Bayesian Haplotype Inference via the Dirichlet Process

• Activity Number: 118
• Type: Invited
• Date/Time: Monday, August 8, 2005 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Eric P. Xing*+ and Roded Sharan
• Companies: Carnegie Mellon University and ICSI, Berkeley
• Keywords: SNP ; Haplotype ; Dirichlet Process ; Bayesian

Abstract

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 to the genetic analysis of disease propensities and other complex traits. In its basic setting, the problem can be formulated as a mixture model, where the mixture components correspond to the pool of haplotypes in the population. The size of this pool is unknown; indeed, knowing the size of the pool would correspond to knowing something 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. 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. The overall result is a flexible Bayesian method reminiscent of parsimony methods in its preference for small haplotype pools. I also will discuss Markovian and hierarchical extensions of the basic DP haplotype model.