Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes

Abstract

To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.

Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

“…We also examined the associations of the IGF‐I‐ and IGFBP‐3‐associated SNPs with anthropometric traits (height, BMI, waist‐to‐hip ratio, and fat percentage) (Heid et al ., 2010; Lango Allen et al ., 2010; Speliotes et al ., 2010), bone mineral density (Estrada et al ., 2012), risk of type 2 diabetes (Voight et al ., 2010; Morris et al ., 2012) and related traits (fasting glucose, 2‐h glucose, HbA1c, fasting insulin, proinsulin, HOMA‐IR, and HOMA‐B) (Dupuis et al ., 2010; Saxena et al ., 2010; Soranzo et al ., 2010), and coronary artery disease (Coronary Artery Disease Genetics C, 2011; Schunkert et al ., 2011; Consortium CAD and Deloukas, 2013) (Table S9, Supporting information). Many nominal associations were expected because of the known influence of the IGF system on these traits.…”

“…Top SNPs associated with levels of IGF‐I and IGFBP‐3 were examined in relationship to other phenotypes using published data on serum metabolites (Suhre et al ., 2011; Shin et al ., 2014), anthropometric traits (Heid et al ., 2010; Lango Allen et al ., 2010; Speliotes et al ., 2010), bone mineral density (Estrada et al ., 2012), diabetes (Voight et al ., 2010; Morris et al ., 2012) and glycemic traits (Dupuis et al ., 2010; Saxena et al ., 2010; Soranzo et al ., 2010), coronary artery disease (Coronary Artery Disease Genetics C, 2011; Schunkert et al ., 2011; Consortium CAD and Deloukas, 2013), and survival beyond 90 years (Broer et al ., 2015). Detailed information of the published datasets used including its references is given in Table S9 (Supporting information).…”

Investigating shared aetiology between type 2 diabetes and major depressive disorder in a population based cohort

“…Single nucleotide polymorphisms (SNPs) found to be associated ( P  ≤ 5 × 10 −8 ) with T2D in two GWAS (comprising 38,840 cases and 114,981 controls [Morris et al, 2012] and 47,979 cases and 139,611 controls comprising a trans‐ancestry GWAS [Mahajan et al, 2014]) were used to perform MR by testing for their association with MDD and current psychological distress. The list consisted of 10 independently associated SNPs from DIAGRAM GWAS that were significant at a genome‐wide level [Morris et al, 2012], and seven further independent loci identified in the DIAGRAM trans‐ancestry T2D GWAS [Mahajan et al, 2014]. 11/17 SNPs were directly genotyped in GS:SFHS and these were the SNPs used in this study (Table II).…”

“…A GWAS of T2D involving 38,840 cases and 114,981 controls found the proportion of genetic variance attributable to common genetic variants to be 49% on the liability scale [Morris et al, 2012]. Unlike MDD GWAS, which have only identified two genome‐wide significant loci to be associated with MDD in Chinese women [CONVERGE, 2015], GWAS of T2D have found 70 loci to be significantly associated with increased risk for T2D.…”

“…Unlike MDD GWAS, which have only identified two genome‐wide significant loci to be associated with MDD in Chinese women [CONVERGE, 2015], GWAS of T2D have found 70 loci to be significantly associated with increased risk for T2D. These 70 loci account for 10.9% of the disease variance of T2D, a substantial portion of the 49% of variance explained by all common SNPs [Morris et al, 2012]. GWAS summary data can be used to test for genetic overlap between traits by creating polygenic risk scores (PRS).…”