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Open Access Methodology article

Efficient Bayesian approach for multilocus association mapping including gene-gene interactions

Pekka Marttinen1* and Jukka Corander23

Author Affiliations

1 Department of Biomedical Engineering and Computational Science, FI-02015 Helsinki University of Technology, Finland

2 Department of Mathematics and Statistics, FI-00014 University of Helsinki, Finland

3 Department of Mathematics, Åbo Akademi University, FI-20500, Finland

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BMC Bioinformatics 2010, 11:443  doi:10.1186/1471-2105-11-443

Published: 2 September 2010

Abstract

Background

Since the introduction of large-scale genotyping methods that can be utilized in genome-wide association (GWA) studies for deciphering complex diseases, statistical genetics has been posed with a tremendous challenge of how to most appropriately analyze such data. A plethora of advanced model-based methods for genetic mapping of traits has been available for more than 10 years in animal and plant breeding. However, most such methods are computationally intractable in the context of genome-wide studies. Therefore, it is hardly surprising that GWA analyses have in practice been dominated by simple statistical tests concerned with a single marker locus at a time, while the more advanced approaches have appeared only relatively recently in the biomedical and statistical literature.

Results

We introduce a novel Bayesian modeling framework for association mapping which enables the detection of multiple loci and their interactions that influence a dichotomous phenotype of interest. The method is shown to perform well in a simulation study when compared to widely used standard alternatives and its computational complexity is typically considerably smaller than that of a maximum likelihood based approach. We also discuss in detail the sensitivity of the Bayesian inferences with respect to the choice of prior distributions in the GWA context.

Conclusions

Our results show that the Bayesian model averaging approach which explicitly considers gene-gene interactions may improve the detection of disease associated genetic markers in two respects: first, by providing better estimates of the locations of the causal loci; second, by reducing the number of false positives. The benefits are most apparent when the interacting genes exhibit no main effects. However, our findings also illustrate that such an approach is somewhat sensitive to the prior distribution assigned on the model structure.