Most disease-associated genetic variants are pleiotropic, affecting multiple genetically correlated traits. Their pleiotropic associations can be mechanistically informative: if many variants have similar patterns of association, they may act via similar pleiotropic mechanisms, forming a shared component of heritability. We developed pleiotropic decomposition regression (PDR) to identify shared components and their underlying genetic variants. We validated PDR on simulated data and identified limitations of existing methods in recovering the true components. We applied PDR to three clusters of five to six traits genetically correlated with coronary artery disease (CAD), asthma, and type II diabetes (T2D), producing biologically interpretable components. For CAD, PDR identified components related to BMI, hypertension, and cholesterol, and it clarified the relationship among these highly correlated risk factors. We assigned variants to components, calculated their posterior-mean effect sizes, and performed out-of-sample validation. Our posterior-mean effect sizes pool statistical power across traits and substantially boost the correlation (r²) between true and estimated effect sizes (compared with the original summary statistics) by 94% and 70% for asthma and T2D out of sample, respectively, and by a predicted 300% for CAD.

大多数与疾病相关的遗传变异是多效性的，影响多种遗传相关性状。它们的多效性关联可以在机制上提供信息：如果许多变体具有相似的关联模式，它们可能通过相似的多效性机制发挥作用，形成遗传性的共同组成部分。我们开发了多效分解回归（PDR）来识别共享成分及其潜在的遗传变异。我们在模拟数据上验证了 PDR，并确定了现有方法在恢复真实成分方面的局限性。我们将 PDR 应用于与冠状动脉疾病 (CAD)、哮喘和 II 型糖尿病 (T2D) 遗传相关的三组（每组五到六个性状），产生生物学上可解释的成分。对于 CAD，PDR 确定了与 BMI、高血压和胆固醇相关的成分，并阐明了这些高度相关的危险因素之间的关系。我们将变体分配给组件，计算其后验平均效应大小，并进行样本外验证。我们的后验平均效应大小汇集了跨特征的统计功效，并将样本外哮喘和 T2D 的真实效应大小和估计效应大小（与原始汇总统计数据相比）之间的相关性 (r ² ) 分别提高了 94% 和 70%， CAD 预计将增加 300%。