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Bioinformatics Advance Access published online on February 23, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn071
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Disease Association Tests by Inferring Ancestral Haplotypes Using a Hidden Markov Model

Shu-Yi Su , David J. Balding and Lachlan J.M. Coin

Department of of Epidemiology and Public Health, Imperial College, London W2 1PG, UK.

To whom correspondence should be addressed. Lachlan J.M. Coin, E-mail: l.coin{at}imperial.ac.uk


  Abstract

Motivation: Most genome-wide association studies rely on single SNP analyses to identify causal loci. The increased stringency required for genome-wide analyses (with per-SNP significance threshold typically {approx}10–7) 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 based association method (AncesHC) which addresses many of these problems. It can be applied to biallelic or multiallelic markers typed in haploid, diploid, or multiploid organisms, and also handles missing genotypes. Our model is free from the assumption of a rigid block structure but recognises a block-like structure if it exists in the data. We employ a Hidden Markov Model (HMM) to cluster the haplotypes into groups of predicted common ancestral origin. We then test each cluster for association with disease by comparing the numbers of cases and controls with 0, 1, and 2 chromosomes in the cluster. We demonstrate the power of this approach by simulation of case-control status under a range of disease models for 1,500 outcrossed mice originating from eight inbred lines. Our results suggest that AncesHC has substantially more power than single-SNP analyses to detect disease association, and is also more powerful than the cladistic haplotype clustering method CLADHC.

Availability: The software can be downloaded from http://www.imperial.ac.uk/medicine/people/l.coin

Contact: l.coin{at}imperial.ac.uk

Associate Editor: Prof. Martin Bishop

Received on January 9, 2008; revised on February 5, 2008; accepted on February 11, 2008

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