Genome-Wide Association Study Using Targeted Marker Subsets to Account for Population Structure and Relatedness Identifies More Genomic Signals Associated with Polygenic Traits in Maize (Zea mays L.).

The unified mixed linear model (MLM) has been one of the most widely-used approaches for associating markers with quantitative traits because it effectively controls for false positive signals arising from the population structure and familial relatedness typically present in a diversity panel. One potential drawback of this approach is that all of the markers being tested for associations are also being used to account for both population structure and relatedness, which could lead to a reduction in the power to detect significant marker-trait associations. Previous work has shown that it is possible to increase power by calculating kinship matrices with markers that are not located on the chromosome where the tested marker resides. To quantify the amount of additional significant signals one can expect using this so-called K_chr model, we reanalyzed several polygenic and complex traits in a maize (Zea mays L.) diversity panel that have been previously assessed using the traditional unified MLM. For the polygenic traits, we demonstrated that the K_chr model could find more significant associations, especially in high LD regions. This finding is underscored by our identification of novel genomic signals proximal to the tocochromanol biosynthetic pathway gene ZmVTE1 and a ratio of tocotrienols. We conclude that the K_chr model can detect more intricate sources of allelic variation underlying polygenic traits, and should therefore become more widely used for GWAS.