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Originally published as Genetics Published Articles Ahead of Print on April 15, 2007.
Genetics, Vol. 176, 1197-1208, June 2007, Copyright © 2007
doi:10.1534/genetics.107.071696
A Bayesian Multilocus Association Method: Allowing for Higher-Order Interaction in Association Studies
Anders Albrechtsen*,
,
,1,
Sofie Castella*,,
Gitte Andersen,
Torben Hansen,
Oluf Pedersen and
Rasmus Nielsen
* Bioinformatics Centre, University of Copenhagen, 2100 Copenhagen, Denmark, Department of Biostatistics, University of Copenhagen, 2100 Copenhagen, Denmark and Steno Diabetes Center, 2820 Gentofte, Denmark
1 Corresponding author: The Bioinformatics Centre, Universitetsparken 15, 2100 Copenhagen, Denmark.
E-mail: albrecht{at}binf.ku.dk
For most common diseases with heritable components, not a single or a few single-nucleotide polymorphisms (SNPs) explain most of the variance for these disorders. Instead, much of the variance may be caused by interactions (epistasis) among multiple SNPs or interactions with environmental conditions. We present a new powerful statistical model for analyzing and interpreting genomic data that influence multifactorial phenotypic traits with a complex and likely polygenic inheritance. The new method is based on Markov chain Monte Carlo (MCMC) and allows for identification of sets of SNPs and environmental factors that when combined increase disease risk or change the distribution of a quantitative trait. Using simulations, we show that the MCMC method can detect disease association when multiple, interacting SNPs are present in the data. When applying the method on real large-scale data from a Danish population-based cohort, multiple interactions are identified that severely affect serum triglyceride levels in the study individuals. The method is designed for quantitative traits but can also be applied on qualitative traits. It is computationally feasible even for a large number of possible interactions and differs fundamentally from most previous approaches by entertaining nonlinear interactions and by directly addressing the multiple-testing problem.
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Genetics 2007 176: NP.