The Causal Pivot: A Structural Approach to Genetic Heterogeneity and Variant Discovery in Complex Diseases

Abstract

We present the Causal Pivot (CP) as a structural causal model (SCM) for analyzing genetic heterogeneity in complex diseases. The CP leverages one established causal factor to detect the contribution of a second suspected cause. Specifically, polygenic risk scores (PRS) serve as known causes, while rare variants (RV) 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-LRTs robust power and superior error control compared to alternatives. We apply the CP-LRT to UK Biobank (UKB) data, analyzing three exemplar diseases: hypercholesterolemia (HC, LDL-c [≥] 4.9 mmol/L; nc=24,656), breast cancer (BC, ICD10 C50; nc=12,479), and Parkinsons disease (PD, ICD10 G20; nc=2,940). For PRS, we utilize UKB-derived values, and for RVs, we analyze ClinVar pathogenic/likely pathogenic variants and loss-of-function mutations in disease-relevant genes: LDLR for HC, BRCA1 for BC, and GBA for PD. Significant CP-LRT signals were detected for all three diseases. Cross-disease and synonymous variant analyses serve as controls. We further develop ancestry adjustment using matching and inverse probability weighting, and we extend the CP to examine oligogenic burden in the lysosomal storage pathway for PD. The CP reveals an approach to address heterogeneity and is an extensible method for inference and discovery in complex disease genetics.