Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression

Journal Article

Although many methods are available to test sequence variants for association with complex diseases and traits, methods that specifically seek to identify causal variants are less developed. Here we develop and evaluate a Bayesian hierarchical regression method that incorporates prior information on the likelihood of variant causality through weighting of variant effects. By simulation studies using both simulated and real sequence variants, we compared a standard single variant test for analyzing variant-disease association with the proposed method using different weighting schemes. We found that by leveraging linkage disequilibrium of variants with known GWAS signals and sequence conservation (phastCons), the proposed method provides a powerful approach for detecting causal variants while controlling false positives. © 2013 Long et al.

Full Text

Duke Authors

Cited Authors

  • Long, N; Dickson, SP; Maia, JM; Kim, HS; Zhu, Q; Allen, AS

Published Date

  • 2013

Published In

Volume / Issue

  • 9 / 6

PubMed ID

  • 23762022

International Standard Serial Number (ISSN)

  • 1553-734X

Digital Object Identifier (DOI)

  • 10.1371/journal.pcbi.1003093