Genome‐Wide Association Studies in Plants

Abstract

Cheap genome sequencing technology has made it possible to search for genomic variants called single nucleotide polymorphisms (SNPs) for hundreds of individuals. Linking these genomic variants to phenotypes is the main goal in running genome‐wide association studies (GWAS). SNPs can be discovered and called using different technologies and methods, and subsequent quality control must be performed taking into account the species of study and genotyping techniques. GWAS can be performed using different mathematical approaches, demonstrated within the current range of software packages, which are used to perform the GWAS and interpret the subsequent results. Key Concepts GWAS is a powerful tool to associate genomic variants with phenotypes. Quality control is core to any good GWAS. Numerous powerful tools now exist to make running a GWAS straightforward. Interpreting the output is still a challenge, especially in the presence of hidden confounding factors. GWAS can be performed using different types of genotyping data, each with its own advantages and disadvantages.

Genome-Wide Association Study of Seed Folate Content in Common Bean

“…They had an average minor allele frequency (MAF) of 0.3 and the data contained 3.5% missing data ( Supplementary Table 2 ). Since P. vulgaris is self-pollinated and all accessions were highly inbred, markers exceeding a threshold of 40% heterozygosity were removed as they were presumed to reflect detection of paralogous loci rather than true heterozygosity ( Anderson et al, 2019 ). After this filter was applied, 5,068 markers remained in the dataset ( Supplementary Table 2 ).…”

Identification of candidate genes for LepR1 resistance against Leptosphaeria maculans in Brassica napus

“…Putative QTL were derived from the significant SNPs identified through MLM GWAS. A QTL was defined either as a 100 kb upstream and downstream region of each significant SNP ( Anderson et al., 2020 ) or a region with two significant SNPs located ≤ 15 Mb from each other on the same chromosome ( Martínez-Montes et al., 2018 ), also integrating a 100 kb region upstream and downstream from the SNP. For QTL comparison, the previous QTL (chromosome A02) based on LepR1 introgressed lines and structural variants, derived from the results of L. maculans containing AvrLep1 screened at the cotyledon stage ( Larkan et al., 2016 ; Dolatabadian et al., 2019 ), and based on adult plant survival rate and average internal infection of the stem at maturity stage ( Fikere et al., 2020b ) (considered as the QTL outlier) were physically mapped to B. napus cv Darmor bzh v9 using MapChart 2.32 for comparison ( Voorrips, 2002 ).…”

Genetic and molecular analysis of stem rot (Sclerotinia sclerotiorum) resistance in Brassica napus (canola type)

“…The QTL/candidate SNPs identified in this study were compared with previously reported QTL from 12 different studies by using BlastN analysis against the B. napus reference genome ‘ Darmor-bzh’ (v4.1) [ 64 ]. To ascertain potential candidates, all genes within a ±100,000 bp upstream and downstream region from any significant SNPs were compiled [ 65 ]. The putative function of each gene in B. napus was then determined using the UniProt Knowledgebase [ 66 ].…”