High-Resolution GWAS Indicates that Gene Regulation and Neofunctionalization are Strong Drivers of Phenotypic Variation in Maize

Phenotypic variation in natural populations results from a combination of genetic effects, environmental effects, and gene-by-environment interactions. Despite the vast amount of genomic data becoming available, many pressing questions remain about the nature of genetic mutations that underlie adaptive variation (that is, variation not associated with genetic diseases). We used a collection of 41 phenotypes measured in the maize Nested Association Mapping (NAM) population to analyze patterns of high-resolution GWAS hits from among of 28.9 million single-nucleotide polymorphisms (SNPs) and ~800,000 copy-number variants (CNVs). We show that genic and intergenic regions show opposite patterns of enrichment and effect size, implying a tradeoff between the probability that a given mutation will have an effect and the potential size of that effect. We also find that genes tagged by GWAS are enriched for regulatory functions and are ~50% more likely to have a paralog than expected by chance, indicating that gene regulation and neofunctionalization are strong drivers of phenotypic variation. These results will likely apply to many other organisms, especially ones with large and complex genomes like maize.