Genome-wide association (GWAS) and epistatic (GWES) studies along with expression studies in soybean [Glycine max (L.) Merr.] were leveraged to dissect the genetics of Sclerotinia stem rot (SSR) [caused by Sclerotinia sclerotiorum (Lib.) de Bary], a significant fungal disease causing yield and quality losses. A large association panel of 466 diverse plant introduction accessions were phenotyped in multiple field and controlled environments to: (1) discover sources of resistance, (2) identify SNPs associated with resistance, and (3) determine putative candidate genes to elucidate the mode of resistance. We report 58 significant main effect loci and 24 significant epistatic interactions associated with SSR resistance, with candidate genes involved in a wide range of processes including cell wall structure, hormone signaling, and sugar allocation related to plant immunity, revealing the complex nature of SSR resistance. Putative candidate genes [for example, PHYTOALEXIN DEFFICIENT 4 (PAD4), ETHYLENE-INSENSITIVE 3-LIKE 1 (EIL3), and ETHYLENE RESPONSE FACTOR 1 (ERF1)] clustered into salicylic acid (SA), jasmonic acid (JA), and ethylene (ET) pathways suggest the involvement of a complex hormonal network typically activated by both necrotrophic (ET/JA) and biotrophic (SA) pathogens supporting that S. sclerotiorum is a hemibiotrophic plant pathogen.

The objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. The predictive ability of six GP models were compared, and the impact of marker density and training population size on the predictive ability was investigated. Cross-prediction among environments was tested to determine the effectiveness of the prediction models. GP models had similar prediction accuracies for all experiments. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size (from 50% to 90% of the total of individuals). The GP model effectively predicted WM resistance across field and greenhouse experiments when each was used as either the training or validation population. The GP model was able to identify WM-resistant accessions in the USDA soybean germplasm collection that had previously been reported and were not included in the study panel. This study demonstrated the applicability of GP to identify useful genetic sources of WM resistance for soybean breeding. Further research will confirm the applicability of the proposed approach to other complex disease resistance traits and in other crops.

No abstract