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ARS Home » Midwest Area » Urbana, Illinois » Soybean/maize Germplasm, Pathology, and Genetics Research » Research » Publications at this Location » Publication #340468

Research Project: Characterization, Management, and Utilization of Soybean Genetic Resources

Location: Soybean/maize Germplasm, Pathology, and Genetics Research

Title: Genome-wide association mapping of resistance to a Brazilian isolate of Sclerotinia sclerotiorum in soybean genotypes mostly from Brazil

Author
item Wei, Wei - University Of Illinois
item Mesquita, Ana Carolina - Universidade Federal De Uberlândia
item Figueiro, Adriana - Universidade Federal De Uberlândia
item Wu, Xing - University Of Illinois
item Manjunatha, Shilpa - University Of Illinois
item Wickland, Daniel - University Of Illinois
item Hudson, Matthew - University Of Illinois
item Juliatta, Fernando - Universidade Federal De Uberlândia
item Clough, Steven

Submitted to: BMC Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/21/2017
Publication Date: 11/7/2017
Citation: Wei, W., Mesquita, A., Figueiro, A., Wu, X., Manjunatha, S., Wickland, D.P., Hudson, M.E., Juliatta, F.C., Clough, S.J. 2017. Genome-wide association mapping of resistance to a Brazilian isolate of Sclerotinia sclerotiorum in soybean genotypes mostly from Brazil. BMC Genomics. 18:849.

Interpretive Summary: Sclerotinia Stem Rot (SSR) is a highly damaging disease of soybean that occurs in cooler soybean-growing regions worldwide. Breeding for resistance to SSR remains challenging in crops like soybean, because instead of a single gene providing strong defense (relatively easy for breeders), multiple genes work together and provide only partial resistance to SSR. If breeders had genetic markers that allowed them to rapidly identify regions of soybean chromosomes that contained genes for resistance to SSR, they could use modern breeding technologies to produce soybean varieties with new combinations of SSR resistance genes. In an effort to find these markers, we evaluated 420 soybean lines for their resistance to SSR and the presence of nearly 50,000 genetic markers. The analysis found that regions on three soybean chromosomes contained genes for SSR resistance. The three regions were identified by two different computational methods, which adds credence to the importance of their role in SSR resistance. These experiments will be useful to scientists working to produce soybean plants with improved resistant to SSR and using molecular techniques to identify chromosomal regions containing genes for disease resistance.

Technical Abstract: Sclerotinia Stem Rot (SSR), caused by the fungal pathogen Sclerotinia sclerotiorum, is ubiquitous in cooler climates where soybean crops are grown. Breeding for resistance to SSR remains challenging in crops like soybean, where no single gene provides strong resistance, but instead, multiple genes work together to provide partial resistance. If breeders knew what genes were associated with enhanced defense, they could use that information to design effective molecular markers in marker assisted selection (MAS). We evaluated 420 soybean genotypes for their sensitivity to SSR, and identified single nucleotide polymorphisms (SNPs) within this group to conduct a genome-wide association study (GWAS). The genotypes were selected based on reports of resistance, and from three different breeding programs in Brazil, two commercial, one public. We inoculated the plants using the cut stem method, and scored disease lesions in centimeters along the plant stems at 4 days post inoculation. Lesion length showed a normal distribution within the population. Prior to inoculation, DNA was collected and used in constructing genotype-by-sequencing (GBS) libraries. Sequencing the GBS libraries and aligning reads allowed for the identification of SNPs. Highly heterozygous genotypes and SNPs were removed, leaving 325 genotypes. We evaluated different SNP calling and genome-wide association study (GWAS) methods, using the TASSEL 5 GBS v2 or an in-house SNP-calling pipeline, as well as by using a Mixed Linear Model with Genome Association and Prediction Integrated Tool (GAPIT) or a Fixed and Random Model Circulating Probability Unification (FARMCPU) model which handles linkage disequilibrium differently than GAPIT. The in-house pipeline performed better than the TASSEL 5 GBS v2 pipeline, and we concluded that, using the in-house SNP-calling pipeline: both GAPIT and FARMCPU identified significance on chromosomes 1, 3, and 19, but FARMCPU also identified significance on chromosomes 5, 13, and 20, whereas GAPIT also found significance on chromosome 18. These similar and yet different results show that the computational method used can strongly impact SNP associations in soybean, a plant with a high degree of linkage disequilibrium.