A statistical method for integrative analysis in precision medicine

摘 要：Recent international projects, such as the Encyclopedia of DNA Elements (ENCODE) project, the Roadmap project and the Genotype-Tissue Expression (GTEx) project, have generated vast amounts of genomic annotation data measured at the multiple layers, e.g., epigenome and transcriptome. On the other hand, increasing evidence suggests that seemingly unrelated phenotypes can share common genetic factors, which is known as pleiotropy. A big challenge in integrative analysis is how to put pleiotropy and annotation into a unified model and automatically select most relevant genomic features from a potentially huge set of genomic features. In this talk, we introduce a flexible statistical approach, named IPAC, to integrating pleiotropy and annotation for characterizing functional roles of genetic variants that underlie human complex phenotypes. IPAC enabled us to automatically perform feature selection from a large number of annotated genomic features and naturally incorporate the selected features for prioritization of genetic risk variants. IPAC not only demonstrated a remarkably computational efficiency (e.g., it took about 2~3 minutes to handle millions of genetic variants and thousands of functional annotations), but also allowed rigorous statistical inference of the model parameters and false discovery rate control in risk variant prioritization. With the IPAC approach, we performed integrative analysis of genome-wide association studies on multiple complex human traits and genome-wide annotation resources, e.g., Roadmap epigenome. The analysis results revealed interesting regulatory patterns of risk variants. These findings undoubtedly deepen our understanding of genetic architectures of complex traits and contribute to precision medicine.