Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression |
| |
Authors: | Nanye Long Samuel P. Dickson Jessica M. Maia Hee Shin Kim Qianqian Zhu Andrew S. Allen |
| |
Affiliation: | 1.Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina, United States of America;2.Department of Biostatistics, Roswell Park Cancer Institute, Buffalo, New York, United States of America;3.Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, United States of America;University of British Columbia, Canada |
| |
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. |
| |
Keywords: | |
|
|