Interrogating type 2 diabetes genome-wide association data using a biological pathway-based approach.
OBJECTIVE
Recent genome-wide association 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.


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 case subjects and 2,938 control subjects. We sought additional evidence from summary level data available from the Diabetes Genetics Initiative (DGI) and the Finland-United States Investigation of NIDDM Genetics (FUSION) studies. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm (GSEA). A total of 439 pathways were analyzed from the Kyoto Encyclopedia 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-signaling 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.


CONCLUSIONS
Common variants involved in type 2 diabetes risk are likely to occur in or near genes in multiple pathways. Pathway-based approaches to genome-wide association data may be more successful for some complex traits than others, depending on the nature of the underlying disease physiology.
Perry
John R B
JR
Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, Magdalen Road, Exeter, UK
McCarthy
Mark I
MI
Hattersley
Andrew T
AT
Zeggini
Eleftheria
E
Weedon
Michael N
MN
Frayling
Timothy M
TM
D000465:Algorithms; D001695:Biology / Q000379:methods; D016022:Case-Control Studies; D002874:Chromosome Mapping; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics*; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D016017:Odds Ratio; D020641:Polymorphism, Single Nucleotide; D012016:Reference Values