Fine Mapping Causal Variants with Functional Annotations

• Activity Number: 310 - SPEED:Statistical Methods for GWAs, Genetics, Genomics, and Other Omics Studies, Part 2
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 9:25 AM to 10:10 AM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Sheila Gaynor* and Xihong Lin
• Companies: Harvard T.H. Chan School of Public Health and Harvard
• Keywords: fine mapping; statistical genetics; functional annotation

Abstract

Fine mapping genetic regions to identify candidate causal variants is a critical post-GWAS analysis that prioritizes variants for future study. Functional annotations have proven to be a useful resource in identifying likely causal variants; however, their inclusion in fine mapping methods is limited primarily because fine mapping is challenged by high computational costs. We propose an efficient algorithm for fine mapping variants incorporating functional annotations. We leverage integrative functional annotation scores as well as principal components of functional annotations to capture a multi-dimensional summary of variant functionality. We focus on the inclusion of tissue- and phenotype-specific annotations to identify likely causal variants within regions highly associated with traits and diseases. We demonstrate the performance of our method at identifying different numbers of causal variants within a region via simulation. We apply our method to complex traits and diseases in large-scale consortiums, which are well suited to fine mapping given their high sample size and SNP density.