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CROP BREEDING, GENETICS & CYTOLOGY

Inheritance of Partial Resistance to Sclerotinia Stem Rot in Soybean

^a Upland Crop Division, National Crop Exp. Stn., Suwon 441-100, South Korea
^b Dep. of Crop Sciences, University of Illinois, Urbana, IL 61801 USA

bdiers{at}uiuc.edu

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
Little is known about the inheritance of partial resistance in soybean [Glycine max (L.) Merr.] to sclerotinia stem rot, caused by the fungal pathogen Sclerotinia sclerotiorum (Lib.) de Bary. This information would be useful to soybean breeders who are developing cultivars with resistance to sclerotinia stem rot. Our objectives were to study the inheritance of partial resistance to sclerotinia stem rot and to present the initial results from mapping quantitative trait loci (QTL) that confer this resistance. The research was conducted by testing 152 F₃-derived lines from a cross between a partially resistant cultivar, Novartis Seeds S19-90 (formerly Northrup King), and a susceptible cultivar, Williams 82, for resistance to sclerotinia stem rot and agronomic traits at two Michigan locations in each of 2 yr. These lines were also evaluated for 123 genetic markers to map resistance genes. The resistance of the lines, measured with a disease severity index (DSI), was normally distributed across environments. Significant (P < 0.05) genotypic variation, genotype x location, and genotype x year interactions were observed for DSI. The broad-sense heritability estimate for DSI across both locations and years was 0.59. More severe disease was significantly correlated with greater lodging, later date of maturity, later R1 date, and greater plant height. Three QTL, explaining 10, 9, and 8% of the variability for DSI across environments, were mapped with genetic markers. Two of these QTL were also significantly associated with disease escape mechanisms such as plant height, lodging, and date of flowering. The other QTL was not significantly associated with escape mechanisms and may map one or more genes involved in physiological resistance to the disease.

Abbreviations: DSI, disease severity index • LOD, likelihood of odds • PCR, polymerase chain reactions • QTL, quantitative trait loci • RAPD, random amplified polymorphic DNA • RFLP, restriction fragment length polymorphism, SSR, simple sequence repeat

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
SCLEROTINIA STEM ROT OF SOYBEAN, also known as white mold, is caused by the fungal pathogen Sclerotinia sclerotiorum (Lib.) de Bary. Sclerotinia stem rot has recently become an important soybean disease in the northern USA and southern Canada (Wrather et al., 1997). Most infections of soybean by S. sclerotiorum are believed to occur by the fungus first colonizing flower petals. The fungus then infects and girdles the stem, causing plant death. The pathogen needs wet soil and canopy conditions at flowering for infection to occur (Grau, 1988).

Although no soybean genotypes which are completely resistant to S. sclerotiorum have been identified, some have partial resistance (Boland and Hall, 1987; Grau et al., 1982; Kim et al., 1999; Nelson et al., 1991). Partial resistance can provide economically useful disease control and is a breeding objective in many soybean improvement programs. However, little is known about the inheritance of resistance to this disease in soybean, and this has limited the development of optimal breeding strategies. Furthermore, it is not known what proportion of the resistance in the field is the result of physiological resistance or escape mechanisms. These escape mechanisms could include flowering date, lodging, canopy architecture, and maturity, which have all been shown to be significantly associated with disease severity (Boland and Hall, 1987; Nelson et al., 1991; Kim et al., 1999).

Our objectives were to study the inheritance of partial resistance to sclerotinia stem rot and to present the initial results from mapping QTL that control this resistance.

	Materials and methods

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
Genetic Material
One hundred fifty-two F₃-derived lines were developed by single seed descent from a cross between Novartis Seeds S19-90, which has a high level of partial resistance to sclerotinia stem rot, and Williams 82, which is highly susceptible to the disease (Kim et al., 1999; Dann et al., 1998).

Field Tests
Field experiments were conducted at East Lansing, MI and near Zilwaukee, MI during 1996 and 1997. The soil at the East Lansing location is a Capac loam (fine-loamy, mixed, mesic Aeric Endoaqualf), and the soil at the Zilwaukee location is a Sloan loam (fine-loamy, mixed, superactive, mesic Fluvaquentic Endoaquoll). The lines in the 1996 test were in the F_3:5 generation and the lines in the 1997 test were in the F_3:6 generation. The parents and the population were sown in two replicates of an alpha lattice design at each location. The parents were entered twice in each replicate. Each plot was seven rows wide with an 18-cm row spacing and a length of 3.7 m in 1996 and 6 m in 1997. The 1997 plots were trimmed to a length of 4.3 m at the beginning of pod fill (R5 growth stage) (Fehr et al., 1971). The 1996 East Lansing and Zilwaukee locations were sown on 31 May and 30 May, respectively, at a rate of 494000 seeds ha^-1. The 1997 East Lansing and Zilwaukee locations were sown on 13 May and 14 May, respectively, at a rate of 543000 seeds ha^-1. The East Lansing fields were artificially inoculated prior to planting with sclerotia from S. sclerotiorum obtained from screenings of harvested dry bean [Phaseolus vulgaris L.]. The sclerotia and other screenings were spread onto the field with a fertilizer spreader at the rate of 50 kg ha^-1. The East Lansing fields were sprinkler-irrigated 2.5 mm every evening from 1 wk prior to the first line beginning to bloom (R1) (Fehr et al., 1971) until completion of flowering of all lines. The Zilwaukee fields were naturally infested with S. sclerotiorum and were not irrigated.

Plots were rated for disease severity based on the rating system of Grau et al. (1982) at approximately the beginning of physiological maturity (R7) (Fehr et al., 1971). Thirty plants in the center rows of plots were individually rated on a scale of 0 to 3, where 0 = no symptoms, 1 = lesions on lateral branches only, 2 = lesions on the main stem but little or no effect on pod fill, and 3 = lesions on main stem resulting in plant death and poor pod fill. A DSI was calculated for each plot using the following formula:

A DSI of 0 was given to plots with no diseased plants rated and a DSI of 100 was given to plots with all rated plants killed by the disease.

The date of R1 was measured as the number of days after 30 June when 50% of the plants in the plot reached the R1 growth stage. Maturity date was recorded as the number of days after 31 August when 95% of the pods reached mature pod color (R8) (Fehr et al., 1971). Plant height was recorded at maturity as centimeters from the ground to the average terminal node of plants. At maturity, lodging was rated on the scale of 1 to 5, where 1 = all plants erect and 5 = all plants prostrate. All rows of the plots were harvested with a combine to measure seed yield. Yields were adjusted to a 13% moisture content.

Genetic Marker Analysis
DNA was isolated from greenhouse-grown leaves from 10 F_3:5 plants for each line according to Keim and Shoemaker (1988). Restriction fragment length polymorphism (RFLP) analysis was conducted using DNA clones developed by Dr. R.C. Shoemaker (USDA-ARS, Iowa State Univ.). The procedures for restriction enzyme digestion, gel electrophoresis, Southern blotting, and hybridization followed Kisha et al. (1997).

Simple sequence repeat (SSR) DNA marker analysis was done with primers developed by Dr. P.B. Cregan (USDA-ARS, Beltsville, MD). Polymerase chain reactions (PCR) were done in 10-µL volumes containing 50 ng of genomic DNA, 0.3 µM of 3' and 5' end primers, 1x reaction buffer (20 mM Tris-HCl, pH 8.4, 50 mM KCl), 3 mM MgCl2, 0.4 mM each dNTP, and 0.5 units of AmpliTaq DNA polymerase (Perkin-Elmer Cetus, Norwalk, CT). DNA was amplified according to the thermal cycle profile of Akkaya et al. (1995). The PCR products were separated on 1.0% (w/v) agarose plus 1.0% (w/v) Synergel (Diversified Biotech, West Roxbury, MA) horizontal gels and visualized by ethidium bromide staining.

Random amplified polymorphic DNA (RAPD) analysis was done with random decamer primers obtained from Operon Technologies Inc., Alameda, CA. The PCR reactions had 25-µL volumes containing 50 ng of genomic DNA, 25 ng of primer, 1x reaction buffer (10 mM Tris-HCl, pH 8.3, 10 mM KCl), 3 mM MgCl2, 0.4 mM each dNTP, and 2 units Stoffel fragment DNA polymerase (Perkin-Elmer Cetus). DNA amplification was done using a thermal cycle profile of 4 min at 94°C followed by three cycles of 15 s at 94°C, 15 s at 35°C, 45-s ramp to 72°C, 75 s at 72°C, and additional 34 cycles of 15 s at 94°C, 15 s at 40°C, 45-s ramp to 72°C, 75 s at 72°C. The PCR products were separated on a 1.4% (w/v) agarose horizontal gel and visualized by ethidium bromide staining.

Data Analysis
Analysis of variance was conducted on the field data using PROC GLM of SAS (SAS Institute, 1985). F₃-derived lines, years, locations, replications, and blocks were analyzed as random effects. Pearson product-movement correlations were calculated with PROC CORR of SAS to compare the mean DSI ratings of genotypes at different environments and to compare DSI ratings with ratings of other traits. Seed yields of lines across environments were regressed against DSI by linear regression with PROC REG of SAS. The means of lines across environments for lodging, date of maturity, R1 date, and plant height were regressed against DSI by linear stepwise regression with PROC REG of SAS. Estimates of variance components and broad-sense heritabilities of traits were calculated from mean squares for each environment and across environments (Fehr, 1987). Confidence intervals for heritability estimates were calculated according to Knapp et al. (1985). The Kolomogorov D statistic in PROC UNIVARIATE of SAS (SAS Institute, 1985) was used to test the null hypothesis that the DSI values of lines in the population were normally distributed for each environment and across environments. A P < 0.05 was used to test for a lack of fit.

The testing of linkage among the DNA markers and the mapping of QTL through interval mapping was done with the computer program Mapmaker Exp 3.0/QTL 1.1b (Lander et al., 1987; Lincoln et al., 1992). A minimum likelihood of odds (LOD) of 3.0 and maximum distance of 50 cM was used for testing linkages among markers. A minimum LOD score of 2.4 was used to declare the presence of a significant QTL. Associations between traits and markers on linkage groups significantly associated with DSI in interval mapping were tested with one-factor analysis of variance using PROC GLM of SAS. The markers with the greatest R² values for DSI at each independent QTL were tested in pairs with two-factor analysis of variance using PROC GLM to detect epistatic interactions for DSI. The marker with the greatest R² value for DSI from each independent QTL was included in a multiple regression model with PROC GLM to estimate the total phenotypic variance for DSI (²_p) explained by the QTL. The proportion of genotypic variance explained by the QTL was obtained by: ²_g explained = ²_p explained/h² (Schön et al., 1994).

	Results and discussion

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
Field Testing
The partially resistant parent, Novartis S19-90, exhibited less disease than Williams 82, the susceptible parent, at each environment and across environments (Table 1) . This difference was significant (P < 0.05) at all environments except Zilwaukee in 1996 and was significant for the means across environments. The mean DSI across all lines in the population was not significantly different from the mean of the parents across environments and for each environment, with the exception of East Lansing in 1996. In this environment, the mean DSI of the population was significantly greater than the mean of the parents (Table 1). There was significant (P < 0.05) transgressive segregation for DSI with lines from the population having greater disease than Williams 82 at each environment and across environments except at East Lansing in 1997. No lines from the population had significantly less disease than Novartis S19-90 at any environment or across environments.

View larger version (20K): [in this window] [in a new window]   Fig. 1 Frequency distribution of the mean sclerotinia stem rot disease severity indexes of F3-derived lines and parents across four Michigan environments

There was significant (P < 0.05) genotypic variation for DSI among the lines in the population at each location and across locations. The genotype x location interaction for DSI was significant in 1996 but not in 1997. Across years, the genotype x year and genotype x location interactions were significant, although the genotype x location x year interaction was not. The correlations for DSI values of lines were significant between the 2-yr means of the Zilwaukee and East Lansing locations (0.62) and between the two-location means for 1996 and 1997 (0.49). There was little association between the average DSI value at a location and how well these values correlated with DSI values from other locations. For example, the 1996 East Lansing location had the second highest average DSI value, but the lowest correlations with other locations (Tables 1 and 2) . In contrast, the 1997 East Lansing location had both the greatest average DSI value and the greatest correlations with the other locations. The significant genotype x location and genotype x year interactions indicate that more than one environment is needed to obtain estimates of DSI that are predictive of the performance of lines at other environments.

Greater disease severity was significantly (P < 0.001) correlated with lower yield (Table 3) , with an average yield loss across environments of 30 kg ha^-1 for every unit increase in DSI. Correlations between yield and DSI ranged from -0.38 to -0.83 for individual environments (Table 3), with the correlation being more negative at environments with the most disease.

Genetic Marker Analysis
The parents of the population were tested with RFLP, SSR, and RAPD markers to identify polymorphisms between the parents. For the RFLP markers, 28 (23.5%) of the 119 soybean genomic DNA clones used as hybridization probes detected polymorphisms between the parents. The population was hybridized with 22 RFLP clones that revealed 23 marker loci. Nine (18.4%) of the 49 SSR primer pairs tested revealed polymorphisms between the parents with the Synergel–agarose gel system, and the population was scored for seven SSR loci. For RAPDs, 96 (17.3%) of the 554 decamer primers tested detected polymorphic fragments between the parents. Ninety-three RAPD marker loci from 78 decamer primers producing clearly scorable banding patterns were initially analyzed with a subset of the 27 most resistant and 27 most susceptible lines selected from the 1996 field evaluations. Based on the 1996 data, thirty-four RAPD markers were identified as significantly (P < 0.05) associated with DSI and were used to evaluate the entire population. These were the only RAPD marker data used in the linkage analysis.

Linkage analysis of the 64 marker loci resulted in the formation of seven linkage groups with 14 markers remaining unlinked. We attempted to reconcile our linkage groups with the USDA ARS–Iowa State University soybean genetic map by using SSR and RFLP markers as anchors. The RFLP markers were assigned to linkage groups only after our polymorphic bands were confirmed with the SoyBase (1995) data base as being the same molecular weight as the USDA ARS–Iowa State University mapped fragments for the identical restriction digest. Assuming that a QTL within 20 cM of either end of a linkage group or unlinked marker can be detected, we should have been able to identify QTLs across 1430 cM of the soybean genome estimated to be about 3000 cM (Shoemaker and Olson, 1993).

Interval mapping based on mean DSI values across locations resulted in the identification of three linkage groups with significant QTL (Fig. 2) . The peaks for the LOD plots suggested that there was a QTL between OW13₉₀₀ and BARC-Satt46 on Linkage Group K and a QTL near IaSU-A226H-1 on Linkage Group M (Fig. 2). Linkage Group C2 contained at least two major LOD peaks, suggesting that there may be two separate QTLs on this linkage group. One peak was near OX03₅₁₀ and the other was near OQ18₅₅₀.

View larger version (19K): [in this window] [in a new window]   Fig. 2 Quantitative trait loci likelihood maps indicating likelihood of odds (LOD) scores for sclerotinia stem rot disease severity index across environments for Linkage Groups (a) C2, (b) K, and (c) M. The map distances between the markers are given in centimorgans. The linkage groups are named according to Shoemaker and Specht (1995) and Akkaya et al. (1995)

Two-factor analysis of variance was done for each pair of markers with the greatest R² value (IaSU-A226H-1, OW13₉₀₀, and OAA09₆₀₀) for DSI from each of the three significant linkage groups to test for epistatic interactions. No significant interactions were detected at the P < 0.01 level. A two-factor analysis of variance including OAA9₆₀₀ and OX03₅₀₀, the markers with the greatest R² values from each of the two main LOD peaks on C2, was also done for DSI. The inclusion of both markers in the model did not result in an increase in R² compared with the analysis of OAA9₆₀₀ alone, suggesting that both peaks map the same QTL. A multiple QTL model that contained IaSU-A226H-1, OW13₉₀₀, and OAA09₆₀₀ explained 23% of the phenotypic and 39% of the genotypic variation for DSI across environments. Markers significantly (P < 0.01) associated with DSI across environments from the three significant linkage groups were tested for associations with agronomic traits. Eight out of these ten markers were also significantly associated with yield (Table 5) . The marker alleles associated with lower DSI ratings were associated with greater yield for all marker loci significantly associated with both traits. This was expected because greater disease was negatively correlated with yield (Table 3) and genes that contribute to more resistance should also increase yield under diseased conditions.

Our finding that some markers mapped QTL for both DSI and agronomic traits suggests that we mapped genes that indirectly affected DSI through a direct effect on other traits. The significant markers on Linkage Group C2 are an example of this indirect effect. The marker alleles on this linkage group that were significantly associated with greater DSI were associated with taller plant height and greater lodging (Table 5). Another example is the marker significantly associated with DSI on Linkage Group M. The marker allele significantly associated with greater DSI was associated with later flowering. Mansur et al. (1993) also mapped QTL for plant height, lodging, and yield to IaSU-A397 on C2 (Shoemaker and Specht, 1995). It is unlikely that their QTL is the same as the QTL on C2 we found associated with agronomic traits because IaSU-A397 is more than 100 cM from IaSU-655, the marker that anchors our QTL on C2 (SoyBase, 1995). In contrast to the other linkage groups, the markers significant for DSI on Linkage Group K were not significantly associated with plant height, lodging, maturity, or R1 date. This indicates that the QTL of Linkage Group K affected resistance directly, and not through escape mechanisms.

Breeders would benefit from the ability to select for resistance to sclerotinia stem rot with genetic markers because the disease is difficult to evaluate in the field. Currently, genes controlling physiological resistance can only be selected in disease evaluations. Field variability makes it difficult to obtain good estimates of resistance, and sometimes the disease fails to develop if the weather conditions are hot and dry. The genetic markers on Linkage Groups K that may be linked to gene(s) providing physiological resistance would have great utility in marker-assisted selection. In contrast, marker-assisted selection would not be as beneficial for genes associated with resistance through a direct effect on escape mechanisms such as date of flowering, plant height, and lodging. Genes controlling these traits can be selected easily by breeders in field trials when the disease is not present.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
Research supported by the Michigan Agricultural Experiment Station and grants from the Michigan Soybean Promotion Committee.

Received for publication August 13, 1998.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 

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