Abstract

Objective. Recent genome-wide association (GWA) studies have resulted in a dramatic increase in our knowledge of the genetic loci involved in type 2 diabetes. In a complementary approach to these single marker studies, we attempted to identify biological pathways associated with type 2 diabetes. This approach could allow us to identify additional risk loci.

Research Design and Methods. We used individual level genotype data generated from the Wellcome Trust Case Control Consortium (WTCCC) type 2 diabetes study, consisting of 393,143 autosomal SNPs, genotyped across 1,924 cases and 2,938 controls. We sought additional evidence from summary level data available from DGI and FUSION studies. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm (GSEA). Four-hundred and thirty-nine pathways were analysed from the Kyoto Encylopedia of Genes and Genomes, Gene Ontology and BioCarta databases.

Results. After correcting for the number of pathways tested we found no strong evidence for any pathway showing association with type 2 diabetes (top P_adj = 0.31). The candidate WNT-signalling pathway ranked top (nominal P = 0.0007, excluding TCF7L2; P = 0.002), containing a number of promising single gene associations. These include CCND2 (rs11833537; P=0.003), SMAD3 (rs7178347; P =0.0006) and PRICKLE1 (rs1796390; P =0.001), all expressed in the pancreas.

Conclusion. Common variants involved in type 2 diabetes risk are likely to occur in or near genes in multiple pathways. Pathway-based approaches to GWA data may be more successful for some complex traits than others, depending on the nature of the underlying disease physiology.