Genome-wide association study and genomic selection for tolerance of soybean biomass to soybean cyst nematode infestation

Abstract

Soybean cyst nematode (SCN), Heterodera glycines Ichinohe, is one of the most devastating pathogens affecting soybean production in the U.S. and worldwide. The use of SCN-resistant soybean cultivars is one of the most affordable strategies to cope with SCN infestation. Because of the limited sources of SCN resistance and changes in SCN virulence phenotypes, host resistance in current cultivars has increasingly been overcome by the pathogen. Host tolerance has been recognized as an additional tool to manage the SCN. The objectives of this study were to conduct a genome-wide association study (GWAS), to identify single nucleotide polymorphism (SNP) markers, and to perform a genomic selection (GS) study for SCN tolerance in soybean based on reduction in biomass. A total of 234 soybean genotypes (lines) were evaluated for their tolerance to SCN in greenhouse using four replicates. The tolerance index (TI = 100 × Biomass of a line in SCN infested / Biomass of the line without SCN) was used as phenotypic data of SCN tolerance. GWAS was conducted using a total of 3,782 high quality SNPs. GS was performed based upon the whole set of SNPs and the GWAS-derived SNPs, respectively. Results showed that (1) a large variation in soybean TI to SCN infection among the soybean genotypes was identified; (2) a total of 35, 21, and 6 SNPs were found to be associated with SCN tolerance using the models SMR, GLM (PCA), and MLM (PCA+K) with 6 SNPs overlapping between models; (3) GS accuracy was SNP set-, model-, and training population size-dependent; and (4) genes around Glyma.06G134900, Glyma.15G097500.1, Glyma.15G100900.3, Glyma.15G105400, Glyma.15G107200, and Glyma.19G121200.1 (Table 4). Glyma.06G134900, Glyma.15G097500.1, Glyma.15G100900.3, Glyma.15G105400, and Glyma.19G121200.1 are best candidates. To the best of our knowledge, this is the first report highlighting SNP markers associated with tolerance index based on biomass reduction under SCN infestation in soybean. This research opens a new approach to use SCN tolerance in soybean breeding and the SNP markers will provide a tool for breeders to select for SCN tolerance.

Fig 1. Distribution of adjusted tolerance index among the 234 soybean accessions.

Fig 2. Manhattan plots and QQ-plots for tolerance indexes based on biomass reduction under SCN infestation.

The x-axis of each Manhattan plot represented the chromosome number, whereas the y-axis denoted the LOD (-log10(p-value)). Color coding on the Manhattan plot is chromosome-wise. The x-axis of each QQ-plot represented the expected -log10(p-value), whereas the y-axis displayed the observed -log10(p-value). A: Manhattan plot and QQ-plot resulted from the single marker regression model (SMR). B: Manhattan plot and QQ-plot obtained using the generalized linear model (GLM(PCA)). C: Manhattan plot and QQ-plot generated by the mixed liner model (MLM(PCA+K)).

Fig 3. Boxplots showing genomic selection accuracy for SCN tolerance index for biomass reduction under SCN infestation using 5 statistical models: Bayesian Lasso regression (BLR), genomic best linear unbiased predictor (gBLUP), random forest (RF), ridge regression best linear unbiased predictor (rrBLUP), and support vector machines (SVMs).

For each model, cross-validation was conducted using different levels (2-fold, 3-fold, 4-fold, 5-fold, 6-fold, and 7-fold) in order to assess the effect of population training size on genomic selection accuracy. At each level of cross-validation, SNP set consisting of all SNPs and SNPs with an LOD greater than 2 based on the GWAS on analysis were used for conducting genomic selection. SMR_SNPs denoted the SNPs from the single marker regression model, GLM_PCA_SNPs represented the SNPs from the generalized linear model, and MLM_PCA_K_SNPs corresponded to the SNPs from the mixed linear model in GWAS. Box plot color coding in the above figure is SNP set-wise. Genomic selection was conducted using a total of 100 replications and empty dots were outliers.