On the contribution of genetic heterogeneity to complex traits

Abstract

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.