Although rare non-coding variants (RVs) play crucial roles in complex traits and diseases, understanding their mechanisms and identifying disease-associated RVs continue to be major challenges. Here we constructed a comprehensive atlas of alternative polyadenylation (APA) outliers (aOutliers), including 1334 3′ UTR and 200 intronic aOutliers, from 15,201 samples across 49 human tissues. These aOutliers exhibit unique characteristics from transcription or splicing outliers, with a pronounced RV enrichment. Mechanistically, aOutlier-RVs alter poly(A) signals and splicing sites, and perturbation indeed triggers APA events. Furthermore, we developed a Bayesian-based APA RV prediction model, which successfully pinpointed a specific set of 1799 RVs impacting 278 genes with significantly large disease effect sizes. Notably, we observed a convergence effect between rare and common cancer variants, exemplified by regulation in the DDX18 gene. Together, this study introduced an APA-enhanced framework for genome annotation, underscoring APA’s role in uncovering functional RVs linked to complex traits and diseases.

暂无