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Bioinformatics Advance Access originally published online on July 9, 2008
Bioinformatics 2008 24(18):2030-2036; doi:10.1093/bioinformatics/btn351
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Bayesian survival analysis in genetic association studies

Ioanna Tachmazidou 1,*, Toby Andrew 2, Claudio J. Verzilli 3, Michael R. Johnson 4 and Maria De Iorio 1

1Department of Epidemiology and Public Health, Imperial College, London W2 1PG, 2Twin Research Unit, King's College, London SE1 7 EH, 3Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT and 4Division of Neurosciences and Mental Health, Imperial College, London SW7 2AZ, UK

*To whom correspondence should be addressed.


  Abstract

Motivation: Large-scale genetic association studies are carried out with the hope of discovering single nucleotide polymorphisms involved in the etiology of complex diseases. There are several existing methods in the literature for performing this kind of analysis for case-control studies, but less work has been done for prospective cohort studies. We present a Bayesian method for linking markers to censored survival outcome by clustering haplotypes using gene trees. Coalescent-based approaches are promising for LD mapping, as the coalescent offers a good approximation to the evolutionary history of mutations.

Results: We compare the performance of the proposed method in simulation studies to the univariate Cox regression and to dimension reduction methods, and we observe that it performs similarly in localizing the causal site, while offering a clear advantage in terms of false positive associations. Moreover, it offers computational advantages. Applying our method to a real prospective study, we observe potential association between candidate ABC transporter genes and epilepsy treatment outcomes.

Availability: R codes are available upon request.

Contact: ioanna.tachmazidou{at}imperial.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Martin Bishop

Received on April 9, 2008; revised on June 19, 2008; accepted on July 8, 2008

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