Zea mays Exome

Although approaches for conducting genome-wide association studies (GWAS) are well developed, conventional GWAS requires the high-density genotyping of large numbers of individuals from a diversity panel. Here we report a method for conducting GWAS that does not require the genotyping of large numbers of individuals. Instead XP-GWAS (extreme phenotype GWAS) relies on genotyping pools of individuals from a diversity panel having extreme phenotypes. This analysis measures allele frequencies in the extreme pools, enabling the discovery of associations between genetic variants and traits of interest. This method was evaluated in maize using the well-characterized kernel row number trait, which was selected to enable comparisons between the results of XP-GWAS and a conventional GWAS. An exome-sequencing strategy was employed to focus sequencing resources on genes and their flanking regions. A total of 0.94 million variants was identified and served as evaluation markers; via comparisons among pools, 145 of these variants were identified as being statistically associated with the kernel row number phenotype. These trait-associated variants were significantly enriched in regions identified by the conventional GWAS. XP-GWAS was able to resolve several linked QTL and detect trait-associated variants within a single gene under a QTL peak. XP-GWAS is expected to be of particular valuable for detecting genes or alleles responsible for quantitative variation in species that do not have access to extensive genotyping resources, such as wild progenitors of crops, orphan crops and other poorly characterized species such as those of ecological interest.