Understanding shared genetic architecture is essential to interpreting disease comorbidities and trait correlations. We introduce SBayesAPP, a Bayesian model that integrates GWAS summary statistics with functional annotations to jointly estimate annotation-stratified SNP effect-size correlation and pleiotropic variant proportion (co-polygenicity) between traits, dissecting genetic correlation and coheritability enrichment across annotations. Simulations and real data analyses show improved accuracy and interpretability over existing methods. In type 2 diabetes analyses with 15 traits, SBayesAPP reveals clear tissue- and cell-type-specific enrichment and distinguishes mechanisms driven by few large-effect variants versus many modest-effect variants. The analysis of smoking and lung cancer prioritizes lung and immune cells, and identifies cell-type-specific genetic correlations driven by either pleiotropic or lung-cancer-specific variants, consistent with a causal relationship model. For schizophrenia and educational attainment, despite near-zero genome-wide genetic correlation, cell-type-specific correlations range from -0.20 to 0.21, with strong (co)heritability enrichment and high co-polygenicity found in dopaminergic neurons and oligodendrocytes. These results highlight the ability of SBayesAPP to resolve annotation-specific genetic sharing and uncover biological mechanisms across complex traits.
Qu, J., Zhao, T., Lin, T., Li, A., Liu, S., Chauquet, S., Visscher, P. M., Wray, N. R., Yengo, L., Zeng, J., Cheng, H.
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