Bayesian logistic regression using a perfect phylogeny

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.