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Standard approaches to analysing data in genome-wide association studies(GWAS) ignore any potential functional relationships between genetic markers.In contrast gene pathways analysis uses prior information on functionalstructure within the genome to identify pathways associated with a trait ofinterest. In a second step, important single nucleotide polymorphisms (SNPs) orgenes may be identified within associated pathways. Most pathways methods beginby testing SNPs one at a time, and so fail to capitalise on the potentialadvantages inherent in a multi-SNP, joint modelling approach. Here we describea dual-level, sparse regression model for the simultaneous identification ofpathways, genes and SNPs associated with a quantitative trait. Our method takesaccount of various factors specific to the joint modelling of pathways withgenome-wide data, including widespread correlation between genetic predictors,and the fact that variants may overlap multiple pathways. We use a resamplingstrategy that exploits finite sample variability to provide robust rankings forpathways, SNPs and genes. We test our method through simulation, and use it toperform pathways-driven SNP selection in a search for pathways, genes and SNPsassociated with variation in serum high-density lipoprotein cholesterol (HDLC)levels in two separate GWAS cohorts of Asian adults. By comparing results fromboth cohorts we identify a number of candidate pathways including thoseassociated with cardiomyopathy, and T cell receptor and PPAR signalling.Highlighted genes include those associated with the L-type calcium channel,adenylate cyclase, integrin, laminin, MAPK signalling and immune function.

Pathways-driven Sparse Regression Identifies Pathways and Genes Associated with High-density Lipoprotein Cholesterol in Two Asian Cohorts