We present a novel approach to address genome association studies between single nucleotide polymorphisms (SNPs) and disease. We propose a Bayesian shared component model to tease out the genotype information that is common to cases and controls from the one that is specific to cases only. This allows to detect the SNPs that show the strongest association with the disease. The model can be applied to case-control studies with more than one disease. In fact, we illustrate the use of this model with a dataset of 23,418 SNPs from a case-control study by The Welcome Trust Case Control Consortium (2007) with 2,000 patients with diabetes type 1, 2,000 with diabetes type 2 and a control group with 3,000 individuals. We carry out a simulation study to assess the sensitivity and specificity of our model to detect SNPs with excess risk. Our results show that the method we propose here can be a very useful tool for this type of studies. The model has been implemented in the bayesGen library of the R statistical package.


Genetics | Statistical Models