QUANTITATIVE TRAIT LOCI ANALYSIS OF RESISTANCE TO WHITE MOLD
(SCLEROTINIA SCLEROTIORUM) IN COMMON BEAN (PHASEOLUS VULGARIS)

By
Wezi Esther Mkwaila

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

Plant Breeding, Genetics and Biotechnology- Crop and Soil Sciences- Doctor of Philosophy
2013

i

ABSTRACT
QUANTITATIVE TRAIT LOCI ANALYSIS OF RESISTANCE TO WHITE MOLD
(SCLEROTINIA SCLEROTIORUM) IN COMMON BEAN (PHASEOLUS VULGARIS)
By
Wezi Esther Mkwaila
White mold, caused by Sclerotinia sclerotiorum (Lib.) De Bary, is a serious yield reducing
fungal pathogen of common bean (Phaseolus vulgaris L.). The objective of this study was to:
identify QTL for resistance to white mold and associated agronomic traits in two inbred
backcross line (IBL) populations derived from the recurrent black bean parent Tacana; two
recombinant inbred line (RIL) pinto bean populations derived from a common parent AN-37 and
to explore the role of defense genes during infection of bean genotypes with contrasting
responses to white mold.
In the IBL populations selective phenotyping failed to detect QTL for field disease resistance but
other agronomic traits less sensitive to environmental conditions were detected. Four novel
QTLs for white mold resistance were identified in the greenhouse straw test on bean
chromosomes Pv03, Pv07, Pv09 and Pv11, respectively and two previously mapped QTL were
also validated on Pv02 and Pv04.
The two pinto bean RIL populations were genotyped with SSR, InDel and SNP markers resulting
in two linkage maps of 1183 and 953 cM and a total of fifty QTL were identified in different
years for eight traits (white mold disease incidence, seed yield, days to flowering, days to
maturity, 100 seed weight, canopy height, lodging and the straw test). These QTL were located
across all 11 chromosomes with LOD scores ranged from the threshold of 2.5 to 10.5. Six new
QTL for yield were identified on Pv01, Pv02, Pv03, Pv05 and Pv09. Seven new QTL for white
mold were identified on Pv01, Pv04, Pv07 and Pv08 and two were validated on Pv02 and Pv03.
ii

Analysis of defense genes in response to white mold infection showed variable temporal
transcription suggesting that the resistance reported in different bean genotypes to white mold
could be due to different defense pathways. The induction of defense genes at different time
points in the contrasting genotypes confirm the quantitative nature of the observed resistance to
white mold in common bean.

iii

DEDICATION
To the memory of Victor Baraza Msowoya; my inspiration, my hero.

iv

ACKNOWLEDGEMENTS
I would like to thank my major advisor Dr. James Kelly for guiding and supervising my research. I
am truly grateful to have benefited from his vast experience in plant breeding. I appreciate his
patience while watching me grow personally and professionally.
I would also like to thank members of my guidance committee, Dr. Ray Hammerschmidt, Dr.
Rebecca Grumet and Dr. Dechun Wang for being supportive and encouraging throughout my studies.
Their insightful comments improved my research tremendously.
I am also grateful for all the members of the dry bean lab who generously shared their lab,
greenhouse and field skills to enhance my research.
My husband Andrew for believing in me and never giving up even when there was no reason to
hope. Thank you.

v

TABLE OF CONTENTS
LIST OF TABLES ......................................................................................................................... ix
LIST OF FIGURES ....................................................................................................................... xi
CHAPTER 1
LITERATURE REVIEW ............................................................................................................... 1
Common bean ............................................................................................................................. 1
Taxonomy and Diversity......................................................................................................... 2
Domestication ......................................................................................................................... 2
Races ....................................................................................................................................... 3
White mold.................................................................................................................................. 4
Disease Management .............................................................................................................. 5
Breeding for Resistance .......................................................................................................... 6
Agronomic traits and heritability ............................................................................................ 8
Disease Screening ................................................................................................................... 9
Marker assisted selection ...................................................................................................... 10
Quantitative Trait Locus Mapping ............................................................................................ 13
Objectives ................................................................................................................................. 15
REFERENCES ......................................................................................................................... 16
CHAPTER 2
IDENTIFICATION OF QTL FOR AGRONOMIC TRAITS AND RESISTANCE TO WHITE
MOLD IN WILD AND LANDRACE GERMPLASM OF COMMON BEAN .......................... 25
Introduction ............................................................................................................................... 25
Materials and Methods .............................................................................................................. 26
Plant Material ........................................................................................................................ 26
Greenhouse Evaluations........................................................................................................ 27
Field Trials ............................................................................................................................ 27
Genotyping of Mapping Populations .................................................................................... 29
Statistical Analysis ................................................................................................................ 29
Results ....................................................................................................................................... 31
Discussion ................................................................................................................................. 44
REFERENCES ......................................................................................................................... 51
CHAPTER 3
QTL ANALYSIS OF WHITE MOLD RESISTANCE AND AGRONOMIC TRAITS IN TWO
PINTO BEAN (PHASEOLUS VULGARIS) RIL POPULATIONS.............................................. 56
Introduction ............................................................................................................................. 56
Materials and methods ........................................................................................................ 59
Plant material ........................................................................................................................ 59
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SSR and InDel Marker Genotyping ...................................................................................... 62
SNP Genotyping ................................................................................................................... 63
Phenotypic Data Analysis ..................................................................................................... 64
Genetic Linkage Map ............................................................................................................ 65
QTL Estimation .................................................................................................................... 66
Map Integration ..................................................................................................................... 67
Results ....................................................................................................................................... 67
Frequency distributions and variation across the years ........................................................ 67
Heritability Estimates............................................................................................................ 77
Trait Correlations .................................................................................................................. 78
Genotyping and Map Construction ....................................................................................... 80
QTL Analysis in AP630 population ..................................................................................... 95
Yield (SY) ......................................................................................................................... 99
White Mold Disease Incidence (WM) ............................................................................ 100
Straw Test (WM) ............................................................................................................ 101
Days to Flowering ........................................................................................................... 102
Days to maturity.............................................................................................................. 102
Seed weight ..................................................................................................................... 103
Lodging ........................................................................................................................... 104
QTL Analysis in AP647 half-sib population ...................................................................... 104
Seed yield ........................................................................................................................ 106
Straw Test ....................................................................................................................... 106
Number of days to flowering .......................................................................................... 107
Number of days to maturity ............................................................................................ 107
100 Seed weight .............................................................................................................. 108
Plant canopy height ......................................................................................................... 108
Discussion ............................................................................................................................... 108
Frequency Distributions ...................................................................................................... 108
Correlation analysis ............................................................................................................ 111
Genetic Linkage Mapping................................................................................................... 112
Yield QTL Analysis ............................................................................................................ 113
Chromosome Pv01 .......................................................................................................... 113
Chromosome Pv02 .......................................................................................................... 113
Chromosome Pv03 .......................................................................................................... 114
Chromosomes Pv05 and Pv09 ........................................................................................ 114
White mold QTL Analysis .................................................................................................. 116
Chromosome Pv01 .......................................................................................................... 116
Chromosome Pv02 .......................................................................................................... 116
Chromosome Pv03 .......................................................................................................... 117
Chromosome Pv04 .......................................................................................................... 117
Chromosome Pv07 .......................................................................................................... 118
Chromosome Pv08 .......................................................................................................... 119
Days to flowering and maturity .......................................................................................... 119
Plant canopy height and lodging ......................................................................................... 120
Conclusion .............................................................................................................................. 121
REFERENCES ....................................................................................................................... 122
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CHAPTER 4
PRELIMINARY ANALYSIS OF DEFENSE GENES IN RESPONSE TO WHITE MOLD
INFECTION IN DIFFERENT BEAN (PHASEOLUS VULGARIS) CULTIVARS ................... 128
Introduction ............................................................................................................................. 128
Materials and Methods ............................................................................................................ 130
Results ..................................................................................................................................... 133
Defense gene expression ..................................................................................................... 133
Glucanase ........................................................................................................................ 133
Phenylalanine Ammonia Lyase (PAL) ........................................................................... 135
Polygalacturonase Inhibitor Protein 1 ............................................................................. 137
Discussion ............................................................................................................................... 139
REFERENCES ....................................................................................................................... 141
APPENDIX ................................................................................................................................. 144

viii

LIST OF TABLES
Table 2.1:

Means, ranges and heritability estimates for white mold resistance and agronomic
traits in the ‘Tacana’ x PI 318695 (TW) IBL population in three greenhouse
screening and field environments at the Montcalm Research Farm, Michigan from
2004 to 2006……………………………………………………………………..35

Table 2.2:

Means, ranges and heritability estimates for white mold resistance and agronomic
traits in the ‘Tacana’ x PI 313850 (TL) IBL population in three greenhouse
screening and field environments at the Montcalm Research Farm, Michigan from
2004 to 2006.…...………………………………………………………………..36

Table 2.3:

Persons phenotypic correlation coefficients (r) between disease incidence, yield
and agronomic traits across three field environments and greenhouse straw test
evaluations in the TW (above the diagonal) and TL (below the diagonal) IBL
populations……………………………………………………………………….37

Table 2.4:

Putative QTL for white mold resistance and agronomic traits identified in field
and greenhouse environments in ‘Tacana’ x PI 318695 (TW) IBL population
evaluated in Michigan during 2004-2006……………………………………..…38

Table 2.5a:

Putative QTL for white mold resistance and agronomic traits identified in field
and greenhouse environments in ‘Tacana’ x PI 313850 (TL) IBL population
evaluated in Michigan during 2004-2006………….…………………………….39

Table 2.5b:

Putative QTL for white mold resistance and agronomic traits identified in field
and greenhouse environments in ‘Tacana’ x PI 313850 (TL) IBL population
evaluated in Michigan during 2004-2006……………………………..…………40

Table 3.1:

Means ranges and heritability estimates for white mold resistance and agronomic
traits in the AN-37 x P02630 (AP630) RIL population in greenhouse screening
and field environments at the Montcalm Research Farm, Michigan from 2007 to
2010………………………………………………………………………………75

Table 3.2:

Means ranges and heritability estimates for white mold resistance and agronomic
traits in the AN-37 x P02647 (AP647) RIL population in greenhouse screening
and field environments at the Montcalm Research Farm, Michigan in 2007, 2010
and 2011…………………………………………………………………………76

Table 3.3:

Persons phenotypic correlation coefficients (r) between disease incidence, yield
and agronomic traits across three field environments and greenhouse straw test
evaluations in the AP647 (above the diagonal) and AP630 (below the diagonal)
RIL populations………………………………………………………………….79

ix

Table 3.4a:

QTL for white mold resistance and yield identified in field and greenhouse
environments in AP630 RIL population evaluated in Michigan during 20072011.………………………………………………………………………..……96

Table 3.4b:

QTL for agronomic and phonological traits identified in field and greenhouse
environments in AP630 RIL population evaluated in Michigan during 20072011………………………………………………………………………………98

Table 3.5:

QTL for white mold resistance and agronomic traits identified in field and
greenhouse environments in AP647 RIL population evaluated in Michigan during
2007, 2010 and 2011……………………………………………………………105

Table 4.1:

Primers designed and used in semi quantitative reverse transcription-polymerase
chain reaction (RT-PCR) for amplifying defense-associated genes of Phaseolus
vulgaris…………………………………………………………………………132

Table 4:2:

Effect of inoculation of the cultivars with Sclerotinia sclerotiorum………...…133

Table A.1:

Single Marker Analysis for white mold and yield in AP630…………………..149

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LIST OF FIGURES
Figure 2.1:

Integrated bean linkage map showing relative positions of QTL for white mold
resistance and agronomic traits evaluated over three years (2004-2006) in two
inbred backcross line populations TW and TL at Montcalm Research Farm,
Michigan, USA…………………………………………………………………..41

Figure 3.1:

Population distributions for white mold incidence, straw test and agronomic traits
in the AP630 population…………………………………………………………68

Figure 3.2:

Population distributions for white mold incidence, straw test and agronomic traits
in the AP647 population…………………………………………………………70

Figure 3.3:

Bean linkage map showing relative positions of QTL for white mold resistance
and agronomic traits evaluated over four years (2007-2010) a recombinant inbred
line populations AP630 at Montcalm Research Farm, Michigan, USA…………82

Figure 3.4:

Bean linkage map showing relative positions of QTL for white mold resistance
and agronomic traits evaluated over four years (2007, 2010 and 2011) a
recombinant inbred line populations AP647 at Montcalm Research Farm,
Michigan, USA…………………………………………………………………..89

Figure 4.1:

RT-PCR product analysis for the glucanase gene, indicating the differences in
signal strength at four different time points in four bean cultivars challenged with
Sclerotinia sclerotiorum………………………………………………………...134

Figure 4.2:

Cultivar -specific differential expression of glucanase gene in infected bean plants
at four time points………………………………………………………………135

Figure 4.3:

RT-PCR product analysis for the Phenylalanine ammonia lyase gene, indicating
the differences in signal strength at four different time points in four bean
cultivars challenged with Sclerotinia sclerotiorum…………………………….136

Figure 4.4:

Cultivar -specific differential expression of Phenylalanine ammonia lyase gene in
infected bean plants at four time points………………………………………...137

Figure 4.5:

RT-PCR product analysis for the polygalacturonase inhibitor protein gene,
indicating the differences in signal strength at four different time points in four
bean cultivars challenged with Sclerotinia sclerotiorum……………………….138

Figure 4.6:

Cultivar -specific differential expression of polygalacturonase inhibitor protein
gene in infected bean plants at four time points………………..………………139

Figure A. 1:

SSR MAP….……………………………………………………….…………..147

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CHAPTER 1

LITERATURE REVIEW
Common bean
Common bean (Phaseolus vulgaris L.) is the most important legume for direct human
consumption worldwide (Broughton et al., 2003). Dry beans provide a major source of quality
protein which is high in lysine and therefore complements most cereals. In addition beans are
high in carbohydrates, fiber, and minerals (calcium, potassium, phosphorus iron, zinc and
magnesium) (Azarpazhooh and Boye, 2012). Bean consumption is high in Latin America and
East Africa with a per-capita consumption of over 60kg per year in countries like Rwanda
(Miklas and Singh, 2007). Consumption is also increasing in the developed countries as beans
are promoted to combat such chronic illnesses like diabetes, kidney disease, hypertension and
certain cancers (Bennink, 2002; Thompson et al., 2009). Apart from their health benefits, beans
play an important role in environmental sustainability through nitrogen fixation and their ability
to be intercropped (Bliss, 1993; Vance, 2001).
Global bean production worldwide was estimated to be at 23.3 million metric tons in 2011
representing an increase of over 70% from 1980. Most of the production is concentrated in the
Americas with Brazil being the largest producer while the U.S. ranks second at 1.3MMT
(FAOSTAT, 2011). The area under dry bean production in the U.S. has increased by a factor of
7% while Michigan which produces 13% of all US beans has decreased by 40% since 1980
(Kelly and Cichy, 2012). Significant production increases have also been observed in Africa
(145%), Asia (36%) and Europe (35%) during the same period (FAOSTAT, 2011).

1

Taxonomy and Diversity
Common bean is diploid species that belongs to the family Fabaceae and genus Phaseolus
(2n=22). It is a predominantly self-pollinating crop. The genus Phaseolus is made up of five
domesticated species namely P. vulgaris (common bean), P. coccineus (scarlet runner bean), P.
dumosus or P. polyanthus (year long bean), P. acutifolius (tepary bean) and P. lunatus (lima
bean). These species belong to the four Phaseolus gene pools namely the primary, secondary,
tertiary and quaternary respectively (Freytag and Debouck, 2002; Gepts, 2000). Crosses made in
and between the primary and secondary gene pools result in viable hybrids especially when
common bean is used as the female parent while those crosses with the tertiary gene pool require
embryo rescue. However no successful hybrids have been produced between lima bean and
common bean (as summarized by Singh, 2007).

Domestication
Common bean was domesticated around 10,000-8,000 years BP and early evidence indicated
two centers of origin namely Mesoamerica and Andean America (Gepts et al., 1986; Gepts,
1998). This was supported by archeological findings that showed that wild forms are present in
both regions and complementary varieties of the species also exist independently in both
geographical areas (Kaplan, 1981). Molecular evidence also suggested that there are two centers
of domestication. These are in the forms of variation observed in phaseolin protein markers
(Gepts and Bliss, 1986), allozymes (Koenig and Gepts, 1989), RFLP patterns on nuclear DNA
(Becerra and Gepts, 1994) and mitochondrial DNA (Khairallah et al., 1990). Reproductive
isolation as observed in weak hybrids and segregation distortion between the two groups also
lends more support to this theory (Gepts, 1985; Koinange and Gepts 1992). Recent genetic
2

studies however suggest that domestication of all common beans occurred in the Mesoamerican
region. Bitocchi et al. (2012) analyzed nucleotides at five different legume anchor marker loci in
both wild and domesticated P. vulgaris collected from the two regions and the intermediate
region and their results indicated that both gene pools originated in central Mexico and moved to
Northern Mexico and south to Argentina.

Races
Common bean (P. vulgaris) is divided into two major gene pools of Middle America and Andean
based on their phylogenetic distances (Gepts and Bliss, 1986). The Andean gene pool is
composed of large seeded kidney and cranberry bean races namely Chile, Nueva Granada and
Peru while the Middle American gene pool is made up of four races namely Mesoamerica,
Durango, Jalisco and Guatemala which consist mainly of small and medium sized beans (Singh
et al., 1991; Beebe et al., 2000). In general the Middle American gene pool has more diversity
and genetic variability than the Andean gene pool possibly due genetic bottlenecks occurring
during domestication and limited migration from the center of origin (Bitocchi et al., 2013; Porch
et al., 2013)
Pinto beans, which belong to the medium seeded Durango race, are the most commonly grown
dry bean class in the US (Teran et al., 2009). Most Durango race beans have predominantly
indeterminate prostrate growth habit. The Mesoamerican race consists of the small seed sized
bean market classes which include black and navy beans. Black beans represent the largest
commercial bean class in Michigan (41,000 ha harvested in 2009 representing 51% of MI and
58% of U.S. production (USDA NASS, 2012).

3

In addition to obvious seed trait differences, other major differences exist among classes in
growth habit, disease reaction, and adaptation. The diversity in seed size, shape, and color
permits the targeting of commercial classes to meet specific regional and cultural market needs.
In addition the variability in the Phaseolus genus enables breeders to explore diverse germplasm
as a source of favorable traits in the improvement of the crop. Most breeding programs aim at
improving yield, pest and disease resistance and nutritional quality and abiotic stresses (Kelly
and Cichy, 2012). There are many diseases that affect common bean including anthracnose,
common bacterial blight, viral mosaics and white mold (Singh and Schwartz, 2010). In the US
Midwest white mold is ranked by farmers as the most important disease limiting production
(Lamey et al., 2000; Webster and Kelly, 2000).

White mold
White mold caused by the ascomycete fungus Sclerotinia sclerotiorum (Lib.) de Bary is an
important disease of many economically important crops worldwide although it is a more
common pathogen in temperate regions (Purdy, 1979). The fungus is a necrotroph and attacks a
wide host range that includes more than 400 species, primarily dicotyledonous species (Boland
and Hall, 1994). The fungus produces compact resting bodies called sclerotia on the surface of
diseased plants as well as inside infected stems and pods. These melanized resting structures
survive in a dormant state in the soil when conditions are not favorable (winter/dry) for
germination and may persist in the soil for 4 to 5 years (Steadman, 1983). Sclerotia can
germinate carpogenically or through the growth of mycelia depending on environmental
conditions. The sclerotia on the soil germinate carpogenically due to prolonged periods of
continuous soil moisture and produce slender stalks with a disk or cup shaped apothecium in
4

which asci and ascospores are produced. When discharged the spores are blown by the wind.
Direct infection of intact healthy tissues from germinating ascospores is not common in beans
however initial infection occurs when ascospores germinate on senescing flower petals allowing
the fungus to form infection hyphae and colonizes leaf, stem and pod tissues (Lumsden, 1976).
Stems can get invaded near the soil line while pods or branches may be directly infected if they
are in contact with the soil. Typical symptoms appear as water soaked lesions on stems and pods
which expand rapidly under moist conditions resulting in a watery rotten mass covered by white
fungal growth. Infected tissue will appear white due to bleaching by oxalic acid produced by the
fungus (Schwartz et al., 1989). Oxalic acid was thought to be a necessary pathogenicity factor as
shown by Godoy et al. (1990) who demonstrated that oxalic acid deficient mutants of S.
sclerotiorum were non-pathogenic. Recent research however indicates that oxalic acid is a
virulence factor and that different isolates of S. sclerotiorum produce varying levels of oxalates
(Rollins and Dickman, 2001). The acid has a direct toxic effect through lowering of the pH of
the environment within the middle lamellae, and binding calcium to form insoluble oxalate
crystals and promoting the activity of cell wall degrading enzymes (Hegedus and Rimmer,
2005).

Disease Management
Yield losses resulting from white mold infection of beans in irrigated regions of the U.S. have
been estimated from 20 to 100% (Kerr et al., 1978; Purdy, 1979) suggesting an integrated
approach to control white mold. Agronomic practices that help control white mold in the field
involve reducing the efficiency of sclerotia and ascospore germination by controlling humidity.
High moisture within the plant canopy creates a favorable microclimate for ascospore
5

germination such that increasing plant and row spacing helps in managing the disease. This
however may not always be economically feasible. Timing irrigations to allow drying of plant
canopy and avoiding excessive irrigation during flowering and after petal drop also reduces
disease severity (Schwartz and Steadman, 1980). Other cultural practices such as avoiding
excessive nitrogen fertilizer that stimulates excessive vegetative growth; deep plowing and crop
rotation contribute toward disease management. The use of fungicides such as benomyl
(Schwartz, 1980) and more recently biocontrol agents like Coniothyrium minitans

(a

mycoparasite to Sclerotinia sclerotiorum) (Budge and Whipps, 2001) have been effective in
controlling the disease in the field. Transgenic approaches have been demonstrated in other crops
like soybean (Glycine max) and sunflower (Helianthus sp) where the overexpression of the
wheat germin gene OXO which encodes for oxalate oxidase has resulted in the reduction of
disease symptoms. Oxalate oxidase works by breaking down oxalic acid into carbon dioxide and
hydrogen peroxide (Donaldson et al., 2001; Hu et al., 2003; Lane, 2000). Transformation of
common bean using the OXO gene has not been reported. Breeding for resistant varieties of
beans therefore is the most economical and effective way of controlling the disease in common
bean.

Breeding for Resistance
Resistance to white mold is known to be complexly inherited in common bean (Fuller et al.,
1984). When crosses are made between resistant and susceptible cultivars the progeny exhibit
continuous variation indicating that trait is controlled by many genes and is affected by
environmental conditions. Bean lines with complete resistance have not been identified but
moderate levels of resistance to white mold have been identified in diverse common bean
6

genotypes (Miklas and Grafton, 1992; Kolkman and Kelly, 2002; Miklas et al., 2006a; Maxwell
et al., 2007; Mkwaila et al., 2011). The moderate levels of resistance are usually enhanced by
plant morphological avoidance mechanisms (Miklas et al., 2001; Kolkman and Kelly, 2002;
Ando et al., 2007). There are several growth habits or plant architectural mechanisms that
contribute to disease escape in common beans. Type I growth habit which is an erect determinate
bush in contrast to the prostrate Type III indeterminate (Singh, 1982) reduces the severity of the
disease by minimizing plant contact with the moist ground (Fuller et al., 1984). The density of
the canopy also plays an important role in the development of the disease in the field by
influencing the penetration of light and the circulation of air. The porosity of the canopy is a
product of the plant growth habit, structure and branching patterns (Ando et al., 2007). A dense
closed canopy creates a favorable microclimate for the germination of sclerotia, growth of the
hyphae and development of infection structures on the plant surfaces (Schwartz et al., 1987). The
ability of the plant to resist lodging will also determine whether the plant escapes white mold
infection. Genotypes with stiffer stems have better standing ability that promotes air circulation
and therefore avoid the disease (Ender and Kelly, 2005). These traits are moderately heritable
and can be visually scored in the field (Miklas et al., 2004), however they do not always predict
how a cultivar will respond to white mold infection in the field. For example the indeterminate
type II cultivar Bunsi has an open porous canopy and partially prostrate growth habit that
exhibits moderate levels of field resistance, whereas erect determinate cultivars like Newport and
Midland exhibit high infection levels in the field (Kolkman and Kelly, 2002). The environment
also has an impact on the severity of the disease in the field as some determinate open canopy
cultivars behave as resistant phenotypes when grown in semiarid regions (Park et al., 2001).
Another factor that contributes to disease avoidance is early maturity which enables the plant to

7

escape infection due to less vegetative growth (Park et al., 1993; Kolkman and Kelly, 2003).
More recently Miklas et al. (2013) reviewed the role of plant architecture related traits that
contribute toward the white mold resistance. Canopy porosity plant height and lodging were
correlated with disease severity in the field however under high disease pressure these traits were
not effective in reducing white mold infection levels. While direct selection for white mold
resistance may be conducted based on disease scores in the field and greenhouse, many
resistance lines exhibit poor agronomic performance and are not useful for release as cultivars.
Most resistant sources are associated with linkage drag of unfavorable characteristics such as low
yield or late maturity. The most commonly used resistant check and parent in white mold studies,
G122 (cranberry bean) (Steadman et al., 2010) is low yielding and does not dry down uniformly
to enable direct harvesting. Other sources of resistance like VCW54 obtained from introgression
from P. coccineus (Singh et al., 2009) are equally unadapted and low yielding. The inbred
backcross lines (IBL) introgressed from exotic accessions (Mkwaila et al., 2011) exhibit
characteristics such as late maturity, small seed size associated with negative linkage drag from
unadapted parental germplasm. Combining selection for white mold resistance with desirable
agronomic traits is essential for the recovery of useful phenotypes with resistance suitable for
variety release (Kelly et al., 2012).

Agronomic traits and heritability
Estimates of heritability have been reported for agronomic traits in a range of different
populations being evaluated for white mold resistance in the field. Results vary depending on
population and traits. Kolkman and Kelly (2002) and Ender and Kelly (2005) reported moderate
estimates (0.24-0.25) for lodging and yield in navy and black bean populations respectively. In
8

inbred backcross line populations broad sense heritabilities were low in ‘Tacana’ x wild (TW)
accessions cross while in the ‘Tacana’ x landrace (TL) population the estimates were higher
(>0.30) for the number of days to flowering and maturity while low (0.14) for lodging (Mkwaila
et al., 2011). In two RIL populations Soule et al. (2011) found higher heritability estimates
(>0.48) for white mold resistance canopy height, days to maturity and lodging. More recently
high broad-sense heritability estimates were obtained in a population of climbing beans for such
traits as seed weight and days to maturity while yield and yield components had relatively low
heritabilities (Checa and Blair, 2012). Trait correlations in these populations also exhibit varying
levels and direction of relationship indicating possibility of generating a selection index. Miklas
(2013) recently reported high positive correlations between canopy height and field disease
scores (r = 0.20 to 0.82) and negative correlations between plant canopy porosity and disease
incidence (r = -0.33 to -0.62).

Disease Screening
The interaction of avoidance mechanisms and environmental conditions makes it impossible to
separate physiological resistance from disease avoidance in the field. Physiological resistance is
controlled by genetic factors that may inhibit infection or spread of the pathogen in the host
tissue. There are several methods of detecting physiological resistance to white mold in crops.
Artificial inoculation techniques employ substrates colonized with fungal mycelia like agar plug,
carrot, celery pieces. Ascospores are usually not used because they require long periods (4-6
weeks) to produce in vitro (Boosalis et al., 2000; Cline and Jacobsen, 1983) and will not always
result in infection under controlled environments. Some methods include limited term
inoculation where a mycelial-infested celery stem, is inoculated onto the plant and then removed
9

from the plant after a specified time (Hunter et al., 1981). In the detached leaf assay an agar plug
is placed in the center of a detached leaf or trifoliate and disease is measured by size of necrotic
lesion and in the cotyledon inoculation test (Steadman, 1997); a mycelial infested agar is placed
on a germinating plant cotyledon prior to abscission (Kull et al., 2003). An indirect method for
screening was developed by Kolkman and Kelly (2000) which employs oxalic acid solution
(40mM) to screen for resistance. In the oxalate assay, seedlings are placed in oxalic acid solution
and resistance is rated based on the extent of wilting. More recently a novel inoculation method
called the drop mycelium test was developed in soybean (Chen and Wang, 2005). The method
involves spraying a suspension of mycelia cultured in nutrient broth. This method has not been
tested on beans however. The most commonly used method for screening bean germplasm for
white mold resistance in the greenhouse is the straw test (Petzoldt and Dickson, 1996). In this
test, mycelia from the expanding edge of a white mold colony on agar are applied with a soda
straw or 10ul pipette tip to the severed end of a bean seedling plant and disease development is
rated based on the progression of the fungus down the stem from the point of inoculation. The
current broad categories are as follows: 1-3 (resistant) is where the lesion does not reach the first
node; 4-6 (moderate) is where the lesion passes the first node but does not reach the second
node; 7-9 (susceptible) is where the lesion extends to the third internode and may lead to plant
death.

Marker assisted selection
To expedite germplasm enhancement and cultivar development marker assisted selection (MAS)
is being incorporated in efforts to breed for white mold resistance. Markers are scored in place of
the desired phenotypic with the aim of correlating the pattern to the genes or loci associated with
10

the trait in question. The sequence of polymorphism or banding pattern of the molecular marker
is indicative of the presence or absence of a specific gene or chromosomal segment that is known
to carry a desired allele. Since phenotypic disease scoring for white mold is confounded by plant
architecture and environment in the field, MAS offers an efficient and reliable way to separate
avoidance from genetic resistance through tagging genes or genomic regions that are associated
with physiological resistance.

To realize the full potential of MAS, markers linked to disease resistance loci must be identified
and possibly separated from those associated with more visible architectural avoidance traits.
This is achieved through the construction of linkage maps which indicate the position and
relative distances between markers along chromosomes. The basis of mapping is that genes and
markers segregate through chromosome recombination such that genes or markers that are close
together will be transmitted together from parent to progeny more frequently than genes or
markers that are located further apart. Several types of markers have been employed in mapping
and tagging disease resistance in common beans. The first linkage map was created using a
combination of biochemical markers and restriction fragment length polymorphisms (RFLP)
markers (Vallejos et al., 1992). RFLPs are based on the differential hybridization of cloned DNA
to DNA fragments in a sample of restriction enzyme digested DNAs; and are specific to a single
clone/restriction enzyme combination. Later Random Amplified Polymorphic DNA (RAPD)
markers were anchored to the map (Skroch at al., 1996). RAPD markers are based on the
differential PCR amplification of a sample of DNAs from short oligonucleotide sequences.
Locus specific co dominant markers been developed by amplifying, sequencing and cloning
RAPD markers to obtain sequence characterized amplified region (SCAR) markers (Melotto et
al., 1996). Amplified Fragment Length Polymorphisms (AFLP) have also been used to study
11

genetic diversity in beans (Tohme et al., 1996). These loci are generated using a procedure that
combines restriction digestion and polymerase chain reaction (PCR) amplification. AFLPs are
particularly useful in mapping because they generate a large number of polymorphic loci with a
single amplification such that a genome can be saturated quickly. Targeted Region Amplified
Polymorphism (TRAP) markers are also PCR-based and they take advantage of the available
EST database sequence information to generate polymorphic markers targeting candidate genes.
Essentially an 18-mer primer is derived from the EST sequence and pairs it with an arbitrary
primer that targets the intronic and/or exonic region (AT- or GC- rich core) (Hu and Vick, 2003).
Since TRAP markers are anchored based on arbitrary primers to amplify coding regions in the
genome, the resulting polymorphism should be reflective of diversity within functional genes.
Miklas et al. (2006b) showed that TRAP markers were linked to new QTL for disease resistance.
Another kind of markers is the sequence related amplified polymorphisms (SRAP). SRAP
markers have a great affinity to amplify gene-rich regions (Li and Quiros, 2001). They have
potential for candidate gene analysis of QTL, and have been used to measure genetic diversity in
pumpkin (Cucurbita pepo L.) germplasm (Ferriol et al., 2003). Since the development of simple
sequence repeats (SSR) for common bean (Yu et al., 2000) more studies are increasingly relying
on these markers to tag for important traits. These markers, also known as microsatellites consist
of tandemly repeated nucleotide motifs that are a common feature of most eukaryotic genomes
(Morgante et al., 2002). Since they have a number of repeats and due to the high allelic diversity,
microsatellites are valuable as molecular markers particularly for closely related individuals. In
addition they are codominant which eliminates the need for progeny testing of heterozygotes.
The first SSR based map was constructed by Blair et al (2006) and since then subsequent maps
have been integrated into the bean consensus map (Freyre et al., 1998; Vallejos et al., 1992).

12

A densely populated genome map enables the dissection of quantitative traits. With advances in
genomics, the P. vulgaris genome has been sequenced using the Andean accession G19833 as
the reference genotype. Single nucleotide polymorphisms (SNPs) were identified by aligning the
whole genome sequence with paired end sequences of 19 other bean genotypes using the
Illumina genome analyzer. SNPs are the most abundant polymorphisms in the genome
(estimated at 1.8 million in common bean). Out of these a subset of about 9,000 loci were
reliably scored on the genotypes of the BeanCAP collection and were also segregating in
Jalo/BAT93 and Stampede/Red Hawk mapping populations and these were developed into a
Illumina GeneChip consisting of 6,000 SNPs (Cregan, 2012). Due to their abundance, maps
constructed using SNP cover most of the genome at a higher resolution.

Quantitative Trait Locus Mapping
Quantitative trait loci (QTL) analysis relies on detecting genotypic differences responsible for
variation in trait means at a marker locus, by associating the phenotype and genotype of an
organism. Several QTL studies have been conducted using different marker systems to identify
white mold resistance in beans derived from different genetic backgrounds. The RIL populations
developed for QTL identification in dry beans have been derived from several sources of white
mold resistance PC-50 (pompadour bean), NY6020-4 (snap bean), Bunsi (navy bean) and G122
(cranberry bean). In a PC-50/XAN-159 RIL population, Park et al. (2001) identified three QTL
explaining 26% of the variance for reaction to white mold infection on linkage groups Pv04,
Pv07, and Pv08, and one QTL explaining 24% of the variance for reaction in the straw test.
Three QTL were also identified on Pv04, Pv07, and Pv08, explaining 27% of field reaction to
white mold in the same study. Miklas et al. (2001) detected a QTL on Pv07 near the Phaseolin
13

(Phs) locus explaining 38% and 26% of the variance for straw test and field response,
respectively in an A 55/G 122 RIL population. A QTL on Pv01 conditioning 18% of the variance
for field response was associated with the fin locus and a QTL explaining 34% of the variance
for canopy porosity, suggesting a role of this locus in disease avoidance. In Bunsi/Newport and
Huron/Newport RIL populations, (Kolkman and Kelly, 2003) found QTL on Pv02 and Pv07 that
account for 12% and 17% of the variance in disease severity, respectively. A QTL conditioning
16% of the variance in oxalate resistance was also detected on Pv07. In a Benton/NY 6020-4
RIL population, Miklas et al. (2003) identified QTL on Pv06 and Pv08 that account for 12% and
38% of the variance in straw test disease reaction and 13% and 26% of the variance in field
disease reaction, respectively. Ender and Kelly (2005) detected QTL on Pv02, Pv05, Pv07, and
Pv08 that accounted for 10.1%, 10.7%, 14.7%, 9.2 % of the variance in field disease reaction,
respectively, in a Bunsi/Raven RIL population. Miklas et al. (2007) also reported significant
QTL on Pv02 and on Pv03 explaining from 8.7-22.7% and 5.3-15.7% of the variance white
mold response in the field respectively, in a pinto x navy cross. Some of the markers associated
with the QTL were recently validated in an independent study in snap beans (Chung et al., 2008).
Other possible sources of white mold resistance in beans are scarlet runner bean (P. coccineus)
(Miklas et al., 1998) and a wild Mexican accession (Terpstra and Kelly, 2007; Mkwaila et al.,
2011). Based on these QTL studies marker assisted breeding has been implemented for QTL on
Pv07, Pv08 (Miklas, 2007) and Pv02 (Ender et al., 2008) associated with resistance and plant
architectural avoidance traits. More recently Soule et al. (2011) showed that resistance in the
kidney bean line VA19 colocalized with QTL on Pv02 and Pv08. Interestingly the partial
resistance in VA19 is not derived from previously recognized resistance sources. The
colocalization of QTL to genomic regions where prior QTL were detected adds support to

14

validity of these QTL and more importantly provides breeders with tools to enhance resistance to
white mold.

Objectives
The objectives of this research were to identify in both greenhouse and field experiments QTL
associated with white mold resistance and agronomic traits including yield under white mold
pressure in two inbred backcross populations developed from crosses between wild and landrace
bean accessions exhibiting partial resistance to white mold in greenhouse evaluations with the
recurrent parent ‘Tacana’, a black bean cultivar possessing field resistance and/or architectural
avoidance to white mold and in two recombinant inbred line populations of type II pinto beans
developed from a common resistance source AN-37 and to explore the pattern of expression of
three defense genes in response to white mold infection in different common bean cultivars.

15

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49:179-186.
Tohme, J., D.O. Gonzalez, S. Beebe, and M.C. Duque. 1996. AFLP analysis of gene pools of a
wild bean core collection. Crop Science. 36:1375-1384.
Vallejos, C.E., N.S. Sakiyama, and C.D. Chase. 1992. A molecular marker-based linkage map of
Phaseolus vulgaris L. Genetics. 131:733-740.
Vance, C.P. 2001. Symbiotic nitrogen fixation and phosphorus acquisition: plant nutrition in a
world of declining renewable resources. Plant Physiology. 127:390-397
Webster, D., and J.D. Kelly. 2000. Commentary on germplasm enhancement and cultivar
development. p. 55–59. In S. Singh (ed.) Bean research, production, and utilization.
Proc. Idaho Bean Workshop. Twin Falls, ID. 3–4 August, 2000.
Yu, K., S.J. Park, V. Poysa, and P. Gepts. 2000. Integration of simple sequence repeat (SSR)
markers into a molecular linkage map of common bean (Phaseolus vulgaris L.). Journal
of Heredity. 91:429-434.

24

CHAPTER 2

IDENTIFICATION OF QTL FOR AGRONOMIC TRAITS AND RESISTANCE TO
WHITE MOLD IN WILD AND LANDRACE GERMPLASM OF COMMON BEAN

Introduction
In humid temperate regions, white mold is rated by bean producers as the most serious disease
threat to the common bean crop (Webster and Kelly 2000). Fewer seeds per pod, decreased seed
size, and, in severe cases, plant death contribute to severe yield losses associated with white
mold infection (Kerr et al. 1978, Steadman 1983). Resistance to white mold is considered a
complexly inherited trait with both physiological and architectural avoidance components but no
source of complete resistance is currently available in cultivated bean germplasm (Soule et al.
2011). A wide array of both Andean and Middle American genotypes have been evaluated for
reaction to white mold (Terán and Singh 2010) but it is probable that resistance to S.
sclerotiorum exists in wild and landrace bean germplasm. Prior screening of wild and landrace
plant accessions of P. vulgaris from the core germplasm collections have identified accessions
possessing partial resistance to white mold in greenhouse evaluations (Grafton et al. 2002,
Kolkman 2000, Miklas et al. 1999). In order to exploit potential physiological resistance present
in undomesticated sources, the resistance must first be transferred into locally adapted
backgrounds to facilitate field testing. One method that has been widely used to introgress exotic
germplasm into cultivated background is the inbred backcross line (IBL) method (Tanksley and
Nelson 1996). Quantitative trait loci (QTL) analysis can be conducted on the IBL populations to
detect significant QTL from the undomesticated donor parent. The IBL breeding method has
been used successfully to introduce quantitative variation for enhanced biological nitrogen
fixation into cultivated beans (Bliss 1993) and root rot resistance from landrace cultivars
25

(Román-Avilés and Kelly 2005). The objective of this study was to identify in both greenhouse
and field experiments QTL associated with white mold resistance and agronomic traits including
yield under white mold pressure in two IBL populations developed from crosses between wild
and landrace bean accessions exhibiting partial resistance to white mold in greenhouse
evaluations with the recurrent parent ‘Tacana’, a black bean cultivar possessing field resistance
and/or architectural avoidance to white mold.

Materials and Methods

Plant Material
This study was conducted on two IBL populations developed by Ender (2003). The first
population, referred to herein as the TW population, consisted of 89 BC2F3:4 IBLs derived from
a cross between ‘Tacana’, recurrent parent, and PI 318695. Tacana is a Middle American black
bean cultivar from Mexico (López Salinas et al. 1997) and was selected for this study because of
its upright architecture and partial resistance to white mold observed in the field in Michigan
(Ender 2003). PI 318695 is a tiny seeded (3.6 g/100 seeds) photoperiod sensitive wild bean
accession from Mexico that exhibited partial resistance to white mold in greenhouse screening
evaluations (Grafton et al. 2002, Kolkman 2000). Two backcrosses to ‘Tacana’ were required in
order to generate IBLs exhibiting sufficient agronomic adaptation to temperate climate in
Michigan. The second population, referred to herein as the TL population, consisted of 75
BC1F4:5 IBLs derived from a cross between Tacana, recurrent parent and PI 313850 accession.
PI 313850 is a large-seeded (64.4g/100 seeds) unadapted Andean landrace from Peru that was
identified as having partial resistance to white mold based upon straw test data (Kolkman 2000,
26

Miklas et al. 1999). In the case of the TL population one backcross to Tacana was sufficient to
develop agronomically adapted IBLs for field testing in Michigan.

Greenhouse Evaluations
The greenhouse straw test, as described by Petzoldt and Dickson (1996) was used to evaluate
both the TW and TL populations for physiological resistance to white mold. The straw test was
conducted on all IBLs and parents each consisting of three replications for four separate years
0

(2003-2006). The greenhouse was maintained between 20 to 24 C and plants were misted daily
to promote disease development. Eight days after inoculation plants were rated for disease
progression on a scale of 1 – 9 as described by Petzoldt and Dickson (1996) and modified by
Miklas et al. (1999). The straw test has been widely used to measure physiological resistance to
white mold in common bean (Pérez-Vega et al. 2011, Terán and Singh 2010).

Field Trials
Selective phenotyping was used to screen the IBL populations for resistance to white mold under
field conditions, due to space limitations. The method of selective phenotyping implies the
selection of the extremely high and low scoring individuals from the continuous distribution of a
quantitative trait, and has been shown to be a powerful method for mapping (Jannink 2005). The
IBL lines displaying the extreme ends of the distribution for resistance/susceptibility based on
initial greenhouse straw test studies were further characterized for reaction to white mold in the
field from 2003 to 2006. A subset of 30 IBLs from both the TW and TL populations was
identified based on reaction to white mold using the straw test in 2003 (data not shown). Twenty
of the most resistant and ten most susceptible genotypes of each population were selected for
27

field evaluation. Due to problems associated with evaluating excessively late maturing IBLs the
subset of IBLs chosen for planting in 2004, 2005, and 2006 were further selected after 2003 field
season based upon earlier maturity (<100d) while maintaining the same range in reaction to
white mold in the straw test. Only the 2004-2006 field data was utilized for QTL analysis of the
TW and TL populations for disease incidence and agronomic traits in field environments.

Plots were planted in naturally infested soil at the Montcalm Research Farm in Montcalm
County, MI. The experimental design each year was an 8 x 8 lattice with three replications.
Entries included 30 IBLs from each population, the recurrent parent, Tacana and three checks
‘Bunsi’, ‘T-39’ and 115M black bean (Wright and Kelly 2011). Four row plots measuring 6 m in
length with 0.5 m row spacing were planted. The interior two rows of each plot were planted
with the IBLs and the exterior two rows were planted with a susceptible cultivar ‘Matterhorn’.
To enhance white mold disease development in the plots, rainfall was supplemented with
overhead irrigation as needed and vigorous plant growth and development was ensured using
standard tillage, fertilization, and weed and insect control practices. All plots were rated for
white mold and agronomic characteristics immediately prior to harvest when plants had reached
physiological maturity. White mold disease was visually rated as a combination of incidence
and severity on a scale of 1-9, where 1 = no diseased plants and 9 = 80-100% diseased plants
and/or 60-100% infected tissue (Miklas et al. 2001). Agronomic traits that may impact white
mold incidence and severity were also measured. Such traits included canopy height, days to
flower and maturity, and lodging from 1 to 5, where 1 = no lodging and 5 = excessive lodging.
Harvest occurred at maturity, when the center two rows of each plot were individually pulled and
28

threshed. Seed yield and the weight of 100 seeds were calculated after harvest and adjusted to
18% moisture content.

Genotyping of Mapping Populations
Plant tissue for DNA extraction was collected from three week old greenhouse plants from four
to six individuals of each IBL and the parental genotypes from both populations according to
method of Haley et al. (1994). SSR, SRAP and TRAP markers were screened on the parents of
both IBL populations following protocols for SSR markers (Blair et al. 2003) and for SRAP and
TRAP markers (Terpstra et al. 2006, Miklas et al. 2006). Only markers polymorphic between
parents of either or both populations were genotyped on the entire population and marker
genotyping was conducted on either 6% polyacrylamide gels or precast 4% agarose gels
depending on the size of the polymorphism of interest.

Statistical Analysis
Data generated from all greenhouse and field experiments were analyzed as randomized
complete block designs using PROC MIXED both individually and across field and greenhouse
tests (SAS 1995). Phenotypic correlation coefficients for all measured field and greenhouse traits
2

were calculated using PROC CORR (SAS, 1995). Narrow sense heritability h was estimated on
a progeny mean basis for the traits (Hallauer and Miranda 1981): as

 g2
h2  2 2
 g   gy / y   e2 / ry
29

where σ g

2

2

2

= the variance due to genotypes, σ y = the variance due to years, σ gy = variance
2

due to the interaction between genotype and year and σ e = experimental error, r= replications
2

and y = years. Exact 90% confidence intervals were calculated for h estimates were calculated
as described by Knapp et al. (1985). All marker data for each population was entered into the
JoinMap 4.0 software program to develop genetic linkage groups for each population (Van
Ooiijen 2006). Linkage maps for both populations were anchored to the bean integrated map
based on the presence of previously mapped SSR markers on each respective linkage group
(Blair et al. 2003).

Markers associated with resistance to white mold in either field or

greenhouse evaluations and markers associated with select agronomic traits associated with
white mold avoidance were detected. Initially single marker analysis was performed with QTL
Cartographer (Basten et al. 2001) followed by interval mapping using MapQTL (Van Ooiijen
2004). Subsequently, multiple-QTL model (MQM) analysis, also known as composite interval
mapping, was performed, using markers near LOD peaks >2.0 as covariates to increase the
power and precision of QTL mapping. If new peaks appeared, these were also employed as a
cofactor until no further cofactor effect was found. Cofactors without an effect were not
included. The set of cofactors that was in use when the LOD profile stabilized was subsequently
used in restricted MQM mapping. This method uses all selected cofactor markers (2-18 in this
study), except the ones on the linkage group where the QTL is located. In QTL analysis, the
mapping step size was 1 cM and additive effects were fitted.

30

Results
Disease incidence (DI) ranged from a low of 30% in 2004 to a high of 57% in 2006 in the TW
population over the three years of field testing (Table 2.1). A very similar trend was observed in
the TL population across years (DI ranged from 29% to 56%, Table 2.2). White mold ratings
were highest in both populations in 2006 when above average rainfall was recorded in July, and
scores were lowest in 2004 when the weather was exceptionally hot and dry (146mm below 30
year average). These data support the major role that weather plays in the incidence and
development of white mold. Despite the higher white mold incidence in 2006 individual IBLs
with low scores (DI~30%) were observed in both populations indicating that genetic resistance
was expressed despite high white mold pressure. Seed yield under disease pressure followed very
similar trends in both populations. Yields were highest in 2004 since supplemental irrigation
favored plant growth and development as high temperatures prevented development of white
mold (Tables 2.1 and 2.2). The lowest yields were recorded in 2006 when the incidence of white
mold was the highest and reflects the negative impact high white mold pressure has on the yield
of common beans. Other factors contributed to yield as white mold incidence was similar in 2003
and 2005 yet the yields were substantially different in both populations. The lower yields in 2005
were the result of dry conditions in August (50mm below 30-year average) during critical pod
filling period, whereas some of the top yields of individual IBLs were recorded in 2004. Seed
weight followed very similar trends as yield in both populations in 2004 as the lowest seed
weights were observed in 2005 when overall yields were low due to drought stress. Since white
mold contributed to the low yields in 2006, seed weight was not negatively affected to the same
extent. Seed weight was generally lower than commercial standards, particularly in the TW
population, due in part to the undomesticated wild parent and in part to the effects of disease
31

pressure. Overall seed weights were smaller in the TW population as compared to values in the
TL population derived from the larger sized landrace accession. Canopy height, lodging, days to
flower and maturity were consistent across populations and years with the lowest lodging scores
recorded in the highest yielding year (2004, Tables 2.1 and 2.2). All phenotypic traits except
disease incidence were significantly different among IBLs over three years in both populations
except days to maturity and height in TL population.
Heritability estimates for the traits in the TW population were low to moderate except for the
straw test (Table 2.1). Heritability of field disease incidence was noticeably low indicating that
only 14% of the observed variation in the field was due to genotypic differences. Plant height
and lodging also exhibited low heritability estimates. In the TL population heritability estimates
were higher for each of the traits however the trend was the same. The straw test had a high
heritability estimate (93%) whereas field disease incidence was substantially lower at 21%
(Table 2.2).
Disease incidence was negatively correlated with yield, and seed weight but was positively
correlated with height and lodging in the TW population (Table 2.3). Yield was strongly
positively correlated with seed weight, and days to flowering, while being negatively correlated
with height and lodging. Lodging was positively associated with seed weight and days to
maturity. In the TL population yield was negatively correlated with seed weight and lodging but
was positively correlated with canopy height (Table 2.3). The number of days to flowering was
also positively correlated with plant height while lodging was negatively correlated with plant
height. Days to maturity was positively correlated with disease incidence in the field.
A total of 106 polymorphic markers were used to genotype the TW population. These markers
which included 39 SSR, 29 SRAP, and 38 TRAP markers and 72 markers; were placed on a total
32

of 17 linkage groups covering 251.2 cM (not shown). Common SSR markers between the TW
map and the bean core maps BAT93/Jalo EEP558 (BJ, Freyre et al. 1998) and DOR364/G19833
(DG, Soule et al. 2011) were used to anchor linkage groups B2, B3, B4, B7, B9, and B11to the
TW map. The DG map contains 246 markers of which 78 are microsatellites anchored by Blair
et al. (2006b) and these were used to place previously unmapped markers especially TRAP’s and
SRAP’s based on relative positions and orientation. Significant QTL identified by CIM from the
TW population are presented in Table 2.4. A QTL for white mold resistance based on the
greenhouse test was found on B2 (Fig. 2.1). This QTL explained about 14% of the phenotypic
variation and was associated with straw test conducted in 2004, STRAW04 (Table 2.4). Another
QTL was identified on B3 in association with the same straw test; STRAW04. This QTL had R

2

value of 13%. Based on straw test, STRAW06, a QTL was found on linkage group B9 which
accounted for 16% of the phenotypic variation.

Seven agronomic traits were also analyzed through interval mapping and these included disease
incidence, seed yield, 100 seed weight, days to flower and maturity, plant height and lodging
given the diversity between the parents. There were no significant QTL for disease incidence in
the field, but two QTLs for seed weight were found on B3 and on B9 based on 2006 field data.
The QTL on B3 accounted for 21% of the phenotypic variation while the other QTL on B9
accounted for 49% of the variation in seed weight (Table 2.4). The same QTL on B4 were
associated with plant height in 2006 and lodging in 2004. These two traits were also linked on
B7 accounting for 48% of the phenotypic variation in each case. The alleles for the QTL on B4
came from Tacana parent while the QTL on B7 originated from the wild parent (Table 2.4). Fine
mapping of this interval would further separate the different trait QTL.
33

A total of 81 polymorphic markers were genotyped in the TL population, comprising 20 SSR,
and 61 SRAP markers. Five linkage groups were anchored to B2, B3, B4, and B7 and B11 based
on SSR markers located on the two core maps (Fig. 2.1). Significant deviations from expected
Mendelian segregation (1:1) segregation rates were observed. Markers showed significant
distortion throughout the linkage groups in favor of Tacana alleles. However, less than half (27
of 55), of the distorted markers were placed on linkage groups in the TL population. The
distortion however was not expected to affect QTL detection because the association between
marker class and phenotype remains the same. Different straw tests were conducted on the TL
population but significant QTL were only detected in 2006 on B4, B7 and B11 of the TL genetic
linkage map (Table 2.5, Fig. 2.1). These QTLs accounted for 22 %, 33% and 15% phenotypic
variation on the three linkage groups respectively.
Analysis of field disease incidence scores revealed no significant QTL in any of the three
seasons. However, significant QTL for yield under disease pressure were detected on B2 across
all three years on a region spanning about 10cM (Fig. 1). These QTL accounted for 38%, 31%,
TL

and 19% of the phenotypic variation from 2004-2006. QTL SY4.5

TL

on B4 and QTL SY7.2

on B7 explained 30% and 36%; and from 31% to 16% of the phenotypic variation for yield in
2005 and 2006, respectively. There was also a coincidence with a QTL for seed weight (in 2006)
2

on B2 (14%), a second QTL on B7 based on 2005 and 2006 data (R = 21% and 27%,
2

respectively) and a third on B11 in 2006 (R =25%). These QTL are adjacent to the yield QTL at
a distance of about 5 cM. Three QTLs for lodging were identified on B3, B4 and B7 based only
on 2005 drought season. These QTL explained 16%, 38%, and 28% of the variation, respectively
(Table 2.5).
34

Table 2.1:
Means, ranges and heritability estimates for white mold resistance and agronomic traits in the ‘Tacana’ x PI 318695
(TW) IBL population in three greenhouse screening and field environments at the Montcalm Research Farm, Michigan from 2004 to
2006
1

1

1

2

Heritability

2004
mean (range)

2005
mean (range)

2006
mean (range)

Genotypes
IBLs

Years

h (90% CI)

5.2 (3.2-7.1)

7.6 (3.6-9.0)

7.1 (4.9-9.0)

***

****

0.69 (0.59-0.76)

30 (22-41)

43 (23-67)

57 (27-83)

Ns

****

0.14 (0.10-0.41)

3450 (2789-4144)

1714 (1064-2654)

1378 (560-2117)

***

****

0.31 (0.27-0.59)

19.5 (15.1-23.5)

14.8 (10.5-17.4)

17.8 (15.2-20.2)

**

****

0.34 (0.20-0.61)

Days to Flower

44.2 (39.0-48.0)

41.2 (37.5-43.0)

42.6 (41.0-44.0)

*

****

0.33 (0.20-0.61)

Days to Maturity
7
Lodging

97.2 (94.6-101.3)

98.4 (96.0-100.0)

96.2 (92.6-99.1)

**

****

0.38 (0.12-0.64)

1.3 (0.9-2.4)

2.8 (2.0-4.5)

1.8 (0.9-3.0)

*

****

0.14 (0.10-0.50)

Height (cm)

47.9 (45.7-51.5)

50.7 (43.0-55.0)

52.6 (48.0-54.5)

*

****

0.10 (0.09-0.39)

Trait
3

Straw Test
Disease Incidence
4
(%)
Seed Yield

5

Seed weight

6

* ** *** ****

, , ,

2

significant at P ‹ 0.05, 0.01and 0.001and 0.0001 levels, respectively; ns= not significant based on ANOVA over years

1

These experiments were conducted at the Montcalm Research Farm each year on 30 IBLs previously selected for reaction to white
mold in straw test in 2003.
2
Narrow sense heritability calculated on a progeny mean basis, (90% Confidence Limits).
3

Greenhouse straw tests on 75 IBLs were rated on a scale of 1-9, where 1 = no infection and 9 = total plant collapse.

4

Disease incidence is resistance to white mold measured on a 1-9 scale, where 1 = no diseased plants and 9 = 80-100% disease in the
plot.
5
Yield is expressed in kilograms per hectare adjusted to 18% moisture.
6

Seed weight is expressed as the weight in grams of 100 seeds.

7

Lodging is measured on a scale of 1 – 5, where 1 = erect and 5 = prostrate or 100% lodged.
35

Table 2.2: Means, ranges and heritability estimates for white mold resistance and agronomic traits in the ‘Tacana’ x PI 313850 (TL)
IBL population in three greenhouse screening and field environments at the Montcalm Research Farm, Michigan from 2004 to 2006
1

1

1

2

2004
mean (range)

Straw Test

Seed Yield

5
6

Seed weight
Days to Flower
Days to Maturity
7

Lodging
Height (cm)
* ** *** ****

, , ,

Heritability

4.1 (2.9-8.3)

5.9 (3.0-9.0)

***

****

0.93 (0.90-0.94)

29 (22-44)

36 (23-57)

56 (30-83)

Ns

****

0.21 (0.13-0.27)

1882 (795-2498)

1534 (560-2173)

***

****

0.89 (0.80-0.93)

22.1 (19.7-26.3)
44.4 (39.0-48.0)
96.3 (94.6-99.5)

17.0 (14.8-20.6)
41.6 (37.5-44.5)
98.5 (97.0-100.0)

19.5 (17.2-26.1)
42.5 (41.0-44.1)
95.7 (94.1-98.1)

**
*
Ns

****
****
****

0.90 (0.83-0.94)
0.75 (0.56-0.85)
0.21 (0.13-0.27)

1.1 (0.9-1.6)
47.7 (43.9-51.3)

4

Disease Incidence(%)

Genotypes
IBLs
Years

3483 (2296-4211)

3

2006
mean (range)

3.2 (2.4-5.3)

Trait

2005
mean (range)

2.3 (1.4-3.9)
52.7 (45.5-55.0)

1.3 (0.9-3.0)
53.2 (50.0-54.0)

*
Ns

****
****

0.13 (0.10-0.48)
0.36 (0.12-0.62)

2

h (90% CI)

significant at P ‹ 0.05, 0.01and 0.001and 0.0001 levels, respectively; ns= not significant based on ANOVA over years

1

These experiments were conducted at the Montcalm Research Farm each year on 30 IBLs previously selected for reaction to white
mold in straw test in 2003.
2
Narrow sense heritability calculated on a progeny mean basis, (90% Confidence Limits).
3

Greenhouse straw tests on 75 IBLs were rated on a scale of 1-9, where 1 = no infection and 9 = total plant collapse.

4

Disease incidence is resistance to white mold measured on a 1-9 scale, where 1 = no diseased plants and 9 = 80-100% disease in the
plot.
5
Yield is expressed in kilograms per hectare adjusted to 18% moisture.
6

Seed weight is expressed as the weight in grams of 100 seeds.

7

Lodging is measured on a scale of 1 – 5, where 1 = erect and 5 = prostrate or 100% lodged.

36

Table 2.3: Persons phenotypic correlation coefficients (r) between disease incidence, yield and agronomic traits across three field
environments and greenhouse straw test evaluations in the TW (above the diagonal) and TL (below the diagonal) IBL populations
Seed
Days to
Canopy
Days to
Disease
Straw
Seed Yield
Weight
Flower
Height
Lodging
Maturity
Incidence
Test
****

Seed Yield

--

0.57

0.38

Seed Weight

-0.36*

--

0.37

Days to
Flower

0.32

-0.54**

Canopy
Height

0.83****

Lodging

****
****

-0.45

****

-0.38

****

-0.10

0.53

--

-0.09

-0.38

-0.53***

0.44**

--

-0.59****

0.33

-0.10

Days to
Maturity

0.22

0.26

Disease
Incidence

0.06

Straw Test

0.18

* ** *** ****

, , ,

****

****

-0.55

0.05
-0.25

***

-0.26

****
****

*

-0.01
-0.10

-0.01

-0.17

0.10

0.11

0.48

-0.47**

--

0.30

-0.01

0.24

0.14

--

-0.11

0.03

-0.26

0.16

0.25

-0.24

0.53**

--

0.02

-0.17

0.11

0.12

0.10

0.02

-0.10

--

significant at P ‹ 0.05, 0.01, 0.001, and 0.0001 levels, respectively.

37

****

0.18

****
*

-0.16

*

-0.10
0.15

*

Table 2.4: Putative QTL for white mold resistance and agronomic traits identified in field and greenhouse environments in ‘Tacana’ x
PI 318695 (TW) IBL population evaluated in Michigan during 2004-2006.

2

Linkage
Group

Indicative
1
marker

LOD
2
score

Adjusted R
(%)

Additivity

B2

Me2Em4.350

2.88

14.3

-0.65

B3

BM189

2.52

13.2

-0.49

B9

F6R8.600

2.78

15.7

-0.52

2006
2006

B3
B9

BM189
F1R8.300

2.93
3.07

21.0
49.1

1.51
0.50

2006
2006

B4
B7

F5R5.180
BM210

3.20
5.67

29.0
48.0

0.71
-0.42

2004
2004

B4
B7

BMd-15
F1R1.275

2.60
5.67

10.8
48.0

0.04
-0.42

Year

2004

WM2.3

2004

WM3.3

2006

Trait
Straw Test

Assigned
QTL

WM9.2

BR,GC,TW
TW
TW

3

4

Seed Weight

Canopy height

Lodging

1
2
3
4

Primer information for SSR markers online at: http://www.css.msu.edu/bic/PDF/Bean SSR Primers 2007.pdf.
LOD: Log of odds
Proportion of the phenotypic variance explained by the QTL at peak LOD using MQM mapping with cofactor selection.
Effect of substituting a single allele from one parent to another. Positive values indicate allele from Tacana and negative from Wild.

38

Table 2.5a: Putative QTL for white mold resistance and agronomic traits identified in field and greenhouse environments in ‘Tacana’
x PI 313850 (TL) IBL population evaluated in Michigan during 2004-2006.
Indicative
1
marker

LOD score

Adjusted R (%)

Additivity

B4

F1R2.550

2.78

22.0

-0.94

B7

F11R3.200

3.90

32.8

-1.09

B11

BMd-33

2.15

15.4

-0.74

B2
B2
B2

BMd-47
BMd-47
BMd-47

3.56
3.13
2.86

37.8
30.9
19.3

3.99
3.72
2.61

B4
B4

F16R8.100
BMd-16

4.38
4.15

30.4
35.6

3.76
3.04

B7
B7

F5R5.225
F5R5.225

3.81
2.63

30.5
15.5

3.43
2.16

Year
2006

Trait
Straw Test

Linkage
Assigned QTL Group
WM4.2

2006

WM7.5

R31,TL
TL

2006

WM11.1

2004
2005
2006

SY2.1

2005
2006

SY4.5

2005
2006

SY7.2

TL

2

2

3

4

Seed Yield

1
2
3

TL

TL
TL

Primer information for SSR markers online at: http://www.css.msu.edu/bic/PDF/Bean SSR Primers 2007.pdf.
LOD: Log of odds.
Proportion of the phenotypic variance explained by the QTL at peak LOD using MQM mapping with cofactor selection.

4

Effect of substituting a single allele from one parent to another. Positive values indicate allele from Tacana and negative from
landrace.

39

Table 2.5b: Putative QTL for white mold resistance and agronomic traits identified in field and greenhouse environments in ‘Tacana’
x PI 313850 (TL) IBL population evaluated in Michigan during 2004-2006.
2

Linkage
Group

LOD
2
score

Adjusted R
(%)

Additivity

2005

B7

F15R5.200

2.99

20.5

-1.86

2006
2006
2006

B2
B7
B11

PV-gccacc001
F1R8.300
F15R5.700

2.50
3.07
6.10

13.7
27.0
24.6

-1.46
0.50
-3.38

2005
2005

B3
B4

BM189
F16R8.100

2.68
3.00

15.5
38.2

-0.80
-0.41

2005

B7

F5R5.225

3.62

28.1

-0.41

2005
2005

Trait
Seed Weight

Indicative
1
marker

B11
B11

F11R3.100
BMd-33

2.71
2.27

19.5
18.0

1.28
1.26

Year

Assigned
QTL

3

4

Lodging

Days to flower

1
2
3

Primer information for SSR markers online at: http://www.css.msu.edu/bic/PDF/Bean SSR Primers 2007.pdf.
LOD: Log of odds.
Proportion of the phenotypic variance explained by the QTL at peak LOD using MQM mapping with cofactor selection.

4

Effect of substituting a single allele from one parent to another. Positive values indicate allele from Tacana and negative from
landrace

40

Fig. 2.1: Integrated bean linkage map showing relative positions of QTL for white mold
resistance and agronomic traits evaluated over three years (2004-2006) in two inbred backcross
line populations TW and TL at Montcalm Research Farm, Michigan, USA.
B2-B11=bean linkage groups, WM= greenhouse straw test, DF= number of days to flowering,
HT= plant height, LDG= lodging, SW= 100 seed weight and SY=seed yield. QTL identified by
population and year. Bar sizes indicate LOD support ranges for the significant QTL.

41

Fig. 2.1 (cont’d).

42

Fig. 2.1 (cont’d).

43

Fig. 2.1 (cont’d).

Discussion
The low heritability estimates for field disease incidence underscores the difficulty of evaluating
white mold in the field and the importance of using separate greenhouse tests to assess
physiological resistance to white mold in common bean. The high heritability estimates for the
straw test indicates that it is a valuable screen to identify underlying mechanisms of resistance
independent of confounding plant avoidance traits expressed in the field. Similar modest
44

heritability estimates for field white mold incidence and agronomic traits in common bean have
been reported previously (Miklas et al. 2004, 2007). It is noteworthy that the economic trait of
importance to growers, yield under disease pressure, had higher heritability estimates. The higher
estimates for most traits in the TL population could be attributed to better combing ability
between the two cultivated parents as opposed to the absence of desired agronomic traits in the
wild parent in the TW population.
The association between disease incidence, plant height and lodging emphasizes the interplay
between architectural traits and physiological resistance mechanisms. Lodging is also associated
with disease development, specifically by reducing canopy porosity and assisting in development
of a microclimate favorable for disease development (Coyne et al. 1974). The correlation
between height and disease incidence may result from taller IBLs being more prone to lodging,
although lodging and height were not correlated in the TW population. The straw test ratings
were not significantly associated with disease incidence in either population, underscoring the
complexity of white mold resistance and the interplay between plant avoidance mechanisms
observed in the field and the partial physiological resistance of the original accessions assayed in
the straw test. The correlation between days to maturity and lodging could be the consequence of
the associations between disease incidence these two traits, as early flowering IBLs showed more
disease and lodging is a consequence of disease development. The strong correlation between
lodging and days to maturity may indicate that late maturing IBLs are either more prone to
lodging as a result of increased vegetative growth or that linkage between these domesticationrelated traits was not broken in this population. Days to maturity was used as a selection criterion
when selecting the IBLs tested in the field, and the narrow range of maturities evaluated may be
responsible for the lack of correlations observed between days to maturity and other traits. As

45

expected disease incidence was negatively correlated with yield, and seed weight, as disease
development has been shown to reduce yield and seed size in other studies (Steadman 1983, Kerr
et al. 1978). In the TL population the negative correlation between seed weight and yield was
expected as these are compensatory yield component traits in crosses between large and small
seeded genotypes from the different gene pools (Blair et al. 2006a). The positive correlation
between disease incidence and the number of days to maturity confirms that in some genotypes
earliness serves as an escape mechanism from severe white mold infection (Kolkman and Kelly
2002).
The location of the QTL for resistance on B2 based on the straw test in TW population suggests
that it is the same QTL as were previously detected in Bunsi/Raven (Ender and Kelly 2005) and
G122/CO72548 (Maxwell et al. 2007) populations. The nearest SSR markers on the DG map
were BM142 and BM143. This is the tenth QTL identified in this region (Soule et al. 2011)
which also contains plant defense genes (PGIP and CHS). This QTL identified in the TW
population is designated WM2.3

BR,GC,TW

according to the new QTL nomenclature guidelines

(Miklas and Porch 2010). Based on the numerous QTL found in this region it is likely that allelic
variations in PGIP or CHS genes may contribute to white mold resistance on B2. The QTL on
B3 appears to be a new locus as the previous QTL reported in this region is WM3.1

AN

near

marker BMd-1 in the Aztec/ND88-106-04 population (Miklas et al. 2007). This new QTL is
located near BMd-36 which is over 40cM away from previously described QTL (Soule et al.
2011, Pérez-Vega et al. 2011) and therefore is designated as WM3.3

TW

, the third QTL for white

mold resistance on B3. A third QTL was detected on the proximal end of B9 suggesting that it is
a new locus as previously reported QTL on B9 in the G122/CO72548 population (Maxwell et al.

46

2007) was located on the distal end of the DG map. This QTL is located near F6R8.600 marker
on B9 is designated WM9.2

TW

. The nearest previously anchored marker to this QTL is BM114

which is found in the region of gluc gene in the BJ core map. Plant glucanases are suggested to
play several physiological roles including plant protection. When fungal pathogens like S.
sclerotiorum attack plants, glucanases aid in the release of polysaccharides from cell wall
degradation and these act as elicitors which induce defense response in plants (Stintzi et al.
1993).
There was significant deviation from the expected genetic ratio in the TL population.
Segregation distortion is a common phenomenon in plant mapping studies. In a previous QTL
mapping study in bean using IBLs, segregation distortion was also noted (Blair et al. 2006a).
The published study was not based on an inter-gene pool cross per se, but rather a cross between
an Andean cultivar and a wild bean that is genotypically distinct from both major gene pools of
common bean (Blair et al. 2006a) and it is speculated that the distorted ratios observed may be
the direct result of inadvertent selection for pod, or seed domestication traits during population
development. It is possible that inadvertent selection also contributed to the segregation
distortion and absence of specific linkage groups including B1, which contains several
domestication genes, in the TL population, as some IBLs were discarded during population
development due to poor adaption to Michigan field conditions (Ender 2003). The deviant
markers however did not significantly affect the accuracy of the genetic map or marker-trait
associations. Three QTL for white mold resistance based on the straw test were identified. The
QTL on B4 in the TL population is probably the same as WM4.2

R31,TL

identified in R31

population as it is in the same vicinity as SSR marker BMd-15 in the DG map (Soule et al.
2011). The QTL on B7 is a novel QTL because it maps near marker BM160 which is on the
47

distal end of B7 away from where the previously reported QTL were located on the DG map.
The new QTL differs from the WM7.1

AG,PX,XC

QTL in the A55/G122 (Miklas et al. 2001), PC-

50/XAN-159 (Park et al. 2001), and Xana/Cornell 49242 (Pérez-Vega et al. 2011) populations;
from the WM7.2

BN,BR

QTL in the Bunsi/Newport (Kolkman and Kelly 2003) and Bunsi/Raven

(Ender and Kelly 2005) populations; from the WM7.3
al. 2011); and from the WM7.4

XC

R31

QTL in the R31 population (Soule et

QTL in the Xana/Cornell 49242 population (Pérez-Vega et al.

2011). Approximately 12 cM was added to the integrated B7 linkage group since B7 is mostly
composed of SRAP markers in this study. The new QTL is designated WM7.5

TL

. No QTL have

been reported for white mold resistance on B11. The QTL on this linkage group accounted for
15.4% of the variation in the TL population and is designated WM11.1

TL

.

A better understanding of the associations between disease resistance and agronomic traits
contributes to the development of more efficient strategies to select for resistance to white mold
in common bean. Yield under disease pressure is very important since it is the primary trait of
interest in most breeding programs. A major QTL, SY2.1

TL

that ranged from 19 to 37% of the

variation for yield under white mold pressure over three years was detected on B2 in the TL
population. The consistency of yield QTL on B2, B4 and B7 underpins the accuracy of mapping
for the trait. Kolkman and Kelly (2003) previously found the SY7.1

BN

QTL for yield under

white mold pressure on B7 that explained 36% of the phenotypic variation. Blair et al. (2006a)
also mapped four QTL for yield on B4. The two QTL SY4.5

48

TL

and SY7.2

TL

from this study are

independent of those previously described. Seed weight and disease resistance colocalized on B3
(TW population) and on B7 and B11 (TL population, Fig. 1). QTL for seed weight, and white
mold resistance were previously mapped to B2 (Kolkman and Kelly 2003). Seed weight is a
component of yield which is a complex trait like white mold. To make genetic gains in yield the
individual components like seed weight have to be improved individually. The colocalization of
white mold resistance and seed weight offers the opportunity for simultaneous selection for yield
and white mold resistance. Other traits like plant height and lodging are an important part of the
architectural escape mechanisms of common bean from white mold. Beattie et al. (2003) also
found QTL for plant height on B4 but it only accounted for only 10% of the phenotypic
variation. The occurrence of these two traits in the same region on B4 and B7 in both populations
confirms that these two traits may affect one another and in turn influence the reaction of bean
plants to white mold. The QTL for lodging and plant height were in the same locations on B4 as
TL

the yield QTL SY4.5

. QTL for lodging have previously been identified on B3 (Ta’ran et al.

2002) on B4 and B7 (Beattie et al. 2003, Wright and Kelly 2011) with variation ranging from 13
to 18%.

The lack of significant QTL for disease incidence could be attributed to the small sample size
and the extreme variation in environmental conditions for the development of white mold in the
field. Selective genotyping and DNA pooling has been previously used to map QTL for white
mold (Kolkman and Kelly 2003, Miklas et al. 2007) but it is only recommended when the
reference populations are genetically narrow. We report here the application of selective
phenotyping on a whole genome scale for QTL mapping when field space for extensive
2

phenotyping is limited (Jannink 2005). The high R values for yield, plant height and lodging are
49

indicative of how relatively consistent these traits are from year to year in comparison to disease
incidence. The consistency of yield QTL across the years in the TL population suggests that
yield under disease pressure can be used as a selection index for improving white mold
resistance in common bean. In summary, selective phenotyping did not permit the detection of
QTL for white mold resistance based on field disease incidence. Four new QTLs for white mold
were identified and two previous QTL were confirmed based only on the straw test. QTL for
yield under white mold pressure were identified on B2, B4 and B7 in the TL population.
Deviation of markers from expected ratio in the inter gene pool TL population was observed but
did not affect the QTL analysis..

50

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51

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Terpstra, K.A., E.M. Wright, and J.D. Kelly. 2006: Protocol for visualizing sequence related
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55

CHAPTER 3
QTL ANALYSIS OF WHITE MOLD RESISTANCE AND AGRONOMIC TRAITS IN
TWO PINTO BEAN (PHASEOLUS VULGARIS) RIL POPULATIONS.

Introduction
White mold caused by the ascomycete fungus Sclerotinia sclerotiorum (Lib.) de Bary is an
important disease of common bean (Phaseolus vulgaris) where average yield losses are
estimated to range from 20 to 80% under levels of severe infection (Kerr et al., 1978; Purdy,
1979). Controlling white mold requires an integrated approach. Cultural practices such as
increasing plant and row spacing and reducing excessive irrigation at flowering help in managing
the disease. However breeding resistant bean varieties is the most economical and effective way
of controlling the disease. The earliest attempts at dissecting the nature of resistance to white
mold involved the screening of a wide array of genetic materials to identify sources of resistance.
Adams et al. (1973) identified black turtle soup (black bean) and some inter specific crosses
between Phaseolus vulgaris and P. coccineus as having some moderate levels of resistance to
white mold. In greenhouse screening Anderson et al. (1978) identified cultivars like Sanilac and
Aurora that possess moderate levels of resistance to the disease. A single dominant gene
responsible for resistance was identified in inheritance studies of interspecific crosses between P.
coccineus and P. vulgaris (Abawi et al., 1978). Schwartz et al. (2006) also reported that
resistance from P. coccineus is qualitative and under the control of a single gene. Similarly
Genchev and Kiryakov (2002) screened progeny from a bayo breeding line A195 which
exhibited disease inheritance patterns consistent with a single recessive gene. However none of
the genes identified in these early studies have been mapped or verified in independent studies.
Consequently they have not been deployed in breeding programs. Many inheritance studies show

56

that resistance to white mold is a complex trait that is under the control of multiple genes (Fuller
et al., 1984). Park et al. (1999, 2001) developed a recombinant inbred line (RIL) mapping
population between PC-50 and XAN-159 to study the inheritance of white mold resistance
following infection with different Sclerotinia isolates. Three quantitative trait loci (QTL) were
identified on bean chromosomes Pv01 and Pv04 accounting for 33% of the variation observed in
reaction to isolate 152 and four QTL that explained 54% of the variance for reaction to isolate
279 were mapped to Pv01, Pv03, and Pv04. Miklas et al. (2001) identified a QTL on Pv07 near
the seed protein Phaseolin (Phs) locus that explained 38% of the variance for straw test and 26%
field response, respectively in a RIL population from G122 (a landrace from India with partial
resistance to white mold) and A55 a Type II Middle American black bean. In the same
population a QTL on Pv01 conditioning 18% of the variance for field response was associated
with the determinate fin locus and another QTL explaining 34% of the variance for canopy
porosity, suggesting that this locus contributes resistance through plant avoidance mechanisms
(Miklas et al., 2001). Kolkman and Kelly (2003) found QTL on Pv02 and Pv07 that accounted
for 12% and 17% of the variance in disease severity in Bunsi/Newport and Huron/Newport RIL
populations, respectively. A QTL conditioning 16% of the variance for oxalate resistance was
also detected on Pv07. Miklas and Delorme (2003) identified QTL on Pv06 and Pv08 that
accounted for 12% and 38% of the variance in straw test disease reaction and 13% and 26% of
the variance in field disease reaction, respectively in a Benton/NY 6020-4 RIL population. Ender
and Kelly (2005) mapped QTL on Pv02, Pv05, Pv07, and Pv08 that accounted for 9 to 15% of
the variance in field disease reaction, respectively, in a navy x black (Bunsi/Raven) RIL
population. Miklas et al. (2007) reported significant QTL on Pv02 and on Pv03 explaining from
up to 23% of the variance to white mold in the field in Aztec pinto /ND88-106-04 navy bean RIL

57

population. Maxwell et al. (2007) identified QTL for white mold resistance on Pv01, Pv02 and
Pv08 in both field and greenhouse tests in a G122/C072548 population. Soule et al. (2011)
mapped QTL in two populations Benton/VA19 and Raven/I9365-31 on Pv02, Pv04, Pv05, Pv06,
Pv07 and Pv08 that accounted for 9 to 33% variation in field, greenhouse straw test and nonwounding tests. Perez-Vega et al. (2012) also mapped QTL associated with five different isolates
of S. sclerotiorum on Pv01, Pv03, Pv06, and Pv07 in an Andean Xana/Cornell 49242 (white x
black bean) RIL population. Mkwaila et al. (2011) identified QTL associated with white mold
resistance in two inbred backcross line (IBL) populations from Tacana and a Mexican wild and
Peruvian landrace bean accession on Pv02, Pv03, Pv04, Pv07, Pv09, and Pv11 that accounting
for up to 32% observed variation in both straw test and field disease incidence. All these studies
emphasize that the utility of QTL analysis for MAS depends on the population from which the
QTL was identified. While some of the loci colocalize between studies, the markers associated
with the trait may not segregate in different populations so breeders need to develop specific
market class populations to identify markers for selection for resistance to white mold.
Pinto beans which belong to the Middle American Durango race are the most common market
class grown in North America (Teran et al., 2009). They have medium size seed with a light tan
color and an irregular mottled dark brown pattern (Freytag and Debouck, 2002). Early landraces
and some traditional cultivars include the Common pinto (Teran et al., 2009), Othello (Burke et
al., 1995) and Maverick (Grafton et al., 1997). These cultivars have a typical indeterminate,
prostrate, type III growth habit that favors disease development. Plants develop a dense leaf
canopy and easily lodge causing bean pods to come in contact with the ground (Grafton et al.,
1988; Miklas et al., 2006). These decumbent types are therefore unsuited for direct harvest and
are also readily attacked by pathogens especially white mold. In recent years, new type II

58

indeterminate upright cultivars have been bred to avoid white mold (Kelly, 2000). More recent
releases include Santa Fe (Kelly et al., 2010), Lariat and Stampede (Osorno et al., 2010) and a
private cultivar La Paz. The main objective of this study was to identify and validate QTL
associated with white mold resistance and agronomic traits in two pinto bean RIL populations
derived from the same resistant parent.

Materials and methods

Plant material
Two half-sib RIL populations of 94 F4:7 lines derived from a common parent were developed
through single seed descent (SSD). The common parent AN- 37 registered as USPT–WM-1 was
released as a white mold tolerant germplasm (Miklas et al., 2006). AN-37 has Type IIb growth
habit, open canopy, stay-green trait, but has an unacceptable small pinto seed. AN-37 was
developed from Aztec pinto/ND88-106-04 navy cross where white mold resistance was derived
from the navy parent (Miklas et al., 2007). The other parents P02630 and P02647 used in the
development of the AP630 and AP647 RIL populations were MSU breeding lines that possessed
high yield potential, superior seed quality, and upright plant type, but were susceptible to white
mold. F1 plants were grown and selfed in the greenhouse at East Lansing, MI during the spring
of 2005. The F2 populations were grown at Saginaw, MI during summer 2005 and over 100
single plants were randomly harvested in each population. Two cycles of SSD were conducted
in the greenhouse at East Lansing, MI. No selection was practiced as the RILs were advanced
through the F2:3 and F3:4 generations during 2005/06 winter. Single plants were harvested and

59

F4:5 single rows were grown in 2006 and harvested in bulk to produce sufficient seed for future
yield evaluations. In 2007, 109 RILs of AP630 population and 99 RILs of the AP647 population
and parents were grown at the Montcalm Research Farm (MRF) near Entrican, MI in a field
naturally infested with white mold. The soil type at the Montcalm Research Farm sites is a
combination of Eutric Glossoboralfs (coarse-loamy, mixed) and Alfic Fragiorthods (coarseloamy, mixed, frigid). The experimental design each year was a 96 entry randomized complete
block design (RCBD) with three replications except in 2007 when only two replications were
planted per population. The extra RILs were evaluated in small RCBD experiment in 2007. In
later years, both populations were reduced to 94 RILs by eliminating lines that were not adapted
to local growing conditions and the population size better fitted the field layout for irrigation
purposes. The interior two rows of each 4 row plot were planted with the RILs and the exterior
two rows were planted with a susceptible cultivar. The great northern cultivar ‘Matterhorn’ was
used as the susceptible genotype in all years except 2007, when the susceptible pinto cultivar
‘Othello’ was used. In order to enhance white mold disease development in the plots, rainfall
was supplemented with overhead irrigation as needed and vigorous plant growth and
development was ensured using standard tillage, fertilization, and weed and insect control
practices. All plots were rated for white mold incidence and agronomic characteristics
immediately prior to harvest when plants had reached physiological maturity. White mold
disease was visually rated as a combination of incidence and severity on a scale of 1-9, where 1
= no diseased plants and 9 = 80-100% diseased plants and/or 60-100% infected tissue (Miklas et
al., 2001).

Agronomic traits that impacted white mold incidence and severity were also

measured. These traits included canopy height, days to maturity, measured as the number of
days from planting when 90% of the pods where physiologically mature, and lodging from 1 to
60

5, where 1 = no lodging and 5 = excessive lodging. Harvest occurred at maturity, when the
center two rows of each plot were individually pulled and threshed. Seed yield and the weight of
100 seeds were calculated after harvest and adjusted to 18% moisture content. Data on height,
lodging, days to maturity and white mold incidence was collected at physiological maturity for
four years (2007-2010) for AP630 and three years (2007, 2010 and 2011) for AP647 populations.
Greenhouse straw tests were conducted from 2008-2010 for AP630 and 2010-2011 for AP647 to
detect physiological resistance as described by Petzoldt and Dickson (1996). The straw test was
carried out on four separate occasions, each consisting of three replications. On each occasion,
clay pots with a 15 centimeter diameter were filled with Bacto potting mix (Michigan Peat Co.,
Michigan, USA) and four seeds were planted per pot. Each pot served as a replicate in a
randomized complete block design. After germination and emergence, pots were thinned to
three plants per pot. Plants were inoculated 28 days after planting as described by Petzoldt and
Dickson (1996), with the following modifications. The growing tip of the plant was excised with
a razor blade and discarded. Petri dishes of two to three day old cultures of S. sclerotiorum
(collected from Montcalm Research Farm) on PDA were used as the inoculum source. The PDA
plugs were placed in the top end of 10 uL pipette tips, which were then applied over the cut stem.
A PDA plug without fungus was first placed in the pipette tip followed by a PDA plug with
fungus in order to prevent the fungal plug from drying out prior to infection of the plant. The
o

greenhouse was maintained between 20 and 24 C and plants were misted daily to promote
disease development. Eight days after inoculation plants were rated for disease progression on a
scale of 1 – 9 as described by Petzoldt and Dickson (1996) and modified by Miklas et al. (1999).
The rating scale is as follows: 1 = no symptoms, 2 = invasion of the stem past the site of
inoculation but not to the first node, 3 = invasion of the stem to the first node, 4 = invasion of the
61

internode slightly past the first node, 5 = invasion to the middle of the internode, 6 = invasion to
the second node, 7 = invasion slightly past the 2nd node, 8 = invasion to the middle of the second
internode and beyond, 9 = total plant collapse.

SSR and InDel Marker Genotyping
Plant tissue was obtained from emerging leaves of 3 four week old plants pooled into one sample
for each genotype from each population and DNA extraction was conducted using a mini-prep
chloroform extraction protocol (Haley et al., 1994). SSR markers were screened on the parents
of AP630 population. Markers that were polymorphic between the parents of the population were
genotyped on the entire population. The polymorphic markers from the first population were
screened on the second population. Bands were amplified using the following polymerase chain
reaction (PCR) specifications, as modified from Blair et al. (2003): 20 uL reactions containing
0.1 mM forward SSR primer, 0.1 mM reverse SSR primer, 1X PCR Buffer (Invitrogen,
Carlsbad, CA), 1.5 mM magnesium chloride, 0.2 mM dNTP mixture, 1 unit Taq polymerase
(Invitrogen, Carlsbad, CA, USA), 2 uL DNA template, and ultra-pure water to volume amplified
in a 96-well PTC-100 Programmable Thermal Controller (MJ Research, Inc.) with the following
profile [94°C for 5 minutes; 30 cycles of 94°C for 1 minute, 47°C for 1 minute, and 72°C for 1
minute; 72°C for 5 minutes].

All SSR markers were screened on 6% polyacrylamide gels.

Depending on the specific size polymorphism for an individual marker, SSR marker genotyping
was conducted on 6% polyacrylamide gels. Insertion Deletion markers (InDels) (Moghaddam et
al., 2013) were also screened on the parents of both RIL populations and polymorphic markers
were genotyped on the entire population. The PCR reaction conditions for the InDel markers
were as follows, 20 uL reactions containing 2uL of 5 uM forward primer, 2uL of 5 uM reverse

62

primer, 10 uL GoTaq Master mix (Invitrogen, Carlsbad, CA), 2 uL DNA template, and 5uL
ultra-pure nuclease free water to volume amplified in a 96-well PTC-100 Programmable Thermal
Controller (MJ Research, Inc.) with the following profile 3 min at 95˚C for one cycle, 20 sec at
95˚C -30 sec at 55˚C-1 min at 72˚C for 45 cycles, 10min at 72˚C for one cycle. Polymorphic
markers were then genotyped on either 6% polyacrylamide gels or 3% agarose gels, depending
on the size of the polymorphism of interest.
All polyacrylamide gels were run on 38 x 50 cm Sequi-Gen GT sequencing cells (BioRad
Laboratories Inc., Hercules, CA, USA) or 6% non-denaturing gels on vertical rigs (CBS
Scientific) as described by Wang et al. (2003) to separate marker fragments. The denatured PCR
product was mixed with 8 uL of formamide loading buffer comprised of 98% formamide; 10mM
EDTA, pH 8.0; 1.0 mg/ml bromophenol blue; and 1.0 mg/ml xylene cyanol. Upon loading,
samples were electrophoresed at 50-80 watts for between 1.5 and 3 hours. Visualization of
fragments was accomplished using a silver staining kit procedure (Promega # Q4132).
Depending on the marker, gels were scored either for presence or absence of the desired band or
scored for the size of the band. 10 and 100 base pair ladders were included on each gel to
facilitate the size estimation of bands. All agarose gels used were cast at 3% agarose in TAE
buffer containing ethidium bromide (EmbiTec, San Diego, CA, USA). All agarose gels were run
between 80 – 100 Volts for 0.5-1.5 hours in TAE buffer. Bands were visualized with ultraviolet
light and scored for either presence or absence of the desired band or for the size of the desired
band.

SNP Genotyping

63

SNP genotyping for both populations was conducted through the BeanCAP (www.beancap.org)
project at the Soybean Genomics and Improvement USDA Laboratory (Beltsville Agricultural
Research Center) in Maryland. The assays were performed on the Illumina HD Infinium Assay
Platform as recommended by the manufacturer (Illumina). Genomic DNA was denatured and
amplified isothermally overnight and the amplified product was fragmented, precipitated and
resuspended in hybridization buffer. The DNA was then loaded on the BARCBEAN6K_3
Genechip for hybridization while the unhybridized fragments were washed away. The captured
DNA served as the template for single base extension and determining the genotype call for the
sample. The Genechip was then imaged using laser to record high resolution images of the
genotype call. The SNP call data were then analyzed in Illumina Genome Studio software via
auto call. The data was also manually adjusted for allele calls (the author acknowledges receipt
of SNP marker data developed by the BeanCAP project at the Soybean Genomics and
Improvement USDA Laboratory (Beltsville Agricultural Research Center) in Maryland by C.
Quigley and P. Cregan (www.beancap.org).

Phenotypic Data Analysis
The trait data collected in each year were analyzed as separate variables using linear mixed
models. Normality and homogeneity of variance assumptions were checked using box plots and
Levene’s test for unequal variances. When the Levene’s test results were statistically significant
at p<0.01, the analysis with unequal variances was performed by comparing performance of
statistical model with homogeneous and heterogeneous variances. The model that generated the
lowest Akaike Information Criterion (AIC) value was selected for further analyses. After
checking for assumptions of zero mean and genetic variance the following model was adopted

64

y = μ + Re pi +RILj + eij,

where μ is the grand mean overall RILs, Repi is the random effect of block, RILj is the random
effect of the RIL and eij is the residual (non-genetic effects) associated with a replication k from
RIL j in block i. Since RCB design was used, block was assumed random so that differences
between blocks could be recovered and RILs were fixed and for variance components year was
considered a replication. The variance components of the random effects were estimated using
the maximum likelihood method. All analysis of variance was performed by PROC MIXED in
SAS software version 9.3 of the SAS System for Windows (2002). Heritability estimates were
computed using mean squares of RILs and mean squares of error (residual) to obtain broad sense
heritability. Transgressive segregation was determined for those RILs whose phenotypic values
were outside the range of the parental values.

Genetic Linkage Map
Preliminary maps were constructed from the segregation data using JoinMap 4.0 (Van Ooiijen,
2006) and QTL IciMapping (Li et al., 2007) software was employed for the final maps. Pairwise
recombination was estimated using Kosambi function and markers were ordered within a linkage
group with a minimum LOD score of 3.0 using a Recombination Counting and Ordering (REC)
algorithm. Linkage groups were assigned chromosome names based on marker position on
integrated linkage maps. Linkage groups with less than three markers of unknown positions were
considered artifacts. Segregation distortion was examined by testing the goodness of fit to the

65

expected Mendelian ratio 1:1. Map integration was conducted using relative positions of markers
within each population.

QTL Estimation
The underlying assumption for all the traits under study was that they were controlled by
multiple genes. The multiple QTL model was employed for each trait per year. The MQM analysis
was conducted in QTL IciMapping (Li et al., 2007) using the function ICM-ADD which takes
into account additive effects of the genotypes. Markers with more than 50% missing genotypes
were eliminated. The first step was to scan the map for QTL single marker analysis with a step
window of 1 cM and stepwise regression probability set at p = 0.001. Consequently a
permutation test (1000) was conducted to determine the threshold level for significant QTL in
simple interval mapping and the logarithm of odds (LOD) score was determined at 2.5. The
composite interval mapping model was fitted to remove the non- significant QTLs and to
generate the final QTL model. The direction and size of QTL model was determined and the
proportion of variance due to the QTL was also estimated from the analysis output with support
2

ranges. Since there were a lot of markers in the model the R was adjusted to limit the
overestimation of QTL effects according to the formula below.
2

2

R adj = 1- [(1-R ) (n-1)] / n-k-1,
where k is the number of markers and n is the population size.
The map and estimated positions of the QTL were converted from QTL IciMapping (which
presents QTL as a single point on the map) to MapChart (Voorrips, 2002) format to indicate
LOD support ranges.

66

Map Integration
The two maps were integrated in QTL IciMapping by adding the SSR data set to the SNP data.
Map order and genetic distances were determined using default parameters of 2.0 LOD and
Kosambi function. Where marker order and recombination distances were significantly
discordant with known positions the markers were combined based on cM position with the SNP
map as the reference. Single marker analysis followed by composite interval mapping were
conducted on the half sib population AP647 based on SNP and SSR markers segregating
between the two parents AN-37 and P02647 using the same parameters described in mapping the
AP630 population.

Results

Frequency distributions and variation across the years
Significant variation was observed as shown by the data ranges among all the traits in the RIL
populations which suggest that initial random selection in the segregating F2 population was
unbiased. Across the years all the trait means exhibited mostly normal distribution (Fig. 3.1 and
3.2). There were no bimodal distributions indicating all the traits are continuous under
quantitative inheritance.

67

60
40
20
0
32.5 35 37.5 40 42.5 45 47.5 50

Disease Incidence
Number of RILs

Number of RILs

Yield
30
20

10
0
20 25 30 35 40 45 50 55 60
Disease Incidence %

Yield X100 kg ha-1

40

Flowering
Number of RILs

Number if RILs

100 Seed Weight
30
20
10
0
32

34

30
20
10
0

36
38
40
42
100 Seed weight (g)

28 30 32 34 36 38 40 42 44 46
Number of days to flowering

Figure 3.1 Population distributions for white mold incidence, straw test and agronomic traits in the AP630 population. Parents are
indicated by arrows.

68

Lodging

40
30
20
10
0
90

92 94 96 98 100 102 104
Number of days to maturity

Number of RILs

Number of RILs

Maturity
30

20
10
0
1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3
Lodging (1-5)

Straw Test

40
30
20
10
0
48

49

50 51 52 53
Canopy Height (cm)

54

Number of RILs

Number of RILs

Height
20

10

Figure 3.1 (cont’d).

69

0
2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7
Straw Test (1-9)

Disease Incidence

30

20
10
0
28

30

32

34

36

38

40

42

Number of RILs

Number of RILs

Yield
30
20

10
0
20 25 30 35 40 45 50 55 60 65 70 75 80
Disease Incidence (%)

Yield x100 kg ha-1

Flowering

40
20
0
28 30 32 34 36 38 40 42 44 46
100 Seed weight (g)

Number of RILs

Number of RILs

100 Seed weight
40
30
20
10
0
39

40 41 42 43 44 45
Number of days to flowering

Figure 3.2 Population distributions for white mold incidence, straw test and agronomic traits in the AP647 population. Parents are
indicated by arrows.

70

40
20
0
93

95
97
99 101 103
Number of days to maturity

Lodging
Number of RILs

Number of RILs

Maturity
20
10
0

1.5 1.7 1.9 2.1 2.3 2.5 2.7 2.9 3.1 3.3 3.5
Lodging (1-5)

Straw Test

40
20
0
48 49 50 51 52 53 54 55 56 57
Canopy height (cm)

Number of RILs

Number of RILs

Height

Figure 3.2 (cont’d).

71

40
30
20
10
0
2

3

4
5
6
Straw Test (1-9)

7

In 2007 disease incidence ranged from 11 to 89% in the AP630 population and from 11 to 94%
in the AP647 population (CV=41.5% in AP630 and CV=33.0% in AP647). The AP630
population ranged in yield from 3203 to 5152 kg ha

-1

-1

with a mean yield of 4177 kg ha while

-1

-1

AP647 had yield ranging from 2867 to 4648 kg ha with a mean yield of 3763 kg ha . Since
only two replications of each population were evaluated in 2007 variability for yield was low;
CV=11.5% in AP630 and CV=10.4% in AP647. Seed weight ranged from 29.4g -44.9g (CV=
5.2%) with a mean of 35.9g in AP630 and 26.3g - 42.7g in AP647 with a mean of 35.5g
(CV=6.7%). Days to flowering ranged from 41.1-47.5 days with an average of 43.0 days (CV=
2.0%) in the AP630 population while AP647 had similar range and mean and CV=2.2%. Days to
maturity ranged from 92-110 days with a mean of 97 days (CV=2.1%) in AP630 and 92 -104
days and an average of 98.1 days (CV=2.3%) in AP647. Lodging ranged from 1-3 with a mean
of 2 (CV=23%) in AP630 and a similar range and an average of 1.8 (CV=26%) in AP647.
Canopy height ranged from 42.0-52.5 cm with a mean of 47.5 cm (CV=3.0%) in AP630 and
41.5-54.0 cm and a mean of 48.4 (CV=3.7%) in AP647 (Tables 3.1; 3.2).
In 2008 disease incidence in the AP630 population ranged from 12 to 36% with a mean of 20%
and high variability (CV=48%). Disease pressure was low despite the addition of 13 irrigations
to promote disease development. Maximum temperatures during the growing season (June to
o

September) were as high as 33 C and may have suppressed disease development. Yields ranged
from 3360 to 5824 kg ha

-1

with a mean yield of 4480 kg ha

-1

and variability was low

(CV=9.7%). Seed weight ranged from 30.8-42.2g with a mean of 36.8g (CV=4.3%). Lodging
ranged from 1-3 with a mean of 1.5 (CV=37.4%) while days to flowering ranged from 34-45
days with an average of 40 days (CV=2.1%). Days to maturity ranged from 92.5-99.5 days with
72

a mean of 94.3 (CV= 1.7%). Canopy height ranged from 48.5-53.3 cm with a mean of 50.4 cm
(CV=3.7%). The straw test ranged from 1-6 with a mean of 5.3 (CV= 16.1%) (Table 3.1).
In 2009 disease incidence in the AP630 population ranged from 16.4 to 75.0% with a mean of
43.5% but variability was high (CV=40.4%). Yields ranged from 1982 to 3729 kg ha
mean yield of 3068 kg ha

-1

-1

with a

with low variability (CV=15.2%). Seed weight ranged from 26.5 to

44.7g with a mean of 37.6g (CV=5.4%). Lodging ranged from 1-3 with a mean of 1.9
(CV=37.1%) while days to flowering ranged from 41-52.0 days with an average of 47.6 days
(CV=1.8%). Days to maturity ranged from 97.3 to 106.5 days with a mean of 101.5 (CV= 4.0%).
Canopy height ranged from 51.5 to 59.0 cm with a mean of 55.7 cm (CV=5.6%). The straw test
ranged from 1-8 with a mean of 4.96 and moderate variability (CV= 14.2%) (Table 3.1).
In 2010 disease incidence ranged from 18.9 to 92% with a mean of 52.2% (CV=41.4 %) in the
AP630 population and from 14 to 92 % in AP647 with a mean of 47.7% but variability was high
(CV=47.2%). AP630 ranged in yield from 1814 to 3595 kg ha
ha

-1

-1

with a mean yield of 2878 kg

and moderate variability (CV=16.2%) whereas the yield ranged from 2564 kg ha

kg ha

-1

with a mean of 3259 kg ha

-1

-1

to 4132

in the AP647 population (CV=15.6%). Seed weight

ranged from 26.4g to 40.0g with a mean of 33.6g (CV=6.6%) in AP630 and from 27.1g to 40.8g
with a mean of 33.1g in the AP647 population (CV=6.5%). Days to flowering ranged from 37.341.3 days with an average of 38.6 days (CV= 2.8%) in the AP630 population while AP647 had a
range of 37.3-41.3 days and mean of 39.1 and CV=3.3%. Days to maturity ranged from 90 days
to 97 days with a mean of 94 days (CV=3.3%) in AP630 and 92 days to 101 days and an average
of 95 days (CV=2.7%) in AP647. Lodging ranged from 1 to 4 with a mean of 2.9 (CV=39%) in
AP630 and a similar range and an average lodging score of 2.6 (CV=48.6%) in AP647. Canopy
73

height ranged from 47.0 cm to 55.3 cm with a mean of 51.3 cm (CV=5.8%) in AP630 and 45.0
cm to 57.0 cm and a mean of 51.2 (CV=5.8%) in AP647. The straw test ranged from 3.0-8.0
with a mean of 3.37 in AP630 (CV=11.0%) and 1.00-9.00 with a mean of 3.6 (CV= 12%) in
AP647 (Tables 3.1 and 3.2).
In 2011 disease incidence in the AP647 ranged from 16 to 72% with a mean of 48% and was
-1

associated with high variability (CV=47%). Yields ranged from 2620 to 5107 kg ha with a
mean yield of 3606 kg ha

-1

and moderate variability (CV=13.3%). Seed weight ranged from

33.2 to 50.6g with a mean of 41.7g (CV=5.0%). Lodging ranged from 1.0 to 4.5 with a mean of
2.6 but variability was high (CV=40.3%) suggesting the scores were not very reliable. Days to
flowering ranged from 37-47.0 days with an average of 42.2 days (CV=2.8%). Days to maturity
ranged from 93 to 102 days with a mean of 97 (CV= 2.4%). Canopy height ranged from 50 to 60 cm with a mean of 54.5 cm (CV=5.4%). The straw test ranged from 1.0-9.0 with a mean of
3.66 and moderate variability (CV=10.0%) (Table 3.2).

74

Table 3.1: Means ranges and heritability estimates for white mold resistance and agronomic traits in the AN-37 x P02630 (AP630)
pinto bean RIL population in greenhouse screening and field environments at the Montcalm Research Farm, Michigan from 2007 to
2010
1

2007 mean
(range)

2008 mean
(range)

2009 mean
(range)

2010 mean
(range)

Grand
Mean

Heritability

53.2 (11.1-88.9)

20.2 (11.9-36.3)

43.5 (16.4-79.4)

52.2 (18.9-92.1)

42.3

0.23 (0.03-0.41)

----

5.0 (1.0-6.0)

5.0 (1.0-8.0)

3.4 (3.0-8.0)

4.5

0.46 (0.29-0.58)

4177 (3203-5152)

4480 (3360-5824)

3069 (1982-3729)

2878 (1814-3595)

3650

0.53 (0.37-0.64)

35.9 (29.4-44.9)

36.8 (30.8-42.8)

37.6 (26.5-44.7)

33.6 (26.4-40.0)

36.0

0.74 (0.65-0.80)

Days to Flower

43.0 (41.1-47.5)

40.0 (34.5-45.5)

47.6 (41.5-53.0)

38.6 (36.7-41.3)

42.3

0.62 (0.49-0.70)

Days to Maturity

97.7 (92.0-110.0)

94.3 (92.0-99.5)

101.5 (94.0-108)

94.3 (90.0-102.7)

97.0

0.34 (0.11-0.49)

2.0 (1.0-3.0)

1.50 (1.0-2.5)

1.9 (1.0-3.0)

2.9 (1.0-4.7)

2.1

0.23 (0.03-0.41)

50.4 (48.5-53.5)

55.7 (51.5-59.0)

51.3 (44.7-57.0)

51.23

0.25 (0.01-0.43)

Trait
Disease Incidence
2
(%)
3

Straw Test (1-9)
-1 4

Yield (kg ha )

Seed weight (g)

6

Lodging (1-5)

5

Canopy Height (cm) 47.5 (42.5-52.5)

2

h (90% CI)

1

Broad sense heritability calculated on a progeny mean basis, (90% Confidence Limits).

2

Disease incidence is resistance to white mold measured on a 1- 9 scale, where 1 = no diseased plants and 9 = 80-100% disease in the
plot.
3
Greenhouse straw tests on the RILS were rated on a scale of 1- 9, where 1 = no infection and 9 = total plant collapse.
4

Yield is expressed in kilograms per hectare adjusted to 18% moisture.

5

Seed weight is expressed as the weight in grams of 100 seeds.

6

Lodging is measured on a scale of 1 – 5, where 1 = erect and 5 = prostrate or 100% lodged.

75

Table 3.2: Means ranges and heritability estimates for white mold resistance and agronomic traits in the AN-37 x P02647 (AP647)
pinto bean RIL population in greenhouse screening and field environments at the Montcalm Research Farm, Michigan in 2007, 2010
and 2011.
2

2007 mean (range)

2010 mean (range)

2011 mean (range)

Grand
Mean

Heritability h

40.4(11.1-94.4)

47.7 (14.4-92.1)

48.0 (16.0-7.02)

45.4

0.49(0.61-0.31)

-

3.6 (1.00-9.00)

3.7 (1.00-9.00)

3.6

0.67 (0.75-0.55)

3763 (2867-4648)

2941 (2290-3690)

3606 (2620-5107)

3437

0.58 (0.68-0.43)

35.5 (26.3-42.7)

33.1(27.1-40.8)

41.7 (33.2-50.6)

36.8

0.88 (0.91-0.84)

Days to Flowering

43.4 (41.0-47.5)

38.9 (37.3-41.3)

42.2 (37.0-47.0)

41.5

0.74 (0.80-0.65)

Days to Maturity

98.1 (92.5-104.0)

95.1 (92.0-101.0)

97.2 (93.0-102.0)

96.8

0.72 (0.79-0.62)

Lodging (1-5)

1.8 (1.0-3.5)

2.6 (1.0-4.3)

2.6 (1.0-4.5)

2.3

0.46 (0.59-0.26)

Canopy Height (cm)

48.4 (41.5-54.0)

51.2(45.0-57.0)

54.5 (50-60.0)

51.4

0.59 (0.69-0.44)

Trait
Disease Incidence (%)
3

Straw Test (1-9)
-1 4

Yield (kgha )

Seed weight (g)

5

6

2

1

(90% CI)

1

Broad sense heritability calculated on a progeny mean basis, (90% Confidence Limits).

2

Disease incidence is resistance to white mold measured on a 1- 9 scale, where 1 = no diseased plants and 9 = 80-100% disease in the
plot.
3
Greenhouse straw tests on the RILS were rated on a scale of 1- 9, where 1 = no infection and 9 = total plant collapse.
4

Yield is expressed in kilograms per hectare adjusted to 18% moisture.

5

Seed weight is expressed as the weight in grams of 100 seeds.

6

Lodging is measured on a scale of 1 – 5, where 1 = erect and 5 = prostrate or 100% lodged.

76

Heritability Estimates
2

Broad sense heritability estimates (h at p=0.001) for the traits in AP630 population ranged from
2

2

2

low to high except for the straw test (h =0.46), yield (h =0.53), seed weight (h =0.74) and
2

2

days to flowering (h =0.62) (Table 3.1). Field disease incidence (h =0.23) was noticeably low
2

in comparison to the greenhouse straw test (h =0.46) indicating that only 23% of the observed
variation in the field was due to high levels of variability of this trait. Plant height and lodging
2

2

also exhibited low heritability estimates (h =0.25 and h =0.23 at p=0.05 respectively). Seed
2

weight and yield exhibited relatively higher heritability estimates (h =0.74 and 0.53
2

respectively). Higher heritability estimates for seed weight (h =0.88), days to flowering
2

2

(h =0.74) and maturity (h =0.72) were observed in the AP647 population suggesting relatively
2

2

more genetic control of these traits than yield (h = 0.58), disease incidence (h = 0.49) or
2

lodging (h =0.46); respectively (Table3.2).

77

Trait Correlations
In the AP630 population all agronomic traits were negatively correlated with disease incidence
in the field except for lodging (Table 3.3). There was small negative correlation (r = -0.14 ns)
between the straw test and disease incidence in the field. Yield was positively correlated with
seed weight (r = 0.57 p=0.001), days to flowering (r = 0.38 p=0.001) but negatively correlated
with lodging (r = -0.38), and canopy height (r = -0.45 p=0.001). Seed weight was negatively
correlated with the number of days to maturity (r = -0.25 p=0.05) but was positively correlated
days to flowering (r = 0.37 p=0.001) and lodging (r = 0.53 p=0.001). Plant canopy height was
negatively correlated with all traits in the AP 630 population.
In the AP647 population yield was positively and significantly correlated with all traits except
lodging (r = -0.04 ns) and white mold incidence (r = -0.36 p=0.001). Similarly white mold
affected incidence was negatively correlated to all traits except the number of days to maturity
(r=0.46) and lodging (r = -0.41). The highest correlations were among plant height and other
traits and white mold and other traits. Plant height was positively correlated with seed yield (r =
0.45 p=0.001), seed weight (r =0.38 p=0.001), days to flowering (r= 30 p=0.001) and negatively
correlated with disease incidence (r = -0.41 p=0.001) and lodging (r = -0.21 p=0.05). In both
populations there was no significant correlation either positively or negatively between the straw
test and any of the traits measured in the field (Table 3.3).

78

Table 3.3: Persons phenotypic correlation coefficients (r) between disease incidence, yield and agronomic traits across three field
environments and greenhouse straw test evaluations in the AP647 (above the diagonal) and AP630 (below the diagonal) pinto bean
RIL populations
Seed
Yield

Disease
Incidence

100 Seed
weight

Days to
Flowering

Days to
maturity

Lodging

Canopy
Height

Straw Test

Seed Yield

--

-0.36**

0.52***

0.38**

0.27**

-0.04

0.45***

0.00

Disease
Incidence

-0.30**

--

-0.26*

-0.17

0.46***

0.41***

-0.41***

0.02

100 Seed
weight

0.57**

0.06

--

0.37**

0.00

0.02

0.38**

-0.05

Days to
Flowering

0.38**

-0.17

0.37**

--

0.60***

0.05

0.30**

0.03

Days to
maturity

0.05

-0.47**

-0.25*

0.98***

--

0.18

0.35**

0.07

Lodging

-0.38

0.44**

0.53**

-0.38**

0.20*

--

-0.21*

-0.12

Canopy Height

-0.45**

-0.45**

-0.10

-0.09

-0.30**

-0.12

--

0.08

Straw Test

-0.01

-0.14

-0.10

-0.16

-0.15

-0.03

0.03

--

Significant at *p= 0.05, **p=0.01***, p=0.001 and ****p= 0.0001

79

Genotyping and Map Construction
A total of 440 SSR and 154 InDel markers were screened on the parents AN-37 and P02630 of
the AP630 population. SSRs showed 54% polymorphism while InDels showed 20%
polymorphism between parents.

Only 107 markers polymorphic between parents were

genotyped on the entire AP630 population. The same 107 markers were also screened on the
AP647 population. Of these only 34 consisting of 6 InDels and 28 SSRs were polymorphic
between parents of the second population and were genotyped in population AP647.
An initial map of AP630 was constructed using JoinMap version 3.0. All markers significantly
deviating from the expected ratio of 1:1 were eliminated at this stage and an initial map of
325cM was generated (not shown). However the map distances and marker order were not
concordant with existing linkage maps because most markers were clustered together within
short (<5) cM distances and there were many unanchored small groups. QTL IciMapping was
then used to order markers and chromosomes resulting in markers located across eight linkage
groups of variable length. Since most of the SSR have been previously mapped and InDels have
known positions on the P. vulgaris map, these were used to identify the bean chromosomes as
anchor markers. A final map of 727 cM was obtained with 107 markers located approximately
every 8 cM and was then used for QTL mapping of the AP630 (Appendix A1 SSR Map).
The two populations were also screened with the BARCBEAN6K_3 Genechip consisting of a
total of 5,398 SNP markers. The two populations were genotyped resulting in an average
hybridization rate of 45% for the AP630 and 44% for AP647 population. A total of 1024 markers
were polymorphic in AP630 and 1016 markers were polymorphic in the AP647 population.
Marker distortion was observed only in 5% of the SNPs in AP630 and these markers were
excluded in final map if there was a duplicate locus. The final map assembly of the AP630

80

population resulted in 11 linkage groups corresponding to the 11 chromosomes of P. vulgaris.
The average marker distance was estimated at 1.2 cM however there were regions of both high
and low recombination on some linkage groups. The maximum distance between adjacent
markers was approximately 22 cM on Pv04. The integrated map of AP630 covered a total of
1183 cM (Figure 3.3). Marker distortion was observed in 10% of the SNPs in AP647 but these
were included in the analysis. The total map distance for AP647 was 953 cM represented on 11
linkage groups corresponding to 11 bean chromosomes with an average marker distance of 1.0
cM. Both high and low recombination regions were observed in AP647 and the maximum
distance between two loci was 45 cM on Pv01 (Figure 3.4).

81

Pv01

Fig. 3.3: Genetic linkage map showing relative positions of QTL for white mold resistance and
agronomic traits evaluated in a pinto bean recombinant inbred line population AP630 grown over
four years (2007-2010) at the Montcalm Research Farm, Michigan, USA. SY= Seed Yield,
WM= White mold.
82

Pv02

Fig. 3.3 (cont’d).
.

83

Pv03
Pv04

Fig. 3.3 (cont’d).

84

Pv05

Fig. 3.3 (cont’d).

85

Pv06

Pv07

Fig. 3.3 (cont’d).

86

Pv08

Pv09

Fig. 3.3 (cont’d).
87

Pv10

Pv11

Fig. 3.3 (cont’d).

88

Pv01

Pv02

Fig. 3.4: Genetic linkage map showing relative positions of QTL for white mold resistance and
agronomic traits evaluated in a pinto bean recombinant inbred line population AP647 grown over
three years (2007, 2010, 2011) at the Montcalm Research Farm, Michigan, USA. SY= Seed
yield, WM= White mold.

89

Pv03

Pv04

Fig. 3.4 (cont’d)

90

Pv05

Pv06

Fig. 3.4 (cont’d).

91

Pv08
Pv07

Fig. 3.4 (cont’d).

92

Pv10

Pv09

Fig. 3.4 (cont’d).

93

Pv11

Fig. 3.4 (cont’d).

94

QTL Analysis in AP630 population
Eight traits namely yield, white mold disease incidence, 100 seed weight, days to flowering, days
to maturity, plant height, lodging, and greenhouse disease severity (straw test) were analyzed
through the multiple QTL model. Thirty six QTL were identified in different years for seven
traits (white mold disease incidence, seed yield, days to flowering, days to maturity, 100 seed
weight, lodging and the straw test) and these QTL were located on 10 linkage groups. LOD
scores ranged from the threshold of 2.5 to 10.47 (Table 3.4, Fig. 3.3).

95

Table 3.4a: QTL for white mold resistance identified in field and greenhouse environments in pinto bean RIL population AP630.
evaluated in Michigan during 2007-2011

Trait Name

Year QTL Name

Disease Incidence

2007

WM1.3

2010

WM1.3

2008

WM3.1

2010

WM3.1

2008

WM8.5

2008

WM2.2

2007

WM7.7

2009

WM2.2

2010

WM2.2

2008

WM7.6

2009

WM7.6

Straw Test

AP630
AP630
AN,AP630
AN,AP630
AP630
BN..AP630*
AP630
BN..AP630*
BN..AP630*
AP630
AP630

Chromosome

Indicative Marker

Pv01

IAC101

Pv01

1

2

2

3

4

R (%)

Add

3.31

33.0

14.83

BM157

2.52

19.9

1.46

Pv03

IAC66

3.42

22.8

-1.76

Pv03

Pvat008

2.79

27.5

-1.48

Pv08

Bmd-15

3.47

25.1

-1.50

Pv02

IAC69

4.03

22.9

-1.57

Pv07

BARCBEAN6K_3-SNP1180 3.56

22.3

-0.57

Pv02

IAC69

2.79

33.7

-1.71

Pv02

IAC69

8.18

41.5

-3.04

Pv07

BMd40

7.13

40.2

-3.00

Pv07

NDSU_IND_7_34.5855

4.41

41.9

3.02

96

LOD

Table 3.4a (cont’d).
Trait Name

Year QTL Name

Seed yield

2007

SY2.2

2008

SY2.2

2009

SY2.2

2008

SY5.4

2009

SY5.4

* WM2.2

AP630
AP630
AP630
AP630
AP630

Chromosome

Indicative Marker

Pv02

BM142

Pv02

1

LOD

2

2

3

4

R (%)

Add

3.37

11.6

-1.70

BM142

3.90

26.0

-3.81

Pv02

BM142

3.25

30.0

-4.30

Pv05

NDSU_IND_5_28.0255

2.77

32.3

-14.82

Pv05

Bmd-28b

3.24

30.4

14.53

BN,HN,BR,AN,R31,BV,AP630

1

Primer information for SSR markers online at: http://www.css.msu.edu/bic/PDF/Bean SSR Primers 2007.pdf. InDels available at
www.beancap.org/Indel-Markers-bean-NDSU.xls and SNPs available online www.beancap.org.
2
LOD: Log of odds.
3

Proportion of the phenotypic variance explained by the QTL at peak LOD using MQM mapping with cofactor selection.

4

Effect of substituting a single allele from one parent to another. Positive values indicate allele from AN-37 and negative from
P02630.

97

Table 3.4b: QTL for agronomic and phenological traits identified in field and greenhouse environments in pinto bean RIL population
AP630 evaluated in Michigan during 2007-2011.
1

Trait Name

Year Chromosome

Indicative Marker

LOD

Days to flowering

2007
2008
2009
2009
2010
2008
2008
2009
2010
2008
2008
2008
2009
2010
2010
2008
2010
2010
2008
2009

IAC64
BARCBEAN6K_3-SNP0695
BARCBEAN6K_3-SNP2604
BARCBEAN6K_3-SNP0695
BNG91R2
BMd-36
BNG91R2
PVBR93
IAC15
BARCBEAN6K_3-SNP0695
BARCBEAN6K_3-SNP0455
BARCBEAN6K_3-SNP1335
BARCBEAN6K_3-SNP3261
BARCBEAN6K_3-SNP2448
BARCBEAN6K_3-SNP1153
BARCBEAN6K_3-SNP1701
BARCBEAN6K_3-SNP1706
BARCBEAN6K_3-SNP0012
BARCBEAN6K_3-SNP0860
BARCBEAN6K_3-SNP3962

2.58
3.99
3.28
5.13
3.75
5.1
2.9
2.9
5.6
10.5
7.0
5.7
2.8
5.1
5.7
6.5
7.5
3.7
2.7
3.6

Days to maturity

Seed weight

Lodging

Pv03
Pv03
Pv01
Pv03
Pv05
Pv03
Pv05
Pv05
Pv08
Pv03
Pv07
Pv10
Pv05
Pv03
Pv10
Pv09
Pv08
Pv09
Pv06
Pv11

1

2

2

3

4

R (%)

Add

19.7
9.4
10.8
20.4
12.4
23.5
21.3
15.5
31.7
40.0
21.5
19.6
15.0
23.7
19.6
27.6
25.3
15.1
14.0
67.6

-5.07
1.01
-0.62
0.75
1.26
-3.92
-3.96
-3.99
-1.69
0.96
-0.72
0.72
1.12
1.08
0.72
-1.50
-2.00
-1.19
-0.15
-0.51

Primer information for SSR markers online at: http://www.css.msu.edu/bic/PDF/Bean SSR Primers 2007.pdf. InDels available at
www.beancap.org/Indel-Markers-bean-NDSU.xls and SNPs available online www.beancap.org
2
3
LOD: Log of odds. Proportion of the phenotypic variance explained by the QTL at peak LOD using MQM mapping with cofactor
selection.
4
Effect of substituting a single allele from one parent to another. Positive values indicate allele from AN-37 and negative from P02630
98

Yield (SY)
Seven QTL associated with yield were identified on chromosomes Pv02 and Pv05 though
composite interval mapping. In 2007 a QTL in a 16 cM interval between BM142 and Bm187 on
Pv02 that accounted for 11.6% of the observed variation associated with alleles from P02630 had
a LOD score of 3.37. In 2008 a QTL in the same 16 cM interval between BM142 and Bm187 on
Pv02 that accounted for 26% of the observed variation associated with alleles from P02630 had a
of LOD score of 3.9. In 2009 a QTL in the same 16 cM interval between BM142 and Bm187 on
Pv02 that accounted for 30% of the observed variation associated with alleles from P02630 had a
LOD score of 3.25. In 2008 a QTL in a 12 cM interval between BARCBEAN6K_3-SNP1431
and BARCBEAN6K_3-SNP2054 towards the distal end of Pv02 accounted for 16% of the
observed variation associated with alleles from AN-37 and had a LOD score of 3.2. In 2009 a
QTL in the interval between BARCBEAN6K_3-SNP1893 and BARCBEAN6K_3-SNP1107 on
Pv02 that accounted for 13.5% of the observed variation associated with alleles from P02630 had
a LOD score of 2.9. In 2008 a QTL in a 10 cM interval between NDSU_IND_5_28.0255 and
BMd28b on Pv05 that accounted for 32% of the observed variation associated with alleles from
AN-37 had a LOD score of 2.77. In 2009 a QTL in the same 10 cM interval between
NDSU_IND_5_28.0255 and BMd28b on Pv05 that accounted for 30% of the observed variation
associated with alleles from P02630 had a LOD score of 3.24 (Table 3.4, Fig. 3.3).
In addition, QTL for yield were also identified on Pv03, Pv08 and Pv11 using single factor
analysis. In 2009 markers associated with yield on Pv03 spanned from 87-106 cM
(BARCBEAN6K_3-SNP1986-BARCBEAN6K_3-SNP1048) having LOD scores ranging from
2.5-3.4 and accounting for 12-18% of the phenotypic variation with alleles from P02630. In 2009
QTL associated with yield on Pv08 spanned from 14-28 cM (BARCBEAN6K_3-SNP1107-

99

BARCBEAN6K_3-SNP2085) having LOD score of 2.5 and accounting for 11.4% of the
phenotypic variation with alleles from P02630. In 2008 QTL associated with yield on Pv08 at 62
cM (BARCBEAN6K_3-SNP0642-BARCBEAN6K_3-SNP0644 region) had a LOD score of 3.3
and accounted for 17% of the phenotypic variation with alleles from P02630. In 2007 a QTL
associated with yield on Pv11 at 21 cM (BARCBEAN6K_3-SNP3543) had a LOD score of 2.5
and accounted for 13% of the phenotypic variation with alleles from AN-37 (Appendix Single
Marker Analysis)

White Mold Disease Incidence (WM)
Five QTL on chromosomes Pv01, Pv03 and Pv08 were associated with disease incidence in the
field. In 2007 a QTL in a 10 cM interval between NDSU_IND_1_16.1931 and IAC101 on Pv01
that accounted for 33% of the observed variation associated with alleles from AN-37 had a LOD
of score 3.31. In 2010 a QTL in a 20 cM interval between NDSU_IND_3_50.0189 and IAC67
on Pv03 that accounted for 22% of the observed variation associated with alleles from AN-37
had a LOD score of 3.4. In 2009 a QTL in the same 20 cM interval between
NDSU_IND_3_50.0189 and IAC67 on Pv03 that accounted for 28% of the observed variation
associated with alleles from AN-37 had a LOD score of 2.8. In 2010 a QTL in the same 20 cM
interval between NDSU_IND_3_50.0189 and IAC67 on Pv03 that accounted for 19% of the
observed variation associated with alleles from AN-37 had a LOD score of 2.9. In 2010 a QTL in
a 12 cM interval between BMd15 and IAC15 on Pv03 that accounted for 25% of the observed
variation associated with alleles from P02630 had a LOD score of 3.5 (Table 3.4, Fig.3.3).
Using single marker analysis, QTL for white mold disease incidence were also identified on
Pv02 and Pv03. QTL associated with disease incidence in 2008 spanned a 85-122 cM region

100

(BARCBEAN6K_3-SNP1431-BARCBEAN6K_3-SNP3575) on Pv02 with a LOD score of
approximately 3.0 that accounted for 13% of the phenotypic variation from P02630 alleles. QTL
at 36, 99-107 and 122 cM on Pv03 were associated with disease incidence in 2010 (LOD 2.53.3) that explained 12.6-17.9% of the observed variation and the alleles originated from P02630
(Appendix Single Marker Analysis).

Straw Test (WM)
Six QTL associated with greenhouse straw test were identified on chromosomes Pv02 and Pv07
though composite interval mapping. In 2008 a QTL in a 10 cM interval between BM14 and
IAC69 on Pv02 that accounted for 23% of the observed variation associated with alleles from
P02630 had a LOD score of 4.0. In 2009 a QTL in the same 10 cM interval between BM14 and
IAC69 on Pv02 that accounted for 34% of the observed variation associated with alleles from
P02630 had a LOD score of 2.8. In 2010 a QTL in the same 10 cM interval between BM14 and
IAC69 on Pv02 that accounted for 42% of the observed variation associated with alleles from
P02630 had a LOD score of 8.8. In 2007 a QTL in a 10 cM interval between BARCBEAN6K_3SNP1180 and BARCBEAN6K_3-SNP3159 on Pv07 that accounted for 23% of the observed
variation associated with alleles from P02630 had a LOD score of 4.0. In 2008 a QTL in a 15 cM
interval between BMd40 and NDSU_IND_7_34.5855 on Pv07 that accounted for 42% of the
observed variation associated with alleles from AN-37 had a LOD score of 4.0. In 2009 a QTL in
a 13 cM interval between BM46 and NDSU_IND_7_34.5855 on Pv07 that accounted for 40% of
the observed variation associated with alleles from P02630 had a LOD score of 7.0 (Table 3.4,
Fig. 3.3).

101

Days to Flowering
Five QTL associated with the number of days to flowering were identified on chromosome Pv01
Pv03 and Pv05 though composite interval mapping. In 2009 a QTL in a 16 cM interval between
BARCBEAN6K_3-SNP2604 and BARCBEAN6K_3-SNP1579 on Pv01 that accounted for 11%
of the observed variation associated with alleles from P02630 had a LOD score of 3.28. In 2007
a QTL in a 5 cM interval between BARCBEAN6K_3-SNP0695 and BARCBEAN6K_3SNP1049 on Pv03 that accounted for 9% of the observed variation associated with alleles from
AN-37 had a LOD score of 4.0. In 2009 a QTL in a 15 cM interval between BARCBEAN6K_3SNP0695 and BARCBEAN6K_3-SNP1049 on Pv03 that accounted for 20% of the observed
variation associated with alleles from AN-37 had a LOD score of 5.0. In 2010 a QTL in a 15 cM
interval between BNG91R2 and BARCBEAN6K_3-SNP2249 on Pv05 that accounted for 12%
of the observed variation associated with alleles from AN-37 had a LOD score of 3.8 (Table 3.4
and Fig. 3.3).

Days to maturity
Ten QTL associated with the number of days to maturity were identified on chromosomes Pv03,
Pv05, Pv07, Pv08 and Pv10 using composite interval mapping. In 2008 a QTL in a 15 cM
interval between BMd-36 and IAC65 on Pv03 that accounted for 23.5% of the observed
variation associated with alleles from P02630 had a LOD score of 5.1. In 2008 a QTL in a 6 cM
interval between BARCBEAN6K_3-SNP0695 and BARCBEAN6K_3-SNP1049 on Pv03 that
accounted for 40% of the observed variation associated with alleles from AN-37 had a LOD
score of 10.5. In 2010 a QTL in an 8 cM interval between BARCBEAN6K_3-SNP2448 and
BARCBEAN6K_3-SNP2826 on Pv03 that accounted for 24% of the observed variation

102

associated with alleles from AN-37 had a LOD score of 5.1. In 2008, 2009 and 2010 markers
BNG91R2, PVBR93 and BARCBEAN6K_3-SNP3261 on Pv05 associated with days to maturity
explained 15-21% of total variation observed (LOD 2.75-2.93) and the contributing alleles
originated from both parents. In 2008 a QTL in a 3 cM interval between BARCBEAN6K_3SNP0455 and BARCBEAN6K_3-SNP2705 on Pv07 that accounted for 21.5% of the observed
variation associated with alleles from P02630 had a LOD score of 6.9. In 2010 a QTL in a 12 cM
interval between IAC15 and BMd-15 on Pv08 that accounted for 32% of the observed variation
associated with alleles from P02630 and a LOD score of 5.6. A QTL explaining 19.6% of the
total variation in 2008 spanned a 12cM region between BARCBEAN6K_3-SNP1153 and
BARCBEAN6K_3-SNP1335 markers on Pv10 with a LOD score of 5.7 and associated with
alleles from AN-37 (Table 3.4 and Fig. 3.3).

Seed weight
Three QTL associated with 100 seed weight were identified on chromosomes Pv08 and Pv09
through composite interval mapping. A QTL explaining 25% of the total variation in 2008
spanned a 20 cM region between BARCBEAN6K_3-SNP4296 and BARCBEAN6K_3SNP1567 markers on Pv09 with a LOD score of 6.0 and associated with alleles from P02630. In
2010 a QTL in a 10 cM interval between BARCBEAN6K_3-SNP1701 and BARCBEAN6K_3SNP1703 on Pv08 that accounted for 28% of the observed variation associated with alleles from
P02630 had a LOD score of 6.5. In 2010 a QTL in a 9 cM interval between BARCBEAN6K_3SNP0012 and BARCBEAN6K_3-SNP4296 on Pv09 that accounted for 15% of the observed
variation associated with alleles from P02630 had a LOD score of 3.71 (Table 3.4 and Fig. 3.3).

103

Lodging
Two QTL were identified on chromosome Pv06 and Pv11. In 2008 a QTL in a 23 cM interval
between BARCBEAN6K_3-SNP 860 and BARCBEAN6K_3-SNP870 on Pv06 that accounted
for 14% of the observed variation associated with alleles from P02630 had a LOD score of 2.67.
In 2009 a QTL in a 48 cM in the interval between BARCBEAN6K_3-SNP3962 and
BARCBEAN6K_3-SNP5100 on Pv11 that accounted for 68% of the observed variation
associated with alleles from P02630 and had a LOD score of 3.57 (Table 3.4 and Fig. 3.3).

QTL Analysis in AP647 half-sib population
A total of 1016 SNP and 34 SSR markers were segregating in the AP647 population. Eight traits
namely yield, white mold disease incidence, 100 seed weight, days to flowering, days to
maturity, plant canopy height, lodging and greenhouse straw test were analyzed through single
marker analysis and interval mapping (IM). Fourteen QTL spread across 10 linkage groups were
identified in different years associated with seed yield, the greenhouse straw test, 100 seed
weight, and number of days to maturity, number of days to flowering and plant canopy height.
LOD scores ranged from 2.5 to 6.9 for these traits (Table 3.5, Figure 3.5)

104

Table 3.5: QTL for white mold resistance and agronomic traits identified in field and greenhouse environments in pinto bean RIL
population AP647 evaluated in Michigan during 2007, 2010 and 2011
Trait Name
Seed yield

Year QTL Name
AP647
2007 SY3.4
2010
2010
2011

Straw test

2011
2011

Seed weight
Days to maturity
Canopy Height
Days to flowering

2011
2007
2010
2007
2011
2007
2010
2010

AP647

SY1.1

AP647

SY9.3

AP647

SY2.4

WM1.4
WM4.3
WM8.6

AP647
AP647
AP67

Chromosome Indicative Marker

1

2

2

3

LOD

R (%)

Add

Pv03

BARCBEAN6K_3-SNP1547

3.0

45.1

3.0

Pv01

BARCBEAN6K_3-SNP0035

3.2

50.9

4.6

Pv09

BARCBEAN6K_3-SNP1486

2.6

40.7

4.2

Pv02

BARCBEAN6K_3-SNP2187

2.6

18.4

-1.2

Pv01

BARCBEAN6K_3-SNP2824

4.0

35.8

2.3

Pv04

BARCBEAN6K_3-SNP3545

3.4

42.9

2.5

Pv08
Pv05
Pv01
Pv01
Pv07
Pv11
Pv04
Pv06

BARCBEAN6K_3-SNP3948
BARCBEAN6K_3-SNP2049
BARCBEAN6K_3-SNP5327
BARCBEAN6K_3-SNP1909
BARCBEAN6K_3-SNP2437
BARCBEAN6K_3-SNP3331
BARCBEAN6K_3-SNP2342
BARCBEAN6K_3-SNP0006

2.6
2.9
2.7
6.2
5.3
7.0
3.6
3.6

15.0
21.7
15.9
58.0
48.8
42.2
24.2
19.3

4

0.8
1.4
-0.6
5.2
-5.2
-2.9
-0.5
-0.5

1

Primer information for SSR markers online at: http://www.css.msu.edu/bic/PDF/Bean SSR Primers 2007.pdf. InDels available at
www.beancap.org/Indel-Markers-bean-NDSU.xls and SNPs available online www.beancap.org
2
LOD: Log of odds.
3

Proportion of the phenotypic variance explained by the QTL at peak LOD using MQM mapping with cofactor selection.

4

Effect of substituting a single allele from one parent to another. Positive values indicate allele from AN-37 and negative from P02647

105

Seed yield
Four QTL associated with seed yield were identified on chromosomes Pv01, Pv02 Pv03 and
Pv09 through composite interval mapping. A QTL in marker interval BARCBEAN6K_3SNP0035 and BARCBEAN6K_3-SNP5178 at 3 cM on Pv01 had a LOD score of 3.20 that
accounted for 60.9% of the observed variation associated with alleles from AN-37in 2010. In
2011 a QTL in marker interval BARCBEAN6K_3-SNP2187 and BARCBEAN6K_3-SNP0446
at 116 cM on Pv02 had a LOD score of 2.64 that accounted for18.4% of the observed variation
associated with alleles from P02647. In 2007 the QTL in marker interval BARCBEAN6K_3SNP1547 and BARCBEAN6K_3-SNP2524 at position 74 cM on Pv03 had a LOD score of 2.99
that accounted for 75.1% of the observed variation associated with alleles from AN-37. A QTL
in marker interval BARCBEAN6K_3-SNP1486 and BARCBEAN6K_3-SNP2388 at 66 cM on
Pv09 had a LOD score of 2.57 that accounted for 40.7% of the observed variation associated
with alleles from AN-37 in 2010 (Table 3.5, Figure 3.4)

Straw Test
Three QTL associated with greenhouse straw test were identified on chromosome Pv01 Pv04
and Pv08 though composite interval mapping in 2011. A QTL in marker interval
BARCBEAN6K_3-SNP2824 and BARCBEAN6K_3-SNP1630 at position 19 cM on Pv01 had a
LOD score of 4.01 that accounted for 35.8% of the observed variation associated with alleles
from AN-37. A QTL in marker interval BARCBEAN6K_3-SNP3545 and BARCBEAN6K_3SNP0992 at position 18 cM on Pv04 had a LOD score of 3.37 that accounted for 42.9% of the
observed variation associated with alleles from AN-37. A QTL in marker interval
BARCBEAN6K_3-SNP3948 and BARCBEAN6K_3-SNP4375 at position 51 cM on Pv08 had a

106

LOD score of 2.55 and accounted for 15% of the observed variation associated with alleles from
AN-37 (Table 3.5, Figure 3.4).

Number of days to flowering
Three QTL associated with the number of days to flowering were identified on chromosomes
Pv04 Pv06 and Pv11 through composite interval mapping. In 2010 a QTL spanning the interval
between BARCBEAN6K_3-SNP2342 and BARCBEAN6K_3-SNP0666 at 7 cM on Pv04 had a
LOD score of 3.64 that explained 25.2% of phenotypic variation with alleles from P02647. On
Pv06

the

QTL

spanned

the

interval

between

BARCBEAN6K_3-SNP0006

and

BARCBEAN6K_3-SNP1094 at 79 cM with a LOD score of 3.63 that explained 19.3% of
phenotypic variation with alleles from P02647. In 2007 the QTL in marker interval
BARCBEAN6K_3-SNP3331 and BARCBEAN6K_3-SNP4345 at position 68 cM on Pv11 had a
LOD score of 6.97 that accounted for 82.2% of the observed variation associated with alleles
from P02647 (Table 3.5, Figure 3.4).

Number of days to maturity
One QTL associated with the number of days to maturity was identified on chromosome Pv01
though composite interval mapping in 2010 in the marker interval BARCBEAN6K_3-SNP5327
and BARCBEAN6K_3-SNP2322 at position 15 cM. The QTL had a LOD score of 2.66 and
accounted for 15.9% of the observed variation associated with alleles from P02647 (Table 3.5,
Figure 3.4).

107

100 Seed weight
One QTL associated with seed weight was identified on chromosome Pv05 though composite
interval mapping in 2007 in the marker interval BARCBEAN6K_3-SNP2049 and
BARCBEAN6K_3-SNP1785 at position 38 cM. The QTL had a LOD score 2.94 and accounted
for 21.7% of the observed variation associated with alleles for smaller seed size from AN-37
(Table 3.5, Figure 3.4).

Plant canopy height
Two QTL associated with plant canopy height were identified on chromosome Pv01 and Pv07
through composite interval mapping. In 2007 a QTL in marker interval BARCBEAN6K_3SNP1909 and BARCBEAN6K_3-SNP4049 at position 17 cM on Pv01 with a LOD score 6.21
accounted for 58% of the observed variation associated with alleles from AN-37. In 2011 a QTL
spanned the interval between BARCBEAN6K_3-SNP2437 and BARCBEAN6K_3-SNP0462 at
83 and 86 cM on Pv07 had LOD score of 5.33 that explained 49% associated with alleles from
P02647 (Table 3.5, Figure 3.4).

Discussion

Frequency Distributions
The continuous distributions exhibited by all the traits confirm the quantitative nature of
inheritance of the traits measured in this study. Desirable phenotypes beyond the range of either
of the parents were detected in a few individuals and one of the RILs in the AP630 population
that exhibited yield potential combined with high levels of resistance to white mold was released

108

as the new pinto bean cultivar Eldorado (Kelly et al., 2012). The variation in yield and disease
scores across the years emphasizes the need for multi-year testing to map white mold resistance
in bean genetic populations. Transgressive segregation was also observed in both directions for
all the traits in both populations as RILs expressed trait mean values above or below parental
means. These two populations represent an intra-gene pool and intraracial cross and
transgressive segregation in these two populations was advantageous in that it enabled the
phenotypic expression of underlying genetic variation in the parental lines. The higher yields and
lower disease scores in AP630 in comparison to AP647 could be due to better general combining
ability between AN-37 and P02630 parents as opposed to the P02647 parent. Better combining
ability was exhibited as greater yield stability observed across the years with the some of the
same RILs genotypes showing consistent yield in different years. These populations proved
ideal for dissecting complex traits through genetic mapping with P02630 being a better genetic
donor for yield traits while P02647 exhibited improved architectural traits.
Estimates of heritability were similar to those previously reported in other bean populations
(Kolkman and Kelly, 2002; Miklas et al., 2004) underscoring the fact that all favorable
agronomic traits need to be considered simultaneously to advance breeding objectives. Park et
al. (2001) reported low heritability estimates (0.23-0.24) for white mold resistance in the PC50/XAN-159 (PX) population however the population was only evaluated in the greenhouse.
Miklas et al. (2004) also reported low to high heritability estimates (0.21-0.90) in a Bunsiderived F5:8 RIL population (Aztec/ND88-106-04 (AN)) evaluated in multiple locations for
white mold resistance and agronomic traits. The range heritability estimates from different
studies suggests that these estimates depend on the genetic background of the material under
evaluation, the size and the level of homozygosity of the population, and the methods of
109

computing variance components. The PX population has introgression alleles from P. acutifolius
introduced for common bacterial blight (CBB) resistance (Park et al., 2001) while AN population
(Miklas et al., 2004) is an intra gene pool cross similar to the two populations under this study.
The higher heritability estimates observed indicate that selection can be effectively implemented
in advanced RIL populations. Alternatively it would be interesting to compute heritability in
multisite experiments in order to make comparison with results obtained from other studies.
While these populations had RILs with a good representation of medium sized pinto seed, seed
size is rarely used as a selection criterion unless crosses are made among highly contrasting
individuals. The high heritability of seed size observed in these populations could be due to the
reproducibility when measuring the trait in comparison to other traits. Seed size is the most
stable trait among yield components followed by the number of pods per plant (Adams, 1967;
Nienhuis and Singh, 1986). While yield is more variable among environments, the negative
correlation with disease incidence allows breeders to use yield as a basis for indirect selection to
improve white mold resistance. This is likely achieved through disease tolerance mechanisms
where plants are still able to produce high yields even under disease pressure. In comparison to
other studies of more diverse crosses (Blair et al., 2006; Singh et al., 2009; Mkwaila et al., 2011;
Checa and Blair, 2012) yields had moderately higher heritability estimates. White mold is a
disease of highly productive environments where management and climatic factors combine to
optimize yields but create local environmental conditions favorable for disease development. In
order to breed for this situation, breeders need to include yield in their selection criterion as these
are the environments where the disease will strike hardest as low yielding lines that escape
disease do not offer the genetic potential needed in these high productivity environments.

110

Correlation analysis
The results from correlation analysis indicate that there is a complex relationship between
agronomic traits and white mold resistance in the field. White mold incidence clearly reduces
yields, and plant vigor and it might be expected that smaller earlier maturing plants would escape
infection. However our data suggests that the longer plants take to reach physiological maturity
the less likely they will get infected. It is probable that plants that mature later tend to remain
physiologically active longer and are able to ward off late infection of the necrotrophic fungus.
Miklas et al. (2004) demonstrated that the stay-green trait (where stems remain green when pods
have reached physiological maturity and have dried down) in the Bunsi cultivar is the main
mechanism regulating the plants ability to tolerate white mold pressure. This trait is not
detectable in the greenhouse straw test. However variable maturity and dry down is a problem
for farmers requiring the use of plant dessicants when direct harvesting bean as green stems at
maturity do not thresh well and the stem sap can stain the bean seed. The negative correlation
between plant height and lodging is not surprising as canopy height is taken at harvest after the
weaker plants have lodged. This observation could be due to advances made in breeding for
upright Type II pinto beans. The bean plant architecture has been modified from the
predominantly shorter decumbent Type III plants to taller upright Type II plants that enable
direct harvesting (Kelly, 2000). In order to maintain the upright architecture Type II plants must
possess stiff stalks that support the full weight of the plant and resist lodging (Ender and Kelly,
2005). Negative correlations between plant height and lodging have also been demonstrated in a
navy x pinto cross although there was also the environmental effect in different experimental
locations (Miklas et al., 2004). The lack of correlation between the greenhouse straw test with all
other traits is not surprising as lines such as VCW54 exhibit resistance in the greenhouse (Singh

111

et al., 2009) but do not perform well in field conditions. Many genotypes screened in the
greenhouse that show high levels of white mold resistance in the straw test are among the lowest
yielding in the field. The straw test is useful in studies that focus on the genetic and physiological
basis of resistance. It is possible that the straw test is affected by such underlying morphological
factors such as structural support through lignin (thick stems, larger cells), the internode length,
and related traits which may have a negative effect on the number of branches and number of
pods and consequently yield. Stem thickness and internode length could also be affecting lodging
in the field which aggravates white mold infections. Alternatively genotypes that do not have
high infection rates under natural conditions are usually more susceptible when challenged with
the mycelia plug in the greenhouse (Kolkman and Kelly, 2001). Breeders would make more
gains in selecting for resistance by focusing on other agronomic traits or developing other more
informative greenhouse tests which closely mimic natural conditions.

Genetic Linkage Mapping
The map sizes (1183 cM and 953 cM for AP630 and AP647, respectively) obtained from this
study are a reasonable representation of the bean linkage map which is estimated at 1200 cM
(Freyre et al., 1998; Soule et al., 2011). The maps from this study have a higher marker density
and are the first attempt to map QTL for white mold and agronomic traits using SNP markers. In
previous white mold mapping studies, genome coverage has ranged from around 200 to 1000 cM
(Ender and Kelly, 2005; Mkwaila et al., 2011; Perez-Vega et al., 2010). When results of JoinMap
and QTL IciMapping are compared, maps of different sizes and orders were generated. JoinMap
had uneven clustering, low map coverage and inconsistent marker order. Based on marker
segregation these discrepancies could not have resulted from biological reasons such as

112

segregation distortion but rather from different computational algorithms.

In contrast the

recombination counting and ordering (REC) method of QTL IciMapping resulted in a more
consistent map compared with other maps of white mold resistance in common bean (Soule et
al., 2011). However it is possible that QTL IciMapping could have overestimated the effects of
2

QTL as observed by the higher R values. Similar results have been reported in mapping studies
in other crops such as soybean where several algorithms have been employed to improve
mapping and QTL analysis (Kim et al., 2012). Several QTLs were identified using different
methods based on the same genotypic and phenotypic data sets and so the use of multiple
algorithms enhances the reliability of maps and QTL detection.

Yield QTL Analysis

Chromosome Pv01
The only QTL detected on Pv01 for seed yield appears to be unique to the AP647 population and
is named SY1.1

AP647

. No prior QTL for yield have been reported on chromosome Pv01. This

QTL is associated with alleles from AN-37 implying that this locus can be used for selection
against low yielding individuals depending on the segregation of the flanking markers.

Chromosome Pv02
The seed yield QTL identified on Pv02 appears to be similar to the previously described QTL
reported by Blair et al. (2006). The common marker between the two maps is the SSR BM142
but the QTL from this study is responsible for greater variation (26%) than that reported in the
Andean x wild P. vulgaris cross used by Blair et al. (2006). The AP630 population exhibited
113

consistent yield performance across the years and so this QTL is more robust and is supported by
2

the relatively higher R values (11-26%). It is interesting to note that in the IBL population of
Tacana x landrace (TL) a similar QTL was identified for yield under disease pressure SY2.1

TL

(Mkwaila et al., 2011). The QTL is flanked by two markers spaced 16cM apart at the distal end
of Pv02 in the current map. However on the consensus map these markers are spaced further
apart and are located near the middle of the linkage group (Soule et al., 2011). This observation
could be due to the fixed order effect when fitting consensus map using different mapping
populations. The other two QTL associated with SNP markers in AP630 are different from
TL,AP630

SY2.1

AP630

SY2.2

.

In the absence of any overlap between loci these QTL are now named
AP630

and SY2.3

, respectively. The QTL for yield on Pv02 in AP647 is not close to

any previously identified QTL and is designated SY2.4

AP647

.

Chromosome Pv03
QTL for yield were also identified on Pv03 by Blair et al. (2006) near SSR markers BM172 and
BM98 and by Beattie et al. (2004) based on RAPD markers grown in different environments.
The QTL from this present study was from AN-37 parent in a single environment and is
AP647

designated SY3.4

.

Chromosomes Pv05 and Pv09
The first QTL reported for yield was identified in navy bean RIL population (Tar’an et al., 2002)
and was associated with SSR marker BNG161. Beattie et al. (2004) also reported two QTL for

114

yield on the same chromosome in a different navy bean RIL mapping population. The yield
QTL on Pv05 identified in this study is different as no QTL for yield have been reported in its
proximity on chromosome Pv05 and therefore is designated SY5.4

AP630

. Similarly, two QTL

have been reported on Pv09 (Tar’an et al., 2002; Blair et al., 2006) and differ from the QTL in
this study which is designated SY9.3

AP647

.

Other researchers have also reported yield QTL on most linkage groups of the bean map. Blair et
al. (2006) and Mkwaila et al. (2011) also identified seed yield QTL on a predominantly SSR map
on Pv04 while Tar’an et al. (2006) found yield QTL on Pv11. Kolkman and Kelly (2002)
identified QTL for yield on Pv07 based on RAPD markers while Wright and Kelly (2011)
J115

reported a major yield QTL SY10.2

on Pv10 using TRAP markers.

All the QTL associated with yield could be further investigated by studying other yield related
components like number of pods per plant. Although seed size is a yield component there was no
co-localization observed with yield QTL. The large number of yield QTL on the bean genetic
linkage map confirms the complex nature of the trait and indicates the pleiotropic effect these
loci may have on each other. The large numbers, small effects and possible interaction between
QTL presents a challenge for breeders if the QTL has only been detected in a single environment
or year. However there is also opportunity to implement QTL pyramiding and introgression to
improve the efficiency of MAS. Since SNP markers have specific known positions in the
genome and map at a high density they can be employed as markers for candidate gene analysis.

115

White mold QTL Analysis

Chromosome Pv01
The QTL for white mold resistance on Pv01 does not share markers with either WM1.1
WM1.2

GC

AG,XC

or

previously mapped by Miklas et al. (2001), Perez-Vega et al. (2012) and Maxwell et

al. (2007). The two former AG and GC populations share a common parent G122 which has
been associated with resistance in both the straw test and disease avoidance in the field due to a
porous canopy. The open porosity of G122 is attributed to the determinate locus fin however
canopy porosity is also influenced by low plant vigor (Miklas et al., 2001). The QTL from this
study designated WM1.3

AP630

is possibly associated with disease avoidance as Bunsi was the

grandparent and used as the source of the resistance. This is demonstrated in greenhouse trials
where Bunsi is ranked as susceptible in the straw test while in the field it exhibits disease
avoidance through open canopy (Kolkman and Kelly, 2003; Ender and Kelly, 2005) and the stay
green trait (Miklas et al., 2004). The QTL associated with disease resistance in the greenhouse
observed in the AP647 population in the greenhouse had alleles from AN-37 parent and is named
WM1.4

AP647

.

Chromosome Pv02
The major QTL WM2.2 previously identified in many studies is likely to be the same as the one
identified here as WM2.2

BN,HN,BR,AN,R31,BV,AP630

. This QTL has been attributed to both

field and greenhouse resistance detected by the straw test. In the BN (Bunsi/Newport) population
the observed resistance was from Bunsi while in the HN (Huron/Newport) population the
116

resistance was conferred by C-20 grandparent (Kolkman and Kelly, 2003). Both Bunsi and C-20
exhibit disease avoidance through open canopy porosity and upright plant architecture. In the BR
(Bunsi/Raven; Ender and Kelly, 2005), AN (Aztec/ND88-106-04; Miklas et al., 2007), R31
(Raven/I9365-31; Soule et al., 2011) populations, the QTL was also associated with reduced
white mold incidence in the field. However, in the BV (Benton/VA19; Soule et al., 2011)
population the resistance was only detected in the greenhouse straw test and non-wounding tests.
The current study supports the finding that the loci has mechanisms associated with both types of
resistance (Soule et al., 2011). This is important as our greenhouse phenotypic data did not
exhibit any correlation with field disease scores. The region on Pv02 has defense genes such as
chalcone synthase, pathogenesis related proteins and polygalacturonic inhibitor proteins which
are induced following infection with many pathogens including S. sclerotiorum.

Chromosome Pv03
The QTL identified on Pv03 in AP630 is associated with similar markers as WM3.1

AN

identified in a navy pinto cross (Aztec/ND88-106-04, AN; Miklas et al., 2007). The AN-37
parent of this current study was a white mold germplasm line released from the AN mapping
population (Miklas et al., 2006). This QTL is likely the same QTL and is therefore named
WM3.1

AN,AP630

.

Chromosome Pv04
The QTL identified on Pv04 in AP647 is a new locus and is in close proximity to a QTL for days
to flowering. This chromosome has QTL associated with both the straw test and white mold
incidence in the field. The other QTL identified on the same linkage group are WM4.1
117

PX

and

WM4.2

R31,TL

(Park et al., 2001; Soule et al., 2011; Mkwaila et al., 2011). The new QTL from

this study is designated WM4.3

AP647

.

Chromosome Pv07
The QTL WM7.6

AP630

on Pv07 was associated with markers BM209 and BARCBEAN6K_3-

SNP1180 which are >30 cM away from other known QTL on this chromosome. The previous
reported QTL were associated with resistance to specific isolates of S. sclerotiorum in the
genotype Xana (Perez-Vega et al., 2012). The resistance associated the WM7.1

AG,PX,XC

QTL

that resides near the Phs seed protein locus on Pv07 is unknown. The resistance observed in
these populations was derived from the Andean G122 genotype, the most commonly used
resistance check in multisite trials (McCoy et al., 2012). Kolkman and Kelly (2003) and Ender
and Kelly (2005) also identified WM7.2

BN,BR

oxalic acid in the greenhouse. WM7.2

BN,BR

QTL based on field resistance and resistance to
share the Bunsi resistance while WM7.3

R31

possesses resistance from I9365-31 derived from an interspecific cross between P. vulgaris/P.
coccineus (Soule et al., 2011; Miklas et al., 1998). Both WM7.4

XC

and WM7.5

TL

were

associated with the straw test in their respective populations (Miklas et al., 2013). Both the Phs
locus identified by Miklas (2007) and the AFLP markers by Ender et al. (2008) have been used
to introgress resistance into Middle American beans. Selection with WM7.6

AP630

QTL could be

improved by cloning an InDel marker NDSU_IND_7_34.5855 near the loci BM209. The QTL at
AP630

the lower end of the chromosome is another new QTL designated WM7.7

118

.

Chromosome Pv08
The QTL identified on Pv08 in AP630 was associated with white mold resistance in the field in
2008. With further validation this is the fifth QTL on this linkage group and is designated
WM8.5

AP630

.

Two of previous QTL WM8.3

B60,GC,BV

and WM8.4

BR,GC,R31

share the

common Bunsi source of resistance. In the B60 population (Bunsi/NY6020-4 snap bean; Miklas
et al., 2003) resistance was based on both straw test and field disease resistance while in BR
(Bunsi/Raven) (Ender and Kelly, 2005) resistance was based on field disease incidence only. The
QTL detected in the AP647 population is at the distal end on the chromosome and is new locus
designated WM8.6

AP647.

The colocalizing of QTL for resistance seem to suggest that the genes

controlling resistance to white mold are part of a similar pathway whether the resistance is
observed in the field or in the greenhouse.

Days to flowering and maturity
The QTL identified on Pv01 in AP630 associated with days to flowering was located on the
same region as the QTL for seed weight. The QTL for days to flowering were also linked to the
number of days to maturity and seed yield in two genomic regions of Pv03. Similarly days to
flowering is also associated with the days to maturity on Pv05. All the alleles from P02630 and
P02647 were associated with late flowering and possibly longer days to maturity. Other
researchers have also identified several loci on the P. vulgaris map associated with flowering.
Blair et al. (2006), and Perez-Vega et al. (2011) have mapped QTL on Pv01, Pv02, Pv06, Pv09,
and Pv11. The number of days to flowering is an important phenological trait not only in relation
to disease resistance and yield but also to other abiotic stresses. In both populations AN-37
flowered earlier and the QTL on Pv05 and the proximal end of Pv03 were influenced by alleles
119

from this parent. All the QTL on Pv04, Pv06 and Pv11 were associated with late flowering in
AP647.
QTL for days to maturity have been mapped previously to Pv01, Pv02 and Pv06 (Perez-Vega et
al., 2010) using mostly AFLP markers and those on Pv09 and Pv11 were based on a growth habit
(GH) morphological marker (Tar’an et al., 2002). The QTL identified on Pv05 in AP630
population appears to be different from that of Blair et al. (2006) which explained 37% total
variation in a single environment. The QTL on Pv01 (AP647) and on the proximal end of Pv03
and Pv05 (AP630) can be used in MAS in selecting for early maturity while QTL for maturity on
the distal regions of Pv03, Pv05 and Pv10 (AP630) are associated with longer days to maturity
coming from the white mold resistant AN-37 parent.

Plant canopy height and lodging
The QTL for plant canopy height on Pv01 in AP647 is a new locus as no QTL for plant height
have been reported on this chromosome. The QTL colocalized with a QTL for shorter days to
maturity. On Pv07 the QTL for plant height could be similar to the one identified in the
Bunsi/Newport population (Kolkman and Kelly, 2002). Plant canopy height is important in
disease avoidance in common bean. A tall plant canopy is associated with more air circulation
and plants that are less likely to be in contact with the moist soil thereby avoiding infection.
Lodging and plant height are closely related in that taller but weak plants tend to lodge more
while upright types with stiff stems tend to remain upright. Canopy height measurements are
taken at harvest time when weaker stemmed plants have already lodged and this explains why
high canopies are correlated with low disease. Lodging helps to create a microclimate under the
decumbent canopy which limits air flow and enhances white mold infections in the field.

120

Conclusion
This study suggests that moderate levels of white mold resistance have been transferred from
navy bean into type II pinto beans. Agronomic traits exhibited moderate heritability estimates
indicating that advances in breeding simultaneously for multiple traits can be made. However
correlations between greenhouse and field disease scores were very weak similar to data
observed in other mapping populations. The straw test is useful in screening diverse genotypes
when trying to identify potential sources of resistance however this resistance has to be
confirmed in the field. This is the first high density mapping study of white mold resistance with
SNP markers in common bean. The markers associated with QTL for white mold resistance on
Pv02, Pv03 and Pv07 can be used for selection or as indicators of regions that warrant future
genomic analysis to identify biological factors involved in conferring the resistance to white
mold in common bean.

121

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122

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CHAPTER 4

PRELIMINARY ANALYSIS OF DEFENSE GENES IN RESPONSE TO WHITE MOLD
INFECTION IN DIFFERENT BEAN (PHASEOLUS VULGARIS) CULTIVARS

Introduction
The development of resistant varieties for quantitatively inherited disease is a challenge to most
breeders. White mold (Sclerotinia sclerotiorum) resistance in common bean (Phaseolus vulgaris)
is known to be a complex trait (Fuller et al., 1984) such that dissection of resistance mechanisms
requires quantitative trait loci analysis. QTL analysis relies on detecting genotypic differences in
trait means at a marker locus, based on the principle of association between the phenotype and
genotype of markers. Several QTL studies have been conducted on different genetic
backgrounds; using different marker systems to identify white mold resistance in beans (Soule et
al., 2011). The focus of QTL analysis however is shifting from simply discovery of QTL
associated with quantitative disease resistance to determining the biological function underlying
the QTL (Kelly and Vallejo, 2005). The interpretation of QTL results is usually limited due to
lack of information on the genetics, biochemistry and physiology underlying trait expression.
Understanding the function of quantitative resistance genes and their mode of expression is
necessary to enable breeders to select which QTL to deploy in marker assisted selection. Some
QTL explain a significant amount of variation but are associated with plant avoidance
mechanisms which confound the discovery of physiological resistance (Kelly and Vallejo, 2005).
QTL that have mapped to the same regions as major resistance genes or defense response in
plants provide a bridge in the understanding the functional significance of QTL. Such QTL also

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serve as a link in identifying candidate genes that influence disease resistance (Geffroy et al.,
2000; Pflieger et al., 2001).
The QTL associated with white mold resistance on chromosome Pv02 has shown consistent
expression in several different independent studies (Kolkman and Kelly, 2003; Ender and Kelly,
2005, Soule et al., 2011) including the current study. This QTL has not been highly correlated
with disease avoidance traits indicating that these loci are associated with physiological disease
resistance. The physiological disease resistance is expressed both in the field and greenhouse
tests. The QTL in this region is in close proximity with three defense response genes namely
chalcone synthase, the pathogenesis related gene PvPR-2 and polygalacturonase inhibiting
protein (PGIP). PGIP are important in inhibitors of cell wall degrading enzymes produced by
most pathogens. The cell wall is the primary barrier that pathogens must overcome in the
infection process. Chalcone synthase catalyses the reactions in the biosynthesis of antimicrobial
phytoalexins which are low molecular weight compounds locally produced in response to
infection (Hahlbrock and Scheel, 1989). PR proteins are also induced in response to infection or
wounding or abiotic stress. Endochitinase and glucanase (located on Pv09) are PR proteins that
are induced by fungal infection (Lamb et al., 1989). Phenylalanine ammonia lyase (PAL) which
catalyzes the conversion of L-phenylalanine into trans-cinnamate is also important in the defense
pathway in response to pathogen attack (Cramer et al., 1989). It is a member of a multigene
family that is located on Pv01 and Pv07 chromomsomes. The detection of QTL for disease
resistance in similar genomic regions seems to suggest that these proteins are involved in host
defense following S. sclerotiorum infection in beans.
The expression of most defense genes varies in different tissues and in timing of expression.
Most genes exhibit low constitutive expression levels. PGIP have been shown to be variably

129

expressed in bean plants (Salvi et al., 1990; Dovidio et al., 2004). In Brassica napus the PGIPs
are differentially regulated in response to Sclerotinia infection and hormone treatment which
demonstrates that these genes are indeed an important part of the plant defense response
(Hegedus et al., 2008).
Bean lines with complete resistance to white mold have not been reported however partial
resistance has been identified in specific cultivars (Kolkman and Kelly, 2002; Miklas and
Grafton, 1992; Miklas et al., 2006). Since there are no specific resistance genes associated with
white mold, it is likely that the physiological resistance detected in greenhouse tests is primarily
dependent on defense response genes. Several sources of partial resistance that have been
identified (Bunsi, G122, PC 50, AN 37, A195) and have been used as parents in developing
mapping populations to study the inheritance of white mold. These genotypes show significant
differences in disease reaction from susceptible genotypes such as Beryl and Matterhorn which
exhibit high infection in the greenhouse screening straw test (Soule et al., 2011).
The objective of this research was to investigate the role of PGIP, Glucanase, and PAL genes in
the defense response of different genotypes following infection with Sclerotina sclerotiorum.

Materials and Methods
Four cultivars of beans AN-37, G122, P02630 and Beryl were chosen based on their different
reaction to white mold in the straw test. G122 is cranberry landrace from India (Miklas et al,
2001), AN-37 is pinto line derived from a navy line carrying resistance from Bunsi a source of
resistance to white mold (Miklas et al., 2006). Both of these cultivars are moderately resistant,
while Beryl (a great northern) and P02630 (MSU pinto breeding line) are susceptible. The plants
were grown in the greenhouse under the same conditions as when conducting the straw test.

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Briefly plants were inoculated 21 days after planting as described by Petzoldt and Dickson
(1996), with the following modifications. The growing tip of the plant was excised with a razor
blade and discarded. Petri dishes of two to three day old cultures of S. sclerotiorum on PDA
were used as the inoculum source. The PDA plugs were placed in the top end of 10 uL pipette
tips, which were then applied over the cut stem. A PDA plug without fungus was first placed in
the pipette tip followed by a PDA plug with fungus in order to prevent the fungal plug from
drying out prior to infection of the plant. The plug was affixed to the freshly cut edge of the
stem with the pipette tip remained in contact with the stem for 8 days. Control plants were
wounded without infection. All pots were kept at 25°C in the greenhouse. Tissue sampling was
conducted from both the necrotic part and the chlorotic area of each lesion within 2 cm of the
lesion edges at 0, 24, 48 hours post inoculation (hpi) and 7 days post-inoculation (dpi) and
immediately frozen in liquid nitrogen prior to RNA extraction. All samples collected had three
biological replicates.
RNA was extracted from plant tissues using both Qiagen RNeasy Plant Kit and the standard
Trizol protocol (Invitrogen). To remove any genomic DNA contamination, RNA was treated
with RNAse-free DNAse I (Promega) followed by enzyme inactivation (2.5 mM EDTA,
65°C/10 min) and ethanol precipitation.
The primers used in this experiment were as shown in Table 4.1. They were designed based on
exons such that DNAse treatment was needed to avoid amplification of genomic DNA.

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Table 4.1: Primers designed and used in semi quantitative reverse transcription-polymerase
chain reaction (RT-PCR) for amplifying defense-associated genes of Phaseolus vulgaris
Gene

Pvpgip 1

Forward Primer Reverse Primer 5’ PCR
Annealing
Accession
5’
product Temperature Number
o
size
( C)
TCTTTGAGAAC CGTCGAATGTG 240
55
AJ864506.1
TGCACT
ATTCCTC

Phenyl
ammonia
lyase

AAGCCATGTCC
AAAGTGCTG

GAGTTCTCCGT
TGCCACCT

240

64

M11939

Glucanase

GCTGTAAGGGC CCAAGTACACA
TCAAGGCCTC
CGTGCGTTGTC

427

65

X53129

Actin

CACCGAGGCA
CCGCTTAATC

55

AB067722

CGGCCACTAGC
GTAAAGGGAA

126

First strand cDNA was synthesized using 2 μg DNAse-treated total RNA using superscript III
Reverse Transcriptase (Invitrogen), following the supplier recommendations. 5 μL of first strand
cDNA was amplified in a final reaction volume of 25 μL containing 1X Taq DNA polymerase
buffer, 1.5 mM MgCl2, 0.2 mM dNTPs, 0.2 μM specific primers, and 5 U Taq DNA polymerase
(Invitrogen). PCR was initially performed at 94°C for 4 min, and then 27 cycles as follows: 94°C
for 1 min, 55°C for 1 min, and 72°C for 1 min. The annealing temperatures were adjusted for
each primer to obtain specific amplification of expected band size. PCR products were resolved
by electrophoresis on a 3% w/v agarose gel. Two replicates of reverse transcription PCR
experiments were performed for all genes analyzed. Gene expression levels were analysed via
density analysis of Gel Doc EZ imager and Image Lab™ software. Amplified product intensity
was expressed as relative absorbance units. The ratio between the relative absorbance units
132

determined for the amplified gene of interest and internal control (Actin) was calculated to
normalize for initial variations in sample concentration and as a control for reaction efficiency (ie
the same intensity in PCR reactions halted at the exponential phase).
Results
Three of the cultivars inoculated with the pathogen S. sclerotiorum showed significant
differences in disease progression and in final disease scores taken at 8 days. There was no
significant difference between AN-37 and P02630 genotypes (Table 4.2).

Table 4:2 Effect of inoculation of the cultivars with Sclerotinia sclerotiorum

Genotype

1

AN-37

Disease score
3a

BERYL

7b

G122

1c

P02630

4a

1

Greenhouse straw tests on the cultivars were rated on a scale of 1- 9, where 1 = no infection and
9 = total plant collapse.
Scores followed by different letters are significantly different from each other (p <0.05).

Defense gene expression
All the results for the non-wounding experiments showed similar expression patterns to the 0 hpi
time line in the challenged plants so they are not presented separately.

Glucanase
In general there was no glucanase expression (Figure 4.1 and 4.2) detected in the cultivars AN37 and P02630 while some low levels of transcription took place at 0 hpi. Gene expression
133

peaked at 24 hpi for AN-37 and G122 and at 48 hpi for P02630. Beryl which had much lower
expression at these time points had no expression at 48hpi. At 7 days all the cultivars had some
level of glucanase present including Beryl which regained expression and P02630 had the
strongest signal relative to the other lines.

0 hpi

24 hpi

48 hpi

7 dpi

AN-37

BERYL

G122

P02630

ACTIN

Figure 4.1 RT-PCR product analysis for the glucanase gene, indicating the differences in signal
strength at four different time points in four bean cultivars challenged with Sclerotinia
sclerotiorum. PCR products, amplified by the primer pairs as described under Materials and
Methods, were separated on a 3 % agarose gel.

134

Glucanase Expression
Relative Absorbance X10000

12
10
8
AN-37

6

BERYL

4

G122

2

P02630

0
0 hdpi

24 hpi
48 hpi
Time of Infection

7 dpi

Figure 4.2: Cultivar-specific differential expression of glucanase gene in bean plants infected
with Sclerotinia sclerotiorum at four time points. Error bars indicate the standard error of means
of two biological replicates of the experiment

Phenylalanine Ammonia Lyase (PAL)
Overall phenylalanine ammonia lyase had low expression levels in response to S. sclerotiorum
inoculation (Figure 4.3 and 4.4) There was no transcription in response to wounding at 0 hpi in
all the cultivars however Beryl and G122 peaked at 24 hrs while the most abundant transcripts
were at 48 hrs in the two pinto lines AN-37 and P02630. All four cultivars retained some enzyme
expression at 7 days.

135

0hpi

24hpi

48hpi

7dpi

AN-37

BERYL

G122

P02630

ACTIN

Figure 4.3: RT-PCR product analysis for the phenylalanine ammonia lyase gene, indicating the
differences in signal strength at four different time points in four bean cultivars challenged with
Sclerotinia sclerotiorum. PCR products, amplified by the primer pairs as described under
Materials and Methods, were separated on a 3 % agarose gel.

136

PAL Expression

Relative Absorbance X10000

16
14
12
10

AN-37

8

BERYL

6

G122

4

P02630

2
0
-2

0 hdpi

24 hpi
48 hpi
Time of Infection

7 dpi

Figure 4.4 Cultivar -specific differential expression of phenylalanine ammonia lyase gene in
bean plants infected with Sclerotinia sclerotiorum at four time points. Error bars indicate the
standard error of means of two biological replicates of the experiment

Polygalacturonase Inhibitor Protein 1
There was also variable expression of PGIP1 in the different cultivars. In contrast to the other
genes PGIP was induced with different signals in response to wounding at 0 hpi. The resistant
line G122 showed the strongest signal at 0 and 48 hpi while AN-37 expressed the gene at 48 hpi
only. At 7 dpi there was no signal except in P02630 (Figure 4.5 and4.6).

137

PvPGIP 1

0hpi 24hpi 48hpi 7dpi

AN-37

BERYL

G122

P02630

ACTIN

Figure 4.5: RT-PCR product analysis for the polygalacturonase inhibitor protein 1 PvPGIP1
gene, indicating the differences in signal strength at four different time points in four bean
cultivars challenged with Sclerotinia sclerotiorum. PCR products, amplified by the primer pairs
as described under Materials and Methods, were separated on a 3 % agarose gel.

138

Pvpgip1 Expression

Relative Absorbance X10000

30
25
20
AN-37

15

BERYL

10

G122
P02630

5
0
0 hdpi

24 hpi
48 hpi
Time of Infection

7 dpi

Figure 4.6 Cultivar -specific differential expression of polygalacturonase inhibitor protein 1
PvPGIP1 gene in bean plants infected with Sclerotinia sclerotiorum at four time points. Error
bars indicate the standard error of means of two biological replicates of the experiment

Discussion
This study was mainly focused on the cultivar specific response of three defense genes following
inoculation with S. sclerotiorum. The expression patterns exhibited by the three genes confirm
that they have a role in defense response following infection with S. sclerotiorum. D’ovidio et al.
(2006) showed that members of the bean PGIP family differ in their response to Phytopthora
megasperma. PGIP 2 was the most abundant transcript in their experiment and PGIP1 was only
induced in wounded plants. Oliveira et al. (2010) showed that PGIP1 reaches peak expression
levels at 72 and 96hpi similar to our results. Our study shows the effect of genotype on the
expression of PGIP 1. The other variation could be due to the differences in response to actual
fungal infection in our study in contrast to using elicitor molecules like salicylic acid and glucans

139

employed by D’ovidio et al. (2006). The induction of PAL at 48 hrs suggests that some
phytoalexins play a role in plant defense following S. sclerotiorum infection (Cramer et al.,
1989). However based on relative amounts of transcripts in comparison to the other two genes
this pathway may only offer generalized defense response.
Glucanase is an important pathogenesis related (PR) protein whose induction has been shown to
be systemically spread to different plant parts following induction (Mauch and Staeheline, 1989).
The most distinct temporal expression was in the pinto lines AN-37 and P02630 suggesting that
it is a distinct defense response however the availability of transcripts in the other genotypes at 0
hpi indicates that the gene also responds to wounding.
On a whole these results confirm the quantitative nature of different levels of resistance to white
mold infection observed in different bean cultivars. These genes could be deployed in
overexpression experiments however due to cultivar difference the results may be variable.

140

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143

APPENDIX

144

Pv01

Pv02

Pv03

Pv05

Pv07

Bm181

13.9

Bm53

24.6

IAC101

33.5

IAC79

51.3

Bm200

64.7

IAC16

80.1

0.0

Bm187

0.0
8.5

IAC64
IAC65

18.2

Bm142

18.3

133M2
BNG91R2

Bm157

24.0

50.7

Bmd18

69.1

IAC69

98.9

IAC90

93.6
100.6

116.2

Bmd24

115.4

Bm148
IAC59

31.8
41.8
44.1

IAC70
GATS54
GATS11

64.6

IAC67

73.5

IAC68

89.1

Bm138

37.2

BMd28a
BMd28b

73.2

PVBR93

22.5

BMd-22

44.1
51.4

BMd-42
pvAT001

61.8

BMd-40a

77.0
84.7
92.1
97.3

BMd-53a
BM209
PVat007
BM213

108.2

PVat008b
BM171
BM210
PVBR269

IAC3

51.2
59.6

BM156
BM46

117.4

IAC66

0.0
8.2

0.0
6.0

129.7
138.1

0.0

Pvcct001

Bmd17

98.4
103.2

Bmd50
Bmd20

Figure A. 1 SSR MAP

145

Pv08

0.0
6.9
14.3
18.4
26.4
29.3
34.8
37.1
40.7
42.2
47.8
52.9
61.6
70.1
75.9
80.6
90.1
100.7

Bm20
Bmd15
IAC15
Bm189
Pvatt001
Pvatg007
Bm202
IAC71
Bmd36a
Bm25
FJ11
PVBR173
Bm79b
Bm185
Bm211
Bmd9
Bmd37
Bm189

Fig A.1 (cont’d).

146

Table A.1. Single Marker Analysis for white mold and yield in AP630

Trait Name
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
YIELD8
WHITEMOLD8
WHITEMOLD8
WHITEMOLD8
WHITEMOLD8
WHITEMOLD8
WHITEMOLD8
WHITEMOLD10
WHITEMOLD10
WHITEMOLD10
WHITEMOLD10
WHITEMOLD10
WHITEMOLD10
WHITEMOLD10
WHITEMOLD10

Marker
1
Name
SNP1048
SNP1050
SNP1047
SNP1991
SNP1992
SNP1993
SNP1990
SNP5118
SNP1989
SNP1986
SNP4002
SNP4187
SNP3065
SNP3916
SNP1728
SNP1729
SNP2069
SNP2068
SNP695
SNP1049
SNP1045
SNP1046
SNP1926
SNP1925
SNP3987
SNP1991
SNP1992
SNP1993
SNP1990
SNP5118
SNP1989
SNP433
SNP432
SNP4002
SNP4187
SNP3065
SNP3916
SNP1728
SNP1729

2

23

4

R (%) Add
Chromosome Position LOD
3
106.49
2.73
13.82
1.41
3
106.49
2.73
13.82
1.41
3
106.49
3.38
17.92
1.61
3
85.52
2.58
12.96
1.55
3
85.52
2.65
13.08
1.57
3
85.52
2.66
12.85
1.56
3
85.52
2.65
13.08
1.57
3
85.52
2.66
12.85
1.56
3
85.52
2.58
12.96
1.55
3
87.17
2.68
14.05
1.54
3
99.81
2.83
13.20
1.37
3
99.81
2.66
12.91
1.36
3
99.81
2.66
12.91
1.36
3
99.81
2.65
13.34
1.38
2
100.34
2.65
12.80
1.35
2
100.34
2.64
13.03
1.37
2
101.43
2.59
12.38
1.33
2
101.43
2.53
12.53
1.34
2
103.68
2.50
13.75
1.42
2
106.49
3.24
16.50
1.54
3
106.49
3.27
16.63
1.55
3
106.49
3.27
16.63
1.55
3
106.49
2.78
13.89
1.41
3
106.49
2.73
13.82
1.41
3
106.49
2.73
13.82
1.41
3
85.52
2.92
13.56
-2.25
3
85.52
3.02
13.82
-2.28
3
85.52
2.93
13.26
-2.25
3
85.52
3.02
13.82
-2.28
3
85.52
2.93
13.26
-2.25
3
85.52
2.92
13.56
-2.25
3
36.22
2.67
13.08
-6.50
3
36.75
2.67
13.03
-6.46
3
99.81
2.89
13.43
-6.55
3
99.81
2.63
12.60
-6.34
3
99.81
2.63
12.60
-6.34
3
99.81
2.95
14.64
-6.84
3
100.34
3.37
16.11
-7.19
3
100.34
3.61
17.40
-7.48
147

Table A.1 (cont’d)
1

2

23

4

Marker Name
R (%) Add
Trait Name
Chromosome Position LOD
WHITEMOLD10 SNP2070
3
101.43
3.50
16.47 -7.26
WHITEMOLD10 SNP2069
3
101.43
3.28
15.03 -6.93
WHITEMOLD10 SNP2068
3
101.43
3.72
17.87 -7.58
WHITEMOLD10 SNP3711
3
101.43
3.19
15.50 -7.06
WHITEMOLD10 SNP696
3
103.68
2.87
14.16 -6.80
WHITEMOLD10 SNP695
3
103.68
3.05
15.00 -7.01
WHITEMOLD10 SNP1049
3
106.49
3.33
16.30 -7.23
WHITEMOLD10 SNP1045
3
106.49
3.02
15.29 -7.01
WHITEMOLD10 SNP1046
3
106.49
3.02
15.29 -7.01
WHITEMOLD10 SNP1926
3
106.49
3.20
14.94 -6.91
WHITEMOLD10 SNP1925
3
106.49
3.34
15.67 -7.08
WHITEMOLD10 SNP3987
3
106.49
3.34
15.67 -7.08
WHITEMOLD10 SNP1050
3
106.49
3.34
15.67 -7.08
WHITEMOLD10 SNP1047
3
106.49
2.69
14.00 -6.73
WHITEMOLD10 SNP1048
3
106.49
3.34
15.67 -7.08
WHITEMOLD10 SNP1902
3
107.03
2.79
13.13 -6.49
WHITEMOLD10 SNP2826
3
122.97
3.21
14.73 -6.87
STRAW1
SNP1425
7
143.72
3.05
17.23 -0.50
YIELD8
SNP644
8
62.25
3.32
16.61 -1.54
YIELD8
SNP643
8
62.25
3.32
16.61 -1.54
YIELD8
SNP642
8
62.25
3.32
16.61 -1.54
YIELD9
SNP4246
8
14.94
2.51
10.89 -1.08
YIELD9
SNP2952
8
16.02
2.80
12.17 -1.15
YIELD9
SNP3786
8
16.56
2.60
11.66 -1.09
YIELD9
SNP3588
8
16.56
2.69
11.45 -1.09
YIELD9
SNP1706
8
19.26
2.99
13.57 -1.19
YIELD9
SNP1702
8
19.26
2.80
12.17 -1.15
YIELD9
SNP2951
8
19.26
2.80
12.17 -1.15
YIELD9
SNP1701
8
19.26
2.80
12.17 -1.15
YIELD9
SNP1708
8
20.35
2.61
11.43 -1.10
YIELD9
SNP1707
8
20.35
2.61
11.43 -1.10
YIELD9
SNP1705
8
20.35
2.99
12.75 -1.15
YIELD9
SNP2085
8
28.16
2.66
11.57 -1.14
YIELD7
SNP3543
11
21.73
2.52
13.10
1.32
1
Primer information for SNPs available online www.beancap.org
2

LOD: Log of odds.

3

Proportion of the phenotypic variance explained by the QTL at peak LOD using MQM mapping
with cofactor selection.
4
Effect of substituting a single allele from one parent to another. Positive values indicate allele
from AN-37 and negative from P02647.
148