biostudies-literatureUnknown112(9)Shaw CAWe present the Causal Pivot (CP) as a structural causal model (SCM) for analyzing genetic heterogeneity in complex diseases. The CP leverages an established causal factor or factors to detect the contribution of additional suspected causes. Specifically, polygenic risk scores (PRSs) serve as known causes, while rare variants (RVs) or RV ensembles are evaluated as candidate causes. The CP incorporates outcome-induced association by conditioning on disease status. We derive a conditional maximum-likelihood procedure for binary and quantitative traits and develop the Causal Pivot likelihood ratio test (CP-LRT) to detect causal signals. Through simulations, we demonstrate the CP-LRT's robust power and superior error control compared to alternatives. We apply the CP-LRT to UK Biobank (UKB) dataAmerican journal of human genetics2232-2246https://www.ebi.ac.uk/biostudies/studies/S-EPMC12461002biostudies-literatureThe Causal Pivot: A structural approach to genetic heterogeneity and variant discovery in complex diseases.PMC12461002Di NShulman JMWilliams CJTan TShaw CAIllera DBelmont JWfalseThe Causal Pivot: A structural approach to genetic heterogeneity and variant discovery in complex diseases.We present the Causal Pivot (CP) as a structural causal model (SCM) for analyzing genetic heterogeneity in complex diseases. The CP leverages an established causal factor or factors to detect the contribution of additional suspected causes. Specifically, polygenic risk scores (PRSs) serve as known causes, while rare variants (RVs) or RV ensembles are evaluated as candidate causes. The CP incorporates outcome-induced association by conditioning on disease status. We derive a conditional maximum-likelihood procedure for binary and quantitative traits and develop the Causal Pivot likelihood ratio test (CP-LRT) to detect causal signals. Through simulations, we demonstrate the CP-LRT's robust power and superior error control compared to alternatives. We apply the CP-LRT to UK Biobank (UKB) data2025-01-01T00:00:00Z2025 Sep2026-06-03T20:30:14.372Z2026-06-01T03:06:43.025ZS-EPMC124610024082959910.1016/j.ajhg.2025.07.012