Multi-Locus Genome-Wide Association Studies for 14 Main Agronomic Traits in Barley

Abstract

The agronomic traits, including morphological and yield component traits, are important in barley breeding programs. In order to reveal the genetic foundation of agronomic traits of interest, in this study 122 doubled haploid lines from a cross between cultivars “Huaai 11” (six-rowed and dwarf) and “Huadamai 6” (two-rowed) were genotyped by 9680 SNPs and phenotyped 14 agronomic traits in 3 years, and the two datasets were used to conduct multi-locus genome-wide association studies. As a result, 913 quantitative trait nucleotides (QTNs) were identified by five multi-locus GWAS methods to be associated with the above 14 traits and their best linear unbiased predictions. Among these QTNs and their adjacent genes, 39 QTNs (or QTN clusters) were repeatedly detected in various environments and methods, and 10 candidate genes were identified from gene annotation. Nineteen QTNs and two genes (sdw1/denso and Vrs1) were previously reported, and eight candidate genes need to be further validated. The Vrs1 gene, controlling the number of rows in the spike, was found to be associated with spikelet number of main spike, spikelet number per plant, grain number per plant, grain number per spike, and 1,000 grain weight in multiple environments and by multi-locus GWAS methods. Therefore, the above results evidenced the feasibility and reliability of genome-wide association studies in doubled haploid population, and the QTNs and their candidate genes detected in this study are useful for marker-assisted selection breeding, gene cloning, and functional identification in barley.

Multi-Locus GWAS of Quality Traits in Bread Wheat: Mining More Candidate Genes and Possible Regulatory Network

“…Due to the stricter Bonferroni correction, the single-locus GWAS methods, including MLM and GLM, were not efficient to detect small effective loci of a complex trait (Wang et al, 2016). Recently, GWAS analysis in many species using the multi-locus methods showed significantly higher efficiency in the detection of small effective loci than the single-locus methods (Hu et al, 2018;Lü et al, 2018;Peng et al, 2018). Excluding the FASTmrEMMA, the four other multi-locus GWAS approaches, which ranged from 89 to 118 and ranged from 2 to 81, found more QTNs than the two single-locus methods ( Table 3).…”

Comparing Single‐SNP, Multi‐SNP, and Haplotype‐Based Approaches in Association Studies for Major Traits in Barley

“…These chromosomal regions represented 67, 90, and 80% of QTLs detected by the single-SNP, multi-SNP, and haplotype-based approaches, respectively, highlighting that the degree of colocalization with previously reported QTLs and genes was high for all three approaches. In a recent study on barley, Hu et al (2018) demonstrated that the use of multilocus models for main agronomic traits can detect more QTLs than traditional QTL analysis: among the 39 QTLs detected, 49% colocalized with same QTL regions in previous studies.…”

“…Several studies have also proved the potential of multilocus and haplotype-based models to identify known and novel associations with traits. In barley, Hu et al (2018) proved that among the QTLs detected by multilocus models, 51%, including some minor-effect QTLs, were novel. Moreover, Mihalyov et al (2017) explored the use of GWAS to characterize stem rust resistance genes in winter wheat and found that a multilocus model improved the identification of new loci with direct breeding applications.…”

“…High‐throughput platforms for genotyping at a relatively low cost are now available for barley, allowing the application of GWAS for dissecting the genetic mechanisms underlying agriculturally important traits and to identify useful markers for breeding programs (Bellucci et al, 2017). Several association studies have been reported in barley for different traits to identify major genes (Ramsay et al, 2011) and many complex agronomic traits (e.g., Cockram et al, 2010; Lorenz et al, 2010; Comadran et al, 2011; Pasam et al, 2012; Visioni et al, 2013; Pauli et al, 2014; Gawenda et al, 2015; Alqudah et al, 2016, 2018; Bellucci et al, 2017; Hu et al, 2018) and to explore resistance for some important pathogens (Roy et al, 2010; Massman et al, 2011; Mamo and Steffenson, 2015; Sallam et al, 2017). The majority of these studies were conducted with a relatively low number of SNPs (1500–9000), which could not fully reveal the genetic bases of quantitative traits.…”

Genome wide association study (GWAS) for morphological and yield-related traits in oil palm hybrids (Elaeis oleifera x Elaeis guineensis)

“…SNPs detected by GBS have allowed the genetic dissection of genotypic variation in complex traits in many plant species [34][35][36]. Specifically, in oil palm the GBS technique has been used for identifying candidate genes related to oil bunch [37], bunch weight [20,37], stem height [21] and oil quality traits studies [38] with the enzymes ApeKI and PstI-MspI.…”