View Program - 2022 Population, Evolutionary, and Quantitative Genetics Conference

Understanding how genetic variants influence multiple traits is an essential foundational question of statistical genetics. Genome-wide association studies (GWAS) have begun characterizing the genetic architecture of complex traits in numerous species, but the molecular mechanisms connecting GWAS hits to their traits are often unclear without extensive forward genetic analyses. Investigation of molecular traits in related pathways, with well-documented biology jointly impacting their levels, provides an opportunity to uncover putative molecular mechanisms. Here, we perform genome-wide association studies (GWAS; n = 94,464 European ancestry individuals) of 16 metabolites clustered at the intersection of amino acid catabolism, glycolysis, and ketone metabolism and systematically evaluate their shared genetic bases.
Among the 213 independent GWAS variants, we find a strong enrichment for genes encoding pathway relevant enzymes (n = 68 variants; 79-fold, P < 2.2e-16) and transporters (n = 46 variants; 13-fold, P < 2.2e-16). We then investigate the joint effects of pleiotropic variants on biologically-related metabolites in the context of their biochemical pathways. This multivariate approach allows for a better understanding of why these variants are associated with and influence the level of these metabolites. We find that variants with effect direction in metabolite pairs opposite the overall genetic correlation of the metabolites, which we define as discordant variants, are more likely to affect enzymes and transporters than other gene types (4.1-fold, P = 0.034). We also find that discordant variants affecting pathway relevant enzymes are more likely to act between, rather than upstream or downstream of, the metabolites for which they are discordant (23-fold, P = 0.0072). Finally, we apply this method to complex trait GWAS hits and identify a coronary artery disease association at PCCB with striking interpretability of effects on disease-relevant pathway metabolites, underscoring the potential of unifying biochemistry with dense metabolomics data to understand the molecular basis of complex traits and diseases.

Integrative pathway analysis of metabolites reveal genetic architecture of complex traits and disease