Inferring gene network from candidate SNP association studies using a Bayesian graphical model: application to a...

Gene network analysis can be a very valuable approach for elucidating complex dependence between functional SNPs in a candidate genetic pathway and for assessing their association with a disease of interest. Even when the number of SNPs evaluated is relatively small (<20), the number of potential gene networks induced by the SNPs can be very large and the contingency tables representing their joint distribution very sparse.

In this paper, we propose a Bayesian model determination for gene network analysis using decomposable discrete graphical models combined with Reversible Jump Markov chain Monte Carlo. We show the application of this approach in a study of 13 SNPs in the DNA repair pathway and their association with breast cancer from a case-control study conducted in Ontario, Canada.

The strength of associations among the SNPs and between the SNPs and the disease status is evaluated by computing the posterior probability of any pair of variables. The corresponding gene network is reconstructed by retaining pair-wise associations with the highest posterior probabilities. In our real data analysis, we found evidence for a particular association between one SNP in the gene POLL and the disease status and also several interesting patterns of association between the SNPs themselves.

This general statistical framework could serve as a basis for prioritizing genes and SNPs that play a major role in breast cancer etiology and to better understand their complex interactions in a specific genetic pathway.