Genetic architecture of wild soybean (Glycine soja) response to soybean cyst nematode (Heterodera glycines)

Abstract

The soybean cyst nematode (SCN) is one of the most destructive pathogens of soybean plants worldwide. Host-plant resistance is an environmentally-friendly method to mitigate SCN damage. To date, the resistant soybean cultivars harbor limited genetic variation, and some are losing resistance. Thus, a better understanding of the genetic mechanisms of the SCN resistance, as well as developing diverse resistant soybean cultivars, is urgently needed. In this study, a genome-wide association study (GWAS) was conducted using 1,032 wild soybean (Glycine soja) accessions with over 42,000 single-nucleotide polymorphisms (SNPs) to understand the genetic architecture of G. soja resistance to SCN race1. Ten SNPs were significantly associated with the response to race 1. Three SNPs on chromosome 18 were localized within the previously identified quantitative trait loci (QTLs), and two of which were localized within a strong linkage disequilibrium block encompassing a nucleotide-binding (NB)-ARC disease resistance gene (Glyma.18g102600). Genes encoding methyltransferases, the calcium-dependent signaling protein, the leucine-rich repeat kinase family protein, and the NB-ARC disease resistance protein, were identified as promising candidate genes. The identified SNPs and candidate genes can not only shed light on the molecular mechanisms underlying SCN resistance, but also can facilitate soybean improvement employing wild genetic resources.

Resistance Components and Interactions of Aphis glycines (Hemiptera: Aphididae) and Heterodera glycines (Tylenchida: Heteroderidae) in Soybean

“…The 'nutrient reservoir activity' molecular function is important in protecting plant tissues that produce surface waxes [79]. Similarly, the 'binding' molecular function that constitutes the higher proportion suggests the Rag QTLs: Rag1 [64], rag1b [69], rag1c [67], Rag2 [76], Rag3 [66,77] , Rag4 [77], rag3 [69] , rag3b [78], Rag3c [75], rag4 [67], Rag5 [68] [82] for understanding the genetic basis of diseases linked to complex, polygenic traits. Classical QTL mapping is limited in its ability to identify allelic diversity and resolve genomes [83], but GWAS can capture all the recombination events undergone during the evolution of sampled genotypes [84].…”

Genome-Wide Association Study for Resistance to the <em>Meloidogyne javanica</em> Causing Root-Knot Nematode in Soybean

“…Based on the SNPs identified by GWAS, using either the peak SNP, GBSRmj961 (30,804,961), or the haplotype composed by the four peak SNPs (SNPs are redundant between them), it was obtained the same results in the haplotype analysis ( Supplementary Table S4). That is, it was possible to appropriately separate the resistant and moderately resistant accessions from susceptible accessions.…”

“…Even previous genetic mapping studies reporting a locus related to resistance to M. javanica in the chromosome 13, as far we know, our study is the first to identified SNPs markers related to M. javanica resistance by GWAS, significantly narrowing down the region, being able to identify potential resistance genes in the locus. Indeed, almost all the GWAS previous reported are focused in soybean cyst nematode (SCN) causing by Heterodera glycines, identifying SNPs related to SCN race 1 [30], race 3 [31,32], SCN HG Type 2.5.7 [22,33].…”

Phenotypic and genomic selection for multi-trait improvement in soybean line and variety development

“…SCN resistance for one race was modeled as consisting of one major genetic locus on chromosome 10 and all of the phenotypic variability was simulated to consist entirely of genotypic variability (Table 5). PRR resistance was modeled as consisting of two QTL on chromosome 1 and 11 that are respectively responsible for 60% and 30% of the total phenotypic variance (Zhang et al 2017). The genetic architecture for lodging resistance consisted of five QTL, two on chromosome 15, one on chromosome 17 and two on chromosome 19.…”

“…However, the genetic architectures for polygenic traits such as yield are not understood with any level of confidence. Estimates of identified QTLs for polygenic traits such as yield seem to be dependent on genetic background (Zhang et al 2017) and environmental influences (Wang et al 2014). In soybeans, 188 QTLs have been identified for yield (Sebastian et al 2010;Fox et al 2015).…”