Bayesian survival analysis in genetic association studies.
BACKGROUND
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.


BACKGROUND
R codes are available upon request.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Tachmazidou
Ioanna
I
Department of Epidemiology and Public Health, Imperial College, London, UK. ioanna.tachmazidou@imperial.ac.uk
Andrew
Toby
T
Verzilli
Claudio J
CJ
Johnson
Michael R
MR
De Iorio
Maria
M
D018528:ATP-Binding Cassette Transporters / Q000235:genetics / Q000378:metabolism; D001499:Bayes Theorem; D003198:Computer Simulation; D004827:Epilepsy / Q000235:genetics; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D006239:Haplotypes; D006801:Humans; D015810:Linkage Disequilibrium; D020641:Polymorphism, Single Nucleotide; D016019:Survival Analysis
D018528:ATP-Binding Cassette Transporters