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Biostatistics Advance Access originally published online on March 23, 2006
Biostatistics 2007 8(1):32-52; doi:10.1093/biostatistics/kxj030
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Bayesian logistic regression using a perfect phylogeny

Taane G. Clark*

Department of Statistics, University of Oxford, Oxford, UK and Department of Epidemiology and Public Health, Imperial College, London, UK taane.clark{at}imperial.ac.uk

Maria De Iorio

Department of Epidemiology and Public Health, Imperial College, London

Robert C. Griffiths

Department of Statistics, University of Oxford, Oxford

* To whom correspondence should be addressed.

Haplotype data capture the genetic variation among individuals in a population and among populations. An understanding of this variation and the ancestral history of haplotypes is important in genetic association studies of complex disease. We introduce a method for detecting associations between disease and haplotypes in a candidate gene region or candidate block with little or no recombination. A perfect phylogeny demonstrates the evolutionary relationship between single-nucleotide polymorphisms (SNPs) in the haplotype blocks. Our approach extends the logic regression technique of Ruczinski and others (2003) to a Bayesian framework, and constrains the model space to that of a perfect phylogeny. Environmental factors, as well as their interactions with SNPs, may be incorporated into the regression framework. We demonstrate our method on simulated data from a coalescent model, as well as data from a candidate gene study of sarcoidosis.

Keywords: Gene tree; Gibbs sampling; Haplotype association; Logic regression; SNP data


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