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

Abstract

SummaryWhite mould of soya bean, caused by Sclerotinia sclerotiorum (Lib.) de Bary, is a necrotrophic fungus capable of infecting a wide range of plants. To dissect the genetic architecture of resistance to white mould, a high‐density customized single nucleotide polymorphism (SNP) array (52 041 SNPs) was used to genotype two soya bean diversity panels. Combined with resistance variation data observed in the field and greenhouse environments, genome‐wide association studies (GWASs) were conducted to identify quantitative trait loci (QTL) controlling resistance against white mould. Results showed that 16 and 11 loci were found significantly associated with resistance in field and greenhouse, respectively. Of these, eight loci localized to previously mapped QTL intervals and one locus had significant associations with resistance across both environments. The expression level changes in genes located in GWAS‐identified loci were assessed between partially resistant and susceptible genotypes through a RNA‐seq analysis of the stem tissue collected at various time points after inoculation. A set of genes with diverse biological functionalities were identified as strong candidates underlying white mould resistance. Moreover, we found that genomic prediction models outperformed predictions based on significant SNPs. Prediction accuracies ranged from 0.48 to 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 mould resistance and provided valuable information regarding breeding for disease resistance through genomic selection in soya bean.

Integrating Coexpression Networks with GWAS to Prioritize Causal Genes in Maize

“…Comparing the similarity of two genes' expression profiles, or coexpression, quantifies the joint response of the genes to various biological contexts, and highly similar expression profiles can indicate shared regulation and function (Eisen et al, 1998). The analysis of coexpression has been used successfully to identify functionally related genes, including in several crop species (Ozaki et al, 2010;Mochida et al, 2011;Swanson-Wagner et al, 2012;Zheng and Zhao, 2013;Obayashi et al, 2014;Sarkar et al, 2014;Schaefer et al, 2014;Michno et al, 2018;Wen et al, 2018), and has been used to characterize GWAS results in Arabidopsis (Arabidopsis thaliana) (Chan et al, 2011;Corwin et al, 2016;Angelovici et al, 2017;Lee and Lee, 2018).…”

Exploring the genetics of lesion and nodal resistance in pea (Pisum sativum L.) to Sclerotinia sclerotiorum using genome‐wide association studies and RNA‐Seq

“…The GWAS approach has been demonstrated in soybean ( Glycine max Merr. L.) resistance to S. sclerotiorum where numerous single nucleotide polymorphisms (SNPs) associated with this quantitative resistance were discovered (Bastien, Sonah, & Belzile, ; Moellers et al., ; Wen et al., ; Wu, Zhao, Liu, et al., ). However, mapping results may discover SNPs that locate in intergenic genomic regions, and the interpretation of a confidence interval relies on the size of linkage disequilibrium (Bush & Moore, ).…”

“…RNA‐Seq and GWAS both have their advantages, and combining them provides a powerful tool to discover not only active genes that express in response to treatments, but also genetic diversity and SNPs associated with the treatment. This combined strategy has been applied to understand white mold resistance and yields in B. napus (Lu et al., ; Wei et al., ) and soybean (Wen et al., ), but not in pea. Because genes that can be found by both GWAS and RNA‐Seq will have higher potential in contributing to white mold resistance, this study aimed to understand and compare the genetics of lesion and nodal resistance by applying both GWAS and RNA‐Seq approaches in the pea‐ S. sclerotiorum pathosystem.…”

Genetic dissection of the gene coexpression network underlying photosynthesis in Populus

“…In this study, we identified 282 eQTNs associated with the expression of 18 genes, and these eQTNs represent 100 gene–gene pairs, which highlights the complexity of the regulatory network (Figure S7). PtoPsbY expression was significantly associated with 17 genetic factors; similarly, PtoLhcb4.2 regulates the expression of eight genes, providing an illustration of the power of eQTN mapping for identifying active regulatory factors in the complex photosynthetic pathway (Wen et al , ). The direct integration of eQTNs with quantitative trait analyses has facilitated the functional interpretation of complex trait association signals in previous studies (Nica et al , ).…”