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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Soybean Genomics & Improvement Laboratory » Research » Publications at this Location » Publication #345528

Research Project: Defining the Genetic Diversity and Structure of the Soybean Genome and Applications to Gene Discovery in Soybean, Wheat and Common Bean Germplasm

Location: Soybean Genomics & Improvement Laboratory

Title: Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean

Author
item Wen, Zhixin - Michigan State University
item Tan, Ruijuan - Michigan State University
item Zhang, Shichen - Michigan State University
item Collins, Paul - Michigan State University
item Yuan, Jiazheng - Michigan State University
item Du, Wenyan - Michigan State University
item Gu, Cuihua - Michigan State University
item Ou, Shujuan - Michigan State University
item Song, Qijian
item An, Yong-qiang - Charles
item Boyse, John - Michigan State University
item Chilvers, Martin - Michigan State University
item Wang, Dechun - Michigan State University

Submitted to: Plant Biotechnology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/24/2018
Publication Date: 3/12/2018
Citation: Wen, Z., Tan, R., Zhang, S., Collins, P.J., Yuan, J., Du, W., Gu, C., Ou, S., Song, Q., An, Y., Boyse, J.F., Chilvers, M.I., Wang, D. 2018. Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean. Plant Biotechnology. https://doi.org/10.1111/pbi.12918.
DOI: https://doi.org/10.1111/pbi.12918

Interpretive Summary: White mold stem rot is a significant soybean disease in North America, although severity of the disease varies depending on the weather. The disease can substantially reduce seed number, seed weight and seed quality, and results in significant yield lost. In 1994, 2004, and 2009, white mold ranked second to soybean cyst nematode in total yield lost in U.S. soybean production. So far, no soybean cultivars with complete resistance to white mold have been developed. Here, USDA-ARS scientists and Michigan State University researchers screened more than 1400 soybean varieties from the USA, China, Japan, Korea, Kyrgyzstan, and Russia for resistance to white mold disease. The scientists discovered novel genes and DNA markers for genes associated with white mold resistance. These genes and markers can be introduced to improve disease resistance to white mold in commercial soybean cultivars. These findings will be useful to breeders at universities, government agencies and private companies who are developing new and improved soybeans with white mold disease resistance.

Technical Abstract: Sclerotinia sclerotiorium (Lib.) deBary is a filamentous phytopathogenic ascomycete fungus capable of infecting a wide range of plants. When infecting soybean [Glycine max (L.) Merr.), this fungus causes an important disease known as Sclerotinia stem rot or white mold. To dissect the genetic architecture of resistance to S. sclerotiorium, a high-density customized single nucleotide polymorphism (SNP) array (52,041 SNPs) was used to genotype two soybean diversity panels. Based on the disease incidence observed in the field and greenhouse environments, genome-wide association studies (GWAS) were conducted to identify quantitative trait loci (QTL) controlling resistance against S. sclerotiorium. In the GWAS, 16 and 11 loci were found significantly associated with resistance in field and greenhouse, respectively. Of these, eight loci localized in previously mapped QTL intervals and two had significant associations with resistance across both environments. The expression changes of genes in the genomic regions identified by GWAS were assessed between resistant genotype and susceptible genotype through a RNA-seq analysis of stem tissues across time points. A set of genes with diverse biological functionalities was identified as strong candidates underlying Sclerotinia stem rot resistance. Genomic prediction models outperformed predictions based on significant SNPs. Prediction accuracies ranged from 0.48 and 0.64 for disease index measured in field experiments. The integrative methods, including GWAS, RNA-seq and genomic selection (GS), applied in this study facilitated the identification of causal variants, enhanced our understanding of mechanisms of white mold resistance, and provided valuable information regarding breeding for disease resistance through genomic selection in soybean.