Multivariate analysis of genome-wide data to identify potential pleiotropic genes for type 2 diabetes, obesity and coronary artery disease using MetaCCA

Background: Although genome-wide association studies (GWAS) have been extensively applied in identifying SNP associated with metabolic diseases, the SNPs identified by this prevailing univariate approach only explain a small percentage of the genetic variance of traits. The extensive previous studies have repeatedly shown type2 diabetes (T2D), obesity and coronary artery disease (CAD) have common genetic mechanisms and the overlapping pathophysiological pathways. Methods: The genetic pleiotropy-informed metaCCA method was applied on summary statistics data from three independent meta-GWAS summary statistics to identify shared variants and pleiotropic effect between T2D, obesity and CAD. Furthermore, to refine all genes, we performed gene-based association analyses for these three diseases respectively using VEGAS2. Gene enrichment analysis was applied to explore the potential functional significance of the identified genes. Results: After metaCCA analysis, 833 SNPs reached the Bonferroni corrected threshold (p < 7.99 × 10−7) in the univariate SNP-multivariate phenotype analysis, and 327 genes with a significance threshold (p < 3.73 × 10−6) were identified as potentially pleiotropic genes in the multivariate SNP-multivariate phenotype analysis. By screening the results of gene-based p-values, we identified 22 putative pleiotropic genes which achieved significance threshold in metaCCA analyses and were also associated with at least one disease in the VEGAS2 analyses. Conclusions: The metaCCA method identified novel variants associated with T2D, obesity and CAD by effectively incorporating information from different GWAS datasets. Our analyses may provide insights for some common therapeutic approaches of metabolic diseases based on the pleiotropic genes and common mechanisms identified.