title: Leveraging prior information to detect causal variants via multi-variant regression.
abstract: 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.
authors.affiliation: Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina, United States of America. n.long@duke.edu
authors.forename: Nanye
authors.initials: N
authors.lastname: Long
authors.forename: Samuel P
authors.initials: SP
authors.lastname: Dickson
authors.forename: Jessica M
authors.initials: JM
authors.lastname: Maia
authors.forename: Hee Shin
authors.initials: HS
authors.lastname: Kim
authors.forename: Qianqian
authors.initials: Q
authors.lastname: Zhu
authors.forename: Andrew S
authors.initials: AS
authors.lastname: Allen
mesh_terms: D015984:Causality; D059472:Exome; D055106:Genome-Wide Association Study; D005838:Genotype; D008962:Models, Theoretical; D012044:Regression Analysis