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Activity Number: 237 - SPEED:Statistical Methods for GWAs, Genetics, Genomics, and Other Omics Studies, Part 1
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #306388
Title: Fine Mapping Causal Variants with Functional Annotations
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

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.

Authors who are presenting talks have a * after their name.

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