Pathway-specific polygenic scores substantially increase the discovery of gene-adiposity interactions impacting liver biomarkers

Polygenic scores (PGS) have been leveraged to detect gene-environment interactions across many complex traits and environmental variables. While PGS×E regression is potentially more powerful than single-variant genome-wide interaction studies (GWIS) due to the aggregation of genetic effects and reduced multiple testing burden, standard PGS reflect many different biological mechanisms, limiting interpretation and potentially diluting pathway-specific interaction signals. Previous work has uncovered significant genome-wide PGS×BMI signal for liver function, but there is an opportunity for additional and more interpretable discoveries. Here, we leverage pathway-specific polygenic scores (pPGS) to discover novel mechanism-specific gene-adiposity interactions. We tested for adiposity interactions impacting three liver-related biomarkers (ALT, AST and GGT) using (1) a standard, genome-wide PGS, (2) an array of pPGS containing variant subsets derived from KEGG pathways, and (3) a GWIS. For ALT, we identified 49 significant pPGS×BMI interactions at a Bonferroni corrected p < 2.7×10 ⁻⁴ , 80% of which were not explained by genes close to the 8 loci found in the associated GWIS. Across all biomarkers, we found interactions of BMI with 83 unique pPGS-based on KEGG pathways. We tested alternate pathway collections, including Hallmark gene sets and the KEGG Medicus database, finding that the choice of pathway collection strongly impacts discovery. Our results support the use of pPGS for well powered and interpretable discovery of pPGS×E interactions with adiposity-related exposures for liver biomarkers and motivate future studies using a broader collection of exposures and outcomes.