Orion/R31-83.

Straw test

Trait†

Canopy

porosity

Canopy

height

Harvest

maturity

White

mold Lodging 7d 11d average

Days to flower 0.01 -0.09 0.64***

0.14 0.16 -0.10 -0.02 -0.10

Canopy porosity

-0.01 -0.12 -0.11 -0.14 -0.10 -0.22 -0.17

Canopy height

0.06 -0.51

** -0.28

** -0.10 0.01 -0.03

Harvest maturity

-0.13 0.38

*** -0.03 -0.05 -0.03

White mold

0.48

*** -0.06 0.13 -0.04

Lodging

-0.15 0.02 -0.08

Straw test

7d

0.69

*** 0.90

11d

0.83

***

average

Greenhouse straw test rated on 1 to 9 scale where 1 is no symptom and 9 completely diseased;

White mold, 1 = no symptoms and 9 = completely diseased; Canopy porosity, 1 = open canopy and 9 =

completely closed canopy; Vigor, 1 = best and 9 = worst; Lodging, 1 = no lodging and 9 = completely

lodged; Harvest maturity and Days to flower = number of days after planting. *, **, ***

denotes significance at 0.05, 0.01 and 0.001 probability level. †Straw test had 3 reps and field trials 2.

88

Table 3.5. Putative QTL positions, likelihood ratios (LR), percentage variance explained (PVE),

and additive effects, for the white mold resistance and agronomic traits identified in field and

greenhouse environments in a BC1F5:7 population of Orion//Orion/R31-83.

Trait Pv

Near

Marker Position Parental %PVE LR

Additive

effect

Greenhouse

Straw test: 11d 2 SNP46675 1,026,678 R31-83 8.46 16.80 -0.33

Straw test:7d 2 SNP45827 24,446,134 R31-83 10.28 20.00 -0.37

Straw test:11d 6 SNP50222 18,501,260 R31-83 9.27 17.39 -0.36

Straw test:11d 7 SNP40392 5,032,878 Orion 9.56 15.21 0.36

Field

Harvest maturity 1 SNP48652 42,854,521 Orion 9.87 13.49 2.25

White mold 1 SNP47097 46,631,601 Orion 10.61 13.46 0.37

Harvest maturity 3 SNP47689 38,799,776 R31-83 11.94 15.25 -2.39

Canopy Porosity 7 SNP40392 5,032,878 R31-83 19.27 21.48 -0.27

89

Figure 3.1. Response of Orion//Orion/R31-83 BC1F5:7

populations to a greenhouse straw test in 2014. Parents are

indicated by arrows.

R31-83

Orion

0

10

20

30

3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5

No

. of

Lin

es

LS Means of Straw test (1-9)

Orion//Orion/R31-83

90

Figure 3.2. Response of Orion//Orion/R31-83 BC1F5:7 populations to white mold and other agronomic traits. Parents are indicated by arrows.

91

igure 3.3. Linkage map for Orion//Orion/R31-83 showing QTL identified for resistance to

white mold

92

igure 3.3. Linkage map for Orion//Orion/R31-83 showing QTL identified for resistance to

white mold