Bayesian Haplotype Inference via the Dirichlet Process
Eric Xing - University of California, Berkeley
Roded Sharan - International Computer Science Institute
Michael Jordan - University of California, Berkeley
The problem of inferring haplotypes from genotypes of single nucleotide polymorphisms (SNPs) is essential for the understanding of genetic variation within and among populations, with important applications tothe genetic analysis of disease propensities and other complex traits. Theproblem can be formulated as a mixture model, where the mixture componentscorrespond to the pool of haplotypes in the population. The size of this poolis unknown; indeed, knowing the size of the pool would correspond to knowingsomething significant about the genome and its history. Thus methods for fitting the genotype mixture must crucially address the problem of estimating a mixture with an unknown number of mixture components. In this paper we present a Bayesian approach to this problem based on a nonparametric prior known as the Dirichlet process. The model also incorporates a likelihood that captures statistical errors in the haplotype/genotype relationship. We apply our approach to the analysis of both simulated andreal genotype data, and compare to extant methods.