Decomposing heritability and genetic covariance by direct and indirect effect paths

Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits.Author summary: Complex human traits are influenced by both genetic and environmental factors. Recent evidence suggests that genetic effects on human traits can sometimes be mediated by the environment. One example is that parental genomes could influence parental behavior and family environment, which, in turn, shape the phenotypes of their children. Since parent and offspring genotypes are correlated, these environmentally mediated effects will be captured by conventional genome-wide association studies and may potentially bias our interpretation of genotype-phenotype associations. In this work, we propose a principled statistical framework named PARSEC to partition the genetic components of single traits and the shared genetic components between traits into direct and indirect effect paths. It marks an important methodological innovation by resolving a limitation in almost all existing genetic correlation estimation approaches and may have broad and impactful applications in future GWAS analysis for complex traits that are affected by parental genetics and family environment.