Leveraging prior information to detect causal variants via multi-variant regression

PLoS Comput Biol. 2013;9(6):e1003093. doi: 10.1371/journal.pcbi.1003093. Epub 2013 Jun 6.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Causality*
  • Exome
  • Genome-Wide Association Study
  • Genotype
  • Models, Theoretical
  • Regression Analysis*

Grant support

This work was supported by the Duke Center for Human Genome Variation fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.