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November 2009 A Bayesian Method for Detecting and Characterizing Allelic Heterogeneity and Boosting Signals in Genome-Wide Association Studies
Zhan Su, Niall Cardin, The Wellcome Trust Case Control Consortium, Peter Donnelly, Jonathan Marchini
Statist. Sci. 24(4): 430-450 (November 2009). DOI: 10.1214/09-STS311

Abstract

The standard paradigm for the analysis of genome-wide association studies involves carrying out association tests at both typed and imputed SNPs. These methods will not be optimal for detecting the signal of association at SNPs that are not currently known or in regions where allelic heterogeneity occurs. We propose a novel association test, complementary to the SNP-based approaches, that attempts to extract further signals of association by explicitly modeling and estimating both unknown SNPs and allelic heterogeneity at a locus. At each site we estimate the genealogy of the case-control sample by taking advantage of the HapMap haplotypes across the genome. Allelic heterogeneity is modeled by allowing more than one mutation on the branches of the genealogy. Our use of Bayesian methods allows us to assess directly the evidence for a causative SNP not well correlated with known SNPs and for allelic heterogeneity at each locus. Using simulated data and real data from the WTCCC project, we show that our method (i) produces a significant boost in signal and accurately identifies the form of the allelic heterogeneity in regions where it is known to exist, (ii) can suggest new signals that are not found by testing typed or imputed SNPs and (iii) can provide more accurate estimates of effect sizes in regions of association.

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Zhan Su. Niall Cardin. The Wellcome Trust Case Control Consortium. Peter Donnelly. Jonathan Marchini. "A Bayesian Method for Detecting and Characterizing Allelic Heterogeneity and Boosting Signals in Genome-Wide Association Studies." Statist. Sci. 24 (4) 430 - 450, November 2009. https://doi.org/10.1214/09-STS311

Information

Published: November 2009
First available in Project Euclid: 20 April 2010

zbMATH: 1329.62437
MathSciNet: MR2779336
Digital Object Identifier: 10.1214/09-STS311

Rights: Copyright © 2009 Institute of Mathematical Statistics

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Vol.24 • No. 4 • November 2009
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