Association mapping in plants in the post-GWAS genomics era

Abstract

With the availability of DNA-based molecular markers during early 1980s and that of sophisticated statistical tools in late 1980s and later, it became possible to identify genomic regions that control a quantitative trait. The two methods used for this purpose included quantitative trait loci (QTL) interval mapping and genome-wide association mapping/studies (GWAS). Both these methods have their own merits and demerits, so that newer approaches were developed in order to deal with the demerits. We have now entered a post-GWAS era, where either the original data on individual genotypes are being used again keeping in view the results of GWAS or else summary statistics obtained through GWAS is subjected to further analysis. The first half of this review briefly deals with the approaches that were used for GWAS, the GWAS results obtained in some major crops (maize, wheat, rice, sorghum and soybean), their utilization for crop improvement and the improvements made to address the limitations of original GWA studies (computational demand, multiple testing and false discovery, rare marker alleles, etc.). These improvements included the development of multi-locus and multi-trait analysis, joint linkage association mapping, etc. Since originally GWA studies were used for mere identification of marker-trait association for marker-assisted selection, the second half of the review is devoted to activities in post-GWAS era, which include different methods that are being used for identification of causal variants and their prioritization (meta-analysis, pathway-based analysis, methylation QTL), functional characterization of candidate signals, gene- and gene-set based association mapping, GWAS using high dimensional data through machine learning, etc. The last section deals with popular resources available for GWAS in plants in the post-GWAS era and the implications of the results of post-GWAS for crop improvement.

Keywords: Association mapping; Candidate gene; Crop improvement; Functional characterization; GWAS; Meta-analysis; Missing heritability; Mixed models; Post-GWAS era; Rare variants.