Disease association tests by inferring ancestral haplotypes using a hidden markov model |
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Authors: | Su Shu-Yi Balding David J Coin Lachlan J M |
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Affiliation: | Department of Epidemiology and Public Health, Imperial College, London W2 1PG, UK |
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Abstract: | Motivation: Most genome-wide association studies rely on singlenucleotide polymorphism (SNP) analyses to identify causal loci.The increased stringency required for genome-wide analyses (withper-SNP significance threshold typically 10–7) meansthat many real signals will be missed. Thus it is still highlyrelevant to develop methods with improved power at low typeI error. Haplotype-based methods provide a promising approach;however, they suffer from statistical problems such as abundanceof rare haplotypes and ambiguity in defining haplotype blockboundaries. Results: We have developed an ancestral haplotype clustering(AncesHC) association method which addresses many of these problems.It can be applied to biallelic or multiallelic markers typedin haploid, diploid or multiploid organisms, and also handlesmissing genotypes. Our model is free from the assumption ofa rigid block structure but recognizes a block-like structureif it exists in the data. We employ a Hidden Markov Model (HMM)to cluster the haplotypes into groups of predicted common ancestralorigin. We then test each cluster for association with diseaseby comparing the numbers of cases and controls with 0, 1 and2 chromosomes in the cluster. We demonstrate the power of thisapproach by simulation of case-control status under a rangeof disease models for 1500 outcrossed mice originating fromeight inbred lines. Our results suggest that AncesHC has substantiallymore power than single-SNP analyses to detect disease association,and is also more powerful than the cladistic haplotype clusteringmethod CLADHC. Availability: The software can be downloaded from http://www.imperial.ac.uk/medicine/people/l.coin Contact: I.coin{at}imperial.ac.uk Supplementary Information: Supplementary data are availableat Bioinformatics online. Associate Editor: Martin Bishop |
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