Abstract
While genome-wide association studies (GWAS) have successfully discovered and replicated thousands of SNPs associated with various traits/diseases, it is a challenge to gain biological insights regarding this association, because over 90% of them are not in protein-coding region. One of the main approaches in the follow up of a GWAS is to examine whether these SNPs from GWAS are associated with the expression levels of certain genes. This leads to the so-called expression quantitative trait loci (eQTL) studies. It is desirable to consider as many expression probes and SNPs as possible simultaneously, to take into consideration the correlations between the expression levels at different gene probes and to make use of biological pathway information. We propose a Bayesian approach in which the prior distribution makes use of the classical heritability concept in genetics and the pathway information from Biology knowledge databases like GO and KEGG. The former helps to avoid the often too conservative practices in genomic studies and the latter helps to provide biological interpretation. The proposed approach can used to look for not only genes but also gene sets associated with a given set of SNPs. A carefully designed MCMC algorithm is proposed to sample the posterior distribution for inference. Our software can handle about 20K expression probes and several hundreds of SNPs within reasonable time. Simulation studies are conducted to evaluate the performance of this method, and we also illustrate the method in analyzing Taiwan lung cancer data. The results show that our approach provides new insights into associations between SNPs and genes/gene sets that could not be revealed in separate analysis.