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

Authors
Journal
PLoS Computational Biology
1553-734X
Publisher
Public Library of Science
Publication Date
Volume
9
Issue
6
Identifiers
DOI: 10.1371/journal.pcbi.1003093
Keywords
  • Research Article
  • Biology
  • Computational Biology
  • Genetics
Disciplines
  • Mathematics
  • Medicine

Abstract

Author Summary The decline in DNA sequencing cost permits the interrogation of potentially all variants across the entire allele frequency spectrum for their associations with complex human diseases and traits. However, the identification of causal variants remains challenging. Existing single variant tests do not distinguish between causal association and association induced by linkage disequilibrium and tend to be underpowered for rare or low-frequency variants, whereas variant grouping methods do not identify individual causal variants. We propose a novel Bayesian hierarchical regression approach that estimates effects of multiple variants on a disease trait simultaneously and incorporates prior information on the likelihood of causality. By simulation, we show that by combining linkage disequilibrium with known genome wide association signals and functional conservation, the proposed method, the first of its kind, is powerful to correctly detect causal variants.

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