Beyond Additivity: Statistical Frameworks for Detecting Secondary Genetic Effects in Large-Scale Association Studies

Modern biobank-scale datasets allow researchers to move beyond traditional additive GWAS models and interrogate richer layers of genetic architecture. Here we present a comprehensive statistical framework for detecting and interpreting non-additive genetic effects, together with systematic scans for variance effects, trait-correlation effects, gene–gene interactions, and gene–environment interactions. We illustrate these approaches with examples from recent large-scale studies of blood lipid traits and white blood cell traits.