Disease association tests by inferring ancestral haplotypes using a hidden markov model

Abstract

Motivation: Most genome-wide association studies rely on single nucleotide polymorphism (SNP) analyses to identify causal loci. The increased stringency required for genome-wide analyses (with per-SNP significance threshold typically ≈ 107) means that many real signals will be missed. Thus it is still highly relevant to develop methods with improved power at low type I error. Haplotype-based methods provide a promising approach; however, they suffer from statistical problems such as abundance of rare haplotypes and ambiguity in defining haplotype block boundaries. Results: We have developed an ancestral haplotype clustering (AncesHC) association method which addresses many of these problems...