Identification of QTL Associated with White Mold Resistance in ...

Published online October 27, 2005

Identification of QTL Associated with White Mold Resistance in Common Bean Marcio Ender and James D. Kelly*

Reproduced from Crop Science. Published by Crop Science Society of America. All copyrights reserved.

ABSTRACT

mental conditions such as temperature and soil moisture on plant avoidance traits that influence white mold development. Disease avoidance mechanisms are associated with plant morphological traits such as upright plant architecture and porous plant canopy (Coyne, 1980; Kolkman and Kelly, 2002) that affect the microclimate conditions within the plant canopy and can limit fungal establishment and development (Schwartz et al., 1978). Stiff-strawed bean genotypes with enhanced standing ability have a better chance of escaping initial white mold infection. However, other traits such as canopy porosity are also effective in minimizing disease development. For example, Bunsi, an indeterminate type II bean cultivar with an open porous canopy and semidecumbent plant architecture, exhibited low levels of white mold infection (Miklas et al., 2004; Park, 1993), whereas, the erect determinate navy bean cultivars Newport and Midland were highly susceptible to white mold (Kolkman and Kelly, 2002). Larger-seeded Andean determinate beans with an open porous canopy, however, behave as resistant phenotypes in semiarid locations (Miklas et al., 2001; Park et al., 2001). Studies conducted in the semiarid western USA under irrigation, support the importance of plant architecture as an avoidance mechanism for white mold. Most of the morphological traits associated with white mold avoidance in common bean are highly heritable and are easily scored in the field (Miklas et al., 2003, 2004). Combining physiological resistance with morphological avoidance mechanisms is the most viable breeding strategy to assure adequate levels of field resistance to white mold in the more humid Midwest (Kolkman and Kelly, 2002; Miklas et al., 2001). Screening for physiological resistance, however, is highly confounded by morphological avoidance mechanisms in the field. Methods proposed to detect physiological resistance to white mold in common bean include the limited-term inoculation method (Hunter et al., 1981), the excised-stem inoculation technique (Miklas et al., 1992a), growing callus on medium containing pathogen filtrate (Miklas et al., 1992b), the straw test (Petzoldt and Dickson, 1996), the leaf-agar plug assay (Steadman et al., 1997), and the oxalate test (Kolkman and Kelly, 2000). The routine use of these tests in breeding programs has been limited since they have produced variable results between labs, but the straw test is currently the most widely used greenhouse screening method. Approaches to combine physiological resistance with

White mold, caused by the necrotrophic fungus Sclerotinia sclerotiorum (Lib.) de Bary, is a serious disease of common bean (Phaseolus vulgaris L.). The use of resistant cultivars is the preferred control strategy, but the approach has been difficult to implement because of quantitative nature of genetic resistance to white mold. The objectives of this study were to identify quantitative trait loci (QTL) associated with resistance to S. sclerotiorum, identify QTL associated with agronomic traits that contribute to white mold avoidance, and locate putative QTL on the integrated bean map. Bulked segregant analysis using multiple trait bulks that included disease severity index (DSI), yield, and days to flower was used to detect QTL associated with field resistance to white mold in a Middle American recombinant inbred line (RIL) population of common bean. QTL that accounted for 9.2 to 14.7% of the phenotypic variation for DSI were located on linkage groups B2, B5, B7, and B8 of the integrated bean map. In addition, QTL were detected for seed size and yield and agronomic traits associated with disease avoidance: days to maturity, days to flower, and lodging. Heritability estimates for DSI were moderate (0.41) and correlations with agronomic traits that included lodging (0.56**), architecture (0.35**), canopy height (⫺0.33**), and yield (⫺0.64**) support the importance of these traits in disease avoidance. Data from the current study provides breeders with critical information on which traits and genomic regions to target as part of an overall strategy to enhance resistance to white mold in common bean.

W

hite mold is a serious disease of common bean that results in major yield loss, and reduced seed quality (Steadman, 1979) as sclerotial bodies produced by the fungus can contaminate the commercial food crop. Strategies for control of white mold in common bean include cultural practices (crop rotation, residue management, irrigation timing), fungicide applications, biological control, and planting of resistant cultivars (Ferraz et al., 1999; Gerlagh et al., 1999; Lamey, 1996; Huang and Erickson, 2000; Steadman, 1979). The use of resistant cultivars is a preferred disease control strategy that has had limited impact because of the paucity of resistance in common bean germplasm (Steadman et al., 2000). Sources of resistance have been identified in small-seeded bean cultivars (Tu and Beversdorf, 1982; Miklas and Grafton, 1992; Kolkman and Kelly, 2002) but introducing resistance into the highly susceptible larger-seeded Durango race pinto and great northern classes has proved challenging (Miklas et al., 2004). The challenge for bean breeders has been to separate physiological resistance from the interaction of environ-

Marcio Ender, Dep. de Fitotecnia, Univ. do Estado de Santa Catarina– UDESC, Lages, SC, Brazil; James D. Kelly, Dep. of Crop and Soil Sciences, Michigan State Univ., East Lansing, MI 48824. Received 21 Jan. 2005. *Corresponding author ([email protected]).

Abbreviations: AFLP, amplified fragment length polymorphism; BJ, BAT93 ⫻ Jalo EEP558 RIL population; CIM, composite interval mapping; cM, centimorgan; DSI, disease severity index; h2N, narrow sense heritability; LG, linkage group; LOD, log of odds; MAS, markerassisted selection; MRF, Montcalm Research Farm; QTL, quantitative trait loci; r 2, coefficient of determination; RAPD, random amplified polymorphic DNA; RIL, recombinant inbred lines; SMA, single marker analysis.

Published in Crop Sci. 45:2482–2490 (2005). Genomics, Molecular Genetics & Biotechnology doi:10.2135/cropsci2005.0064 © Crop Science Society of America 677 S. Segoe Rd., Madison, WI 53711 USA

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ENDER & KELLY: QTL ASSOCIATED WITH WHITE MOLD RESISTANCE IN COMMON BEAN

agronomic avoidance traits have been accomplished by performing a multiple trait bulking strategy for QTL detection (Ronin et al., 1998). Multiple trait bulking permits the identification of QTL conditioning resistance in agronomically acceptable plant types by eliminating the detection of QTL associated with undesirable avoidance mechanisms such as extreme early flowering or maturity or dwarf plants that restrict disease development but limit yield potential (Kolkman and Kelly, 2003). Detection of QTL associated with diverse sources of resistance to white mold should provide the opportunity to combine unique sources of physiological resistance with avoidance mechanisms in common bean, which would not be possible by direct phenotypic selection under white mold pressure in the field. QTL for white mold resistance have been mapped to linkage groups (LGs) B2, B3, B4, B7, B8, and B11 of the integrated bean linkage map (Freyre et al., 1998) in a RIL population between PC-50 and XAN-159 (Park et al., 2001). Six of the seven QTL associated with field resistance were found in the same locations as QTL for physiological resistance detected using the straw test. In a RIL population between G122 and A55, Miklas et al. (2001) detected a QTL on B7 that explained 38% of the phenotypic variation for disease resistance determined by the straw test. The same QTL was also associated with field resistance and mapped near the phaseolin seed protein (Phs) gene on B7. In addition, a second QTL for resistance in G 122 mapped near the fin gene on B1 (Miklas et al., 2001). The fin gene controls determinate growth habit that had previously been associated with resistance in bean genotypes from the Andean gene pool (Coyne, 1980; Schwartz et al., 1987). QTL for resistance were also detected on four LGs, B2, B3, B7, and B8, in a RIL population between the smallseeded cultivars Bunsi and Newport from the Middle American gene pool (Kolkman and Kelly, 2003). One major QTL for disease incidence on B2 was located near several genes that appear to be important in disease defense response (Kelly et al., 2003). The QTL identified by Kolkman and Kelly (2003) were found in regions of the genome associated with either plant architecture or general plant defense response genes, such as PvPR-2 (Walter et al., 1990), and Pgip (Toubart et al., 1992) on B2, the PvPR-1 gene on B3, and seed lectins (Brambl and Gada, 1985) on B7. QTL for DSI on B7 were also significantly associated with yield, seed size, lodging, and days to flower. The QTL detected by Kolkman and Kelly (2003) on B7, did not map near Phs, indicating that the resistance QTL was different from those previously detected in large-seeded, late-maturing, determinate Andean bean genotypes PC-50 and G122 (Miklas et al., 2001; Park et al., 2001). Given that resistance to white mold is a complexly inherited trait, with low to moderate heritability and highly influenced by environmental factors that demands intensive field work to evaluate, marker-assisted selection (MAS) offers a promising approach to improve resistance to white mold in common bean. The identification of new genetic sources of resistance and detection of QTL associated with physiologi-

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cal resistance will provide an important breeding tool to enhance white mold resistance in common bean. The objectives of this study were to: (i) identify and map QTL associated with resistance to S. sclerotiorum in an indeterminate Middle American RIL population of common bean and (ii) identify QTL associated with important agronomic traits and investigate their relationship with resistance to white mold. MATERIALS AND METHODS Plant Material This study was conducted using a population of 98 F4:7 RILs derived from the cross between Bunsi and Raven. Both genotypes are classified as small-seeded race Mesoamerica bean genotypes from the Middle American gene pool (Singh, 1999). Bunsi (a.k.a Ex-Rico 23; Tu and Beversdorf, 1982) is an indeterminate navy bean with a porous plant canopy and physiological resistance to white mold (Schwartz et al., 1987; Miklas et al., 2004; Kolkman and Kelly, 2002). Raven is an indeterminate black bean with upright plant architecture that lacks adequate levels of physiological resistance to S. sclerotiorum (Kelly et al., 1994). The Bunsi/Raven cross was made to eliminate growth habit as a factor confounding the expression of white mold in the current population. Ninety-eight F2 plants were advanced from F2 to F4 generation by the single seed descent method in the greenhouse. F5 seeds of individual F4 plants were bulked and advanced an additional generation in the greenhouse during spring 2000. Seeds harvested from three F5 plants in each F4:5 line were bulked and F4:6 lines were increased in the field, in Saginaw, MI, during summer 2000. No selection for agronomic or phenological traits was made during the development of the RIL population.

Field Trials and Traits The 98 F4:7 and F4:8 lines and the parents, Bunsi and Raven, were evaluated for reaction to white mold in naturally infested field plots at the Montcalm Research Farm (MRF) near Entrican, MI, during 2001 and 2002. Supplemental irrigation was provided, as needed, to enhance the development of white mold. The soil type at the MRF site is a combination of Eutric Glossoboralfs (coarse-loamy, mixed) and Alfic Fragiorthods (coarse-loamy, mixed, frigid). Experimental design was a 10 ⫻ 10 lattice with four replications. Each experimental unit consisted of a four row plots, 6 m in length, with 0.5-m-row spacing. The two center rows of each plot were planted with the RIL, while the two border rows were planted with the highly susceptible cultivar, Midland. Standard agronomic practices for tillage, fertilization, and weed and insect control were followed to ensure adequate plant growth and development. Plots were irrigated during the growing season starting in the third week after planting with 12 mm of water at least once a week, depending on rainfall, to promote uniform disease pressure across the field. During 2001, precipitation from June through September was 355 mm or 28 mm above 30-yr average, and plots were irrigated eight times for an additional total of 127 mm. In 2002, precipitation for the same 4-mo period was 235 mm or 93 mm below the 30-yr average, so the plots were irrigated with an additional 180 mm distributed over 11 irrigations. Plots were rated for disease before harvest, when the plants had reached physiological maturity. Thirty plants per plot were rated for disease on a scale from 0 to 4 (Hall and Phillips, 1996), and DSI was calculated for each plot by the following formula: DSI (%) ⫽ 兺 (rating of each plant) ⫻ 100/ 4 ⫻ (number of plants rated). The RILs were also evaluated


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for agronomic and phenological traits, including yield, seed size (100 seed weight, g), days to flower, days to maturity, architecture, canopy height, and lodging. Days to flower were characterized by the number of days following planting, when 50% of the plants in a plot had at least one open flower. Four weeks after flowering the plots were evaluated for upright architecture by a 1-to-5 scale, where branching was 1 ⫽ fully upright, acute angle ⬍45⬚; 3 ⫽ bush, 45 to 60⬚ angle; and 5 ⫽ lateral with an obtuse angle ⬎60⬚. Lodging was determined at physiological maturity on the basis of a 1-to-5 scale (Kolkman and Kelly, 2002). Canopy height (cm) was measured at plant maturity on each individual plot from six measurements per plot. Days to maturity were calculated as the number of days following planting until 90% of the pods were physiologically mature and the drying down process was initiated. All plots were harvested at maturity, when plots were individually pulled and threshed. Seed yield and weight of 100 seeds were adjusted to 180 g kg⫺1 moisture content.

AFLP and RAPD Analysis Plant tissue for DNA extraction was collected from parental genotypes and individual RILs from 10 plants in each F4:7 line. DNA was extracted from the plant tissue by a mini-prep procedure (Edwards et al., 1991; Haley et al., 1994), and the parents were screened with RAPD primers to detect the 11 markers previously mapped by Kolkman and Kelly (2003) on B2, B3, B7, and B8. AFLP analysis was performed by means of the restriction enzymes EcoRI and MseI (Vos et al., 1995). Double digestion, adaptor ligation, pre-amplification, and selective amplification were performed as previously proposed by Vos et al. (1995) with the following modifications described by Hazen et al. (2002). Preamplification of 2 ␮L of restriction ligation product was combined with 25 ng of EcoRI ⫹A and MseI ⫹ C, 0.5 mM dNTP, 1⫻ PCR buffer, 0.5 U Taq polymerase (Promega, Madison, WI), 1.5 mM MgCl2, total volume 20 ␮L, and amplified in a 96-well PTC-100 Programmable Thermal Controller (MJ Research Inc., Waltham, MA) programmed with the following profile: 94⬚C/2 min, 26 cycles; 94⬚C/1 min, 56⬚C/1 min, 72⬚C/1 min; 72⬚C/5 min. The preamplification PCR product was diluted 6⫻ with sterile water and 1 ␮L of diluted product was used in 20 ␮L of selective amplification reaction (25 ng EcoRI ⫹ ANN primer, 30 ng MseI ⫹ CNN primer, 0.2 mM dNTPs, 1⫻ PCR buffer, 0.4 U Taq polymerase, 1.5 mM MgCl2) and amplified via the following PCR profile: 94⬚C/2 min; 94⬚C/30 s, 65⬚C/30 s, 72⬚C/1 min, 12 cycles, 0.7⬚C/cycle; 94⬚C/30 s, 56⬚C/30 s, 72⬚C/1 min, 23 cycles; 72⬚C/2 min. The selective amplification product was combined with 8 ␮L of 6⫻ formamide loading buffer (Sambrook and Russell, 2001) and electrophoresed for 2.5 h at 80 W on a 6% (w/v) polyacrylamide gel cast on a 38 ⫻ 50 cm Sequi-Gen GT sequencing cell (BioRad Labs, Hercules, CA) to separate the AFLP fragments. Fragments were visualized by a silver staining procedure (Promega). Gels were scored for polymorphism on the basis of presence or absence of bands. The 256 AFLP primer pair combinations (EcoRI ⫹ ANN and MseI ⫹ CNN) were first tested on the parents and resistant and susceptible bulks developed using a combination of bulked segregant analysis (BSA; Michelmore et al., 1991) and multiple-trait bulking procedure (Kolkman and Kelly, 2003) to detect associated markers. The resistant bulk consisted of five individuals with low DSI (29.6%) and high yield (4954 kg/ha) whereas susceptible bulk members had high DSI (63.7%) combined with low yield (2881 kg/ha). Individuals in both bulks were chosen in the range of 40 to 46 d to flower to minimize disease avoidance on the basis of early or late flowering trait (Kolkman and Kelly, 2003). In addition to the candidate mark-

ers associated with resistance, all polymorphic bands were scored and included in the construction of the linkage map. Chi-square test for goodness-of-fit was used to test the deviations of the expected segregation ratio of 9:7 (for F4 derived lines) or 1:1 (RILs).

Statistical Analysis A total of 124 molecular markers (118 AFLP ⫹ 6 RAPD) were analyzed and divided into LGs by the Joinmap software (Stam, 1993). The LGs were first anchored through common markers previously mapped on the integrated bean map. All 28 AFLP primer combinations were first tested for polymorphism against the parents of the BAT93/Jalo EEP558 (BJ) RIL mapping population (Freyre et al., 1998), and only those polymorphic markers were tested in the BJ population (64 RILs) and mapped to the integrated bean map using MAPMAKER/EXP (Lander et al., 1987). Significant markers associated with resistance to white mold and other traits were identified through single marker analysis (SMA) and composite interval mapping (CIM) analyzed by software QTL Cartographer (Basten et al., 2001) using a window size of 5 cM. Additive effects and coefficient of determination (R2) were calculated by QTL cartographer. Threshold LOD scores for individual traits were determined through a permutation test, with 1000 permutations (Churchill and Doerge, 1994). The Pearson correlation coefficients (r ) were calculated by PROC CORR (SAS, 1995). The RIL population was analyzed across the 2 yr as a RCBD by PROC GLM (SAS, 1995). Estimates of heritability and confidence interval estimates for all traits were calculated on entry mean basis, where h2 ⫽ ␴2g/␴2e/ry ⫹ ␴2ge/y ⫹ ␴2g, (Fehr, 1987; Knapp et al., 1985).

RESULTS AND DISCUSSION QTL Associated with Resistance to White Mold Significant genetic variation for white mold DSI scores in the Bunsi/Raven RIL population and the two parental cultivars, Bunsi and Raven was observed in the two field experiments. Data on reaction to white mold, days to flower, and yield followed a normal distribution in the combined environments and the RILs exhibited transgressive segregation over the parents for all six traits evaluated (Table 1). In the combined analysis, the parental genotypes differed significantly for DSI, lodging, and seed size, whereas nonsignificant differences were observed for yield, days to flower, and maturity. Disease pressure was adequate in both years, but the variation in precipitation and temperature between the two years resulted in higher levels of DSI (44.9%) in 2001 compared with levels (27.2%) in 2002. DSI scores were significantly correlated between years (r ⫽ 0.33**), however, despite differences in disease pressure. The cumulative precipitation during the critical 2-mo period for white mold infection and development (August to September 2001) was 256 mm, compared with 105 mm for the same period in 2002. Precipitation during this period was 79 mm above the long-term average in 2001 and 72 mm below the average in 2002. In addition, the average maximum temperature in September 2001 was 21⬚C compared with 26⬚C in September 2002. High temperatures combined with reduced precipitation reduced disease development in 2002, despite the supplemental irrigation applied to the experiment.


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Table 1. Means, range, and narrow sense heritability estimates (h2N) of disease severity index for white mold and five agronomic traits in 98 F4–derived recombinant inbred line (RIL) population derived from the cross Bunsi/Raven, grown at Montcalm, MI, in 2001 and 2002. Parental means Trait DSI‡ (%)


Yield (kg ha⫺1) Days to flower Days to maturity Lodging Seed size (g 100 seed⫺1)

Recombinant inbred lines

Year

Bunsi

Raven

Range

Mean

2001 2002 Combined 2001 2002 Combined 2001 2002 Combined 2001 2002 Combined 2001 2002 Combined 2001 2002 Combined

22.5 13.5 18.0 4179 3978 4078 40.5 45.5 43.0 105.0 109.5 107.6 3.5 3.5 3.5 22.7 22.8 22.8

43.8 NS§ 46.5* 45.2** 4544 NS 4011 NS 4278 NS 46.0 NS 47.0 NS 46.5 NS 104.0* 106.5 NS 105.4 NS 2.5 NS 3.0 NS 2.8* 21.5 NS 18.8* 20.2**

23.1–72.6 8.4–51.2 19.7–52.6 2130–5571 3127–5627 3015–5380 36.5–48.4 42.0–52.5 39.5–50.5 103.2–111.7 91.8–116.5 96.3–113.1 2.0–4.9 1.6–4.1 1.8–4.5 18.9–26.1 16.7–26.2 18.3–25.7

44.9 27.2 36.1 4136 4495 4315 43.8 45.3 44.6 104.9 105.8 105.4 3.3 3.1 3.2 22.2 21.0 21.6

Heritability (h2N) (90% CI†)

0.41 (0.18–0.58) 0.56 (0.38–0.68) 0.79 (0.71–0.85) 0.82 (0.75–0.87) 0.76 (0.67–0.83) 0.90 (0.85–0.93)

* Significant at 0.05 level of probability. ** Significant at 0.01 level of probability. † Confidence Interval (Knapp et al., 1985). ‡ DSI ⫽ disease severity index. § NS ⫽ Not significant.

Narrow sense heritability estimates for white mold DSI rating were moderate (hN2 ⫽ 0.41) in the Bunsi/Raven RIL population in the combined field trials (Table 1). Previous estimates, using Bunsi navy bean as the resistance source in field trials, ranged from 0.47 to 0.82 in navy/navy bean populations (Kolkman and Kelly, 2002), from 0.56 in Washington to 0.36 in North Dakota in the same navy/pinto population (Miklas et al., 2004) to 0.70 in a fourth navy/navy population (Miklas and Grafton, 1992). In three Andean populations, h2N estimates for DSI ranged from 0.65 (straw test) to 0.78 (field test; Miklas et al., 2001) to 0.23 (Park et al., 2001), whereas estimates ranged from 0.62 (field) to 0.73 (straw test; Miklas et al., 2003) in a snap bean population. Heritability estimates for physiological resistance on the basis of greenhouse tests such as oxalate assay (0.19; Kolkman and Kelly, 2000) or juvenile stem test (0.05 to 0.30; Roberts et al., 1982) were generally low. The wide range in heritability estimates for white mold resistance suggests that the inheritance of white mold resistance is influenced by parental genotype, the testing procedures employed, disease severity at evaluation, and the interaction of agronomic and environmental factors. Since quantitative resistance is highly influenced by the environment, multiple evaluations over locations are recommended when rating for white mold to ensure more effective differentiation of resistance among lines in a segregating population. High heritability estimates for yield (0.56), days to flower (0.79) and maturity (0.82), lodging resistance (0.76), and seed size (0.90; Table 1) are supported by similar estimates in previous studies (Kolkman and Kelly, 2002; Miklas et al., 2003). The high heritability estimates for these agronomic traits emphasizes the importance role that disease avoidance traits could play in the indirect selection for white mold resistance under field conditions. Twenty-eight AFLP primer combinations were iden-

tified by multiple trait bulking and BSA and a total of 124 molecular markers (118 AFLP and 6 RAPD) were mapped in the Bunsi/Raven RIL population. Markers associated with QTL for resistance to white mold and for agronomic traits detected with CIM were supported by SMA and different QTL associated with resistance to white mold were identified in the two field experiments. Linkage map construction, using Joinmap (Stam, 1993), placed 118 markers on 12 LGs for a total of 227.5 cM, and seven LGs (B2, B4-a, B4-b, B5, B7-a, B7-b, B8) were anchored to the integrated map (Freyre et al., 1998). B4-a, B4-b, B7-a, and B7-b, were associated with different regions of B4 and B7, respectively, but five additional LGs (LG1-5) remained unassigned because of lack of polymorphism in BJ RIL population (Ender, 2003). Five LG (B2, B5, B7-b, B8, LG-2, and LG-5) possessing QTL for resistance to white mold were detected in 2001 field experiment by SMA and three of these genomic regions on B2, B5, and B8 were confirmed by CIM (Fig. 1, Table 2). Six LG (B2, B5, B7-b, B9, LG-2, and LG-5) with QTL for resistance to white mold were identified in 2002 by SMA, but only B7-b was confirmed by CIM. The discussion will focus only on the QTL confirmed by the CIM analysis. A QTL that accounted for 10.1% (R2) of the phenotypic variability for DSI was identified on B2 near the RAPD marker O12.1600. A second QTL on B2 that explained 8.7% of DSI (Table 2, Fig. 1) mapped close to O15.1800, detected previously in a different RIL population (Kolkman and Kelly, 2003). Support for the presence of a resistance QTL comes from the presence of three AFLP markers EAGAMCAT165, EACTMCAC120, EACCMCCT310 that mapped to the same position as O15.1800 on B2 (Fig. 1). QTL associated with resistance to white mold (Kolkman and Kelly, 2003), common bacterial blight [caused by Xanthomonas axonopodis pv. phaseoli (Smith 1897) Vauterin, Hoste, Kersters &



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Fig. 1. Linkage map from 98 F4 derived recombinant inbred lines originated from a cross between Bunsi and Raven. Linkage groups B2, B5, B7-b and B8 correspond to the integrated map (Freyre et al., 1998) and QTL for white mold resistance were identified by composite interval mapping analysis over two years, 2001, 2002. AFLP primers used: E ⫽ EcoRI primer; M ⫽ MseI primer, followed by three selected bases and fragment size (bp). PvPr-2, Pgip, ChS, Lec-2 and Lec-3 are defense response genes mapped to the integrated bean map.

Swings 1995, Nodari et al., 1993], and root rot [caused by Fusarium solani (Mart.) Sacc. f. sp. phaseoli (Burkholder) W.C. Snyder & N.H. Hans, Schneider et al.,

2001; Roma´n-Avile´s and Kelly, 2005) were also identified in this region of B2. In addition, several genes identified in host defense response have been located near O15.1800


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Table 2. Markers most closely linked to QTL for resistance to white mold and selected agronomic traits in a common bean RIL population grown in 2001 and 2002. Trait


DSI (%)

Yield (kg ha⫺1)

Days to flower

Days to maturity Lodging

Seed size (g 100 seed⫺1)

Marker

Linkage group

QTL position

LOD value

R2

Additive effect‡

O12.1600 O15.1800 EACTMCAT85 EAACMCTT223 P9.1750 EAGAMCTG190 EAATMCAA400 EACTMCAT85 EACTMCAA330/320†† EAGAMCTG190 EACCMCTC86 EAGGMCAC78 EACCMCTT550 EAACMCTT223 EACTMCTT670 EAAAMCAA104 EAATMCAA400 EACTMCAA580 EAACMCTT223 EAAAMCAG210 O15.1800 EACTMCAT260 EACCMCTT550 EAACMCTT223

B2 B2 B5 B7-b B7-b B8 B2 B5 B7-a B8 B2 B7-a B7-a B7-b B2 B7-b B2 B7-a B7-b B8 B2 B5 B7-a B7-b

cM 8.0 21.0 27.7 8.6 14.8 1.4 10.1 25.7 33.9 1.4 21.6 1.1 13.6 8.6 22.6 17.1 0.01 15.8 8.6 0.01 21.0 6.9 11.6 8.6

2.77* 2.60† 2.54† 3.51** 2.91* 2.96* 3.39* 2.89* 5.57** 4.68** 5.09** 5.42** 6.21** 15.00** 12.42** 5.68** 3.18* 9.29** 3.66** 2.80* 7.06** 3.37* 7.76** 4.28**

% 10.1 8.7 10.7¶ 14.7# 14.2# 9.2¶ 9.7 7.5 21.5 12.7 7.6 8.4 14.8¶ 35.7 28.5 9.8 8.5 29.8 9.6 6.4 20.1 9.3 25.7 9.4

3.15(B)§ ⫺2.66(B) 3.16(B) ⫺4.17(B) ⫺4.01(B) 2.93(B) ⫺171(B) ⫺122(B) ⫺158(B) ⫺158(B) 0.57(R ) ⫺0.60(B) ⫺0.96(R ) ⫺1.21(B) 2.06(R ) ⫺1.21(R ) 0.17(B) 0.33(B) ⫺0.17(B) 0.15(B) 0.78(B) ⫺0.49(R ) ⫺0.83(R ) 0.49(B)

† Significant at 0.1 level of probability based on 1000 permutations. * Significant at 0.05 level of probability based on 1000 permutations. ** Significant at 0.01 level of probability based on 1000 permutations. ‡ Estimated additive effect of substituting one allele of Raven by one allele of Bunsi. § The letter in parentheses indicates the parent where the marker is present, B ⫽ Bunsi; R ⫽ Raven. ¶ QTL detected only in 2001. # QTL detected only in 2002. †† Codominant marker.

indicating that this region of B2 may be important for disease resistance. Pgip, a polygalacturonase-inhibiting protein (Toubart et al., 1992), chalcone synthase (ChS; Ryder et al., 1987) and PvPR-2, a low molecular weight acidic protein induced during fungal elicitation (Walter et al., 1990), are all located in this region of B2. Differences in PvPR gene arrangements were detected between bean genotypes resistant or susceptible to anthracnose [caused by Colletotrichum lindemuthianum (Sacc. & Magnus) Lams.-Scrib.; Walter et al., 1990] indicating that polymorphism between PvPR as well as other defense response-related genes may also contribute to quantitative resistance. Pgip could be an important defense mechanism since polygalacturonases are produced by S. sclerotiorum during the infection process. In addition, the ChS gene (Ryder et al., 1987) located near O15.1800, codes for an enzyme required in the isoflavonoid phytoalexins biosynthesis, and a second enzyme, phenylalanine ammonium-lyase (PAL) in the same pathway may also be important in conditioning resistance to S. sclerotiorum. Slower lesion development was associated with greater PAL activity in bean cultivars infected with S. sclerotiorum (Miklas et al., 1993). The colocalization of defense response genes with QTL for resistance to white mold may suggest that a general resistance response occurs to S. sclerotiorum infection in common bean. Four AFLP markers associated with QTL for resistance to white mold mapped to B5. EACTMCAT85 was the marker closest to this QTL that contributed up to 10.7% of phenotypic variation for DSI (Table 2, Fig. 1). The same marker was also associated with QTL for yield

(R2 ⫽ 7.5). In previous studies, Park et al. (2001) identified a QTL (R2 ⫽ 11%) near D05.1100 on B5 that contributed to partial physiological resistance to only one isolate of S. sclerotiorum in the straw test. An absence of polymorphism for D05.1100 in the Bunsi/Raven population prevented a direct comparison of the location of the two QTL. Four markers associated with QTL for resistance to white mold were detected on B8 in 2001 trial. EAGA MCTG190 was the marker most closely associated with the QTL that accounted for 9.2% of the phenotypic variability for DSI (Table 2, Fig. 1). Candidate QTL associated with white mold resistance on B8 (Kolkman and Kelly, 2003) could not be confirmed in the current study. In addition, the QTL that mapped between AH05.1000 and D1468 markers on B8 in the snap bean line NY6020-4 (Miklas et al., 2003) appears to be different from the QTL detected in the current study since the QTL mapped more than 30 cM apart. The QTL in NY6020-4 that accounted for 26% of phenotypic variability for disease reaction in the field, also mapped adjacent to QTL for partial resistance from PC 50 (Park et al., 2001). Two LG, which independently mapped to B7, but could not be combined in a single analysis, will be referred to as LG B7-a and B7-b. Nine markers associated with QTL for DSI in 2002 mapped to B7-b, which was anchored to B7 on the integrated map (Freyre et al., 1998) with the H12.1050 marker. EAACMCTT223 was the closest marker to this QTL that accounted for 14.7% of the phenotypic variability for DSI in 2002 (Table 2, Fig. 1). In the same region of B7, Kolkman and Kelly


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(2003) also identified a major QTL for resistance to white mold along with QTL for agronomic traits that included days to flower and maturity, architecture, lodging, seed size, and yield. One of the most useful markers detected on B7 by Kolkman and Kelly (2003) was EAACMCTT130, which also corresponded to one of the nine linked markers in the current study. The QTL associated with the EAACMCTT130 marker accounted for 15.8% of the phenotypic variability for DSI (Kolkman and Kelly, 2003). In addition, genes for seed lectins (phytohaemoglutanins) also mapped to this region of B7 and may have a putative role in plant defense (Shewry and Lucas, 1997). QTL for resistance to white mold located near the Phs locus on B7, were previously detected by Miklas et al. (2001) and Park et al. (2001) and those QTL correspond to B7-a in this study. The major QTL for DSI identified in the current study, however, appears to be different from the QTL detected on B7 by Miklas et al. (2001) in G122 and by Park et al. (2001) in PC-50. QTL for DSI were detected on B7-a and LG2 using SMA, but the QTL were not confirmed by CIM. The lack of consistency between methods does not necessarily mean that these QTL are false positives, but population size and environmental conditions influence different QTL detection methods resulting in a lack of expression and detection of a QTL at a significant level of probability. The QTL for resistance to white mold identified in the Bunsi/Raven population on B2 and B7 support previous results of Kolkman and Kelly (2003) who also used Bunsi as the resistance source. Given that different QTL were identified in various years would suggest that other traits may play an interactive role in resistance to white mold, particularly those traits associated with plant morphology and disease avoidance in the field.

Agronomic Traits and Resistance to White Mold A better understanding of the associations between DSI and phenological and agronomic traits should contribute to the development of more efficient strategies to select for resistance to white mold in common bean. DSI was significantly correlated with yield in both years, however the correlation coefficient was lower in 2002 (r ⫽ ⫺ 0.45**) compared with 2001 (r ⫽ ⫺0.77**) (Table 3). The lower correlation coefficient could be a consequence of lower disease pressure since white mold did not develop to the same levels because of effects of Table 3. Phenotypic correlations between disease severity index for white mold and phenological and agronomic traits in a 98 F4 derived RIL population from a Bunsi/Raven cross grown in Montcalm, Michigan in 2001 and 2002. Yield Days to flower Days to maturity Lodging Architecture 100-seed weight Canopy height

DSI 2001†

DSI 2002

DSI Combined

⫺0.77** NS NS 0.51** NS ⫺0.21* ⫺0.22*

⫺0.45** 0.43** 0.26** 0.65** 0.42** ⫺0.26* ⫺0.31**

⫺0.64** 0.33** NS 0.56** 0.35** ⫺0.22* ⫺0.33**

* Significant at 0.05 level of probability. ** Significant at 0.01 level of probability. † DSI ⫽ Disease Severity Index.

higher temperatures and lower precipitation in 2002. Lodging was correlated with DSI in both years but for different reasons of cause and effect. Increased lodging in 2001 probably was a consequence of higher disease pressure since the disease developed early after flowering and highly susceptible genotypes lodged because of disease. In contrast, in 2002, more prostrate plants contributed to high DSI by promoting a more favorable microclimate for the disease development under the decumbent plant canopy. Architecture was not associated with DSI in 2001, whereas more upright lines had less white mold (r ⫽ 0.42**) only in 2002. None of the phenological or architecture traits were correlated with DSI in 2001 (Table 3). The severity of the white mold infection in 2001 (Table 1) appears to have overridden the plant avoidance traits, whereas significant positive correlations with plant architecture and days to flower and maturity were detected under less disease pressure in 2002. Enhanced physiological resistance is needed in the humid Midwest, when environmental conditions that favor disease development negate the positive effects of plant architectural avoidance traits. QTL associated with phenological and agronomic traits were detected on B2 and B7 in both years. Significant QTL for days to flower (EACCMCTC86; R2 ⫽ 7.6%), days to maturity (EACTMCTT670; R2 ⫽ 28.5%), and lodging (EAATMCAA400; R2 ⫽ 8.5%) were detected on B2 (Table 2, Fig. 1). EAACMCTT223 was associated with a QTL for DSI in 2002 in the same region of B7-b where QTL were detected for days to flower (R2 ⫽ 35.7), and seed size (R2 ⫽ 9.4) in both years and for lodging (R2 ⫽ 9.6) in 2002 (Table 2). A second QTL for DSI linked to EAAAMCAA104 (R2 ⫽ 9.8) was also associated with days to maturity on B7-b. On B7-a, QTL associated with yield (EACTMCAA330/320; R2 ⫽ 21.5%), days to flower (EAGGMCAC78; R2 ⫽ 8.4%), lodging (EACTMCAA580; R2 ⫽ 29.8%), and seed size (EACCMCTT550; R2 ⫽ 25.7%) were identified. The QTL that explained 21.5% of phenotypic variation for yield across the two years was detected near EACTMCAA330/320 on B7-a. EACTMCAA330/320 was linked to EACTMCAA75, which mapped near the Phs gene on B7 in the BJ population. Previous workers mapped QTL for seed size (Kelly et al., 2003) and QTL for white mold resistance (Miklas et al., 2003; Park et al., 2001) to this region of B7. Individual QTL for DSI detected in the Bunsi/Raven population accounted for up to 15% of the phenotypic variability, similar to values reported for QTL for resistance to S. sclerotiorum in soybean [Glycine max (L.) Merr.] grown in four environments (Kim and Diers, 2000). Two of the QTL in soybean were also significantly associated with disease avoidance traits such as plant height, lodging, and flowering date. QTL for branching pattern, seed weight, and flowering date were also shown to be associated with resistance to white mold in sunflower (Helianthus annuus L; Mestries et al., 1998). The limited genomic coverage (228 cM) in the current study represents approximately 20% of the estimated total length (1200 cM) of the bean genome (Kelly et al., 2003). The partial coverage is the direct result of the narrow genetic base of the RIL population and the



clustering of the AFLP markers on two LGs B2 and B5 (Fig. 1). AFLP clusters in soybean were shown to mark the heterochromatic regions surrounding the centromeres (Young et al., 1999). The physical distance separating the markers in a cluster may actually be quite large, since it is spread across a region exhibiting little or no recombination. The high level of cytosine methylation observed in heterochromatic regions surrounding the centromere suggests the presence of a single EcoRI/ MseI cluster on B2 and B5. The detection of two LGs B4-a, B4-b and B7-a, B7-b that mapped to B4 and B7, respectively, does not support the hypothesis of one cluster per chromosome in the centromere region. Clustering to the heterochromatic telomeric regions, however, would result in two clusters that may be too distant to map on a single LG when the number of markers is limited. If clustering is attributed to the use of the methylation insensitive restriction enzyme EcoRI in the AFLP analysis, further studies to expand genomic coverage could be conducted in Bunsi/Raven population by including AFLP markers using PstI/MseI restriction enzymes.

CONCLUSIONS The detection of QTL is highly dependent on the environment where the trait is measured. Environmental conditions or even small microclimate variations can affect the expression of QTL for resistance to white mold in common bean. The detection and location of the QTL is also affected by the population size (Miklas et al., 2001). The population used in this study was developed from a cross between Bunsi and Raven bean cultivars that are adapted to Michigan environmental conditions. A population derived from parents with a narrow genetic background is considered more useful for detection of QTL with small effects. QTL are more easily identified in populations from wide crosses, but such QTL may be associated with broad genetic differences in gene pools instead of the differences associated with complex agronomic traits of economic importance. On the basis of the narrow genetic background of the Bunsi/Raven RIL population in this study, only a limited coverage (20%) of the bean genome was attained. The lack of variation in growth habit between parents should have improved the detection of QTL related to physiological resistance because differences in growth habit are known to be associated with contrasting levels of white mold resistance in common bean (Kolkman and Kelly, 2003). Since Bunsi and Raven exhibit distinct plant architecture, the effect of upright plant type on resistance to white mold was observed under different disease pressure in the 2 yr of this study. MAS could be used to improve resistance to white mold in common bean by combining different genomic regions (B2, B5, B7) associated with resistance. The QTL detected on B5, for example, appears to represents a new source of physiological resistance for white mold. The identification of QTL for different agronomic traits associated with white mold resistance is crucial because it allows bean breeders to enhance resistance by selecting multi-

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ple QTL from different LGs regardless of the type of resistance mechanism. Combining QTL for physiological resistance with plant avoidance traits is suggested as a preferred strategy to improve resistance to S. sclerotiorum in common bean. ACKNOWLEDGMENTS The first author was supported by a scholarship from CAPES-Brazil.

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