On the contribution of genetic heterogeneity to complex traits

Genetic heterogeneity, where different alleles or loci are responsible for similar phenotypes, reduces the power of genome-wide association studies and can cause misleading results. Although many striking examples have been identified, the general importance of genetic heterogeneity for complex traits is unclear. Here, we use a novel interpretative machine-learning approach to look for evidence of genetic heterogeneity in plants and humans. Our approach helps identify new loci/alleles influencing trait variation in several agriculturally important species, and we show that at least 6% of maize eQTL, half of them newly identified, exhibit evidence of allelic heterogeneity. Finally, we search for evidence of synthetic associations in human GWAS data, and find that as many as 3-5% may be affected. Our results highlight the need to take genetic heterogeneity seriously, and provide a simple approach for doing so.