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3:: ‘ Iiiiiiiiflilii
" LIBRARY "
Mlchlgan State
University

 

 

 

This is to certify that the

dissertation entitled .

GENETIC DISSECTION OF RESISTANCE TO WHITE MOLD
(SCLEROTINIA SCLEROTIORUM) IN COMMON BEAN (PHASEOLUS VULGARIS)

presented by
JUDITH MARIE KOLKMAN

has been accepted towards fulfillment
of the requirements for ]

DOCTOR OF PHILOSOPHY degree in Plant Breeding and l
Genetics Program
Department of Crop and
Soil Sciences

%@ 7%

/ Major professor /
Date fi7‘ X " O 0 ‘

MSU is an Affirmative Action/Equal Opportunity Imtitution 0-12771

 

PLACE IN RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE

DATE DUE

DATE DUE

 

 

tug/1t w

 

 

 

 

 

 

 

 

 

 

 

 

 

11/00 W14

GENETIC DISSECTION OF RESISTANCE TO WHITE MOLD
(SCLEROTINIA SCLEROTIOR UM) IN COMMON BEAN (PHASEOL US VULGARIS)

By

Judith Marie Kolkman

A DISSERTATION

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

DOCTOR OF PHILOSOPHY

Plant Breeding and Genetics Program
Department of Crop and Soil Sciences

2000

ABSTRACT

GENETIC DISSECTION OF RESISTANCE TO WHITE MOLD
(SCLEROTINIA SCLEROTIORUM) IN COMMON BEAN (PHASEOLUS VULGARIS)

By

Judith Marie Kolkman

White mold, caused by Sclerotinia sclerotiorum (Lib.) De Bary, is a destructive
yield-limiting fiingal disease of common bean (Phaseolus vulgaris L.). Breeding for
resistance in bean offers a stable, long-term strategy to reduce yield loss to white mold in
common bean. The objectives of this study were to i) determine if oxalate, a primary
pathogenicity factor of S. sclerotiorum, could be used to indirectly screen for
physiological resistance to white mold in bean, ii) study the inheritance of resistance to
white mold, iii) dissect the relationship between agronomic avoidance mechanisms and
physiological resistance in three populations, and iv) identify markers linked to
quantitative trait loci (QTLs) conferring resistance to white mold in bean, using selective
multivariate genotyping based on single and multiple phenotypic traits.

An indirect greenhouse test for physiological resistance to white mold was
developed by evaluating for host resistance to oxalate (OR). Cut bean seedlings were
placed in a 20 mM oxalate solution in the greenhouse, and rated based on differences in
wilting response. Resistance to oxalate in 27 elite genotypes was correlated to field
ratings of white mold disease severity index (DSI; r = 0.58“) and disease incidence (DI;
r = 0.57"), and negatively correlated to yield (r = - 0.50”). New exotic sources of

resistance to white mold were identified using the oxalate test.

Two genetic populations segregating for resistance, and one advanced line
population, were evaluated for OK DSI, DI, and agronomic avoidance traits in multiple
environments. Heritability estimates were moderate for DSI (0.49) and DI (0.42), and
low for OR (0.19) in a 98-entry Bunsi/Newport (BN) population; heritability estimates
were higher for DSI (0.82) and DI (0.76) and moderate for OR (0.54) in a 28-entry
Huron/Newport (HN) population. Resistance to oxalate was significantly correlated to
D81 and D1 in an advanced line population of 27 entries but not in the BN or HN
populations. Different agronomic avoidance mechanisms were correlated to D81 and D1
in each of the genetic populations that were not significant factors in the advanced line
population.

The BN population was evaluated for markers linked to QTLs for resistance to
white mold in the field and greenhouse. Markers were identified using selective
multivariate genotyping comprised of DNA bulking strategies using genotypes fi'om the
extreme phenotypes for single and multiple traits. Markers linked to QTLs for resistance
to S. sclerotiorum were identified in each of the DNA bulked screening methods, and were
consistent across field environments and populations. In the BN population, the most
significant marker on linkage group B2 was associated with D81 (12%) and D1 (13%),
while individual markers on linkage group B7 were associated with OR (9%), D81 (17%),
D1 (13%), and yield (3 7%). A unique locus for detemtinate growth habit in navy bean
was located on B7. In the HN population, one marker on B2 was also associated with
DSI (40.3%) and DI (35.4%), while one marker on B7 was associated with OR (24.3%)
and yield (47.0%). Markers identified in this study will be used in marker-aided breeding

for resistance to white mold in common bean.

ACKNOWLEDGMENTS

There are many people I need to acknowledge who have been a very important
part of my research and personal life at Michigan State University. First, I must express
my gratitude to my major advisor, Dr. J .D. Kelly, who has been a remarkable mentor and
example to follow. His knowledge regarding Phaseolus and plant breeding is
unsurpassed, and I am fortunate to have had such a strong individual as an advisor. I want
to thank him for his patience when field sites met with disaster, when I was experimenting
with new greenhouse techniques or taking more field observations than I probably needed,
for teaching me how to write papers, and for giving me room to grow as an individual. I
wish to thank my committee members, Dr. Rebecca Grumet, Dr. Ray Hammerschmidt,
Dr. Pat Hart, Dr. Brian Diers, and Dr. Mitch McGrath, for their warm smiles, open doors,
and interesting discussions. I would particularly like to thank Dr. Ray Hammerschmidt for
helping me see through the white mold fog during my first year at MSU.

The field research component of my research was quite intense, and I wish to
thank all of the individuals who assisted me in trying to establish white mold trials. I
would like to thank Jerry, Norm, Dick, Greg, and the Thayer family for assistance in
creating successfiil white mold trials - which hasn’t been taken for granted! I would like

to thank Kelly, Missy, Renate, Roger, and Nat for taking time to assist me in field ratings -

iv

which hasn’t been taken for granted!

1 thank Sam Hazen for teaching me how to run AF LPs, and then watching me do
so in his lab for months and months. I thank the individuals in the lab - Maeli, Kristin,
Halima, Esther, Mark, Marcio, Veronica, Ricardo, Roberto, and Shitaye, for their daily
banter. I thank the individuals in the Lenski lab for great fun and research discussions. I
thank Dr. Jim Hancock for getting me hooked into the running game, and for being an
incredibly enthusiastic supportive individual - it has not been taken for granted. I thank
Dr. Richard Allison, and the individuals in his lab for their fiiendship. I also would like to
acknowledge my appreciation for having an office by Dr. Joanne Whallon, who is a good
friend and mentor.

I thank my fiiends and family, for their love, support, fiiendship, and laughter
shared during my stay at MSU. I want to thank the Thayers for providing me with one of
my soulmates - Shamu. I would also like to personally thank my other soulmate, Chris,
for his unending support and understanding. A person could be so lucky to have these

two soulmates.

TABLE OF CONTENTS

LIST OF TABLES ..................................................... vii
LIST OF FIGURES ...................................................... x
INTRODUCTION ....................................................... 1
References ...................................................... 18
CHAPTER 1
AN INDIRECT TEST USING OXALATE TO DETERMINE PHYSIOLOGICAL
RESISTANCE TO WHITE MOLD IN COMMON BEAN ...................... 26
Introduction ..................................................... 26
Materials and Methods ............................................. 28
Results and Discussion ............................................ 31
References ...................................................... 42
CHAPTER 2
RELATIONSHIP OF AGRONOMIC TRAITS AND RESISTANCE TO WHITE MOLD
IN THREE BEAN POPULATIONS ........................................ 46
Introduction ..................................................... 46
Materials and Methods ............................................. 48
Results ......................................................... 54
Discussion ...................................................... 66
References ...................................................... 73
CHAPTER 3

MOLECULAR MARKER DISSECTION OF QTLS CONFERRING RESISTANCE TO
WHITE MOLD AND GROWTH HABIT IN TWO NAVY BEAN POPULATIONS . . 76

Introduction ..................................................... 76

Materials and Methods ............................................. 79

Results and Discussion ............................................ 85

References ..................................................... 1 15
APPENDIX

Appendix A .................................................... 122

Appendix B .................................................... 124

vi

LIST OF TABLES

Table 1.1 Mean squares of the greenhouse oxalate test scores, and
the field ratings for disease severity index and disease
incidence, for 27 bean genotypes across three

environments. .............................................. 32
Table 1.2 Ratings of resistance to oxalate in the greenhouse oxalate

test for three individual tests and the combined test scores

for 27 common bean cultivars across the three tests. ................ 34
Table 1.3 Pearson correlation coefficients of oxalate test ratings to the

disease severity index, disease incidence, and yield for

common bean genotypes tested in three greenhouse oxalate

tests and corresponding field tests at the Montcalm

Research Farm. ............................................ 35

Table 1.4 Field ratings of white mold disease severity and incidence
for the 27 bean cultivars in three individual field tests and
combined field tests across the three tests (years), at the
Montcalm Research Farm in 1996, 1997, and 1998. ................ 36

Table 1.5 Ratings of resistance to oxalate in the greenhouse oxalate
test for selected wild, landrace and cultivated Phaseolus
vulgaris gerrnplasm. ......................................... 37

Table 2.1 Analysis of variance for resistance and agronomic traits in
the advanced line population, for 27 common genotypes
tested across three greenhouse assays and three field
environments (1996-1998). ................................... 55

Table 2.2 Analysis of variance for resistance and agronomic traits in
the Bunsi/Newport population, tested across three
greenhouse assays and three field environments (1997-
1998). .................................................... 56

vii

Table 2.3

Table 2.4

Table 2.5

Table 2.6

Table 2.7

Table 2.8

Table 2.9

Table 2.10

Table 2.11

Table 3.1

Analysis of variance for resistance and agronomic traits in
the Huron/Newport population, tested across three
greenhouse assays and four field environments (1996-

1998) ...............................................

Means and ranges for resistance and agronomic traits for
the advanced line population tested across individual and

combined environments .................................

Means and ranges for resistance and agronomic traits for
the Bunsi/Newport population tested across three individual

environments. .........................................

Means and ranges for resistance and agronomic traits for
the Huron/Newport population tested across individual

..... 57

.58

..... 59

environments. .............................................. 60

Parental and progeny means, progeny ranges, and estimates
of heritability (hz) for resistance and agronomic traits in the
Bunsi/Newport and Huron/Newport populations tested

across combined environments. ................................ 61

A comparison of Pearson correlation coefficient (r) for
resistance to oxalate, disease severity index, and disease
incidence to resistance and agronomic traits between the 27
common genotypes in the advanced line population, and the

BN and HN populations across combined environments. ............ 62

Pearson correlation coefficient (r) for resistance to oxalate,
disease severity index, disease incidence, and agronomic
traits in the individual environments for the advanced line

population. ................................................ 63

Pearson correlation coefficient (r) for resistance to oxalate,
disease severity index, disease incidence, and agronomic
traits in the individual environments for the Bunsi/Newport

population. ...........................................

Pearson correlation coefficient (r) for resistance to oxalate,
disease severity index, disease incidence, and agronomic
traits for individual environments in the Huron/Newport

population. ...........................................

DNA pooling strategies based on single or multiple traits ......

viii

..... 64

..... 65

..... 83

Table 3.2

Table 3.3

Table 3.4

Table 3.5

Table 3.6

Table A7

Table A8

RAPD and AF LP markers and corresponding linkage group
designation, identified through selective multivariate
genotyping with 4 sets of resistant and susceptible DNA
bulks: Multivariate (M), Disease Severity Index (S),
Disease Incidence (I), and Resistance to Oxalate (O) (‘-’ =

no polymorphic bulk) ...............................

Summary of markers identified on linkage group B2 in the
BN population, found to be most closely linked to the QTL
for significant resistance and agronomic traits including
phenotypic variability associated with the marker in
combined (P<0.01) and individual environments, effect of
the presence of the marker across environments, and

SMG/DNA bulking strategy used to identify marker. ......

Summary of markers identified in the BN population, to be
most closely linked to the QTL for resistance to oxalate
(OR), including phenotypic variability (P<0.01) associated
with the marker, effect of the presence of the marker, and

SMG/DNA bulking strategy used to identify the marker. . . .

Summary of markers identified on linkage group B7 in the
BN population, to be most closely linked to the QT L for
significant resistance and agronomic traits, including
phenotypic variability associated with the marker in
combined (P<0.01) and individual environments, the effect
of the presence of the markers on the phenotype, and the

SMG/DNA bulking strategy used to identify marker. ......

Phenotypic variability (P<0.01) for OR, DSI, DI and yield
associated with markers in HN p0pulation in combined and

individual environments. ............................

Chi-square goodness of fit test for segregation of RAPD

and AF LP markers on Linkage Groups B2 and B7. .......

Chi-square goodness of fit test for segregation of RAPD
and AF LP markers on Putative Linkage Groups B3 and B8.

ix

......... 87

......... 92

......... 95

......... 96

........ 110

........ 122

........ 123

Figure 3.1

Figure 3.2

Figure 3.3

Figure 3.4

LIST OF FIGURES

Linkage map with 13 RAPD markers, 10 AF LP markers,

and one phenological marker, constructed using

Mapmaker/Exp, form 98 F 3-derived lines form the

Bunsi/Newport population. Distance are in Kosambi cM

units, and are listed on the left-hand side of the linkage

groups. Locations of known genes are listed in italics.

Linkage groups B2, B7, B3 and B8 and gene location

correspond to the integrated linkage map (Freyre et al.,

1998). .................................................... 86

Phenotypic distributions for 98 lines of the Bunsi/Newport

population evaluated for resistance to oxalate, disease

severity index, disease incidence, and agronomic traits that

were associated with resistance traits (see Chapter 2)

including: days to flowering, architecture, lodging, days to

maturity, seed size, and yield. Location of mean values for

Bunsi (B) and Newport (N) are located at arrows ................... 9O

LOD scores for interval mapping analysis of i) disease

severity index, ii) disease incidence, and iii) lodging on

Linkage Group B2 in the Bunsi/Newport population. LOD

scores above the threshold level (—) indicate experiment-

wise error rate of 0.05, determined using 1000

permutations. The x-axis corresponds to centimorgan

distance and marker intervals located on Figure 3.1. ................ 91

LOD scores for interval mapping analysis of i) disease

severity index, ii) disease incidence, and iii) resistance to

oxalate on Linkage Group B7 in the Bunsi/Newport

population. LOD scores above the threshold level (—)

indicate experiment-wise error rate of 0.05, determined

using 1000 permutations. The x-axis corresponds to

centimorgan distance and marker intervals located on

Figure 3.1. ................................................ 94

Figure 3.5

Figure 3.6

Figure 8.1

LOD scores for interval mapping analysis of i) days to

flowering, ii) seed size, iii) lodging, and iv) yield on

Linkage Group B7 in the Bunsi/Newport population. LOD

scores above the threshold level (—) indicate experiment-

wise error rate of 0.05, determined using 1000

permutations. The x-axis corresponds to centimorgan

distance and marker intervals located on Figure 3.1. ................ 99

LOD scores for interval mapping analysis of resistance to

oxalate on Linkage Group B3 (tentative) in the

Bunsi/Newport population. LOD scores above the

threshold level (—) indicate experiment-wise error rate of

0.05, determined using 1000 permutations. The x—axis

corresponds to centimorgan distance and marker intervals

located on Figure 3.1 ........................................ 101

Phenotypic distribution across four environments for 28

recombinant inbred lines of the Huron/Newport population

evaluated for marker association to disease severity index, disease
incidence, resistance to oxalate, and yield (see Chapters 2

and 3). Location of mean values for Huron (H) and

Newport (N) are located at arrows ............................. 124

xi

INTRODUCTION

White mold, caused by the ascomycete, Sclerotim'a sclerotiorum (Lib.) De Bary,
is a destructive yield-limiting fungal disease that seriously affects common bean
(Phaseolus vulgaris L.) production in temperate regions (Steadman, 1983; Haas and
Bolwyn, 1972, Purdy, 1979; Wallen and Sutton, 1967). S. sclerotiorum has a wide host
range of over 400 plant species, including many other important crop species, such as
canola (Brassica napus L.), sunflower (Helianthus annuus L.), alfalfa (Medicago sativa
L.), soybean [Glycine max (L.) Merrill] and peanut (Arachis hypogaea L.; Boland and
Hall, 1994). White mold epidemics in common bean occur during seasons of high yield
potential. Total seed yield is reduced due to lower number of seeds produced per plant,
reduced number of pods per plant and smaller seed size (Kerr et al., 1978; Steadman,
1979). In a recent survey of Michigan dry bean production, 64% of the 80 respondents
indicated that white mold was the number one disease problem, followed by root rot
(23%), bacterial blight ( 17%), and anthracnose (10%) (DiFonzo, 2000).

White mold infections are initiated during and after flowering, when canopy
closure produces microclimate conditions that stimulate germination of the stipe and
development of apothecia from soil-bome sclerotial bodies. Microclirnate conditions that
influence development of apothecia include 20 to 25 °C temperatures and an extended
period of soil and leaf wetness (Abawi and Grogan, 1975; Boland and Hall, 1987a;
Grogan and Abawi, 1975; Weiss et al., 1980a; Weiss et al., 1980b). Ascospores disperse
into the plant canopy and initially require a carbon and nitrogen nutrient source, such as

senescent flower blossoms, for germination. Senescent flowers have been identified as

1

the primary source of infection in common bean (Natti, 1971; Tu; Abawi et al., 1975 ;
Haas and Bolwyn, 1972 Hunter et al., 1978; Cook et al., 1975). Ascospores cannot infect
healthy green leaf tissue (Sutton and Deverall, 1983; Tariq and J effiies, 1984), but will
infect senescent leaf tissue (Purdy, 195 8). Secondary infections involve the direct
infection of mycelial growth (myceliogenic germination) fi'om sclerotial bodies onto
senescent leaf tissue (Tu, 1989a). Less than 10% of the senescent bean flowers identified
on the ground, however, had been infected by mycelium produced directly from sclerotial
bodies (Cook et al., 1975), indicating that secondary infections in bean is much less
significant than flower blossom infections.

Developing mycelia proceed to infect the plant by exuding copious amounts of
oxalic acid into the plant tissue (Maxwell and Lumsden, 1970). Oxalate chelates calcium
from the pectate fiaction of the xylem and associated pit vessels (Sperry and Tyree,

198 8), causing the entry of air leading to xylem embolism and subsequent wilting.
Oxalate was identified as a primary mode of pathogenesis for S. sclerotiorum (Godoy et
al., 1990). Non-oxalate producing mutants of S. sclerotiorum were not able to infect bean
leaf tissue, whereas mutant isolates that reverted to normal oxalate-production regained
the ability to infect the bean leaf. The exuded oxalate provides an optimal pH for
function of the polygalacturonases produced by the pathogen during the infection process
(Marciano et al., 1983; Lumsden, 1976). Endo-polygalacturonase was found to be
abundant within 24 hours of infection of bean leaves and was associated with the
advancing margins of young lesions. It was suggested that the endo-polygalacturonase
contributes to the hydrolysis of the middle larnellae of bean cells (Lumsden, 1976). In
apple tissue infected with S. sclerotiorum, this effect was identified one to two cells in

2

advance of the hyphae. Pectin methylesterase was also detected early in infection in
advancing hyphae. The invading mycelium may play an important role by making
substrate more available for the polygalacturonases. Exo-polygalacturonase, associated
with the growth of the fungus, appears at lesion margins after 48 hour, and was associated
with mycelial dry weight (Lumsden, 197 6).

Populations of S. sclerotiorum are predominately clonal. Mutation, and
occasional genetic exchange and recombination provide new sources for variability
(Kohli and Kohn, 1998). Sixty-four isolates of S. sclerotiorum, originating fi'om 17
different dicot host species in seven different countries, were evaluated for virulence
using a leaf assay, and for genetic diversity using random amplified polymorphic DNA
(RAPD). Eighty-nine percent of the isolates were statistically similar in virulence, and
RAPD marker data did not differentiate intraspecific virulence variation (Steadman et al.,
1999). RAPD and virulence data failed to differentiate isolates from different hosts, or
different geographic origin. Variation in virulence found between isolates has been
contributed to oxalate secretion (Dutton and Evans, 1996). Hypovirulence found in S.
sclerotiorum was associated with the presence of double-stranded RNAs, and due to
reduced or delayed production of oxalic acid (Zhou and Boland, 1999).

Symptoms of white mold on bean plant include wilting, lesions, bleached stems,
and the presence of sclerotial bodies. The firngal mycelium proceeds to spread throughout
the plant and produces hardened sclerotial bodies, which are tightly packed mycelial
bodies. The sclerotial bodies are returned to the soil in the crop residue and provide the
inoculum source for the next season. In a three year period, sclerotial bodies that were
planted at 5, 12.5, and 20 cm depths were able to produce apothecia in culture ensuring

3

longevity of inoculum in a three year crop rotation (Cook et al., 1975).

Agronomic management practices that result in reduced production of apothecia,
less exposure to inoculum, or less development of disease, were found to contribute to
reduced white mold in the field environment. A decrease in plant row width, resulting in
higher plant density increased the levels of white mold in common bean (Park, 1993;
Steadman et al., 1973). The relationship between high-yielding environments and
reduced white mold potential is complex. Agronomic management practices that aim to
maximize yield potential, typically provide optimal conditions for white mold infection.
Higher yields have been associated with low to moderate levels of white mold infection.
Heavy white mold pressure, however, has been found to severely reduce yield potential
(Kerr et al., 1978). Optimizing yield often involves the use of overhead irrigation.
Irrigation increases canopy density, thereby increasing soil surface moisture, leaf wetness,
and humidity within the microclimate (Weiss et al., 19803; Blad et al., 1978).

Microorganisms have been studied as biological agents to control sclerotial
producing pathogens. Coniothyrz'um minitans Campbell is one example of a
hyperparasite that has been investigated for control of sclerotinia wilt in sunflower,
caused by S. sclerotiorum (Huang, 1992). The direct application of C. minitans into the
soil furrow of naturally infested field sites was found to reduce the incidence of
sclerotinia wilt in sunflower by 42 to 56% (Huang, 1980). In a monoculture of a
susceptible crop, such as sunflower, white mold incidence was found to decrease, due to
the presence of this parasitic fungus.

Protection of flowers and competition for nutrients on flowers by micro-
organisms has also been studied as a method of biocontrol. In a greenhouse study,

4

mutant cultures of Epicoccum purpurascens offered improved protection bean flowers to
S. sclerotiorum ascospore infection compared to wild type strains (Zhou and Reeleder,
1990). Ascospore germination on flowers was inhibited in bean when blossoms where
sprayed with Erwinia herbicola. A short duration, up to 1 day, of protection is offered by
the bacterial strains, and population levels were found to be dependent on environmental
conditions, such as temperature. The short duration of ascospore germination inhibition,
as well as the difficulties in sustaining an adequate population level at temperatures
conducive to S. sclerotiorum infection limit the usefulness of the bacterial strains for
biocontrol (Y uen et al., 1994).

Fungicide application for white mold control has met with limited success.
Greenhouse studies indicate that fungicide (benomyl) application on bean leaves (not the
flowers) did not protect plants fi'om being infected by ascospores. Protection of bean
fiom ascospore infection was shown to occur only when the flowers were sprayed with
fungicide (Hunter et al., 1978). Field studies have indicated that spraying of fungicides,
such as benomyl, a few days before full bloom, provided effective control of white mold.
Benomyl provided control for up to 9 days past spraying on senescent and dead blossoms
(N atti, 1971). Application of benomyl was more effective at decreasing white mold
severity at pre-bloom than full bloom and was not effective at the post bloom stage. In
another study, application of benomyl was most effective at firll bloom (Morton and Hall,
1989). The most efficient control of white mold was dependent upon the number of
flowers that received fungicide protection. Fungicide application is costly and must be
timed in order for maximum flower blossom coverage. Application of ftmgicides for
control of white mold is low in Michigan dry bean production. In 1999, Benlate

5

(benomyl) was applied to only 11.4% of 23, 000 bean acres surveyed, most likely due to
a dry season. The number of acres sprayed with fungicide for control of white mold is
generally low considering white mold was cited by growers as the major disease problem
of dry bean (DiFonzo, 2000).

Herbicides offer a potential alternative measure for control of white mold, through
potential reduction of inoculum, or suppressed disease levels. The treatment of sclerotial
bodies with triazines, such as atrazine, resulted in an increase in the amount of carpogenic
germination. Apothecia development, however, was either abnormal or absent (Casale
and Hart, 1986; Radke and Grau, 1986). The application of the herbicide Lactofen
(Cobra) was found to reduce infection levels of white mold in soybean fields with heavy
disease pressure (Dann, et al., 1999). Glyceollin accumulation was identified in soybean
leaves treated with lactofen, and corresponded to reduced lesion size in a leaf assay
inoculated with S. sclerotiorum (Dann, et al., 1999). An induced resistance response was
observed in soybeans treated with 2,6—dichloroisonictinic acid (INA) and
benzothiadiazole (BTH). Greenhouse leaf assays, and field trials determined that
susceptible genotypes had a reduced level of white mold when treated with INA or BTH
(Dann et al., 1998).

The development of bean cultivars with resistance to white mold is an effective
strategy in reducing yield losses to white mold. Physiological resistance to white mold
has been described in several navy bean cultivars, such as Bunsi (also known as Ex Rico
23) and C-20, (Schwartz etal., 1987; Miklas et al., 1992; Tu 1985; Kelly et al., 1984).
Mechanisms of physiological resistance may include several factors, including protection
by cutinase (Parker and Koller, 1999), phytoalexin production (Sutton and Deverall,

6

1984), and resistance to oxalic acid (Tu, 1985; Tu, 1989). Bean leaves and hypocotyls
treated with ascospores exhibited a hypersensitive reaction, with phaseollin and
phaseollidin phytoalexins accumulating in leaf tissue, and kievitone accumulating in the
hypocotyls. Treatment of bean leaves and hypocotyls with mycelium produced water-
soaked lesions, with no phytoalexin production in the leaf tissue, and only kievitone
production in the hypocotyls. Phaseollin and kievitone production increased when
mycelium-treated hypocotyls were transferred from a 18 to 28°C environment (Sutton and
Deverall, 1984).

Oxalic acid has been useful in screening for white mold resistance in several
crops. An initial leaf test demonstrated that sunflower cultivars were sensitive to oxalic
acid and differentiated a response that corresponded to field resistance (Noyes and
Hancock, 1981). Gerrninating seeds on an oxalate-based medium proved to be a useful
tool in selecting for resistance to S. sclerotiorum in alfalfa and crimson clover (Trzfolium
incarnatum; Rowe, 1993). In soybean, excised stems of 12 cultivars were placed in vials
containing a 40 mM oxalic acid solution (W egulo et al., 1998) and rated for lesion length.
Correlation coefficients of field resistance among the 12 cultivars and 24 analyses of the
oxalic acid stem test were highly variable. In order to increase physiological resistance to
white mold in oilseed rape (Brassica napus), an oxalate oxidase gene isolated fi‘om barley
(Hordeum vulgare L.) roots was introduced into oilseed rape via transformation
(Thompson et al., 1995). Leaves fiom transgenic plants that showed oxalate oxidase
activity were found to have resistance to an oxalate solution, as compared to control plant
reactions.

Common bean has shown differentiation in response to oxalate. The uptake of

7

oxalic acid by petioles of excised primary leaves of a resistant cultivar, Bunsi was shown
to be much slower than that of two susceptible cultivars, Kentwood and Seafarer (Tu,
1985). Lesion area was also shown to correspond in this fashion, in a range of oxalate
concentrations fiom 1 mM, to 80 mM. Structural damage to the plasma membranes and
chloroplasts was more severe in the susceptible ‘F leetwood’, compared to the resistant
cultivar, Bunsi (Tu, 1989b). Chloroplast degradation was also found in beans exposed to
oxalic acid secreted by the pathogen (Tariq and Jeffries, 1985).

A change in plant architectural traits was found to influence levels of white mold
in common bean (Coyne, 1980). Bunsi, an indeterminate navy bean, has an open porous
canopy which has been associated with reduced levels of white mold in the field (Tu and
Beversdorf, 1982; Park, 1993). Upright indeterminate navy beans escape white mold
infection or spread, in comparison to determinate navy bean types, due in part to a
narrower canopy, resulting in a drier microclimate underneath the canopy (Park, 1983).
Alternatively, the open, porous canopy of larger-seeded determinate beans was identified
as an architectural avoidance mechanism in semi-arid production regions (Coyne et a1,
1974; Weiss et al., 1980a; Schwartz et al., 1987). Dense canopies generally resulted in
higher white mold severity than porous canopies, due to the development of a favorable
microclimate within the canopy (Blad et al., 1978; Coyne, 1980). Fewer apothecia were
produced underneath the open determinate canopy of the dark red kidney bean cultivar,
Charlevoix, and the upright canopy of the indeterminate small white bean, Aurora,
compared to the dense canopy of several prostrate type III great northern bean cultivars
(Schwartz and Steadman, 1978). Many factors influence the production of apothecia and
ascospores, the infection of flowers, and the subsequent spread of disease throughout the

8

plant tissue. The onset of flowering and production of apothecia, however, generally
occur at canopy closure under appropriate moisture conditions (Boland and Hall, 1987).
The interaction between management practices, such as row width, and plant architectural
traits, such as an open porous canopy, or upright architecture can affect levels of white
mold in the field.

Sclerotinia stem rot has become an increasingly important disease affecting
soybean production in recent years (Wrather et al., 1997). Senescent flowers are initial
infection sites, and microclimate conditions, such as wet soil and wet canopy conditions
at flowering, are required for infection (Grau, 1988). Certain cultivars of soybean have
partial resistance to white mold, including both physiological resistance and escape
mechanisms. Flowering date, plant architecture, and maturity have been identified as
plant avoidance mechanisms to white mold infection in soybean (Boland and Hall, 1987;
Nelson, 1991; Kim et al., 1999). Under field conditions, heritability estimates of
resistance determined in an F 3-derived soybean population ranged fiom 0.30 to 0.71, and
was 0.59 across all field environments. The moderate heritability estimate suggests that
progress can be made in selecting for resistance to white mold in soybean.

S. sclerotiorum can infect the roots, stems, leaves, terminal bud, and capitulurn of
sunflower, causing sclerotinia wilt, stem rot, and heat rot. Sclerotinia wilt generally
originates fiom myceliogenic germination (Huang and Hoes, 1980), whereas the infection
of the head is caused by infection of ascospores. The fungus can infect the plant root
system and stem at an early seedling stage, wheras, the head is susceptible to infection
later in the season. Sclerotinia wilt generally has two cycles for infection. The first cycle

begins with myceliogenic germination from sclerotial bodies onto seedlings via the roots

9

or stems near the surface. The second cycle occurs with the onset of budding and
flowering (Huang and Kozub, 1990). In sunflower, two types of resistance have been
suggested. The first type of resistance is defined as resistance to the penetration of S.
sclerotiorum into the plant. The second type of resistance refers to a resistance to the
extension of mycelium in the plant tissues (Castano et al., 1993). The first plant with an
established root infection becomes the primary infection locus, and infection can spread
from plant to plant via root contact. Factors, such as plant spacing, can inhibit the quick
spread of disease from plant to plant (Huang and Hoes, 1980). Range in heritability
estimates for resistance in sunflower depends upon resistance trait measured, such as leaf
lesion (0.30), petiole score (0.73), stem rating (0.57), stem lesion (0.59), and disease
stems (0.36) (Degener etal., 1998). The moderate heritability estimates across several
disease parameters indicate that selection for resistance to white mold is possible in
sunflower.

Resistance to white mold in bean is complexly-inherited (Fuller et al., 1984;
Miklas and Grafton, 1992). In general, genotype x environment interaction play a major
role in the expression of resistance of white mold in bean. The number of genetic and
environmental factors that can influence the degree of white mold in the field can be
designated into three categories: i) factors that affect the amount of inoculum produced,
ii) factors that affect the exposure of the plant to inoculum, and iii) factors that determine
how quickly the fungus can spread throughout the plant. Factors that affect the amount of
inoculum produced include both management strategies and avoidance strategies. Wider
row widths, open porous plant canopies, and upright plant architecture are three examples
of factors that influence the rrricroclimate underneath the canopy. An increase in the air

10

flow underneath the canopy leads to a drier microclimate, which may reduce the
apothecia production. The second category, includes factors that affect the exposure of
the plant to inoculum. The ascospores require a minimum amount of time of leaf
wetness, conditioned by soil moisture and humidity within the plant canopy. An open,
porous canopy may reduce humidity and the time for initial infection. Ascospores require
senescent flowers for infection, therefore factors affecting flowering characteristics will
affect exposure to inoculum. Initial primary infections are typically senescent flowers
that lodge on stems (Tu, 1989a). Flowers are the initial infection sites, and methods to
protect these are useful in reducing infection levels. The effectiveness of fungicides in
reducing white mold depends upon the ability to efficiently protect the flowers (N atti,
1971; Morton and Hall, 1989; Hunter et al., 197 8). Other factors that may reduce the
exposure of the plant to inoculum include biological competition by organisms such as
Epicoccum, for nutrients on flowers (Zhou and Reeleder, 1990). The third category
influencing resistance to white mold involves factors that affect how fast the fungus
spreads throughout the plant. Physiological resistance mechanisms, such as phytoalexin
production (Sutton and Deverall, 1984) and resistance to oxalate (Tu, 1985), reduce the
rate at which the fungus can infect the plant. Microclirnate may also be a factor in this
category, since, temperature and humidity may affect spread of fungus. A hot, dry
environment is not conducive to fungal growth. Complete resistance to white mold in
bean is not known. Instead, phytoalexin production and/or resistance to oxalate may be
more effective in an environment where the spread of fungus is less rapid due to
microclimate conditions that limit its virulence. Breeding for resistance to white mold
must take into account the various factors which affect the production of inoculum, the

11

exposure of the plant to inoculum, and the resistance of a plant to firngal development
once infection has established. Combining genes for resistance to white mold should
include those that affect avoidance mechanisms as well as those that control physiological
resistance mechanisms.

Progress in breeding for resistance in common bean is hindered by environmental
conditions and factors that confound the expression and detection of physiological
resistance mechanisms. In the field, the detection of physiological resistance can be
masked by architectural avoidance mechanisms, such as an open canopy or upright
architecture, in which the microclimate within the canopy limits production of inoculum
and subsequent infection (Schwartz and Steadman, 1978; Blad et al., 1978; Park, 1993).
Improvement of resistance to white mold in bean, therefore, must take into account the
ability to identify and select individual genotypes with physiological resistance to white
mold, and agronomically-desirable architectural avoidance mechanisms in advanced
breeding trials and differentiating populations. Heritability estimates for resistance to
white mold in bean were found to be lower for physiological resistance, versus field
resistance. In three different populations, estimates of heritability for a lesion length stem
assay were 0.27, 0.38, and 0.66, whereas the estimates for the same three populations in
the field were 0.77, 0.58, and 0.70, respectively (Miklas and Grafton, 1992). Progress in
selection for resistance to white mold in the field environment should be feasible with the
moderate to high estimates of heritability. Lower estimates for physiological resistance,
as measured in the lesion length assay, indicate that physiological resistance is most
likely complexly—inherited, and significantly influenced by the environment. Higher
selection intensity may be required to select for physiological resistance, particularly in

12

the populations with low heritability.

Selection of complexly-inherited traits, such as resistance to white mold, may be
facilitated by the identification of single factors linked to or associated with complex
traits, but unaffected by environmental variation. The association between markers and
quantitatively-inherited traits was first reported between seed coat patterns/color and seed
size of beans. Linkage between bean seed color and size allowed for the indirect
selection of seed size (Sax, 1923). In breeding for disease resistance, markers allow the
plant breeder to select for disease resistance traits without having to handle highly
variable and virulent pathogens (Kelly, 1995). The increased utilization of molecular
markers linked to economically important traits of interest in plants has allowed for the
indirect identification and selection of quantitative trait loci (QTL) (Beckmann and
Soller, 1983; Darvasi and Soller, 1994; Tanksley et al., 1989).

Markers linked to QTLs conferring resistance to white mold have been identified
in both soybean and sunflower. In soybean, three QTLs were associated with disease
severity index (DSI), accounting for 8, 9, and 10 % of the phenotypic variability for DSI
across environments. Two of the QTLs were also associated with plant avoidance
mechanisms, such as plant height, lodging, and date of flowering (Kim and Diers, 2000).
In sunflower, four QTLs were associated with leaf resistance and two QTLs were
associated with capitulum resistance to S. sclerotiorum. One of the QTLs was associated
with both leaf and capitulum resistance. The QTLS accounted for up to 60% of the leaf
resistance, and up to 38% of the capitulum resistance. Agronomic traits, such as seed
weight and oil content were found to have overlapping regions with QTL regions. Apical
branching pattern was suggested as exhibiting the best resistance to infection of the

13

capitulum (Mestries et al., 1998). The association between days to flowering and
resistance to S. sclerotiorum in sunflower was found to be dependent upon the population
(Castano et al., 1993). Plant avoidance mechanisms may play an important role in
resistance to S. sclerotiorum in sunflower.

Marker-assisted selection (MAS) has been advocated as a new tool for plant
breeders to select indirectly for economically important traits (Tanksley et al., 1989).
Markers that are linked to traits of interest offer a unique advantage for selection
purposes. Molecular markers are single Mendelian loci, that are not influenced by
environmental conditions. Complexly-inherited traits are typically difficult to evaluate
due to environmental variation. Markers linked to QTLs controlling complexly-inherited
traits would allow for screening without having to conduct extensive and laborious
testing associated with such traits (Staub and Serquen, 1996).

Markers can be utilized for both foreground and background selection using
MAS. In foreground selection (Melchinger, 1990), flanking markers near the donor QTL
need to be selected in order to introgress a particular QTL into breeding population.
Marker-assisted selection can also be used for background selection in backcross
breeding (V isscher et al., 1996). Selection for the adapted genome reduces the amount of
extraneous donor genome on non—carrier chromosomes thereby limiting the amount of
donor genome surrounding the target QTL. Foreground and/or background selection
using MAS may be useful for introgression of QTLS from more unadapted or wild
material via Advanced Backcross QTL analysis (Tanksley and Nelson, 1996), or Inbred
Backcross Line Development (Bliss, 1993; Butruille et al., 1999).

The efficiency of selection based on markers is dependent upon several factors,

14

such as heritability, selection intensity, selection generation, linkage distance, population
size, economics of MAS, robustness of the marker and QTL (Hospital et al., 1992;
Moreau et al., 2000; Paterson et al., 1991). Traits with low heritability are the most
efficient traits to select for utilizing MAS, since low heritability is associated with
difficulty in phenotyping. Stringent selection pressure must be used in MAS for traits of
low heritability, since the QTLs are linked to traits with high environmental variation
(Knapp, 1998). Marker-assisted selection is most efficient in early generations, since the
marker can be detected in the generations where phenotypic selection is hindered by a
lack of replication and small number of experimental units (Lande and Thompson, 1990;
Stromberg et al., 1994). Flanking marker are desirable, and should be tightly linked to
the QT L, in order to reduce recombination in the region between the marker and the QTL
(F risch et al., 1999). The identification of markers linked to QTLS is dependent upon the
initial population size. When large populations are used, minor QTLs will be more easily
defined. Marker-assisted selection involves extensive effort, in DNA extraction,
genotyping and phenotyping the population. Efficiency in MAS will increase, however,
after one cycle of selection (Moreau et al., 2000), since the initial start-up cost of marker
identification is high. The cost of using markers for screening populations will therefore,
be reduced with each cycle of selection. The efficiency of a marker also increases with
the robustness of the marker across genotypes, generations and environments (Paterson et
al., 1991).

An approach to increase the efficiency of identifying markers linked to QTLS
involves the use of selective genotyping (Lander and Botstein, 1989) and bulked
segregant analysis (Michehnore et al., 1991). A limited number (10 - 14%) of extreme

15

phenotypes are pooled and screened for the presence of polymorphic bands. Primers
identified to have polymorphic bands in the two DNA bulks, are then tested on the entire
population. This approach is particularly useful if a saturated linkage map has not been
developed for the population. The genotypes included in the DNA bulks are important,
since only a small percentage of the population is used for identification of markers.
Biases in marker evaluation may occur when only few genotypes are utilized in marker
identification (Wang and Paterson, 1994). Repetitiveness of screening the DNA bulks
twice, sequentially leads to cost inefficiencies. Computer simulation models indicate that
potential biases in the identification of markers based on extreme values of a single
phenotypes may be limited, and suggest that multiple correlated traits may more
efficiently identify usefirl markers (Ronin et al., 1998). In a recent study of nematode
resistance in citrus, two sets of DNA bulks were used to identify important polymorphic
markers. Markers were first identified in a set of DNA bulks with 6 genotypes each, and
confirmed in a second set with 15 genotypes each (Ling et al., 2000).

The utility of markers linked to QTLs for economically important traits, such as
resistance to white mold, lies in the actual value of MAS. Marker-assisted selection
allows for the identification and selection of traits in genotypes without having to employ
effort into phenotyping a large number of individuals. The strength of the marker is
dependent upon the phenotypic data. In the case of many quantitatively-inherited traits,
accurate phenotypic data requires extensive testing, over multiple variable environments.
The ability to detect useful markers is limited to the individual genotypes in the bulks,
since only a small percentage of the population is included in the screening process.

Utility of markers is dependent upon robustness across multiple environments and

16

populations. Replacing laborious screening of quantitatively-inherited traits with MAS
would have several advantages in a breeding program. Few examples exist at present of
the utilization of MAS for quantitative trait improvement, despite the obvious benefits to
be gained from using MAS in breeding programs. Resistance to white mold is an
excellent example of the potential for MAS, since screening for resistance in the field is
difficult. Physiological resistance to white mold in complexly-inherited and also difficult
to evaluate in greenhouse or field environments. In breeding programs, the detection of
the resistance to white mold in heavily influenced by environmental variation from
season to season which hinders the normal selection procedures for important quantitative
traits, and increases the importance of MAS (Tanksley et al., 1989). In order to detect
markers linked to useful QTLs for resistance to white mold, great care must be taken to
collect accurate phenotypic data. Marker-assisted selection must be based on a data set
that is uncompromised in quality and reproducibility. Breeding for resistance to white
mold in bean would be greatly enhanced with the discovery and use of stable markers that
would allow for selection of physiological resistance without confounding environmental

factors.

17

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23

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24

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25

CHAPTER I
AN INDIRECT TEST USING OXALATE To DETERMINE PHYSIOLOGICAL RESISTANCE

TO WHITE MOLD IN COMMON BEAN

INTRODUCTION

White mold (S. sclerotiorum) is a destructive fungal disease that can infect over
400 plant species, including many important crop species, such as common bean,
sunflower, alfalfa, soybean, rape and peanut (Boland and Hall, 1994). In common bean,
total seed and pod yield are reduced due to lower number of seeds produced per plant,
reduced number of pods per plant, and smaller seed size (Kerr et al., 1978). Progress in
breeding for resistance is hindered by environmental conditions and plant avoidance
mechanisms that confound the expression and detection of physiological resistance
mechanisms in the field. Methods to detect physiological resistance to white mold in
common bean include the limited-term inoculation method (Hunter et al., 1981), excised-
stem inoculation technique (Miklas et al., 1992a), leaf-agar plug assay (Steadman et al.,
1997 ), straw test (Petzoldt and Dickson, 1996), and growing callus on medium
containing pathogen filtrate (Miklas et al., 1992b). Most of these tests utilize a limited
number of genotypes and depend upon fungal mycelium in screening procedures.
Variability in virulence among isolates (Maxwell and Lumsden, 1970; Morrall etal.,
1971; Miklas et al., 1992a; Pratt and Rowe, 1995) and potential pathogen sensitivity to
high temperatures (Abawi and Grogan, 1975; Boland and Hall, 1987) limit greenhouse
screening methods utilizing the pathogen.

White mold mycelium exude copious amounts of oxalate during infection of plant

26

tissue (Maxwell and Lumsden, 1970). Using non-oxalate producing mutants, oxalate was
identified as the primary pathogenicity factor for S. sclerotiorum (Godoy et al., 1990). A
low pH environment (pH 4.0) created by exuded oxalate is optimal for function of the
polygalacturonase produced by the pathogen (Marciano et al., 1983). Differentiation for
resistance to oxalate has been identified in a leaf test in sunflower (Noyes and Hancock,
1981), a germination test in alfalfa and crimson clover (T rifolium incarnatum L.) (Rowe,
1993), an excised stem test in soybean (Wegulo et al., 1998), and a leaf test in transgenic
rape (Thompson et al., 1995). Common bean has also shown genotypic differentiation in
response to oxalate. The uptake of oxalic acid by petioles of excised primary leaves of
the resistant cultivar Bunsi (also known as Ex Rico 23) was shown to be slower than in
two susceptible cultivars, Kentwood and Seafarer (Tu, 1985). Cultivars that were
susceptible to white mold exhibited more severe structural damage to the plasma
membranes and chloroplasts than resistant cultivars when exposed to an oxalate solution
(Tu, 1989).

Breeding for resistance to white mold in common bean is limited by the lack of a
simple, consistent screening method to quickly evaluate a broad array of genotypes for
physiological resistance to white mold. An indirect screening method that bypasses the
need for the plant to flower, would be valuable in screening unadapted, photoperiod-
sensitive germplasm for new sources of resistance to white mold. An indirect screening
method that eliminates the use of the pathogen would also reduce variability often
associated with greenhouse tests. The objective of this study was to develop an indirect
greenhouse test for physiological resistance to white mold in common bean, using

oxalate, a primary pathogenicity factor of S. sclerotiorum.

27

 

MATERIALS AND METHODS

High-yielding common bean genotypes were evaluated for resistance to oxalate in
three greenhouse tests. Thirty (Test 1) or 36 (Tests 2 and 3) genotypes were evaluated,
including cultivars and breeding lines from the navy, black, pink, pinto, great northern,
cranberry, and kidney commercial classes, new sources of resistance from breeding
programs across North America and the Caribbean, as well as genotypes entered in the
National Sclerotinia White Mold Nursery. Twenty seven genotypes were common across
all three tests. In the greenhouse oxalate test, each entry (genotype) was planted in three
15 cm diameter pots containing Baccto High Porosity Planting Mix, with 9 seeds per pot,
and grown under greenhouse conditions with ambient temperature and a 16 hr daylength.
Twenty-day old seedlings (2"d trifoliate emerging) were cut at the base of the stem at
night to avoid wilting due to the potential of high transpiration rates during daylight
hours. A foam stopper was placed around the base of the seedling, and placed in a
perforated foam board in a 78 L plastic container (67 cm length, 47 cm width, 21 cm
depth). Each container held 11 L of a 20 mM oxalic acid solution that had been adjusted
to a pH of 4.0 with NaOH. The perforated foam board was positioned above the solution,
which kept the seedlings upright while the cut stem was immersed in the solution. Four
(Test 2 and 3) or five (Test 1) seedlings (samples) were used for each genotype in each of
the three containers (replications) of the Randomized Complete Block Design (RCBD).
In each experiment, a separate control replication (single container) consisted of two
(Tests 2 and 3) or three (Test 1) seedlings per genotype being placed in a 11 L solution of
distilled water that had been adjusted to a pH of 4.0 with HCl. The three replications and
one control replication were placed in a greenhouse chamber covered in clear

28

polyethylene to minimize wilting as a result of exposure to direct light.

A fourth greenhouse test was designed to evaluate resistance to oxalate in wild,
landrace, and exotic, cultivated materials, including thirty-two accessions from the
Phaseolus core collection that were identified to have physiological resistance to white
mold, as determined via the straw test or leaf agar plug assay (K.F. Grafton, and J .R.
Steadman, personal communication). New sources of unadapted and adapted genotypes
were also incorporated in this test, and included genotypes originating from Mexico,
Peru, Colombia, and California. Two pots with nine seeds per pot, were planted for each
entry, and grown in greenhouse conditions. The oxalate test was initiated 20 days after
planting, and was similar in methodology to the previously mentioned tests. The oxalate
test was designed as an RCBD, with four replications, and three seedlings (samples) per
genotype in each replication. No control replication was utilized in this experiment.

The seedlings were rated for wilting symptoms after 12 to 15 h of exposure to the
oxalate solution (approximately 6 to 9 h of daylight). A 1 to 6 scale was used to measure
wilting, where 1 = no wilting symptoms visible, 2 = 1 leaf with wilting symptoms (the
two unifoliate leaves were rated together as one leaf, and the 3 leaflets of a trifoliate leaf
were rated together as one leaf), 3 = 2 leaves with wilting symptoms, 4 = 3 or more leaves
with wilting symptoms, 5 = petioles collapsing, 6 = main stem (total plant) collapsing.
Wilting symptoms ranged from curled leaf tip, to total loss of turgidity in the entire leaf.

Genotypes in greenhouse Tests 1, 2, and 3, were evaluated for comparison with
reaction to white mold resistance in the field. The field experiments were grown at the
Montcalm Research Farm, Entrican, MI, in 1996 (Test 1), 1997 (Test 2), and 1998 (Test
3). Planting was delayed to the second week in June in all three field experiments to

29

favor disease development. A 0.5 m row spacing was used for the four row plots, with 6
m row length, where the outer two rows were planted with a white mold susceptible
uniform border (‘Midland’), and the inner two rows were planted with the experimental
genotypes. The soil type at the Montcalm Research Farm sites is a combination of Eutric
Glossoboralfs (coarse-loamy, mixed) and Alfie F ragiorthods (coarse-loamy, mixed,
fiigid). Standard agronomic practices for tillage, fertilization, and herbicide were applied
to ensure good crop growth and development. Plots were irrigated during initial
flowering with 13 mm of water at approximately three day intervals, depending upon
rainfall, in order to promote uniform disease pressure across the field. The field
experiments were irrigated with an overhead sprinkler system five times in 1996, three
times in 1997, and six times in 1998. Uniform infection of white mold in dry bean at the
Montcalm Research Farm was identified in previous field studies. Plots were rated for
disease severity and disease incidence (DI) (Steadman 1997; Kolkman and Kelly, 1998;
Steadman et al., 1998) using a ‘quarter scale’ (Hall and Phillips, 1996), shortly before
harvest, when the majority of plants had reached physiological maturity. Thirty plants
per plot were each given a rating from 0 to 4, where 0 = no disease present, 1 = 1 to 25%
of the plant with white mold symptoms, 2 = 26 to 50% of the plant with white mold
symptoms, 3 = 51 to 75% of the plant with white mold symptoms, and 4 = 76 to 100% of
the plant with white mold symptoms. A Disease Severity Index (DSI) was calculated for

each plot on a percentage basis, using the following formula:

2 (rating of each plant)
DSI = x 100
4 x (number of plants rated)

 

30

Disease incidence was calculated as the number of plants out of the thirty individuals
with white mold infection, based as a percentage. Plots were harvested after disease
rating.

All greenhouse experiments were analyzed as RCBDS, using PROC GLM (SAS,
1995). The three field experiments were analyzed separately using PROC LATTICE
(SAS, 1995). The 1996 field experiment was analyzed as a rectangular lattice, and the
1997 and 1998 field experiments were each analyzed as a partially balanced triple lattice.
The 27 common genotypes were analyzed across all three tests (greenhouse) and years
(field) as a RCBD, using PROC GLM (SAS, 1995). Environments were considered as a

random effect, and genotypes as a fixed effect.

RESULTS AND DISCUSSION

Significant genotypic differences were identified in the response to a 20 mM
oxalate solution (Tables 1.1 and 1.2). Preliminary experiments using a subset of
genotypes and a 10, 20, 40, 80, and 100 mM range of oxalate concentrations over time,
indicated that the 20 mM oxalate concentration was suitable for bean. Wilting symptoms
in the control container in each experiment were negligible and not significant, indicating
the importance of oxalate in the appearance of wilting symptoms. The temperature in the
three oxalate tests ranged fi'om 24 to 40 °C (Test 1), 21.5 to 26 °C (Test 2), and 22.5 to
26.5 °C (Test 3), and 23.5 to 25 °C (Test 4). Significant differences between tests (Tables
1.1 and 1.2) indicate the influence of the environment in affecting the estimate of
resistance to oxalate, and the importance of including known resistant and susceptible

control cultivars in each experiment. Significant correlations between the oxalate test

31

Table 1.1. Mean squares of the greenhouse oxalate test scores, and the field ratings for disease severity
index and disease incidence, for 27 beanienogfpes across three environments.

Mean Squares

 

 

 

 

Greenhouse Field
Oxalate Disease Disease

Source df' Test Severity Index Incidence
Genotype 26 2.3399"** 1512.9"** 2566.2""
Environment 2 35 7039"" 8817.5"** 5426.0****
Genotype x Enviromnent 52 0.4713*"* 4187*" 734.4“
Rep (Environment) 6 0.3012 ns 175.9 ns 273.5 ns
Error 156 0.1817 206.1 403.5

 

", *“, *"* Significant at P < 0.01, 0.001 and 0.0001 levels, respectively; ns = non significant.

ratings and field disease ratings were identified in each experiment Tests 1, 2, and 3
(Table 1.3). The highest correlation between the oxalate test and the DSI (r=0.58;
P=0.0015) and DI (r=0.57; P=0.0019) ratings in the field (Table 1.4) was observed with
the 27 genotypes across three years.

The oxalate test results confirm resistance found in several common bean sources
(Tables 1.2 and 1.4). Bunsi, has been identified as resistant in both greenhouse tests (Tu,
1985; Tu, 1989; Miklas et al., 1992a; Miklas et al., 1992b), and field trials (Tu and
Beversdorf, 1982; Schwartz et al., 1987; Miklas et al., 1992a). Bunsi-derived cultivars,
such as Stinger, Crestwood, 192919, N90618, and ND88-106 were resistant to both white
mold in the greenhouse oxalate test and in the field. ‘C-20’ (Kelly et al., 1984) and C-20
- derived cultivars, such as Huron (Kelly et al., 1994), represent another source of navy
bean with physiological resistance (Miklas et al., 1992a) and field resistance. Low
oxalate test ratings verified the presence of physiological resistance to white mold in
Huron (Table 1.2). 19365-3, I9365-14, I9365-5-pk, I9365-19, and 92BG-7, released as

sources of white mold resistance (Miklas et al., 1998), also showed resistance to oxalate.

32

Resistance to oxalate varied in the unadapted photoperiod-sensitive wild and
landrace accessions that were previously identified through two the straw test and leaf
agar plug assay (Table 1.5). Accessions fi'om both the Mexico core collection, such as PI
318695, and from the Central and South American core collection, such as PI 399169, PI
313609, and PI 313598, were resistant to oxalate. Variability in the detection of
physiological resistance across screening methods, is indicative of the variability that can
be found within an accession, as well as the variability for phenotyping an accurate
measure of resistance based on the differing tests. Other new sources of resistance to
oxalate were identified in this test. Two black bean lines fi'om Mexico, Tacana, and
V8025, and two genotypes fiom CIAT, DOR 364 (Beebe et al., 1998) and Sea 5, were
Shown to have resistance to oxalate similar to that of the most resistant check, Huron.
Chaucha Chuga, a cultivar from Peru (Beebe et al., 1998), was also resistant to oxalate.
An Australian navy bean, CH428-4D, with reported resistance to white mold in the field
(Redden and Tatnell, 2000), was also resistant to oxalate. G122, a cranberry bean
(Shonnard and Gepts, 1994; Miklas et al., 2000), was included in all four greenhouse
tests. In the first three tests, G122 had low to moderate levels of susceptibility for
resistance to oxalate (Table 1.2). In the fourth test, 6122 had low to moderate levels of
resistance to oxalate (Table 1.5). This genotype was previously identified as having
resistance to white mold in the field (Kmiecik and Nienhuis, 1998), and most likely has
moderate levels of physiological resistance to white mold, that is highly influenced by

enviromnental conditions.

33

Table 1.2. Ratings of resistance to oxalate in the greenhouse oxalate test for three individual tests and the
combined test scores for 27 common bean cultivars across the three tests.

 

 

Greenhouse Oxalate TestsT
Genotypesx Test 1 Test 2 Test 3 Combined
Midland 4.001 2.75 3.50 3.42
Isles 3.20 3.42 3.17 3.27
Othello 3.73 2.83 3.00 3.20
Frontier 3.93 2.17 2.83 2.98
Newport 3.80 1.50 3.58 2.97
Mackinac 3.53 2.50 2.58 2.87
Weihing 2.47 2.33 3.58 2.81
I9365-31 3.53 2.00 2.67 2.74
N94080 3.40 1.58 2.75 2.58
G122 3.13 1.92 2.33 2.47
Mayflower 3 .27 1.42 2.58 2.43
Raven 2.93 1.50 2.33 2.27
N90618 2.67 1.25 2.75 2.23
OAC Laser 3.07 1.33 2.25 2.23
92130—7 3.07 1.17 1.83 2.03
Stinger 2.53 1.08 2.42 2.02
Crestwood 2.93 1.08 2.00 2.01
I9365-19 2.60 1.00 2.42 2.01
Bunsi 2.67 1.00 2.33 2.00
T39 2.60 1.50 1.83 1.99
Vista 3.00 1.50 1.42 1.98
I9365-5-pk 2.60 1.17 2.17 1.98
ND88-106 2.00 1.33 2.50 1.96
Huron 2.20 1.25 2.08 1.85
I9365-14 2.13 1.17 2.00 1.77
193653 2.60 1.25 1.33 1.73
192919 2.67 1.08 1.33 1.70
Mean’ 2.99 1.72 2.46 2.35
LSD (0.05) 0.69 0.57 0.76 0.40
CV (%) 14.1 20.3 18.9 18.1

 

1 Scale from 1 to 6, where 1 = no wilting (similar to control), 2 = 1 leaf wilting, 3 = 2 leaves wilting, 4 = 3
or more leaves wilting, 5 = petioles wilting, 6 = total plant collapse.

‘ 27 common genotypes across the three tests

1 Mean values for 30 genotypes in test 1, 36 genotypes in tests 2 and 3, and 27 common genotypes in the
combined analysis.

34

Table 1.3. Pearson correlation coefficients of oxalate test ratings to the disease severity index, disease
incidence, and yield for common bean genotypes tested in three greenhouse oxalate tests and
corresponding field tests at the Montcalm Research Farm.

 

 

Oxalate Test Ratings
Field Ratings Test 1‘ Test 2 Test 3 Common‘
(30 genotypes) (36 genotypes) (36 genotypes) (27 genotypes)
Disease Severity Index‘ 0.534M 0.3151 0.457" 0.580“
Disease Incidence” 0.482“ 0.3 18* 0.394“ 0.571"
Yield -0.358* -0.413*"' -0.245 ns -0.505"""

 

*, ”, *** Significant at the P < 0.05, 0.01, and 0.001 levels, respectively; ns = non significant

1 Significant at P < 0.10 level

1 Ratings from oxalate tests 1, 2, and 3 were correlated to field ratings from the 1996, 1997, and 1998 field
environments, respectively

i Means of 27 common genotypes from three oxalate testes were correlated to the combined field ratings
over years.

2 (rating of each plant)

1 Disease Severity Index = x 100
4 x (number of plants rated)

" Disease Incidence = percentage of 30 plants with white mold infection.

 

Resistance to oxalate is a resistance mechanism that may work singly, or more
likely in a combination with a number of plant avoidance mechanisms or alternative
physiological mechanisms to provide consistent levels of resistance to white mold in the
field. Mechanisms can provide plant avoidance to white mold, in which the plant
escapes the initial infection of the pathogen. Favorable conditions for the formation of
apothecia, the corresponding onset of flowering for inoculation via ascospores, and
appropriate temperatures following infection are critical components of the epidemiology
of S. sclerotiorum (Boland and Hall, 1987). Plant avoidance mechanisms, such as early
flowering or maturity, or an open porous canopy may limit the initial inoculation and
subsequent infection of white mold. Physiological resistance mechanisms may not be

restricted to resistance to oxalic acid. Alternative resistance mechanisms at the cellular

35

Table 1.4. Field ratings of white mold disease severity and incidence for the 27 bean cultivars in three
individual field tests and combined field tests across the three tests (years), at the Montcalm Research Farm
in 1996, 1997, and 1998.

 

 

 

 

 

Test 1 (1996) Test 2 (1997) Test 3 (1998) Combined (1996-98)

(33110013951 DSI‘ DI’ DSI DI DSI DI DSI DI

Midland 60.0 70.0 53.3 73.3 39.1 73.2 51.4 74.8
Othello 79.3 87.8 50.6 66.7 12.4 19.6 47.5 58.5
Newport 45.8 60.0 61.4 83.3 25.6 59.7 43.4 65.9
N94080 50.8 72.2 41.9 53.3 9.8 34.8 34.4 53.3
I9365-19 46.7 62.2 42.2 60.0 9.8 28.3 33.9 51.8
Weihing 25.0 34.5 64.4 77.8 11.2 29.4 33.7 47.4
Mackinac 50.8 75.5 25.6 41.1 22.8 62.3 32.5 58.9
I9365-3 36.7 56.7 43.9 62.2 13.0 35.0 31.1 51.1
Raven 30.8 41.1 37.2 50.0 22.1 50.2 30.0 47.0
Frontier 36.7 53.3 45.0 70.0 6.2 17.5 29.4 48.1
T39 21.7 33.3 45.0 57.8 13.9 43.5 27.9 46.3
Vista 18.3 34.4 49.2 66.7 5.0 15.4 23.2 36.7
I9365-14 21.7 47.8 41.7 55.6 3.9 14.0 22.4 38.9
Stinger 9.2 16.7 31.9 47.8 14.1 35.2 18.9 34.8
Crestwood 6.7 13.3 37.2 44.4 7.4 19.6 18.8 29.6
Isles 4.2 6.7 30.8 50.0 14.9 42.8 16.5 32.6
I9365-31 15.8 38.9 27.2 48.9 5.9 17.6 15.6 33.7
6122 10.0 22.2 21.7 32.2 12.2 34.2 14.9 31.1
I9365-5-pk 18.3 34.5 18.3 36.7 5.9 16.6 14.4 30.0
Bunsi 8.3 14.4 30.3 48.9 2.8 8.8 13.4 23.0
Mayflower 14.2 22.2 12.8 21.1 14.3 38.6 13.2 26.7
92BG-7 1.7 6.6 23.1 36.7 13.9 36.6 12.8 27.0
Huron 17.5 36.7 7.8 21.1 10.6 31.4 11.3 28.5
N90618 9.2 17.8 11.4 20.0 6.5 18.9 8.4 17.8
ND88-106 0.0 2.2 18.1 25.6 5.8 17.3 7.4 13.0
192919 0.0 2.2 7.2 16.7 5.5 16.8 3.9 11.1
OAC Laser 1.7 3.3 1.1 2.2 3.9 14.7 3.1 7.8

Mean‘I 24.3 36.7 32.4 47.1 12.2 30.7 22.7 38.0
LSD (0.05) 26.3 31.0 24.7 29.4 18.4 31.6 13.4 18.7
CV (%) 67.7 53.7 47.9 40.6 96.0 67.3 63.2 52.9

1 27 common genotypes across the three tests
2 (rating of each plant)
z Disease Severity Index = x 100

4 x (number of plants rated)

’ Disease Incidence = percentage of 30 plants with white mold infection.
1 Mean values for 30 genotypes in test 1, and 36 genotypes in tests 2 and 3

36

 

Table 1.5. Ratings of resistance to oxalate in the greenhouse oxalate test for selected wild, landrace and

cultivated Phaseolus wigarbjemplasm.

I‘. "J. 7. I o

 

 

oxalate no. of

Genotype origin coreT seed class: seed score’ st. dev. plants

size‘ tested8
PI 263596 Mexico Mex. unknown 38.0 3.7 0.47 12
PI 313348 Mexico Mex. landrace 22.0 3.6 0.74 12
PI 31 1974 Mexico Mex. landrace 13 .0 3 .5 0.51 9
PI 3 13671 Ecuador CASA cultivated 42.0 3 .4 0.32 12
PI 415913 Ecuador CASA uncert. irnpr. st. 56.0 3.3 0.47 12
PI 201354 Mexico Mex. unknown 47.0 3.3 0.82 12
PI 316024 Peru cultivated (nuna) 30.0 3.3 0.19 9
T3147-2‘ Mich/Mex breeding line 27.5 3.3 0.42 12
PI 415936 Ecuador CASA uncert. irrrpr. st. 50.0 3.2 0.51 9
PI 282016 Colombia CASA cultivar 74.0 3.1 0.57 12
PI 417782 Mexico Mex. wild 5.2 3.1 . 3
PI 313425 Mexico Mex. landrace 24.0 3.1 0.74 12
Othello Washington cultivar 43.8 2.9 0.57 12
PI 415906 Ecuador CASA uncert. irnpr. st. 58.0 2.9 0.63 12
PI 415886 Ecuador CASA landrace 56.0 2.8 1.00 12
PI 325653” Mexico Mex. landrace 23.0 2.8 0.43 12
PI 201010 Guatemala CASA wild 9.0 2.8 1.11 12
Newport Michigan cultivar 21 .2 2.8 0.32 12
PI 312018” Mexico Mex. landrace 26.0 2.7 0.67 12
PI 313254 Mexico Mex. landrace 19.0 2.7 1.12 12
PI 3105 15 Honduras CASA cultivated 24.0 2.7 0.38 12
PI 309837 Costa Rica CASA landrace 26.0 2.6 1.13 12
PI 31 1843 Guatemala CASA landrace 32.0 2.5 1.07 9
P1 31 1794 El Salvador CASA landrace 18.0 2.5 0.43 12
PTMex80 Mexico cultivated 27.0 2.5 0.33 12
PI 313850 Peru CASA cultivated 46.0 2.3 0.47 12
PI 310865 Nicaragua CASA cultivated 21.0 2.3 0.90 12
PI 417721 Mexico Mex. landrace 24.0 2.3 0.72 12
T3008-1‘ Mich/Mex breeding line 26.0 2.3 0.84 12
PI 325685 Mexico Mex. landrace 3.6 2.3 0.50 12
PI 319683 Mexico Mex. landrace 34.0 2.2 1.02 9
PI 325691 Mexico Mex. landrace 3.6 2.2 0.00
PI 189016 Guatemala CASA unknown 30.0 1.9 0.42 12
PI 318695 Mexico Mex. wild 3.5 1.9 0.57 12
CH428-4D Australia breeding line 22.0 1.8 0.88 12

37

G122 India breeding line 48.2 1.8 0.88 12

PI 313598 Colombia CASA cultivated 60.0 1.8 0.88 12
PI 313609 Colombia CASA cultivated 73.0 1.8 0.17 12
Chaucha Chuga Peru cultivated 38.8 1.6 0.50 12
V8025 Mexico cultivar 25.0 1.5 0.19 12
DOR 364 CIAT cultivar 22.0 1.4 0.32 12
PI 399169 Nicaragua CASA uncert. impr. st. 24.0 1.3 0.19 12
Sea 5 CIAT breeding line 26.0 1.2 0.33 12
Tacana Mexico cultivar 25.8 1.2 0.33 12
Huron Michigan cultivar 23.9 1.2 0.19 12
mean 2.4

CV, % 25

 

1 Mexico and CASA (Central and South American) core collections

‘ seed class: uncert. impr. st. = uncertain improvement status

1 seed size (g'1005eed") obtained from NPGS/GRIN database; data on seed size for cultivars obtained from
Kelly et al., 1999.

’ oxalate score determined using a scale from 1 to 6, where 1 = no wilting (similar to control), 2 = 1 leaf
wilting, 3 = 2 leaves wilting, 4 = 3 or more leaves wilting, 5 = petioles wilting, 6 = total plant collapse.

‘ Number of plants tested per genotype over the entire 4 replications of the experiment

3 Breeding lines developed for tolerance to drought (Schneider et al., 1997)

" PI 325653 = flesh colored seed only; PI 312018 = black colored seed only; PI 189016 = red colored seed

only
level, such as phytoalexins (Sutton and Deverall, 1984), may be important to white mold
resistance in the field.

Any genotype that escapes infection in the field can significantly skew the
correlation between the greenhouse oxalate test ratings and field disease ratings. OAC
Laser, an upright navy bean cultivar with a porous canopy, does not have high levels of
resistance to oxalate in the greenhouse tests, yet is very resistant to white mold infection
in the field (Tables 1.2 and 1.4). Plant avoidance mechanisms and moderate to low levels
of resistance to oxalate in OAC Laser most likely work in combination to provide
excellent resistance in the field. Two early-flowering cultivars, Isles and Othello, can

have low incidence of white mold in the field, but exhibit high oxalate ratings (Tables 1.2

38

and 1.4). The high oxalate test ratings indicate that both Isles and Othello have little
physiological resistance to oxalate. Alternatively, less adapted germplasm, such as
I9365-19 and I9365-3 (Miklas et al., 1998), were identified to be resistant to oxalate, yet
had high disease ratings in the field (Table 1.4). I9365-l9 and I9365-3 represent usefirl
sources of physiological resistance for introgression into adapted germplasm. Unadapted
germplasm has been identified to carry putative physiological resistance using the straw
test (Miklas et al., 1999). Resistance to oxalate was identified in unadapted germplasm,
including wild, landrace and exotic cultivated material (Table 1.5). The unadapted
genotypes of varying seed size and origin offer a useful source of physiological resistance
to white mold for both large- and small-seeded market classes. New sources of resistance
are important in breeding strategies for improved disease resistance. Few large- or small-
seeded genotypes have been identified that have physiological resistance to white mold.
New sources of resistance allow for the potential to improve resistance in susceptible
market classes, and pyramid genes for resistance in market classes, such as the navy bean,
in order to create more stable resistant cultivars. The success of the oxalate test confirms
the segregation of responses of resistant and susceptible common bean cultivars to
oxalate (Tu, 1985; Tu, 1989) and pathogen filtrate (Miklas et al., 1992b). The oxalate test
indirectly identifies genotypes that have physiological resistance to white mold via
oxalate resistance, bypassing the need for field testing where the detection of
physiological resistance is confounded by plant avoidance mechanisms.

A highly significant negative correlation (r=-0.50; P=0.0072) between the oxalate
test ratings and yield for the 27 cultivars across three field environments implies the
association between resistance to oxalate and high yield under white mold pressure

39

(Table 1.3). The lack of a significant correlation between the oxalate ratings and yield in
the 1998 field trial may be indicative of the lower yield potential during the growing
season.

The oxalate test is useful for determining physiological resistance in the
greenhouse. Photoperiod-sensitive unadapted germplasm can be tested for physiological
resistance, since plants are tested at the seedling stage (2“d trifoliate emerging) and are
therefore, not influenced by flowering (reproductive) traits. A large number of lines can
be evaluated in a relatively short time period. Inoculation of the cut seedlings into a
common solution of oxalate reduces variability that may be observed when utilizing agar
plugs of S. sclerotiorum. The inherent variability within a single isolate (Maxwell and
Lumsden, 1970), or isolate variability from test to test is reduced (Miklas et al., 1992a).
The time between inoculation of seedlings and rating of the response to oxalate is very
Short (12 to 15 h after inoculation) reducing the potential variability in environmental
conditions that exist in a greenhouse over a longer period of time. The rating scale in the
oxalate test was designed to effectively quantify the degree of damage to a genotype
using a quick visual estimate. Extreme high temperatures can limit the ability to screen
effectively using the fungus (Abawi and Grogan, 1975; Boland and Hall, 1987). In the
oxalate test, temperatures up to 40 °C were encountered that did not adversely affect the
correlation between greenhouse and field results. The differential response of common
bean genotypes exposed to an oxalate solution has a highly significant correlation to
corresponding white mold field ratings for DSI and DI, and a highly significant negative
correlation to yield (Table 1.3). Screening genotypes for resistance to oxalate, a primary

pathogenicity factor for S. sclerotiorum, is an efficient indirect method to test for

40

physiological resistance to white mold in common bean, and an effective method to
identify new and unique sources of physiological resistance in wild and unadapted bean

germplasm.

41

REFERENCES

Abawi, GS, and RC. Grogan. 1975. Source of primary inoculum and effects of
temperature and moisture on infection of beans by Sclerotinia sclerotiorum.
Phytopathology 65:300-309.

Beebe, S., F. Pedraza, M. Rojas, J. Gutierrez, and J. Tohme. 1998. A genetic map of
common bean combining RFLP, RAPD, SCAR and AF LP markers. Annu. Rep.
Bean Irnprov. Coop. 41 :95-96.

Boland, G.J., and R. Hall. 1987. Epidemiology of white mold of white bean in Ontario.
Can. J. Plant Pathol. 9:218-224.

Boland, G.J., and R. Hall. 1994. Index of plant hosts of Sclerotinia sclerotiorum. Can.
J. Plant Pathol. 16:93-108.

Godoy, G., J .R. Steadman, M.B. Dickrnan, and R. Dam. 1990. Use of mutants to
demonstrate the role of oxalic acid in pathogenicity of Sclerotinia sclerotiorum on
Phaseolus vulgaris. Physiol. Mol. Plant Pathol. 37:179-191.

Hall, R., and LG. Phillips. 1996. Evaluation of parameters to assess resistance of white
bean to white mold. Annu. Rep. Bean Irnprov. Coop. 39:306-307.

Hunter, J .E., M.H. Dickson, and J .A. Cigna. 1981. Limited-Term Inoculation: a method
to screen bean plants for partial resistance to white mold. Plant Dis. 65:414-417.

Kelly, J .D., M.W. Adams, A.W. Saettler, G.L. Hosfield, and A. Ghaderi. 1984.
Registration of C-20 navy bean. Crop Sci. 24:822.

Kelly, J .D., G.L. Hosfield, G.V. Vamer, M.A. Uebersax, M.E. Brothers, and J. Taylor.
1994. Registration of ‘Huron’ navy bean. Crop Sci. 34:1408.

Kelly, J .D., J. Taylor, N. Blakely, and J. Kolkman. 1999. 1999 Dry Bean Yield Trials.
1999 Research Report: Saginaw Valley Bean & Beet Research Farm and Related
Bean-Beet Research, Michigan State University Agricultural Experiment Station,
pp.69-133.

Kerr, B.D., J .R. Steadman, and LA. Nelson. 1978. Estimation of white mold disease
reduction of yield and yield components of dry edible beans. Crop Sci. 18:275-
279.

Kolkman, J .M., and J .D. Kelly. 1998. Analysis of white mold resistance in dry bean.
Annu. Rep. Bean Irnprov. Coop. 41 :68-69.

42

 

Kmiecik, K.A., and J. Nienhuis. 1998. The use of line G122 as a source of white mold
resistance in breeding improved processing snap beans for the rrridwest. Annu.
Rep. Bean Irnprov. Coop. 41:68-69.

Marciano, P., P. Di Lenna, and P. Magro. 1983. Oxalic acid, cell wall-degrading
enzymes and pH in pathogenesis and their significance in the virulence of two
Sclerotinia sclerotiorum isolates on sunflower. Physiol. Plant Pathol. 22:339-345.

Maxwell, DP, and RD. Lumsden. 1970. Oxalic acid production by S. sclerotiorum in
infected bean and in culture. Phytopathology 60:1395-1398.

Miklas, P.N., R. Delorme, R. Harman, and M.H. Dickson. 1999. Using a subsample of
the core collection to identify new sources of resistance to white mold in common
bean. Crop Sci. 39:569-573.

Miklas, P.N., R. Delorrne, W.C. Johnson, and P. Gepts. 2000. Field and straw test
reactions to white mold in a RIL Population (A55/G122). Annu. Rep. Bean
Irnprov. Coop. 43:76-77.

Miklas, P.N., K.F. Grafton, J .D. Kelly, H.F. Schwartz, and J .R. Steadman. 1998.
Registration of four white mold resistant dry bean germplasm Lines: I9365-3,
I9365-5, I9365-31, and 92BG—7. Crop Sci. 38:1728.

Miklas, P.N., K.F. Grafton, and B.D. Nelson. 1992a. Screening for partial physiological
resistance to white mold in dry bean using excised stems. J. Am. Soc. Hortic. Sci.
117:321-327.

Miklas, P.N., K.F. Grafton, G.A. Secor, and PE. McClean. 1992b. Use of pathogen
filtrate to differentiate physiological resistance of dry bean to white mold disease.
Crop Sci. 32:310-312.

Morrall, R.A.A., L.J. Duczek, and J .W. Sheard. 1971. Variations and correlations within
and between morphology, pathogenicity, and pectolytic enzyme activity in
Sclerotinia from Saskatchewan. Can. J. Bot. 50:767-786.

Noyes, RD, and J .6. Hancock. 1981. Role of oxalic acid in the Sclerotinia wilt of
sunflower. Physiol. Plant Pathol. 18:123-132.

Petzoldt, R., and M.H. Dickson. 1996. Straw test for resistance to white mold in beans.
Annu. Rep. Bean Irnprov. Coop. 39:142-143.

Pratt, R.G., and DE Rowe. 1995. Comparative pathogenicity of isolates of Sclerotinia
trifoliorum and S. sclerotiorum on alfalfa cultivars. Plant Dis. 79:474—477.

43

Redden, R., and J. Tatnell. 2000. Screening common bean breeding lines and
germplasm for resistance to Sclerotinia sclerotiorum. Annu. Rep. Bean Improv.
Coop. 43:160-161.

Rowe, DE. 1993. Oxalic acid effects in exudates of Sclerotinia trifoliorum and S.
sclerotiorum and potential use in selection. Crop Sci. 33:1146-1149.

SAS Institute. 1995. The SAS system for Windows. Release 6.12. SAS Inst, Cary, NC.

Schneider, K.A., R. Rosales-Sema, F. Ibarra—Perez, B. Cazares-Enriquez, J .A. Acosta-
Gallegos, P. Ramirez-Vallejo, N. Wasimi, and J .D. Kelly. 1997. Improving
common bean performance under drought stress. Crop Sci. 37:43-50.

Shonnard G.C., P. Gepts. 1994. Genetics of heat tolerance during reproductive
development in common bean. Crop Sci. 34: 1168-1175.

Schwartz, H.F., D.H. Casciano, J .A. Asenga, and DR. Wood. 1987. Field measurement
of white mold effects upon dry beans with genetic resistance or upright plant
architecture. Crop Sci. 27:699-702.

Steadman, J .R. 1997. 1996 White mold (Sclerotinia) of bean nursery summary. Annu.
Rep. Bean Irnprov. Coop. 40:142.

Steadman, J .R., K.F. Grafton, K. Kmiecik, J.M. Kolkman, M. Kyle Jahn, and R. Mainz.
1998. Bean White Mold Nursery, 1997. Annu. Rep. Bean Irnprov. Coop.
41 :17 3-174.

Steadman, J .R., K. Powers, and B. Higgins. 1997. Screening common bean for white
mold resistance using detached leaves. Annu. Rep. Bean Irnprov. Coop. 40: 140-
141.

Sutton, DC, and B.J. Deverall. 1984. Phytoalexin accumulation during infection of
bean and soybean by ascospores and mycelium of Sclerotinia sclerotiorum. Plant
Pathol. 33:377-383.

Thompson, C., J .M. Dunwell, C.E. Johnstone, V. Lay, J. Ray, M. Schmitt, H. Watson,
and G. Nesbet. 1995. Degradation of oxalic acid by transgenic oilseed rape
plants expressing oxalate oxidase. Euphytica 85: 169-172.

Tu, J .C. 1985. Tolerance of white bean (Phaseolus vulgaris) to white mold (Sclerotinia
sclerotiorum) associated with tolerance to oxalic acid. Physiol. Plant Pathol.
26: 1 1 1-1 17.

Tu, J .C. 1989. Oxalic acid induced cytological alterations differ in beans tolerant or
susceptible to white mould. New Phytol. 112:519-525.

44

 

Tu, J .C., and W.D. Beversdorf. 1982. Tolerance to white mold (Sclerotinia sclerotiorum
(Lib.) De Bary) in Ex Rico 23, a cultivar of white bean (Phaseolus vulgaris L.).
Can. J. Plant Sci. 62:65-69.

Wegulo, S.N., X.B. Yang, and CA. Martinson. 1998. Soybean cultivar responses to
Sclerotinia sclerotiorum in field and controlled environment studies. Plant Dis.
82:1264-1270.

45

CHAPTER 2
RELATIONSHIP OF AGRONOMIC TRAITS AND RESISTANCE To WHITE MOLD IN THREE

BEAN POPULATIONS

INTRODUCTION

White mold, caused by S. sclerotiorum, is a destructive yield-limiting firngal
disease that seriously affects common bean production in temperate regions (Steadman,
1983; Haas and Bolwyn, 1972; Kerr et al., 1978; Purdy, 1979; Wallen and Sutton, 1967).
White mold infections in bean are initiated during flowering, coinciding with canopy
closure and microclimate conditions that stimulate the development of apothecia fi'om
soil-bome sclerotial bodies (Boland and Hall, 1987). Ascospores dispersed into plant
canopy require a nutrient source, such as flowers, for germination (Abawi et al., 1975;
Haas and Bolwyn, 1972; Hunter et al., 1978). Oxalate was identified as a primary
pathogenicity factor for S. sclerotiorum (Godoy et al., 1990). Developing mycelium
proceed to infect the plant by exuding copious amounts of oxalate into the plant tissue,
creating optimal conditions for the firnction of polygalacturonases from the fungus that
break down plant cell walls (Maxwell and Lumsden, 1970; Marciano et al., 1983).
Symptoms of white mold on bean plant include wilting, lesions, bleached stems, and
presence of sclerotial bodies in infected tissue.

Resistance to white mold in common bean is complexly-inherited (Fuller et al.,
1984; Miklas and Grafton, 1992). Physiological resistance to white mold has been
described in several navy bean cultivars, such as Bunsi and C-20, (Schwartz et al., 1987 ;
Miklas et al., 1992; Tu and Beversdorf, 1982; Kelly et al., 1984). Mechanisms of

46

 

physiological resistance may involve several factors, including phytoalexin production
(Sutton and Deverall, 1984), and resistance to tissue damage from oxalic acid (Tu, 1985).
Resistance to oxalate (OR) was found to be significantly correlated to resistance to white
mold in the field in a group of elite high-yielding bean genotypes (Kolkman and Kelly,
2000).

Agronomic management practices that result in reduced production of apothecia
or exposure to inoculum were found to contribute to reduced white mold infections in the
field environment. A decrease in plant row width, resulting in higher plant density will
increase the levels of white mold (Park, 1993; Steadman et al., 1973). Elevating the
canopy of a prostrate highly-susceptible indeterminate great northern bean reduced white
mold infection, and increased yield (Fuller et al., 1984). Plant architectural traits that
influence levels of white mold were also identified in several cultivars. Upright
indeterminate navy beans escape white mold infection or Spread, in comparison to
determinate navy bean types, most likely due to a narrower canopy, resulting in a drier
microclimate underneath the canopy (Park, 1983). Bunsi, an indeterminate navy bean,
has an open porous canopy, which has been associated with reduced levels of white mold
in the field (Tu and Beversdorf, 1982; Park, 1993). Alternatively, the open, porous
canopy of larger-seeded determinate beans was identified as an architectural avoidance
mechanism (Coyne et al., 1974; Weiss et al., 1977; Schwartz et al., 1987). Dense
canopies generally resulted in higher white mold severity than porous canopies, due to the
development of a favorable microclimate within the canopy (Blad et al., 1978; Coyne,
1980). Fewer apothecia were produced underneath the open canopy of the determinate
dark red kidney bean cultivar, Charlevoix, and the upright canopy of the indeterminate

47

 

small white bean, Aurora, compared to the dense canopy of several prostrate type HI
great northern bean cultivars (Schwartz and Steadman, 1978). Many factors influence the
production of apothecia and ascospores, the initial infection, and subsequent Spread of
disease throughout the plant tissue. The onset of flowering and production of apothecia,
however, generally occurs at canopy closure under appropriate moisture conditions
(Boland and Hall, 1987). The interaction between management practices, such as row
width and plant density, and plant architectural traits, such as an open porous canopy, or
upright architecture can affect levels of white mold in the field.

Progress in breeding for resistance in common bean is hindered by environmental
conditions and factors that confound the expression and detection of physiological
resistance mechanisms. The ability to identify and select individual genotypes with
physiological resistance to white mold, and agronomically-desirable, architectural
avoidance mechanisms, in advanced breeding lines and differentiating populations is
essential in breeding for resistance to white mold. The objective of this study was to
determine the relationship between specific agronomic traits, physiological resistance,
measured indirectly as OR, and resistance to white mold in the field in three contrasting

populations.

MATERIALS AND METHODS
Populations:

The first population was an assembly of advanced-line genotypes consisting of
resistant and susceptible elite lines, advanced breeding material, and cultivars. The
advanced line population was evaluated in three years and three greenhouse tests for OR

48

(Kolkman and Kelly, 2000). The three field tests consisted of 30 (Test 1) and 36 (Tests 2
and 3) genotypes, including cultivars and breeding lines fi'om the navy, black, pink, pinto,
great northern, cranberry, and kidney commercial classes, new sources of resistance from
breeding programs across North America and the Caribbean, as well as genotypes entered
in the National Bean White Mold Nursery. Twenty-seven genotypes were common
across all three tests. The 27 genotypes were typically high-yielding cultivars, or
advanced breeding lines previously selected for usefirl agronomic attributes (Kolkman
and Kelly, 2000).

The second population was an 98 line F 3-derived population derived fi'om a
biparental cross between Bunsi, an indeterminate (Type II) resistant cultivar with an open
porous canopy, and Newport, a determinate (Type I) susceptible cultivar (Kelly et al.,
1995; Kolkman and Kelly, 2000). Bunsi has been identified to have physiological
resistance to white mold, OR, and plant avoidance due to an open porous canopy (Tu and
Beversdorf, 1982; Schwartz et al., 1978; Miklas et al., 1992; Tu, 1985; Kolkman and
Kelly, 2000). Ninety-eight F2 lines were advanced in the greenhouse to the F3 generation
using single seed descent. Seed from individual F 3 plants was bulked, and advanced in a
greenhouse. Seed harvested from three F3,4 plants was bulked and F3,5 plants were
increased in a winter nursery in Puerto Rico. No selection for agronomic traits was made
during generation advancement.

The third population was a 28 F 5,6 recombinant inbred line (RIL) population,
developed by single seed descent from a biparental cross between the resistant
indeterminate (Type II) cultivar, Huron, and the susceptible determinate (Type I) cultivar,
N eWport (Kelly et al., 1994). Huron is a C-20 - derived cultivar, and has both

49

physiological resistance to white mold, as determined through OR, and resistance to

white mold in the field (Kolkman and Kelly, 2000).

Physiological Resistance:

Physiological resistance in all three populations was determined indirectly, by
screening the populations for OR (Kolkman and Kelly, 2000). The advanced line
population was evaluated in three separate tests representing genotypes tested in field trial
at the Montcalm Research Farm, Entrican, MI in 1996 (MRF 96; Test 1), 1997 (MRF 97;
Test 2), and 1998 (MRF98; Test 3). Thirty (Test 1) and 36 (Tests 2 and 3; Kolkman and
Kelly, 2000). Four (Test 2 and 3) or five (Test 1) seedlings (samples) were used in each
of the three replications of the randomized complete block design (RCBD). The
Bunsi/Newport (BN) population was evaluated for OR in an RCBD using four
replications over time, using four samples per entry in each of four replications over time.
The Huron/Newport (HN) population was evaluated three times for OR, using five
samples per entry for each of three replications in an RCBD. Twenty-day old seedlings
(2"d trifoliate emerging) were cut at the base of the stem, placed in a 20 mM oxalate
solution (pH = 4.0), and rated for wilting symptoms, using a 1 to 6 scale used to measure

wilting (see Chapter 1 for details).

Field Experiments:
All three populations were evaluated for resistance to white mold in the field. The
advanced line population were grown in the field trials MRF96, MRF97, and MRF 98.
The BN F 3,, population was grown in the field in MRF97, and Sanilac Cooperator Farm

50

(SCF97), and F3” lines were grown in MRF98. Only 88 of the 98 F3“, lines in the BN
population were grown in SCF97, due to limited seed availability. The HN RIL
population was grown in MRF 96, MRF 97, MRF 98, as well as in SCF97. Planting was
delayed to the second week in June in all field experiments to favor disease development.
In the Montcalm Research Farm experiments, plots were 6 m in row length, with a 0.5 m
row spacing in the Montcalm Research Farm experiments. At the SCF97 field Site, all
plots were 3 m in row length, and 0.76 m in row width. The inner two rows of each four
row plot were planted with the experimental line, while the outer two rows were planted
with a highly susceptible cultivar, Midland, used as a uniform border. Standard
agronomic practices for tillage, fertilization, and herbicide were applied to ensure good
crop growth and development at both field sites. In MRF, plots were irrigated during
initial flowering with 13 mm of water at approximately three day intervals, depending
upon rainfall, in order to promote uniform disease pressure across the field. The field
experiments were irrigated with an overhead sprinkler system five times in 1996, three
times in 1997, and six times in 1998. Uniform infection of white mold of dry bean grown
at the MRF, was identified in previous field studies. The field Site at SCF97 was located
in a cooperators field, and selected based on past history of heavy white mold infection in

previous years.

Disease Severity and Incidence:

Plots were rated for disease severity index (DSI) and disease incidence (DI)
(Steadman 1997; Kolkman and Kelly, 2000; Steadman et al., 1998) using a ‘quarter scale’
(Hall and Phillips, 1996), approximately one month prior to maturity (early season

51

rating), and shortly before harvest (final season rating), when the majority of plants had
reached physiological maturity. The change in DSI and DI was calculated as the
difference between the early and final disease ratings. Thirty plants per plot were each
given a rating from 0 to 4, where 0 = no disease present, 1 = 1 to 25% of the plant with
white mold symptoms, 2 = 26 to 50% of the plant with white mold symptoms, 3 = 51 to
75% of the plant with white mold symptoms, and 4 = 76 to 100% of the plant with white
mold symptoms. Disease Severity Index was calculated for each plot on a percentage

basis, using the following formula:

2 (rating of each plant)
DSI = x 100
4 x (number of plants rated)

 

Disease incidence was calculated as the number of plants out of the thirty individuals

with white mold infection, based as a percentage.

Agronomic Traits:

Genotypes in all three populations, were evaluated for the following agronomic
traits: growth habit, days to flowering, mid-season canopy height, mid-season canopy
width, architecture, days to maturity, lodging, yield, and seed size. Growth habit was
determined during the growing season, as either indeterminate (Type H or H1) or
determinate (Type 1). Days to flowering were characterized by the number of days
following planting, when 50% of the plants in a plot have at least one open flower. At

mid-season (post main flower flush) canopy height and width measurements were

52

averaged on each individual plot, from six measurements per plot (three measurements
per row) in all experiments except for the MRF96 trials, where 10 measurements per plot
were taken. Plots were evaluated for architecture at maturity, using a 1 to 5 scale, where
1 = fully upright, 3 = bush, and 5 = prostrate. Days to maturity were calculated as the
number of days following planting, until 90% of the pods were physiologically mature
and drying down. Lodging was determined at maturity, based on a 1 to 5 scale, where 1
= no lodging, 3 = moderate lodging, and 5 = excessive lodging. All plots were harvested
at maturity after the late season ratings were taken. Plots were individually pulled, and
threshed in a Hege combine. Seed weight was recorded and reported at a moisture
content of 18%. Seed size was determined as the weight of 100 seeds adjusted to 18%

moisture content.

Statistical Analysis:

All greenhouse experiments were analyzed as RCBDS, using PROC GLM (SAS,
1995). In the advanced line populations, the MRF96 experiment was analyzed as a
rectangular lattice, and the MRF97 and MRF98 field experiments were each analyzed as
a partially balanced triple lattice using PROC LATTICE (SAS, 1995). The 27 common
genotypes were analyzed across all three tests (greenhouse) and years (field) as a RCBD,
using PROC GLM (SAS, 1995), with environments considered as a random effect, and
genotypes as a fixed effect. The BN and HN populations were evaluated in individual
greenhouse tests and field environments as RCBDS, using PROC GLM (SAS, 1995).
Both genetic populations were analyzed across the three years as a RCBD, using PROC
GLM, with both genotypes and environments considered as random effects. Estimates of

53

 

heritability for all traits were calculated on a plot basis, where h2 = (ogz)/[02/(re) + Ogcz/e +
082] (Hallauer and Miranda, 1988). Growth habit in the genetic populations was either
determinate or indeterminate, and a chi-square goodness of fit test was used to test for a
normal segregation ratio. Pearson correlation coefficients (r) were calculated by PROC

CORR (SAS, 1995).

RESULTS

Significant genotypic variation for OR, DSI, DI, and agronomic traits across
environments was identified in all three populations (Tables 2.1, 2.2, and 2.3). Mean
values and range in DSI and DI indicated that adequate disease pressure was attained in
each environment (Tables 2.4, 2.5, 2.6 and 2.7), except for the MRF98 environment, in
which an early DSI and DI ratings were not taken due to the short season. Parents of the
genetic populations differed for OR, DSI, DI, and all agronomic traits, except lodging.
Significant variation for lodging was identified in the progeny of both BN and HN
populations, even though the parental genotypes were not different. Other architectural
traits may have been segregating in both populations to sufficiently affect transgressive
segregation for lodging. Lodged plants during the growing season may affect the
microclimate to favor white mold development.

The correlations between OR, DSI, DI and agronomic traits were markedly
different in the three populations across environments (Table 2.8). Disease severity was
significantly correlated to D1 (P<0.0001) in each population, and in each environment. In

the advanced line population, OR was Significantly correlated to DSI (r=0.58; P<0.01)

54

Table 2.1. Analysis of variance for resistance and agronomic traits in the advanced line population, for
27 common genotypes tested across three greenhouse assays and three field environments (1996-1998).

 

 

 

 

Source Mean squares
Replication

Resistance Traits: Genotype (G) Environment (E) G x E (Environment)

26 2 252 6
Greenhouse:
OR1 2.3 **** 35.7““ 0.5 **** 0.3
Md;
Final DSII 1512.9 **"'* 8817.5 **** 418.7 *** 175.9
Final DII 2566.2 "" 5426.0 ** 734.4 ** 273.5
Early DSI 243.1 *** 2.4 68.9 "' 37.1
Early DI 1479.4 "** 891.8 244.9 102.2
Agronomic Traits:
Days to Flowering 86.1 "** 519.7 **** 2.5 *"* 3.6 **
Canopy Height 87.7 **"'* 10387.8 "" 25.4 **" 7.6
Canopy Width 57.1 *** 1536.7 “** 20.2 "** 41.5 ****
Architecture 8.0 "** 0.2 0.5 **** 0.2
Lodging 7.4 **** 15.3 "' 0.8 **" 1.8 **"
Days to Maturity 98.3 "" 12992.2 "** 25.1 **** 34.8 "**
Seed Size 1070.9 "** 39.5 18.9 **** 2.7
Yield 76.8 *" 156.8 27.1 **"'* 88.9 “"

 

*, ", "‘3 **" significant at P < 0.05, 0.01, 0.001 and 0.0001 levels, respectively
1 OR = resistance to oxalate; DSI = disease severity index; D1 = disease incidence

and DI (r=0.57; P<0.01), whereas in both the BN and HN populations, OR was not

significantly correlated to DSI or D1.

The only agronomic avoidance trait significantly associated with DSI (r=-0.54;

P<0.01) and DI (r=-0.51; P<0.01) in the advanced population was days to maturity. An

increase in days to maturity was associated with low DSI (r =-0.51; P<0.01) and DI (r=-

0.43; P<0.01) ratings. Days to maturity, however, was the only agronomic avoidance

mechanism that was not associated with DSI or D1 in the BN population. Fewer days to

55

Table 2.2. Analysis of variance for resistance and agronomic traits in the Bunsi/Newport population,
tested across three greenhouse assays and three field environments (1997-1998).

 

 

 

 

Source Mean squares

Replication
Resistance Traits: Genotype (G) Environment (E) G x E (Environment)

97 2 184 6

Greenhouse:
OR: 37.6T - - 40.4 mun
M
Final DSI1 1580.5 "** 12803.7 ” 838.1 **** 828.4 *
Final DI’ 2069.5 "“" 37766.5 **‘"* 1196.5 **** 1278.5 "
Early DSI 394.8 “ 252.4 232.1 "** 185.8
Early DI 1832.2 "* 69591.5 ”* 8652“" 1441.7 **
Agronomic Traits:
Days to Flowering 47.5 **** 8.9 4.6 *"'** 5.9 **
Canopy Height 135.1 **" 260.8 15.0 "" 190.3 “"
Canopy Width 85.4 **** 21774.7 "** 30.2 ***"' 230.3 "“
Architecture 1.0 **‘"* 8.4 ** 0,3 "I" 03 m:
Lodging 3.5 **** 16.2 0.8 .. 4.2 *m
Days to Maturity 89.2 ”*"‘ 56614.3 ""“" 22.8 " 80.1 ”*
Seed Size 33.8 "" 399.1 *"* 3.3 **** 9.0 "u
Yield 107.8 **** 3728.7 **" 44.5 **"'* 83.6 ""*

 

*, ", "‘3 "** significant at P < 0.05, 0.01, 0.001 and 0.0001 levels, respectively

1 significant at P < 0.10

3 OR = resistance to oxalate; DSI = disease severity index; D1 = disease incidence

flowering, a more upright architecture, shorter and narrower canopy at mid-season, and a

lower lodging score were all significantly associated with lower DSI and DI scores in the

BN population (Table 2.8). Alternatively, shorter canopy height at mid-season was the

only agronomic avoidance mechanism to be significantly associated with DSI (r=0.5 9;

P<0.001) or DI (r=0.73; P<0.0001) in the HN population. Correlations between disease

resistance and agronomic traits varied between individual environments, yet general

trends remained similar (Tables 2.9, 2.10, and 2.11). In individual environments,

56

Table 2.3. Analysis of variance for resistance and agronomic traits in the Huron/Newport population,
tested across three greenhouse assays and four field environments (1996-1998).

 

 

 

 

Source Mean squares

Replication
Resistance Traits: Genotype (G) Environment (E) G x E (Environment)

27 3 81 8

Greenhouse:
OR‘ 1.2 ** 27.2 **** 0.6 "M 0.5 *
Ei_e_lg_:
Final DSIz 1362.8 **** 16878.7 *** 24.7 732.8 **
Final DIz 2128.2 **** 47810.3 **** 510.7 1357.7 "
Early DSI 371.6 "* 3409.1 ** 130.0 283.4 *
Early DI 1535.7 ** 28965.7 ** 626.1 ** 1734.7 ***
Agronomic Traits:
Days to Flowering 58.3 **** 641.5 **** 3.6 **"'"' 2.7 *
Canopy Height 62.6"“ 7492.7 *"* 21.3 **“ 60.7 ""
Canopy Width 64.0" 5717.1 "** 27.2 *" 197.7 ****
Architecture 2.7 **** 11.9 ”** 0.5 **""" 0.3 *
Lodging 2.5 "* 23.6" 0.7* 1.7 ***
Days to Maturity 166.3 **** 6900.7 **** 8.1 21.7 *"'
Seed Size 57.3 **** 217.3 *** 3.6"" 12.5 "**
Yield 76.0 ** 1396.7 **** 36.3 **** 59.7 ****

 

*, **, "‘3 ”** significant at P < 0.05, 0.01, 0.001 and 0.0001 levels, respectively

1 significant at P < 0.10

1 OR = resistance to oxalate; DSI = disease severity index; D1 = disease incidence

OR was only significantly associated with DSI and D1 in the advanced line population.

The BN population had a large number of agronomic avoidance mechanisms, such as

days to flowering, canopy width, and lodging, that were correlated to DSI and D1. The

HN population had only few agronomic avoidance traits that were Significantly

associated with DSI and DI, such as canopy height. Overall, striking variability in

correlations between agronomic avoidance mechanisms existed between the advanced

population, and both the BN and HN populations.

57

Table 2.4. Means and ranges for resistance and agronomic traits for the advanced line population tested

across individual and combined environments.

 

 

 

Combined

MRF96’ MRF97T MRF98l Environments
Resistance Traits: mean (range)t mean (range) mean (range) mean (range)
OR 1 3.0 (2.0-4.0) 1.7 (1.0-3.4) 2.5 (1.3-3.6) 2.4 (1.7-3.4)
Final DSI1 (%) 24.3 (0.0-79.3) 32.4 (1.1-70.3) 12.2 (1.7-40.8) 22.7 (3.1-51.4)
Final DI‘I (%) 36.7 (2.2-87.8) 47.1 (2.2-86.7) 30.7 (5.6-81.1) 38.0 (7.8-74.8)
Early DSI (%) 6.8 (0.0-43.1) 6.4 (0.8-16.7) -- 6.5 (0.7-28.3)
Early DI (%) 20.0 (0.0-67.8) 23.8 (3.3-56.7) -- 21.3 (2.8-57.2)

Agonomic Traits:

 

Days to flowering
Canopy height (cm)
Canopy width (cm)
Architecturei
Lodging‘

Days to maturity

Yield (kg'ha")

40.3 (32045.3)

34.1 (27440.3)

35.1 (30.5-39.7)
2.7 (1.0-5.0)
2.2 (105.0)

98.9 (87.3-108.7)
Seed size (g'lOOSeed") 25.7 (16.9-64.9)
3122 (2235-4077)

44.9 (38.7-49.3)

55.6 (46.6-62.6)

42.7 (36.7-52.2)
2.6 (1.04.3)
2.9 (1.04.7)

112.1 (102.3-1193)
27.4 (16.3-79.6)
3380 (21674436)

42.9 (36.7-47.3)
51.9 (40.3-60.8)
42.1 (32.9-47.7)
2.6 (1.0-5.0)
2.3 (1.04.7)
86.9 (80.0-99.3)
25.1 (17.8-58.4)

42.7 (36.6-47.1)
46.8 (40.4-51.2)
40.3 (34.8-44.4)
2.7 (1.04.2)
2.5 (1.24.6)
99.4 (89.9-105.7)
25.6 (17.0-67.6)

3099 (2010-3785) 3212 (2325-3661)

I MRF96 = Montcalm Research Farm, 1996, MRF97 = Montcalm Research Farrrr, 1997; MRF98 =
Montcalm Research F arm, 1998
t Mean values for 30 genotypes in MRF96 (Test 1), 36 genotypes in MRF97 (Test 2) and MRF98 (Test 3),
and 27 combined gentoypes in the combined analysis.
1 OR = resistance to oxalate, where 1 = no wilting symptoms, 2 = 1 leaf with wilting symptoms, 3 = 2
leaves with wilting symptoms, 4 = 3 or more leaves with wilting symptoms, 5 = petioles collapsing, 6 =
main stem collapsing; DSI = disease severity index; D1 = disease incidence
‘Architecture based on a 1 to 5 scale, where 1 = fully upright, 3 = bush, and 5 = prostrate

‘ Lodging based on a 1 to 5 scale, where 1 = no lodging, 3 = moderate lodging, and 5 = excessive lodging.

Yield was Significantly associated with DSI and D1 in both the advanced

population and BN population, but not in the HN population (Table 2.8). In the BN

population, large seed Size was also significantly associated with low DSI (r=-0.44;

P<0.0001) and DI (r=-0.43; P<0.0001). Seed size in the BN population may be both a

strong component of yield, and vary as a result of white mold infection (Kerr et al.,

197 8). Physiological resistance, measured as OR, was significantly associated with yield

58

Table 2.5. Means and ranges for resistance and agronomic traits for the Bunsi/Newport population
tested across three individual environments.

 

 

 

MRF97I SCF97l MRF981
Resistance Traits: mean (range) mean (range) mean (range)
Final DSIx (%) 35.5 (3.9-85.0) 46.5 (0.8-86.1) 32.4 (1.4-76.1)
Final DI‘(%) 47.6 (7.8-95.6) 71.1 (2.2-100.0) 52.0 (5.6-97.8)
Early DSI (%) 18.9 (2.2-59.4) 20.6 (1.1-45.8) --
Early DI (%) 38.2 (5.6-93.3) 62.0 (3.3-98.9) --
Agronomic Traits:
Days to flowering 42.7 (36.7-49.5) 42.7 (37.0-48.7) 42.4 (38.3-47.0)

Canopy height (cm) 49.2 (35.0-58.2) 50.9 (37.5-59.6) 49.2 (33.8-58.1)
Canopy width (cm) 43.4 (34.1-51.5) 59.0 (43.2-70.4) 42.9 (31.8-49.6)
Architecture‘ 2.7 (1.0-3.0) 2.9 (2.0-3.3) 2.5 (1.3-3.7)
Lodging‘ 2.7 (1.7-5.0) 3.3 (1.3-5.0) 3.0 (1.0-5.0)
Days to maturity 111.6 (100.0-121.3) 110.9 (97.0-117.7) 86.8 (83.0-95.7)

Seed size (g‘ lOOseed") 20.9 (17.1-26.4) 22.9 (17.0-29.0) 20.7 (16.7-25.3)
Yield (kg ha") 2740 (16964009) 3481 (1348-4705) 2785 (1853-3717)

 

1 MRF97 = Montcalm Research Farrrr, 1997, SCF97 = Sanilac Cooperator Farm, 1997; MRF98 =
Montcalm Research Farrrr, 1998

‘ DSI = disease severity index; D1 = disease incidence

1 Architecture based on a 1 to 5 scale, where 1 = fully upright, 3 = bush, and 5 = prostrate

’ Lodging based on a 1 to 5 scale, where l = no lodging, 3 = moderate lodging, and 5 = excessive lodging.

within and across environments for both the advanced line (r=-0.51; P<0.01) and BN
population (r=-0.31; P>0.01; Tables 2.8, 2.9, 2.10, and 2.11). Yield was not associated
with OR for the HN population in the individual environments.

Growth habit ranged from determinate (Type I), to indeterminate (Type H), and
indeterminate prostrate (Type III) were observed in the advanced line population.
Growth habit segregated between determinate (Type I) and indeterminate (Type 11)
growth habit in the BN population [x2 = 1.98 (5:3); 0.15<P<0.20], and the HN population
[x2 = 0.57 (1 :1); 0.40<P<0.50]. The indeterminate growth habit was significantly

associated with low OR ratings in the advanced line population (r=-0.56; P<0.01), the

59

Table 2.6. Means and ranges for resistance and agronomic traits for the Huron/Newport population tested
across individual environments.

 

 

MRF96l MRF97T SCF971 MRF981
Resistance Traits: mean (range) mean (range) mean (range) mean (range)
OR1 2.9 (1.7-3.7) 1.8 (1.1-3.2) 2.4 (1.5-3.5) --
Final DSIx (%) 23.0 (1.7-54.4) 20.4 (1.1-50.3) 48.4 (7.8-69.7) 17.6 (3.1-40.0)
Final DIt (%) 36.1 (3.3-81.1) 32.7 (2.2-71.1) 82.2 (21.1-96.7) 34.8 (5.6-68.9)
Early DSI (%) 11.1 (1.4-33.0) 13.5 (0.3-35.8) 23.2 (6.1-46.7) --
Early DI (%) 32.7 (5.6—92.2) 26.1 (1.1-56.7) 61.0 (14.4-90.0) --

Agronomic Traits:

 

Days to flowering

37.2 (31.0-41.7)

43.4 (37.7-47.3)

42.7 (37.3-52.0)

41.2 (36.7-48.3)

Canopy height (cm) 32.4 (28.1-36.9) 52.9 (43.4-56.6) 52.1 (42.1-56.7) 46.8 (37.7-53.3)
Canopy width (cm) 32.5 (28.3-36.4) 40.9 (36.4-44.9) 52.0 (44.8-63.7) 37.7 (29344.7)
Architecture1 2.6 (1.0-3.0) 2.5 (1.0-3.3) 2.8 (2.0-3.7) 1.9 (1.0-3.0)
Lodgingi 2.1 (1.3-3.7) 2.7 (1.7-4.3) 3.3 (2.04.7) 2.3 (1.0-3.3)
Days to maturity 95.8 (88.3-105.3) 104.0 (96.0-113.3) 106.6 (98.0-115.0) 86.5 (80.0-97.0)

Seed size (g'lOOSeed") 20.6(16.9-25.4) 20.8(16.3-25.2) 24.0(19.4-28.4) 22.3(17.7-27.3)

Yield (kg'ha") 2852 (2123-3661) 2869(1827-3440) 3706(19434301) 268409993421)

1 MRF96 = Montcalm Research Farm, 1996, MRF 97 = Montcahn Research Farm, 1997, SCF97 = Sanilac
Cooperator Farm, 1997; MRF 98 = Montcalm Research Farm, 1998

‘ OR = resistance to oxalate averaged over three tests, where 1 = no wilting symptoms, 2 = 1 leaf with
wilting symptoms, 3 = 2 leaves with wilting symptoms, 4 = 3 or more leaves with wilting symptoms, 5 =
petioles collapsing, 6 = main stem collapsing; Greenhouse Tests 1, 2, and 3.

1 Architecture based on a 1 to 5 scale, where l = fully upright, 3 = bush, and 5 = prostrate

‘1 Lodging based on a 1 to 5 scale, where l = no lodging, 3 = moderate lodging, and 5 = excessive lodging.

BN population (r=-O.36; P<0.001), and the HN population (r=-0.50; P<0.01). Growth
habit was not associated with DSI or D1 in the advanced line population, most likely due
to the presence of susceptible indeterminate prostrate Type III cultivars, and determinate
large-seeded cultivars that escaped infection. The determinate growth habit was
significantly associated with high DSI (r=-0.20; P<0.05) in the BN population. Growth
habit was not associated with DSI or DI for the HN population in either the individual or

combined environments. Architectural avoidance mechanisms, such as canopy height

60

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65

‘ OR = resistance to oxalate averaged across three greenhouse tests; DSI = disease severity index; D1 = disease
incidence

and width most likely affected the correlations between field disease ratings in the
population and growth habit.

In all environments, the correlation between DSI and DI was highly significant
(Tables 2.8, 2.9, 2.10, and 2.11). Genotypic variation for the change in DSI between
early and final ratings were significant for all three populations. Significant genotypic
variation in the change in D1 was only identified in the advanced line population. The
only significant association between change from early to final DSI and DI, and OR was
observed in the advanced line population across environments (DSI; r=0.42; P<0.05), at
the MRF 96 environment (DSI; r=0.40; P<0.05).

Heritability estimates for resistance to white mold in the BN population were 0.47
for DSI, and 0.42 for D1 (Table 2.7). Estimates of heritability in the HN population for
DSI (0.82) and DI (0.76) were higher than those for the BN population. Heritability
estimates for OR in the BN population (0.19) and HN population (0.54) were both lower

than the corresponding heritability estimates for DSI and DI.

DISCUSSION

The relationship between resistance and agronomic traits in three different
populations of common bean was investigated in this study. The first population was a
group of advanced cultivars and germplasm of various market classes and geographic
origin. Physiological resistance, as determined through OR, was significantly correlated
to DSI and DI, while agronomic traits, such as days to flowering, canopy height and

width, were not important factors relating to disease levels in the field. The EN

66

 

population, was comprised of F 3-derived lines with no prior selection for high yield
potential. In this population, a weak association existed between OR and DSI in the field
across environments (r=0.18; P<0.10). There was no association between OR and DSI or
D1 in the field in the HN population. Similar results have been found using an alternative
measure of physiological resistance to white mold in bean. The length of fungal lesions
on stems was significantly correlated to white mold resistance in the field, in a small
group of relatively elite germplasm (Miklas et al., 1992). The correlation between the
lesion length and white mold resistance in the field varied, however, in a segregating
genetic populations (Miklas and Grafton, 1992). Genetic correlation between lesion
length and disease resistance in the field was not significant in a cross between Bunsi and
a susceptible determinate navy bean, D76125. In two other populations with a resistant
determinate (Type I) snap bean, NY5262, and differing susceptible indeterminate (Type
III) pinto bean parents, genetic correlations varied between significant and non-significant
correlations. Architectural avoidance mechanisms were cited as reasons for variability in
correlations between a measure of physiological resistance (lesion length), and resistance
to white mold in the field (Miklas and Grafton, 1992). The BN and HN populations both
shared the common susceptible parent, Newport. The variation in correlations between
agronomic and disease-related traits indicate that the choice of resistant parent is very
important in determining potential avoidance mechanisms in a segregating genetic
population. The heritability estimate for OR was much higher in the HN population, than
in the BN population. The oxalate test rates for a wilting response, that can be influenced
by the environmental conditions during the test. The overall mean of OR in the parents
and progeny of the BN population was lower than the tests for the HN population or the

67

advanced line population. The environmental conditions may have influenced wilting
response, and the corresponding heritability estimate. The HN population was at a more
advanced stage of homozygosity than the BN population. In addition, the oxalate test
was repeated three times, with three replications per test in the HN population, whereas
the BN population was tested with four replications over time.

The methodology in rating physiological resistance can also be an important
source of variability of correlations between physiological resistance and field ratings. In
soybean, three unique tests for physiological resistance on 18 cultivars resulted in varying
associations within tests, as well as to the field evaluations (Kim et al., 2000). One of the
methodologies involved placing mycelial plugs on soybean cotyledons. Two mycelial
plug tests were conducted on the same genotypes, and the results between tests were not
significantly correlated to each other. Choice of screening method for determining
physiological resistance is an important factor in evaluating advanced line populations as
well as segregating populations.

Agronomic traits that could contribute to disease avoidance in the field
environment, such as days to flowering and canopy width, were significantly associated
with the presence of disease in the field in the BN population. Days to maturity, which
was a significant factor in the field disease ratings in the advanced line population, was
not associated with the presence of disease in the field for the BN population. In
soybean, agronomic traits, such as flowering date, plant height, lodging and maturity,
have been shown to play a significant role in disease levels in the field in a segregating
soybean population (Kim and Diers, 2000). Bunsi and Newport are both well adapted to
the Michigan environment. Segregation among progeny for architectural traits and

68

phenological traits, such as days to flowering, was large and can greatly affect the
detection of resistance versus avoidance mechanisms. Greater architectural similarity
between Huron and NeWport, resulted in less segregation for architectural traits, and less
architectural avoidance in the HN population. The determinate growth habit in larger-
seeded determinate genotypes was identified with an open porous canopy, and a
component of architectural avoidance in previous studies under the semi-arid conditions
of intermountain states (Coyne, 1980). In the BN and HN navy bean populations, the
indeterminate growth habit had physiological resistance to white mold, whereas the
determinate growth habit was identified with low OR ratings. Determinate navy bean
cultivars such as Newport and Midland, grown in the Midwest region, were very
susceptible to white mold, as seen in the advanced line population (Kolkman and Kelly,
2000)

Heritability estimates varied for traits across populations. In the BN population,
estimates of heritability for the agronomic traits that were significantly correlated to DSI
and DI were generally much higher than those for the OR, DSI and DI. Estimates of
heritability for days to flowering was very high, at 0.90, and moderate to high for canopy
width at 0.65. Heritability estimates for resistance to white mold in the field can be
misleading, if traits with high heritability, such as days to flowering, play a major
avoidance role in the field. If selection in the BN population was based solely on disease
ratings, gain for disease resistance may increase, but such selection may be inadvertently
identifying genotypes with undesirable agronomic avoidance traits that reduce yield. In
the HN population, DSI and DI were only significantly associated with canopy height (h2
= 0.66) across environments. The estimate of heritability for DSI in this population was

69

higher than in the BN population, possibly due less environmental variation in
architectural traits, less variability in days to flowering, and less avoidance mechanisms.
Agronomic avoidance traits, such as a more upright architecture in parents, may have
reduced overall variability in resistance to white mold. Plant breeders making selections
for low DSI or DI cannot rely solely on ratings at the end of the season. The breeder
must know if the population is segregating for agronomically undesirable avoidance
mechanisms that have a negative effect on yield.

The study of complexly-inherited traits is highly dependent upon the physical
environment in which the phenotype is measured, and the genetic composition of the
population. Low to moderate levels of white mold infection have been associated with
higher yields, whereas high levels of white mold infection can result in severe yield loss
(Kerr et al., 1978). The SCF97 environment had a very severe level of white mold
infection compared to the other environments, and the mean yield was higher than in
other environments. Genotypes in the SCF97 environment produced both the highest
yield, as well as the lowest yield in the BN and HN populations (Tables 2.5 and 2.6). A
number of important architectural and phenological avoidance traits, such as days to
flowering, can be of variable importance in differing genetic populations. In the
advanced line population, OR was important in DSI and DI ratings, whereas agronomic
avoidance factors other than days to maturity, were not pertinent in the expression of
resistance. The number of lines that had undesirable agronomic avoidance mechanisms
were few in the advanced line population were few. Architectural traits also varied
greatly in this population, from lines with an open porous canopy, an upright architecture,
dense canopies and prostrate plant types. These genotypes, however, were generally

70

high-yielding, and trials in similar environments resulted in higher overall yields, than
that found in either of the two genetic populations. Agronomic avoidance mechanisms
may mask physiological resistance in the genetic populations with no prior selection for
yield, creating difficulty in selection of superior genotypes.

In genetic populations, an indirect screen for physiological resistance becomes
very important, even if the results are not correlated with field data. Ratings for DSI and
DI at physiological maturity provided sufficient information regarding disease resistance
in the field. There was no significant correlation between OR and the change in D1,
which may indicate that OR plays a more important role in the development and spread
of disease, and a less effect on infection late in the season.

Combining physiological resistance with desirable agronomic avoidance
mechanisms, such as an open porous canopy, or upright architecture, is a valuable
strategy in improving levels of resistance to white mold across environments.
Heritability estimates for DSI and DI were moderate in the BN population, and moderate
to high in the HN population, which suggest that progress can be made in breeding for
resistance to white mold. The genotypes studied in the advanced line population,
generally had fewer agronomic avoidance traits, compared to the BN and HN genetic
populations. Agronomic avoidance mechanisms played a large role in resistance in the
BN population. The HN population also had significant architectural mechanisms
associated with DSI and DI, different fi'om those identified in the BN population.
Generally, navy bean genotypes with a determinate grth habit were found to be very
susceptible to oxalate, and such cultivars were generally very susceptible to white mold in
the field. The choice of parents can be an important factor in dictating how much

71

variability in DSI and DI is attributable to agronomic avoidance traits versus
physiological resistance. Care must be taken in selecting lines from segregating
populations that do not possess undesirable agronomic avoidance traits that could
contribute to lower yield. Early generation selection against highly heritable, undesirable
agronomic avoidance traits, such as early flowering, may be a useful approach in
minimizing the selection of less desirable genotypes. Understanding the type of
variability present in genetic populations is critical if progress is to be made in breeding

for resistance to white mold.

72

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Abawi, GS, and R.G. Grogan. 1975. Source of primary inoculum and effects of
temperature and moisture on infection of beans by Sclerotinia sclerotiorum.
Phytopathology 65:300-309.

Blad, B.L., J .R. Steadman, and A. Weiss. 1978. Canopy structure and irrigation
influence white mold disease and microclimate of dry edible beans.
Phytopathology 68: 1431-1437.

Boland, G.J., and R. Hall. 1987. Epidemiology of white mold of white bean in Ontario.
Can. J. Plant Pathol. 9:218-224.

Boland, G.J., and R. Hall. 1994. Index of plant hosts of Sclerotinia sclerotiorum. Can.
J. Plant Pathol. 16:93-108.

Coyne, DP. 1980. Modification of plant architecture and crop yield by breeding.
HortScience 15: 244-247.

Fuller, P.A., D.P. Coyne, and J .R. Steadman. 1984. Inheritance of resistance to white
mold disease in a diallel cross of dry beans. Crop Sci. 24: 929-933.

0

Godoy, G., J .R. Steadman, M.B. Dickman, and R. Dam. 1990. Use of mutants to
demonstrate the role of oxalic acid in pathogenicity of Sclerotinia sclerotiorum on
Phaseolus vulgaris. Physiol. Mol. Plant Pathol. 37:179-191.

Haas, J .H., and B. Bolwyn. 1972. Ecology and epidemiology of Sclerotinia wilt of white
beans in Ontario. Can. J. Plant Sci. 52: 525-533.

Hall, R., and LG. Phillips. 1996. Evaluation of parameters to assess resistance of white
bean to white mold. Annu. Rep. Bean Improv. Coop. 39:306-307.

Hallauer, A.R., and J .B. Miranda. 1988. Quantitative genetics in maize breeding. Iowa
State University Press, Ames, Iowa, 2"‘1 edition. pp. 468.

Kelly, J .D., M.W. Adams, A.W. Saettler, G.L. Hosfield, and A. Ghaderi. 1984.
Registration of C-20 navy bean. Crop Sci. 24:822.

Kelly, J.D., G.L. Hosfield, G.V. Varner, M.A. Uebersax, M.E. Brothers, and J. Taylor.
1994. Registration of ‘Huron’ navy bean. Crop Sci. 34:1408.

Kerr, B.D., J .R. Steadman, and LA. Nelson. 1978. Estimation of white mold disease

reduction of yield and yield components of dry edible beans. Crop Sci. 18:275-
279.

73

Kim, H.S., and B.W. Diers. 2000. Inheritance of partial resistance to Sclerotinia stern rot
in soybean. Crop Sci. 40:55-61.

Kim, H.S., G.L. Hartman, J .B. Manandhar, G.L. Graef, J .R. Steadman, and B.W. Diers.
2000. Reaction of soybean cultivars to sclerotinia stern rot in field, greenhouse,
and laboratory evaluations. Crop Sci. 40:665-669.

Kolkman, J .M., and J .D. Kelly. 2000. An indirect test using oxalate to determine
physiological resistance to white mold in common bean. Crop Sci. 40:281-285.

Marciano, P., P. Di Lenna, and P. Magro. 1983. Oxalic acid, cell wall-degrading
enzymes and pH in pathogenesis and their significance in the virulence of two
Sclerotinia sclerotiorum isolates on sunflower. Physiol. Plant Pathol. 22:339-345.

Maxwell, DP, and RD. Lumsden. 1970. Oxalic acid production by S. sclerotiorum in
infected bean and in culture. Phytopathology 60:1395-1398.

Miklas, P.N., K.F. Grafton, J.D. Kelly, H.F. Schwartz, and J .R. Steadman. 1998.
Registration of four white mold resistant dry bean germplasm lines: I9365-3,
I9365-5, I9365-31, and 92BG—7. Crop Sci. 38:1728.

Miklas, RN, and KP. Grafton. 1992. Inheritance of partial resistance to white mold in
inbred populations of dry bean. Crop Sci. 32:943-948.

Miklas, P.N., K.F. Grafton, and B.D. Nelson. 1992. Screening for partial physiological
resistance to white mold in dry bean using excised stems. J. Am. Soc. Hort. Sci.
117:321-327.

Morrall, R.A.A., L.J. Duczek, and J .W. Sheard. 1971. Variations and correlations within
and between morphology, pathogenicity, and pectolytic enzyme activity in
Sclerotinia from Saskatchewan. Can. J. Bot. 50:767-7 86.

Noyes, RD, and J .G. Hancock. 1981. Role of oxalic acid in the Sclerotinia wilt of
sunflower. Physiol. Plant Pathol. 18:123-132.

Park, SJ. 1993. Response of bush and upright plant type selections to white mold and
seed yield of common beans grown in various row widths in southern Ontario.
Can. J. Plant Sci. 73: 265-272.

Redden, R., and J. Tatnell. 2000. Screening common bean breeding lines and
germplasm for resistance to Sclerotinia sclerotiorum. Annu. Rep. Bean Improv.
Coop. 43:160-161.

SAS Institute. 1995. The SAS system for Windows. Release 6.12. SAS Inst., Cary, NC.

74

Schwartz, H.F., D.H. Casciano, J .A. Asenga, and DR. Wood. 1987. Field measurement
of white mold effects upon dry beans with genetic resistance or upright plant
architecture. Crop Sci. 27:699-702.

Schwartz, HR, and J .R. Steadman. 1978. Factors affecting sclerotium populations of,
and apothecium production by, Sclerotinia sclerotiorum. Phytopathology 68:
383-388.

Schwartz, H.F., J .R. Steadman, and DP. Coyne. 1978. Influence of Phaseolus vulgaris
blossoming characteristics and canopy structure upon reaction to Sclerotinia
sclerotiorum. Phytopathology 68: 465-470.

Steadman, J .R. 1997. 1996 White mold (Sclerotinia) bean nursery summary. Annu.
Rep. Bean Improv. Coop. 40:142.

Steadman, J .R., K.F. Grafton, K. Kmiecik, J .M. Kolkman, M. Kyle Jahn, and R. Mainz.
1998. Bean White Mold Nursery, 1997. Annu. Rep. Bean Improv. C00p.
41 :173-174.

Steadman, J .R., K. Powers, and B. Higgins. 1997. Screening common bean for white
mold resistance using detached leaves. Annu. Rep. Bean Improv. Coop. 40: 140-
141.

Sutton, DC, and B.J. Deverall. 1984. Phytoalexin accumulation during infection of
bean and soybean by ascospores and mycelium of Sclerotinia sclerotiorum. Plant
Pathol. 33:377-383.

Tu, J .C. 1985. Tolerance of white bean (Phaseolus vulgaris) to white mold (Sclerotinia
sclerotiorum) associated with tolerance to oxalic acid. Physiol. Plant Pathol.
26: 1 1 1-1 17.

Tu, J .C. 1989. Oxalic acid induced cytological alterations differ in beans tolerant or
susceptible to white mould. New Phytol. 112:519-525.

Tu, J .C., and W.D. Beversdorf. 1982. Tolerance to white mold (Sclerotinia sclerotiorum
(Lib.) De Bary) in Ex Rico 23, a cultivar of white bean (Phaseolus vulgaris L.).
Can. J. Plant Sci. 62:65-69.

Wallen, V.R., and MD. Sutton. 1967. Observations on sclerotinia rot of field beans in
southwestern Ontario and its effect on yield. Can. Plant Dis. Surv. 47:116.

Weiss, A., L.E. Hipps, B.L. Blad, and J .R. Steadman. 1980. Comparison of within-
canopy microclimate and white mold disease (Sclerotinia sclerotiorum)
development in dry edible beans as influenced by canopy structure and irrigation.
Agric. Meterol. 22: 11-21.

75

CHAPTER 3
MOLECULAR MARKER DISSECTION OF QTLS CONFERRING RESISTANCE

TO WHITE MOLD AND GROWTH HABIT IN Two NAVY BEAN POPULATIONS

INTRODUCTION

White mold, caused by S. sclerotiorum, is a devastating fungal disease that can
infect over 400 plant species (Boland and Hall, 1994). In common bean, white mold
causes a reduction in yield, due to a decrease in pods per plant, seed size, and seed quality
(Kerr et al., 1978; Steadman, 1979). Under appropriate moisture conditions, typically
found during canopy cover and flowering, apothecia germinate from sclerotial bodies,
producing ascospores that disperse into the plant canopy (Boland and Hall, 1987). The
ascospores germinate on senescent flowers, and the subsequent developing mycelium
invade the plant tissue. Oxalate, a primary pathogenicity factor of S. sclerotiorum
(Godoy et al., 1990), is exuded into the plant tissue, followed by the release of
polygalacturonases (Marciano et al., 1983).

Resistance to white mold in common bean is complexly-inherited (Fuller et a1,
1984; Miklas and Grafton, 1992). Physiological resistance has been described in certain
genotypes, based on different greenhouse assays (Pedzoldt and Dickson, 1998; Miklas et
al., 1992a; Miklas et al., 1992b; Steadman, 1998; Hunter et al., 1981; Kolkman and
Kelly, 2000). Progress in breeding for resistance to white mold has been hindered by the
limited expression and detection of physiological resistance in the field environment.
Avoidance mechanisms, such as an open porous canopy, can play a major role in the
development of disease in the field throughout the season (Fuller et al., 1984; Steadman

76

et al., 1973). Agronomically undesirable avoidance mechanisms, such as early flowering,
may reduce white mold infection levels, but place a major restraint on the ability of the
breeders to select for high-yielding genotypes. Few genotypes have been identified as
resistant in both field and greenhouse assays. Bunsi (Ex Rico 23), an indeterminate navy
bean, has both physiological resistance to white mold and an open porous canopy that
deters white mold development (Tu and Beversdorf, 1982; Tu, 1985; Kolkman & Kelly,
2000). A second navy bean, C-20 (Kelly et al., 1984), and C-20 - derived lines, such as
Huron (Kelly et al., 1994), have also been identified as having resistance to white mold in
both field and greenhouse tests (Miklas et al., 1992a; Kolkman and Kelly, 2000)
Marker-assisted selection (MAS) allows for the identification and selection of
superior genotypes without having to employ undue effort in phenotyping large numbers
of individuals. The difficulty in detection of desirable phenotypes, due to factors such as
environmental variation, hinders normal selection procedures for important quantitative
traits, and increases the importance of MAS (Tanksley et al., 1989). Molecular markers
linked to both qualitative and quantitative traits of economic importance, including
disease resistance, have been identified in common bean (Kelly and Miklas, 1998).
Selective genotyping (Lander and Botstein, 1989) and bulked segregant analysis
(BSA) (Michehnore et al., 1991) have been utilized to efficiently screen large numbers of
polymorphic markers, without having to genotype entire populations. Selective
genotyping involves the identification of a subset, usually 10 - 14% of the genotypes, that
possess extreme phenotypes of the population. A small percentage of the total genotypes
that exhibit extreme phenotypic values for the trait of interest are grouped together, and
either analyzed as individuals, or through BSA, where the DNA of the similar phenotypes

77

are pooled. Selective genotyping and BSA has been used successfiilly in the
identification of QTLS for quantitatively-inherited traits. In a computer simulation study,
the ability to detect markers linked to QTLS for the trait of interest was improved if
alternate DNA bulks were used for traits that were correlated to the main trait of interest
(Ronin et al., 1998). Selective genotyping may be restrictive, when a saturated linkage
map is not available. A small number of individuals are used for both linkage map
construction and QTL estimation, which may result in a bias of the genetic variation that
is present in the population (Wang and Paterson, 1994; Martinez, 1996).

A population was developed from a biparental cross between two navy bean
genotypes Bunsi and Newport, differing in resistance to white mold. An initial study of
the Bunsi-derived (BN) population indicated that certain agronomic avoidance traits, such
as days to flowering, may have confounded the expression or detection of a significant
correlation between physiological resistance, measured as resistance to oxalate (OR) and
white mold disease levels in the field (Chapter 2). DNA bulks comprised solely of a
small number of lines in the extreme phenotypes, may not adequately represent resistant
genotypes in the population. DNA pooling strategies based on a priori knowledge about
the population should help resolve useful markers linked to QTLS, and discern the
location of QTL regions (Wang and Paterson, 1994). Genotyping multiple traits that are
related to the trait of interest have been shown to be effective in identifying QTLS that
may not be detected through screening extreme phenotypes (Ronin et al., 1998). The first
objective of this study, was to identify markers linked to QTLS conferring resistance to
white mold in common bean. The second objective of this study was to determine if
selective multivariate genotyping (SMG), using more than one phenotypic trait in the

78

pooling of DNA bulks, is more efficient than creating DNA bulks from single traits in the

identification of markers linked to QTLS for resistance to white mold.

MATERIALS & METHODS
Plant Material and Marker evaluation:

An F3-derived BN mapping population was generated from a cross between two
navy bean genotypes, Bunsi and Newport, that varied in resistance to white mold. Bunsi
is an elite cultivar with an indeterminate (Type 11) growth habit. Bunsi possesses both
physiological resistance and a porous canopy for avoidance to white mold (Tu and
Beversdorf, 1985; Schwartz et al., 1987; Miklas et al., 1992; Kolkman and Kelly, 2000).
Newport is a susceptible navy bean cultivar with a determinate (Type 1) growth habit
(Kelly et al., 1995; Kolkman and Kelly, 2000). Ninety-eight F2 lines were advanced in
the greenhouse to the F3 generation using single seed descent. Seed of individual F 3
plants was bulked, and advanced in a greenhouse. Seed harvested from three FM plants
were bulked and F 3,, plants were increased in a winter nursery in Puerto Rico. Bulked
F3,6 lines were grown in field trials at the Montcahn Research Farm and Sanilac
Cooperator Farm in 1997. F3,7 lines were grown in a Montcalm Research Farm field trial
in 1998. No selection for agronomic traits was made during generation advancement.

A second recombinant inbred line (RIL) population of 28 individuals was
developed using single seed descent from a cross between Huron and Newport. Huron is
an C-20 - derived cultivar, with an upright plant type and indeterminate growth habit
(Kelly et al., 1994). Huron has both physiological resistance to white mold, via the
greenhouse oxalate test, and resistance to white mold in the field (Kolkman and Kelly,

79

2000)

Plant tissue was harvested from parental genotypes and approximately 10 F 3,7
greenhouse grown plants for each F 3 - derived family from the BN population. DNA was
harvested fi'om approximately 10 greenhouse F5:7 plants from the Huron/Newport (HN)
population. DNA was extracted from the plant tissue using a mini-prep procedure
(Edwards et a1, 1991; Haley et al., 1994b). Parental genotypes of the BN population
were screened for the presence of polymorphic bands with the Polymerase Chain
Reaction (PCR) and approximately 600 Operon random 10-mer primers (Williams et al.,
1990), using Gibco Taq DNA polymerase (Miklas et al., 1993; Haley et al., 1994a).
Approximately 100 primers were found to be polymorphic between the parental
genotypes, Bunsi and Newport. The parents were also screened twice with 111 RAPD
primers from the integrated bean linkage map (F reyre et al., 1998), once with Gibco Taq
DNA polymerase, and once with Stoffel fragment Taq DNA polymerase. Polymerase
Chain Reaction was conducted in a 96-well PTC-100 Programmable Thermal Controller
(MJ Research, Inc) programmed for 3 cycles of l min at 94 °C, 1 min at 35 °C, and 2 min
at 72 °C; 34 cycles of min at 94 °C, 1 min at 40 °C, and 2 min at 72 °C with the final step
extended by 1 s for each of the 34 cycles, and a final extension cycle of 5 min at 72 °C
(Haley et al., 1994a). RAPD markers are identified by the name of the Operon primer,
followed by the size of the polymorphic fragment.

The parental genotypes and DNA bulks were also screened for polymorphic bands
using eight AF LP primer pair combinations (V os et al., 1995). Primer combinations that
produced bands that segregated between the parental genotypes and DNA bulks were

screened on the entire population. Polyacrylamide gel electrophorese (PAGE) was used

80

to separate AF LP fragments. Fragments were visualized using a silver staining
procedure, according to the directions of a commercial silver staining kit (Promega), with
the addition that both the fix/stop and developing solution were partially frozen. Gels
were scored for band polymorphism, estimating band size in reference to a 10 and a 25
base pair DNA ladder. Gels were transferred to chromatography paper (Barrett and
Kidwell, 1998). The first three letters of the AF LP marker names indicate the Eco RI +3
(+ANN) selective nucleotide, while the second three letters indicate the Mse I + 3
(+CNN) selective nucleotides used in this study. The number following the Six letter
enzyme/primer combination represents the Size of the polymorphic fiagment generated by
the specific marker. RAPD and AF LP marker protocols for the HN population were

similar to those described for the BN population.

Traits:

Physiological resistance in both populations was determined indirectly, by
screening the populations for OR (Kolkman and Kelly, 2000). The BN population was
evaluated for OR in an RCBD using four replications. The HN population was evaluated
twice for OR with three replications in a RCBD. Briefly, twenty-day old seedlings (2“d
trifoliate emerging) were cut at the base of the stem and placed in a 20 mM oxalate
solution (pH 4.0). The seedlings were rated for wilting symptoms using a l to 6 scale
(see Chapters 1 and 2 for details).

Both populations were evaluated for resistance to white mold in the field across
several environments in Michigan. The BN population was grown at the Montcalm
Research Farm in 1997(MRF 97) and 1998 (MRF 98), and Sanilac Cooperator Farm in

81

1997 (SCF97). The HN RILs were grown in MRF 97, MRF98, and SCF97, as well as in
MRF in 1996 (MRF96; see Chapter 2). Plots were rated for disease severity index (DSI)
and disease incidence (DI) using a ‘quarter scale’ (Hall and Phillips, 1996), shortly before
harvest, when the majority of plants had reached physiological maturity (see Chapter 2
for details). The DSI was calculated for each plot on a percentage basis, using the

following formula:

2 (rating of each plant)
DSI = x 100
4 x (number of plants rated)

 

Disease incidence was calculated as the number of plants out of the thirty individuals
with white mold infection, based as a percentage. Genotypes were also evaluated for a
number of agronomic traits, including: growth habit, days to flowering, mid-season
canopy height, mid-season canopy width, architecture, days to maturity, lodging, yield,

and seed size (see Chapter 2).

Selective Multivariate Genotyping:

Selective genotyping, using both single traits and multiple traits, was used to
create DNA bulks, and identify significant markers for the BN population. Three sets of
resistant and susceptible DNA bulks were established for extreme phenotypes of DSI, DI,
and OR (see Chapter 2). The multivariate bulks were comprised of lines that were either
resistant and high-yielding, or susceptible and low-yielding, within a fixed flowering

range from 40 - 45 days to flowering (Table 3.1). The 4 sets of DNA bulks were

82

screened with the polymorphic primers, in order to identify markers linked to the

resistance phenotype. Primers that were polymorphic in the bulks were then tested for

polymorphism in the population.

Table 3.1. DNA pooling strategies based on single or multiple traits

 

DNA pool

DNA pool phenotype

 

Disease Severity Index:

81

S2
Disease Incidence:

ll

12
Resistance to Oxalfl;

Ol

02
Multivariate Analysis:

M1

M2

resistant: low DSI’, based on field data

susceptible: high DSI, based on field data

resistant: low DII, based on field data

susceptible: high DI, based on field data

resistant: low ORI score, based on greenhouse data

susceptible: high OR score, based on greenhouse data

resistant: high yielding, low DSI, between 40-45 days to flowering
susceptible: low yielding, high DSI, between 40-45 days to flowering

 

l DSI = disease severity index; DI = disease incidence; OR = resistance to oxalate

Markers were scored for the presence or absence of the RAPD or AF LP band.

Chi-square tests indicated whether the markers were segregating in 5:3 ratio (for F 3-

derived lines) or a 1:1 ratio (in the instance where the DNA collected for each family was

a representative sample of a RIL). Significant markers were identified via analysis of

variance and correlation analysis (SAS, 1995) to indicate linkage between markers, as

well as the confirmation of linkage to resistance or agronomic traits. Linkage and linkage

83

order of markers were determined with MAPMAKER/EXP (Lander et al., 1987), using
the Kosambi mapping function (Kosambi, 1944), a minimum LOD score of 3.0 and a

maximum recombination frequency of 0.30.

Statistical Analysis:

All greenhouse experiments were analyzed as RCBDS, using PROC GLM (SAS,
1995). Greenhouse and field experiments for the BN and HN populations were evaluated
individually as RCBDS, using PROC GLM (SAS, 1995). Both populations were
analyzed across field environments, and greenhouse tests, as a RCBD, using PROC
GLM, with both genotypes and environments considered as random effects. Resistance
and agronomic traits that were significantly (P<0.01) associated with DSI and D1 in the
BN populaton, were initially tested for analysis of variance, and Pearson correlation
coefficient (SAS, 1995).

Traits Significantly associated with DSI and DI across environments in the BN
population (P<0.01) were mapped onto the constructed linkage groups using interval
mapping via QTL Cartographer software program (Basten et al. 1994; Basten et al.,
1999). Threshold LOD scores (95%) for individual traits were determined through a
permutation test, with 1000 permutations (Churchill and Doerge, 1994). Significant
markers that were most closely associated to regions with major QTLS, were confirmed
also via analysis of variance and Pearson correlation coefficient, and tested for
significance in combined and individual environments. The most significant marker(s) in
each linkage group for OR, DSI, DI, and yield was tested in the HN population. Analysis
of variance and correlation analysis were used to detect significance between the marker

84

and OR, DSI, DI, and yield. Effect of single markers was determined using a students t-
test (SAS, 1995). The effect of multiple markers on phenotypic expression of traits by
markers on more than one linkage group was analyzed using multiple regression (SAS,

1995).

RESULTS & DISCUSSION
Marker and QTL associations:

Markers were identified using each DNA bulking method in SMG. The markers
identified in this study were robust across environments, and were also generally robust
across resistant genotypes. In total, 38 polymorphic markers were evaluated in the BN
population. Linkage map construction, using MAPMAKER/EXP placed 23 markers on
four main linkage groups (Figure 3.1), for a total of comprised 220 cM. Markers that were
detected via SMG were included within the four linkage groups (Table 3.2), except for one
unlinked marker, aaccaa195. Marker segregation ratios were tested against both a 5:3
ratio or 1:1 ratio, since small sample size used for DNA extraction may have limited the
ability to adequately sample a segregating F3-derived line in the BN population (Tables A7
and A8). Mapping of F2 or F3 populations with dominant markers is not ideal, since
heterozygous individuals cannot be identified. Mapping RIL or doubled-haploid (DH)
populations with dominant markers is much more efficient, since heterozygous genotypes
are minimal to non-existent in the population (Knapp et al., 1995). In BSA, however,
dominant markers are more informative in F 2 populations than backcross populations,
since recombination frequency is higher and results in closer linkage to the QTL (Mackay
and Caligari, 2000).

85

 

 

 

 

O " ' 012.900 0 - - acccac242 0 - ~ acceacSOO o - -AM13.400
11 e - aggctt85
16 e - aaccaa302
19- - 012.2500
21 T ' P7700
23 — F 611820 L99” 25 - - :03 1550
27 - _ l07.1t2g)§0Uri-2
3O -1 - aaCC _
32 H12.1050L9c 2
P9'1750 Leo-3 37 Y11 350 - - aggcaa87
PvPr-1
:vEr-Z 48 - - habit B3 J
grp _
53 - - acgctt239 B7 52 aacctt144
57 - - acgctt240 38
63 r P BC20.1800
77J - 015.1800

32 ChS

Figure 3.1. Linkage map with 13 RAPD markers, 10 AFLP markers
and one phenological marker, constructed with Mapmaker/Exp, from

98 F3 - derived lines from the Bunsi/Newport population. Distances

are in Kosambi cM units and are listed on the left-hand side of the linkage
groups. Locations of known genes are listed in italics. Linkage groups
B2, B7, B3, and B8, and gene location correspond to the integrated
linkage map (Freyre et al., 1998)

86

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87

The first linkage group had four markers that were anchored in a similar
orientation to Linkage Group B2 of the integrated linkage map (F reyre et al., 1998). The
second linkage group had two anchor markers in common with Linkage Group B7 of the
integrated linkage map. The third and fourth linkage groups each had one marker in
common with Linkage Group B3 and B8 of the integrated linkage map, respectfully.
More markers are needed to anchor these two linkage groups to the integrated linkage
map. One of 13 markers not identified with a linkage group was associated with OR.

Genotype and genotype by environment interactions were significant for all
resistance and agronomic traits in the BN population (see Chapter 2), and the two
parental genotypes varied significantly for all traits measured, except lodging.
Segregation of growth habit was tested to fit a 5:3 ratio using chi-square goodness of fit
test, (x2 = 1.98; 0.15<P<0.20). Disease pressure appeared adequate for each test
environment in the BN population. Parental genotypes segregated for OR, DSI, DI, and
yield. Environmental variation was significant for DSI, DI and agronomic traits.
Although data in individual environments were not always normally distributed, data was
not transformed to correct for skewness (Doerge et al., 1997). Combined environments
were generally normally distributed (Figure 3.2).

Markers associated with QTLS for resistance and agronomic traits were found to
be relatively robust across environments. Markers were also stable between DSI and DI
measurements, which is expected since the phenotypic correlation between DSI and DI
was very high. One major QTL for DSI and DI was identified on linkage group B2, and
was located near RAPD markers BC20.1800 and 015.1800 (Figure 3.3). BC20.1800, the
closest marker associated with this QTL, accounted for 11.6 % of the phenotypic

88

variability for DSI, and 12.6 % of the phenotypic variability for D1 (Table 3.3) across the
three environments. This marker was significant in individual MRF97 and MRF 98
environments, accounting for the most variability in the MRF97 environment for both
DSI (R2 = 13.0%), and DI (R2 = 13.2%). BC20.1800 was not significantly associated to
DSI or DI within the SCF 97 environment, however, possibly due to the extreme high
disease pressure encountered in that particular environment (see Chapter 2).

The region around 015.1800 and BC20.1800 on the integrated linkage map
contains several identified genes that may be important in disease resistance. A P.
vulgaris pathogenesis-related gene, PvPR-Z (Walter etal., 1990), a polygalacturonase-
inhibiting protein, Pgip (Toubert et al., 1992), are located in this region of Linkage Group
BZ. Fungal defense-related genes may be triggered as a general resistance response to
S. sclerotinia infection. Pgip is particularly noteworthy, since polygalacturonase is
generated by the pathogen during white mold infection. Chalcone synthase, (ChS; Ryder
el at., 1987), located near 015.1800, is an isoflavonoid-derived phytoalexin involved in
host defense, and may be important in general resistance to S. sclerotiorum. The
resistance found in this linkage group was most likely due to physiological resistance
associated with a generalized host defense response, but not associated with OR, which

requires some tolerance to the actual chemical. Marker P7.700 was previously identified
to be linked to resistance to F usarium root rot in bean (Schneider and Kelly, 2000). In the
BZ linkage group, there were significant QTLS for lodging (acgctt239; R2 = 7.4%), days
to maturity (012.1600; R2 = 13.5%), and seed size (acgctt240; R2 = 6.9%) across
environments (Table 3.3) that were identified in the analysis of variance, but not in
interval mapping. Disease severity index and DI most likely influenced seed size on B2,

89

Resistance to Oxalate Disease Severity Index

40

N
V

w
0

number of lines
8 '5’

B
V

1 3

2
oxalate score

Days to Flowering

_n
01

 

number__ of lines
0

U!

35 37 39 41 43 45 47 49
days to flowering

 

 

 

Days to Maturity
U)
gas N
§22 ~ V
g B
g11
C

I I I I I - I

 

 

 

90 93 96 99102105108111
days to maturity

N (d
O 0

number of lines
o

O

00
0

number of lines
0

.5 _;
N a

number of lines
a:

Disease Incidence

 

  

5

N
V

  

 

80

0 20
disease severity index (%)

4O 60 100

M
h

 
 

_.
0)

B
V

number of lines
a:

  

O

60
disease incidence (%)

0 20 40 80 100

 

N
O
r

 

 

 

 

 

 

 

 

Architecture Lodging
24
.,, B/N
0)
5.16
“6
3
E 8
3
C
o
2 3 1 2 3 4 5
architecture lodging
Seed Size Yield
B
N (015 '
V V .2
tow a B
u V
8
.I.. |.
15 17 19 21 23 25 27 29 1750 2350 2950 3550
seed size (g100seed1 ) yield (kg ha1 )

Figure 3.2. Phenotypic distribution for 98 lines of the Bunsi/Newport

population evaluated for resistance to oxalate, disease severity index, disease

incidence, and agronomic traits that were associated with resistance traits

(see Chapter 2) including: days to flowering, architecture, lodging, days to

maturity, seed size, and yield. Location of mean values for Bunsi (B) and

Newport (N) are located at arrows.

90

 

 

 

 

 

 

 

 

 

 

3.5 r i) Disease Severity Index 5 _ ii) Disease Incidence
02.0 03
9 9 -----------------------
0.5 1 ' h
-1.0 ‘ ' -1 ‘ '
cM 14 5 11 23 21 cM 14 6 11 23 21 l
015.1800 012.900 015.1800 012.900

 

iii) Lodging

 

 

 

 

cM14‘6 11 2 21
015.1800 012.900

Figure 3.3. LOD scores for interval mapping analysis of i) disease severity index,
ii) disease incidence, and iii) lodging on Linkage Group B2 in the Bunsi/NeWport
population. LOD scores above the threshold level (---) indicate experiment-wise
error rate of 0.05, determined using 1000 permutations. The x-axis corresponds

to centimorgan distance and marker intervals located on Figure 3.1.

91

Table 3.3. Summary of markers identified on linkage group B2 in the BN population, found to be
most closely linked to the QTL for significant resistance and agronomic traits including phenotypic
variability associated with the marker in combined (P<0.01) and individual environments, effect of the
presence of the marker across environments, and SMG/DNA bulking strategy used to identify marker.

 

 

 

 

Linkage group 82
Trait marker EnvironmentI R2 (%) Effect Bulk‘
DSI§ (%) BC20.1800 Across Environments 11.6 9.3 M, S, I
MRF97 13.0
SCF97 ns
MRF98 9.9
DI (%) BC20.1800 Across Environments 12.6 11.1 M, S, I
MRF97 13.2
SCF97 ns
MRF98 10.5
Lodgings acgctt239 Across Environments 7.4 0.3 -
MRF97 5.0
SCF97 3.7 *
MRF 98 4.7 *
Days to Maturity 012.1600 Across Environments 13.5 2.4 -
MRF97 10.5
SCF97 6.1 *
MRF 98 19.1
Seed Size (g IOOSeed") acgctt240 Across Environments 6.9 1.0 I
MRF97 6.9
SCF97 4.5 *
MRF 98 6.2

 

"' significant at P<0.05

l significant at P<0.10; ns = non significant

‘MRF97 = Montcalm Research Farm, 1997; SCF97 = Sanilac Cooperator Farm, 1997; MRF98 =
Montcalm Research Farm, 1998; Across Environments = average across MRF97, SCF97, and MRF98

‘ DNA bulks, where M = multivariate, S = disease severity index, I = disease incidence, and 0 = Oxalate; ‘-
’ = no bulk; see Table 3.1

5 DSI = disease severity index; D1 = disease incidence

’ Lodging based on a 1 to 5 scale, where 1 = no lodging, 3 = moderate lodging, and 5 = excessive lodging.

92

since seed size has been shown to decrease with higher levels of disease infection (Kerr et
al., 1978). Seed size must not be reduced dramatically by disease, however, since yield is
not a significant factor in this linkage group.

The largest linkage group identified in this study was associated to the B7 (Freyre
et al., 1998), by anchor markers H12.1050 and 107.1200 (Figure 3.1). Markers in this
linkage group were significantly associated with 0R, DSI, DI, days to flowering, lodging,
seed size and yield (Figure 3.4; Tables 3.4 and 3.5). 017.820 was associated with 8.8 %
of the phenotypic variability for OR in the greenhouse. Two of the most important
markers, aggctt85 and aacctt130, contributed up to 16.8 % and 15.8 % of the phenotypic
variability for DSI, respectively, and 13.3% and 12.7% of the phenotypic variability for
D1, respectively. The same two markers generated the greatest significance in MRF97,
but were not associated with either DSI or D1 in SCF97 (Table 3.5). AF LP marker
aacctt130 was significantly associated to days to flowering (R2 = 13.9%) across
environments, particularly in SCF97 (R2 = 18.3%). The same two markers, were
significantly associated with days to maturity across environments in the analysis of
variance.

A potential QTL region for architectural traits was also located via analysis of
variance on B7. Marker 107.1200 was associated with architecture (R2 = 9.1%) and
lodging (R2 = 9.1%), across environments (Table 3.5). In a previous unrelated study
involving a population derived fiom a cross of Type II x Type HI genotypes, H12.1050
was associated with plant uprightness, and branch density (Jung et al., 1996). The
association with architectural traits is confirmed in this study, since 107.1200 is linked to

H12.1050 marker on B7. 107.1200 was significantly associated with seed size across

93

 

 

4 _ i) Disease Severity Index ’ ii) Disease Incidence

 

 

 

 

 

 

 

0 i l i I 4 L 0 g l . l a 1

habit acccac242 habit acccac242

 

iii) Resistance to Oxalate

LOD

 

 

 

 

A 4.4. 8‘: 11 :
habit . aacca0242

Figure 3.4. LCD scores for interval mapping analysis of i) disease severity
index, ii) disease incidence, and iii) resistance to oxalate on Linkage

Group B7 in the Bunsi/Newport population. LOD scores above the
threshold level (---) indicate experiment-wise error rate of 0.05, determined
using 1000 permutations. The x-axis corresponds to centimorgan distance

and marker intervals located on Figure 3.1.

94

Table 3.4. Summary of markers identified in the BN population, to be most closely linked to the QTL
for resistance to oxalate (0R), including phenotypic variability (P<0.01) associated with the marker,
effect of the presence of the marker, and SMG/DNA bulking strategy used to identify the marker.

 

 

 

 

 

 

Putative Linkage group B3
Trait Marker Environment R2 (%) Effect Bulk'r
0Rt aaccaa302 Greenhouse 1 1.4 0.19 0
Unlinked Loci
R2 (%) Effect Bulk
aaccaa l 95 Greenhouse 8.8 0. 17 I
Linkage Group B7
R2 (%) Effect Bulk
G17.820 Greenhouse 8.8 0.17 M, I, 0
habit‘ 8.0 0.16 -

 

l DNA bulks, where M = multivariate, S = severity, I = incidence, and 0 = Oxalate; see Table 3.1

‘ OR = resistance to oxalate where l = no wilting symptoms, 2 = 1 leaf with wilting symptoms, 3 = 2
leaves with wilting symptoms, 4 = 3 or more leaves with wilting symptoms, 5 = petioles collapsing, 6 =
main stem collapsing

‘ the indeterminate (Type II) growth habit is associated with resistance to oxalate

environments (R2 = 19.5%), and within individual environments MRF 97 (R2 = 19.8%),
SCF97 (R2 = 23.4%), and MRF98 (R2 = 11.4%). Previously, seed size was thought to
have been affected by white mold, since plants with heavy disease pressure have a
smaller seed size than those in the absence of disease (Kerr et al., 1978). Seed size is a
complex factor that has an affiliation with this particular linkage group for another
reason. Seed size is also affected by the presence of seed proteins. For example, the ‘T’
phaseolin seed protein, located on another region of Linkage group B7, has been
associated with larger-seeded Andean genotypes, such as G122 (Johnson et al., 1996),
whereas the S phaseolin is associated with smaller-seeded genotypes. Other seed

proteins, such as lectins and uridenes with no known effect on seed size have also

95

Table 3.5. Summary of markers identified on linkage group B7 in the BN population, to be most

closely linked to the QTL for significant resistance and agronomic traits, including phenotypic

variability associated with the marker in combined (P<0.01) and individual environments, the effect of
the presence of the markers on the phenotype, and the SMG/DNA bulking strategy used to identify

 

 

 

marker.
Linkage group B7
Trait Marker Environment‘ R2 (%) Effect Bulk1
DSI’ (%) aggctt85 Across Environments 16.8 11.3 M, I, 0
MRF97 15.6
SCF97 ns
MRF 98 6.4 *
aacctt130 Across Environments 15.8 10.9 M, 0
MRF 97 18.9
SCF97 ns
MRF98 4.3 *
DI (%) aggctt85 Across Environments 13.3 11.5 M, I, 0
MRF97 13.6
SCF97 ns
MRF98 6.1 *
aacctt130 Across Environments 12.7 11.1 M, 0
MRF 97 17.2
SCF97 ns
MRF98 3.6 *
Days to Flowering aaccttl30 Across Environments 13.9 1.7 M, 0
MRF97 17.4
SCF97 18.3
MRF98 3.8 1'
Architecture$ 107.1200 Across Environments 9.1 0.2 M, 0
MRF97 11.1
SCF97 3 .6 1'
MRF98 4.2 *
Lodging‘2 107.1200 Across Environments 9.1 0.4 M, 0
MRF97 12.0
SCF97 5.2 "‘
MRF98 6.0 *
Days to Maturity aacctt130 Across Environments 6.0 * 1.6 M, 0
MRF97 6.4

96

SCF97 5.4 *

MRF98 4.7 "'

aggctt85 Across Environments 3.6 l 1.2 M, I ,0
MRF97 8.1
SCF97 3.4 *
MRF98 5.9

Seed Size (g lOOseed") 107.1200 Across Environments 19.5 1.7 M, 0
MRF97 19.8
SCF97 23.4
MRF98 11.4
habit” Across Environments 18.3 1.9 -
MRF97 19.0
SCF97 9.2
MRF98 20.6
Yield (kg ha") 107.1200 Across Environments 36.5 477 M, O

MRF97 42.5
SCF97 9.8
MRF98 25.8

aaccttl30 Across Environments 34.4 462 M, 0
MRF97 45.6
SCF97 5.3
MRF98 28.3

G17.820 Across Environments 27.2 408 M, I, 0
MRF97 33.7
SCF97 5.1
MRF98 17.3

aggctt85 Across Environments 24.9 396 M, I, O
MRF97 26.1
SCF97 6.9
MRF98 13.5

 

* significant at P<0.05

l significant at P<O.lO; ns = non significant

3 MRF97 = Montcalm Research Farm, 1997; SCF97 = Sanilac Cooperator Farm, 1997; MRF98 =
Montcalm Research Farm, 1998; Across Environments = average across MRF97, SCF97, and MRF 98

‘ DNA bulks, where M = multivariate, S = severity, I = incidence, and 0 = Oxalate; see Table 3.1

9 DSI = disease severity index; D1 = disease incidence

‘ Architecture based on a l to 5 scale, where l = fully upright, 3 = bush, and 5 = prostrate

1: Lodging based on a l to 5 scale, where 1 = no lodging, 3 = moderate lodging, and 5 = excessive lodging.
" the indeterminate (Type 11) growth habit is associated with seed size

97

been located at the base of linkage group B7 (Freyre et al., 1998).

The bottom of linkage group B7, appears to be the region associated with yield in
the BN population, and is supported by the very high LOD scores on the linkage map
(Figure 3.5). Most of the markers significantly associated with yield within and across
environments in this linkage group included: 107.1200 (R2 = 36.5%), aacctt130 (R2 =
34.4%), G17.820 (R2 = 27.2%), and aggcttSS (R2 = 24.9%). The most significant marker
for yield, 107.1200, was also the most significant marker for seed size. The markers were
most responsive in the MRF 97 environment, accounting for 26.1% (aggctt85) up to
45.6% (aacctt130) of the phenotypic variability for yield. Interactions between these
markers and the MRF98 environment, which was a lower yielding environment, were
still very significant, with associations ranging from 13.5% (aggctt85) to 28.3%

(aaccttl 30) of the phenotypic variability for yield. The associations between the markers
and yield in the SCF97 environment were significant, but much less than the MRF97 and
MRF98 environments, ranging fi'om 5.1% (G17.820) to 9.8% (107.1200). Among
individual environments, 107 .1200 had the most significant association for seed size in
the SCF 97 environment, accounting for 23.4% of the phenotypic variability. The SCF97
environment, despite having such heavy disease pressure, was by far, the highest yielding
environment, even though the marker associations with yield were lower.

The significance of the marker associations with yield and disease resistance in
this linkage group indicates that there must be important physiological components in this
region. The seed lectins (phytohaemogluttanins), uridenes, and the leghaemoglobbin
genes located near this region may be very important indirect contributors to yield. Seed

lectins have been studied for their potential role in plant defense (Shewry and Lucas,

98

 

 

. . 6.0
3_5 _ 1) Days to Flowering ii) Seed Size

  

415 ’

LOD

31) '

_—--——-—--_--— -—— ——-——c—

1.5 ’

 

 

 

 

 

 

 

 

1
hablt aaccac242 habit aaccac242

 

 

iv) Yield

LOD

   

 

 

 

 

I

W CM‘I 16 1.3. 141 8
ha it a ccac 42 ha'bit "h I

 

 

P
--

Figure 3.5. LOD scores for interval mapping analysis of i) days to flowering,
ii) seed size, iii) lodging and iv) yield on Linkage Group B7 in the
Bunsi/Newport population. LOD scores above the threshold level (---)
indicate experiment-wise error rate of 0.05, determined using 1000
permutations. The x-axis corresponds to centimorgan distance and marker

intervals located on Figure 3.1.

99

1997). Lectins isolated from red kidney bean seeds were found to reduce germ tube
elongation in Botryodiplodia theobromae (Brambl and Gade, 1985). Resistance to
oxalate, DSI, D1, which are yield components, as well as the associations with
architecture, days to flowering, days to maturity, and lodging, indicate that the resistance
identified in this linkage group may be the result of a constitutive structural component of
the plant.

The third linkage group detected in this study was identified in the Oxalate bulks
and was associated only with 0R (Figure 3.1). The RAPD marker, Y11.350, corresponds
to linkage group B3 of the integrated linkage map (Freyre et al., 1998). The most closely
linked marker to a QTL for OR was the AF LP marker, aaccaa302 (R2 = 11.4 %), which is
closely linked to Y11.350 (Figure 3.6; Table 3.4). Flanking Y11.350 on the integrated
linkage map, is another pathogenesis-related protein, PvPR-I (Walter et al., 1990).
Greater marker density is needed to confirm if this is an important region for resistance to
white mold. Significant markers linked to QT Ls for resistance to F usarium root rot in
bean were previously detected near the PvPr-I gene on B3 in both greenhouse and field
tests (Schneider and Kelly, 2000). The identified markers linked to both resistance to
F usarium root rot and resistance to white mold indicates that this region may contain
general defense-related genes.

The fourth linkage group in this study contains 103.1550, which corresponded to
Linkage Group B8 of the integrated linkage map (Freyre et al., 1998; Figure 3.1). This
linkage group was identified using the multivariate, DI and OR bulks. The marker was
present in the susceptible multivariate, DI, and OR DNA bulk for AM13.400, and in the
susceptible DI bulk for 103.1550. In the two examples, however, the band was present

100

 

Resistance to Oxalate

2.4

LOD

 

 

 

l l- L .4 l l 4 l. . l

CN'i 8 l 21 5 16

aaccaa310 acccac500

 

-

Figure 3.6. LOD scores for interval mapping analysis of resistance to oxalate
on Linkage Group B3 (tentative) in the Bunsi/Newport population. LOD scores
above the threshold level (---) indicate experiment-wise error rate of 0.05,
determined using 1000 permutations. The x-axis corresponds to centimorgan

distance and marker intervals located on Figure 3.1.

101

in the resistant Bunsi parent. In this scenario, the presence of the band was associated
with a shorter plant phenotype. No resistance or yield traits were associated with this
linkage group, although markers were associated with canopy height and architecture.

An unlinked loci, aaccaal95, was also identified in the DI bulk, and was found to
be highly associated with 0R (R2 = 8.8%) (Table 3.4). This marker was not found to be
associated with any other agronomic trait, nor was associated with any known inkage
group.

Overall, the three markers G17.820, aaccaa302, and aaccaa195, accounted for up
to 26 % of the phenotypic variability found for OR in the BN population. Resistance to
oxalate appears to be complexly-inherited, and greatly influenced by the environment,
which is consistent with the low estimate of heritability for OR in the BN population
(Chapter 2). Combining selection for BC20.1800 and aggctt85, or BC20.1800 and
aaccttl30 resulted in phenotypic associations for DSI of 27%. A similar coefficient of
determination was identified with these markers and DI. Combining BC20.1800,
aggctt85 and aactt130 produced 30% of the phenotypic variability for DSI, and 27% of
the phenotypic variability for DI. The markers alone, and in combination are

significantly associated with major QTLs for resistance to white mold.

Selective multivariate genotyping was an efficient method for detecting markers
for resistance to white mold in bean. Markers located on B2 were identified using the
multivariate, DSI, and DI DNA bulks (Table 3.2), and not the 0R bulks. The candidate
QTL loci in the BZ region include genes involved in defense response mechanisms,
which would not have been ascertained in a test for resistance to oxalate. Selective

102

multivariate genotyping was also effective in identifying all of the molecular markers on
B7. Only one of the eight molecular markers in this linkage group was detected in all 4
sets of DNA bulks. Four of the markers were identified with the multivariate, DI, and
OR bulks, while two of the markers, 107.1200, and aacctt130, were identified using the
multivariate and OR bulks only. In B7, creating DNA bulks using the extreme
phenotypes from DSI and DI, OR, as well as the multiple traits related to yield and
resistance within a fixed flowering range, was an effective method in determining
markers and QTLS for resistance to white mold. Seven of the eight molecular markers in
B7 were detected using the OR bulks even though OR was not significantly associated to
DSI or DI. As well, all eight of the markers were identified using the multivariate bulks.
Genotyping a chosen set of individuals with specific phenotypes, based on a prior
knowledge about the population, was an efficient method to detect markers that were

linked to the resistance phenotype.

Determinate vs. Indeterminate Growth Habit:

In the BN population, growth habit was treated as a phenotypic marker, since the
progeny were either determinate (Type I) or indeterminate (Type II). The placement of
this phenotypic marker on linkage group B7 was therefore an indirect result of the
mapping of markers related to resistance to white mold. Growth habit was, unexpectedly,
significantly associated with markers on linkage group B7. The growth habit marker was
detected on one end of the linkage group, corresponding to bottom of linkage group B7.
The gene for determinate growth habit, fin, had previously been mapped onto linkage
group B1 (Freyre et al., 1998). The 15 RAPD markers on linkage group B1 of the

103

integrated linkage map were not polymorphic between the Bunsi and Newport parents,
suggesting that the determinate grth habit in the BN population may reside at a
different location in the genome. Differing origins of the determinate growth habit in
populations previously used to map this phenotype, and the mapping population used in
this study validates the likelihood of the contrasting results. Wide crosses, using the
Andean gene pool as the source for the determinate grth habit, were previously used
for mapping the fin gene (F reyre et al., 1998). The determinate growth habit is a
prominent trait in Andean germplasm (Singh et al., 1991; Koinange et al., 1996).
Alternatively, the determinate growth habit in genotypes of the Middle American gene
pool exists mainly in cultivated materials of the navy bean market class (Kelly, 2000).
Few examples exist of the determinate growth habit in more exotic, Middle American
germplasm. Early attempts to introgress the determinate growth habit from Andean
germplasm into navy bean germplasm through breeding proved to be unsuccessful. The
determinate growth habit was, therefore, first introduced into the navy beans using
mutagenesis. The first determinate navy bean cultivar, Sanilac, released in 1956, was the
most widely used source of the determinate growth habit in navy bean breeding programs
worldwide (Kelly, 2000). It is reasonable, therefore, that the determinate grth habit
trait in the navy bean germplasm is controlled by a different locus than that of the fin
gene in Andean germplasm. The determinate growth habit, or terminal inflorescence is
controlled by more than one loci in other plant species, such as Arabidopsis (Ratcliffe et
al., 1998), Brassica (Mirnida et al., 1999) and Pisum (Singer et al., 1999). In Pisum
sativum L., up to six independent mutants characterize various aspects of inflorescence
architecture. This is the first report of the determinate grth habit trait in navy bean

104

germplasm being associated with a completely unique locus, unlinked to the fin locus.
Unique phenotypic differences exist between the determinate grth habit of
navy beans, and genotypes of Andean origin. Determinate navy bean cultivars, such as
Midland and Newport, are generally very susceptible to white mold (Kolkman and Kelly,
2000). Seafarer, a similar determinate navy bean, was found to have a highly
concentrated flowering period characterized by a very short flowering duration, resulting
in the production of a main flush of flowers, many of which do not set pods. A higher
pod abscission rate was identified in determinate navy bean genotypes in comparison to
indeterminate navy and black bean cultivars, and resulted in lower yield in the
determinate navy bean genotypes (Izquierdo and Hosfield, 1983). Alternatively, the
determinate growth habit in larger-seeded genotypes, such as Montcahn, is characterized
by a significantly lower number of flowers, compared to the determinate navy bean,
Seafarer (Subhadrabandhu et al., 1978). The determinate grth habit has been
recognized as a resistance phenotype in larger-seeded bean genotypes (Fuller et al., 1984;
Miklas et al., 2000; Steadman et al., 1973). Isles, a determinate kidney bean, was found
to avoid infection to white mold, compared to highly susceptible determinate navy bean
cultivars, such as Newport and Midland (Kolkman and Kelly, 2000). The determinate
phenotype at the fin locus was associated with resistance to white mold, in an A55/G122
population (Miklas et al., 2000). A QTL for canopy porosity was also located near the fin
locus in the G122, indicating that the resistance phenotype was due to an avoidance
mechanism. Excessive flower production in the determinate navy bean cultivars, may
lead to an increase in potential inoculation sites, and be an overall component of the
susceptibility to white mold in the determinate navy bean. Empirical knowledge from

105

breeders working in this germplasm group indicates that determinate navy bean
genotypes are very susceptible to white mold. In the BN population, growth habit was
also associated with yield, since average yield of the indeterminate genotypes was 364
kg‘ha‘l higher than the determinate genotypes, across three environments. The open
porous canopy of determinate genotypes of Andean origin has been characterized as an
avoidance mechanism in resistance to white mold in the field in semi-arid production

areas (Coyne, 1980) and a characteristic that may limit yield.

Confirmation of Markers in HN Population:

The most significant markers identified in each linkage group for OR, DSI, D1 or
yield, were tested on the HN population (Table 3.6). On linkage group BZ, BC20.1800
segregated in a 1:1 segregation ratio (x2 = 0.57; 0.40<P<0.50) between the parental and
progeny genotypes. BC20.1800 was a significant marker in this population for DSI
accounting for 40% of the phenotypic variability for DSI and 35% of the phenotypic
variability for D1 across environments. In the MRF96 environment, associations between
BC20.1800 and DSI (R2 = 60.9 %) and DI (R2 = 53.2 %) indicated the presence of a
major QTL for resistance in this population. BC20.1800 was only significantly
associated with DSI (P<0.05), representing up to 20.2 % of the variability in the SCF97
environment and was not significantly associated with D1 in SCF97. Heavy disease
pressure in the SCF 97 environment, may have limited the expression of the QTL located
near BC20.1800. The small population size of the HN population may also have been a
limitation for evaluating the marker association in the SCF97 environment.

Markers on linkage group B2 have previously been associated with QTLS for

106

disease resistance in bean. Markers linked to QTL for common bacterial blight (CBB)
resistance were located near the PvPr—Z and ChS genes (Nodari et al., 1993; Jung et al.,
1996). Markers linked to QTLS for resistance to both CBB and web blight were
identified on linkage group BZ, located near ChS and PvPr-2 in another population (Jung
et al., 1996). Plant uprightness, an avoidance mechanism for web blight, was also
mapped to this region of the genome (Jung et al., 1996). Marker P7.700 was also
associated with a QTL for resistance to F usarium root rot on B2 (Schneider and Kelly,
2000). Linkage group B2 may have a cluster of defense-related genes that have broad
application across pathogens.

All markers associated with linkage group B7 were tested on the HN population,
since yield was such a predominantly significant factor within the entire linkage group in
the BN population. Six of the markers did not have the favorable allele of the resistance
phenotype. Marker H12.1050 was polymorphic between the parental genotypes, but the
band corresponding with the marker, was not present in any of the 28 progeny. Only one
marker, G1 7 .820, segregated between the parental and progeny genotypes in the HN
population, and had a normal 1:1 segregation ratio (x2 = 0.14; 0.70<P<0.80). G17.820
was associated with OR (R2 = 24.3%), in addition to yield across environments (R2 = 47 .0
%), and within the MRF96 environment (R2 = 34.5%). It was not associated with yield in
the SCF97 environment, and was not correlated with DSI or D1 in either environment.
As in the BN population, G1 7 .820 was associated with growth habit. Growth habit was,
however, not significantly associated with DSI, DI, or yield in the HN population.
Although the indeterminate phenotype may be associated with genes for resistance to
white mold, selection for the indetemrinate phenotype alone would be insufficient in

107

selecting for resistance. Resistance identified in the Huron genotype may be, in part, due
to the QTL associated with BC20.1800, as well as G17.820. Huron shares two markers
for resistance in common with Bunsi. Huron may have other QTLS conferring resistance
to white mold. A larger population will be needed to test for the presence and effects of
additional QTLs.

Quantitative trait loci for resistance to white mold has previously been identified
on linkage group B7. The Phs locus, located on B7, was found to be associated with up
to 36% of the variability for physiological resistance in an A55/G122 population, as
determined using a straw test (Miklas et al., 2000). Days to maturity was also found to be
associated with the Phs locus in the A5 5/G122 population, where later maturing lines had
less disease (Miklas et al., 2000). Both physiological resistance and avoidance
mechanisms QTLS were located on B7 in a PC-SO/XAN-159 population (Park et al.,
2000a; Park eta1., 2000b). A single RAPD marker, J 09.950, was associated with partial
field resistance, plant height, partial physiological resistance, determined via the straw
test, seed weight, and resistance to CBB in the PC-50/XAN-159 population. RAPD
marker, H12.1050, was previously associated with plant uprightness and branch density,
contributing more evidence of plant avoidance mechanisms located on B7 (Jung et al.,
1996). Significant markers for both physiological resistance and plant avoidance to white
mold reside on linkage group B7.

Resistance genes have been found to be tightly linked. Certain resistance genes,
such as genes for resistance to downy mildew in lettuce (Lactuca serriola L.) are located
in clusters on the genome (Maisonneuve et al., 1994). Mechanisms of resistance genes,
such as protein kinases (PK), nucleotide binding sites (NBS), and leucine-rich repeats

108

have also been found to be conserved resistance genes across species (Kanazin et al.,
1996; Salrneron et al., 1999). Similarity in function and location of resistance genes
suggest a common evolutionary origin for resistance to pathogens in plants (Maisonneuve
et al., 1994; Kanazin etal., 1996; Salrneron et al., 1996; Geffroy et al., 1999).
Microclusters of disease-resistance related sequences were identified in bean when
analyzed with primers based on a conserved NBS found in several plant disease
resistance genes (Rivkin et al., 1999). One of the identified clones from this analysis
mapped close to a gene for rust resistance in bean. Candidate gene analysis using known
defense-related genes may be a useful approach towards identifying genes associated with
resistance to white mold in bean. In sunflower, a candidate gene approach was used to
identify genes for resistance to white mold (Gentzbittel et al., 1998). Homology cloning
of NBS-like genes, and serine-threonine PK-like genes, was used to detect genes for
resistance to downy mildew and white mold. The NBS-like loci were associated with
resistance to downy mildew, and 25 cM away from a PK-like loci that was associated
with resistance to white mold (Gentzbittel et al., 1998). The presence of QTLS for
physiological resistance to white mold in bean on similar linkage groups in several
populations is suggestive of a similar mode of action for resistance (Miklas et al., 2000;
Park et al., 2000a). The same QTL regions for resistance to both Fusarium root rot and
white mold, located on B2 and B3, indicate that similar defense-related genes may
effective against both pathogens.

The markers identified in the BN population are useful tools in understanding
resistance to white mold in bean, and for future marker-aided breeding for resistance to
white mold. QT Ls for resistance to white mold identified in Linkage Groups B2 and B7

109

were significant across environments, and in certain cases, were confirmed in a second

population. High-yielding environments are very conducive to heavy white mold

pressure. The high LOD scores and coefficients of determination between markers on

Linkage Group B7 and yield under white mold pressure, indicates that breeding for high

yield and resistance to white mold is achievable. The indeterminate navy bean has been

Table 3.6. Phenotypic variability (P<0.01) for OR, DSI, DI and yield associated with markers in HN

population in combined and individual environments.

 

 

 

Putative Linkage HN population marker- trait analysis
Marker Group Trait Environment R2 (%)
BC20. 1800 BZ DSI’r Across Environments 40.3
MRF96 60.9
MRF 97 16.4“
SCF 97 20.2““
MRF98 13.2l
DI Across Environments 35.4
MRF96 53.2
MRF 97 ns
SCF97 ns
MRF98 21.0
G17.820 B7 OR Greenhouse 24.3
Yield Across Environments 47.0
MRF96 34.5
MRF97 38.4
SCF97 ns
MRF98 42.5

 

1‘, l = significant at P<0.05 and 0.10, respectively; as = non significant

’ DSI = disease severity incidence; D1 = disease incidence; OR = resistance to oxalate

shown to be higher-yielding, with greater yield stability than the determinate navy bean

(Kelly et al., 1987). QTL analysis of yield under heavy white mold stress confirmed the

significant variation in yield that was observed in the BN population. Combining the

110

resistant cultivars, Bunsi and Huron, may offer an improved phenotype, if Huron was to
carry additional QTLS for resistance that were not identified in the BN population. The
markers identified in this study indicate that improving resistance by MAS in a
Huron/Bunsi hybridization may not be an effective strategy. Phenotypically, Bunsi has
an open porous canopy, while Huron has a more upright plant architecture and tight
canopy. Phenotypic selection for a taller, more upright plant architecture, with an open
canopy may be an efficient strategy for improving resistance to white mold. OAC Laser,
an indeterminate navy bean, is an example of a navy bean with an upright plant
architecture and open porous canopy that has low DSI and DI scores in white mold
environments (Kolkman and Kelly, 2000). Relationships between architectural and seed
size traits have been previously associated with elongation factors, such as intemode
length, structural factors relating to sturdiness, branch angle and number, and
reproductive factors relating to pod distribution traits (Acquaah et al., 1992). Previous
convention has held that physiological resistance and avoidance mechanisms, such as
plant architecture are separate unlinked entities. Markers on B7 contrast that theory,
since QTLS for 0R, days to flowering, days to maturity and architecture were identified
on the same linkage group. The QTL for OR identified on linkage group B7 may be an
indirectly constitutive defense mechanism that is a pleiotropic effect of genes for

architecture, that improve plant structure and rigidity in bean.

QTL Mapping:
The identification of significant QTLs for complexly-inherited traits is highly

dependent upon the environment in which the trait is measured. In many instances,

111

microclimate changes can significantly alter the expression of the trait. A change in
microclimate can play a major role in the expression and detection of resistance to white
mold in bean. Experiments with populations that are used to study such complexly-
inherited traits, must take into account not only the physical environment that the
genotypes are exposed to, but also the phenotypic variability within the population, that
may affect the microclimate. In this study, populations were developed from narrow
crosses between elite navy bean lines that were adapted to the Michigan environment. A
genetically narrow population is ideal for the study of QTLS of minor effect. If a wide
cross is utilized, QTLS will be identified, but may be associated with broad differences in
germplasm groups, rather than minor differences associated with complexly-inherited
traits. The BN population varied for growth habit and plant architecture. More minor
QTLs may be identified in a population derived from a cross between resistant and
susceptible parents that have a similar architecture and growth habit. The importance of
evaluating QTLS in a genetic background that can adequately identify the genetic
potential of QTL regions is the main focus of the Inbred Backcross Method (Bliss, 1993),
or the Advanced Backcross QTL Method (Tanksley and Nelson, 1996). The latter has
been successfully used to introgress QTLs from diverse germplasm, including wild
species, into an adaptive background (Bemacchi et al., 1998) An appropriate genetic
population should be the first priority in studying QTLS, whereas the second priority then
becomes identification of linkage groups, and linkage map construction.

The construction of genetic linkage maps, however, are dependent upon
maximum recombination, assuming adequate coverage of the genome. Genetic maps are

constructed between market classes, gene pools, and species, depending upon genetic

112

similarity within the species of interest. In common bean, an integrated linkage map was
created to bring together three previously mapped populations consisting of
morphological markers, isozymes, RF LP markers, and RAPD markers (F reyre et al.,
1998). The integrated linkage map was very useful in determining the location of QTLS
in the BN population. Genes identified on previous maps that are in the region of the
QTLs identified in the BN population become candidate genes for further study of
possible Mendelian factors controlling the QTLs for resistance to white mold in common
bean. Such genes include the PvPR-Z, Pgip, and ChS on linkage group B2, the seed
lectins on linkage group B7 , and the PvPr-I gene on B3.

The importance of testing for multiple traits that are correlated to the trait of
interest was suggested as an important method in the identification of markers linked to
QTLS (Ronin et al., 1998). Selective multivariate genotyping, using single and multiple
traits to design resistant and susceptible DNA bulks was an efficient strategy for the
identification of markers linked to QTLs in the BN population. In the absence of an
adequate screen for physiological resistance, the multivariate DNA bulk offers an
opportunity to select for a specific set of genotypes with desirable characteristics. This is
an important factor in the study of resistance to white mold in common bean, since
various factors, such as days to flowering may affect the number of lines that avoid
infection, rather than resist infection. The QTLs identified in this study were found in
regions of the genome most likely to have either general plant defense mechanisms, such
as PvPr-I , PvPR-Z, and Pgip, or seed protein factors, which may also have antifungal
activity, as well as an influence on the architectural phenotype. Studies in other species
have shown that general mechanisms of plant defense may play an important role in

113

resistance to white mold. In this study, the most important QTLs in the BN population
were found in regions of the genome that were associated with phenological and
agronomic traits, such as days to flowering, days to maturity, plant architecture and yield.
In soybean, QTLS were found in regions of the genome associated with agronomic traits
such as lodging, and plant height and flowering date (R1; Kim and Diers, 2000). QTLS
for branching pattern, seed weight, oil content and flowering date were also found to be
associated with resistance to white mold in sunflower (Mestries et al., 1998). A structural
component of plant architecture may play an important role in the constitutive resistance
to white mold in crop species. Other general constitutive pathogenesis-related defense
mechanisms may also be important for resistance to white mold in common bean.
Markers were identified that were linked to QTLS for resistance to white mold, in
two main linkage groups of common bean. The markers were relatively stable across
environments, with the exception of being less significant in one environment with
extreme disease pressure. The association of the BC20.1800 marker to resistance to
white mold in linkage group B2 was confirmed in a second RIL population. One marker
on linkage group B7 was associated with OR and yield in the second RIL population.
One of the linkage groups accounted for major variability for yield under white mold
pressure. Markers in the two linkage groups offer unique insight into potential breeding
strategies, such as QTL pyramiding, for the improvement of resistance to white mold in

common bean.

114

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AppendixB
Disease Severity Index Disease Incidence
15 9
... Hv
2 _ 3 H
‘b 0
g 5 - N E Y
3 .
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0 ,
0 20 4O 60 0 20 4O 60 80
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Resistance to Oxalate Yield
9
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oxalate score yield (kg/ha)

Figure B1. Phenotypic distribution across four environments for 28
recombinant inbred lines of the Huron/Newport population evaluated for
marker association to disease severity index, disease incidence, resistance
to oxalate, and yield (see Chapter 2 and 3). Location of mean values for

Huron (H) and Newport (N) are located at arrows.

124

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