Finding the genetic basis of adaptation: reducing complexity to improve trait mapping

Genome-wide association studies (GWAS) have advanced our understanding of trait variation, yet persistent challenges limit their effectiveness in mapping adaptive traits. Population structure, allelic heterogeneity, trait complexity, and structural variation obscure true causal relationships. However, these factors can often be addressed through strategic simplification. Here, we review factors limiting GWAS success in natural populations and show that reducing complexity improves mapping outcomes. We examine how local population mapping, trait decomposition into endophenotypes, and incorporation of structural variation enable the identification of adaptive loci. Using Arabidopsis thaliana as a model system, we demonstrate how these approaches reveal functionally validated variants missed by traditional studies. Finally, we discuss integration strategies and emerging technologies that will advance our understanding of the genetics of adaptation.