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

Publication ,  Journal Article
Long, N; Dickson, SP; Maia, JM; Kim, HS; Zhu, Q; Allen, AS
Published in: PLoS Comput Biol
2013

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.

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Published In

PLoS Comput Biol

DOI

EISSN

1553-7358

Publication Date

2013

Volume

9

Issue

6

Start / End Page

e1003093

Location

United States

Related Subject Headings

  • Regression Analysis
  • Models, Theoretical
  • Genotype
  • Genome-Wide Association Study
  • Exome
  • Causality
  • Bioinformatics
  • 08 Information and Computing Sciences
  • 06 Biological Sciences
  • 01 Mathematical Sciences
 

Citation

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Long, N., Dickson, S. P., Maia, J. M., Kim, H. S., Zhu, Q., & Allen, A. S. (2013). Leveraging prior information to detect causal variants via multi-variant regression. PLoS Comput Biol, 9(6), e1003093. https://doi.org/10.1371/journal.pcbi.1003093
Long, Nanye, Samuel P. Dickson, Jessica M. Maia, Hee Shin Kim, Qianqian Zhu, and Andrew S. Allen. “Leveraging prior information to detect causal variants via multi-variant regression.PLoS Comput Biol 9, no. 6 (2013): e1003093. https://doi.org/10.1371/journal.pcbi.1003093.
Long N, Dickson SP, Maia JM, Kim HS, Zhu Q, Allen AS. Leveraging prior information to detect causal variants via multi-variant regression. PLoS Comput Biol. 2013;9(6):e1003093.
Long, Nanye, et al. “Leveraging prior information to detect causal variants via multi-variant regression.PLoS Comput Biol, vol. 9, no. 6, 2013, p. e1003093. Pubmed, doi:10.1371/journal.pcbi.1003093.
Long N, Dickson SP, Maia JM, Kim HS, Zhu Q, Allen AS. Leveraging prior information to detect causal variants via multi-variant regression. PLoS Comput Biol. 2013;9(6):e1003093.

Published In

PLoS Comput Biol

DOI

EISSN

1553-7358

Publication Date

2013

Volume

9

Issue

6

Start / End Page

e1003093

Location

United States

Related Subject Headings

  • Regression Analysis
  • Models, Theoretical
  • Genotype
  • Genome-Wide Association Study
  • Exome
  • Causality
  • Bioinformatics
  • 08 Information and Computing Sciences
  • 06 Biological Sciences
  • 01 Mathematical Sciences