The genetic architecture of type 2 diabetes

Abstract

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes.

10 Years of GWAS Discovery: Biology, Function, and Translation

“…Scores of genes have been causally implicated in monogenic forms of diabetes (e.g., neonatal diabetes mellitus [MIM: 601410] 73 ), but GWASs have now identified over 100 common variant signals. [74][75][76] Recent efforts to extend GWASs beyond arraybased genotyping and to access a broader range of variants through sequencing (particularly those of lower frequency) have revealed that most genetic variation influencing T2D appears to reside at common variant sites. 74,77 This chimes with the view of T2D as a largely post-reproductive trait and is consistent with a failure to detect compelling empirical evidence that T2D risk alleles have been subject to marked purifying selection.…”

“…[74][75][76] Recent efforts to extend GWASs beyond arraybased genotyping and to access a broader range of variants through sequencing (particularly those of lower frequency) have revealed that most genetic variation influencing T2D appears to reside at common variant sites. 74,77 This chimes with the view of T2D as a largely post-reproductive trait and is consistent with a failure to detect compelling empirical evidence that T2D risk alleles have been subject to marked purifying selection. 78,79 In keeping with the age of these common risk alleles (which predates the diaspora of modern humans out of Africa), most common variant associations for T2D are replicated across major ethnic groups.…”

“…85 90,91 or data from animal models (CDKAL1 [MIM: 611259]). 92 Finally, the accumulation of data on coding variants (via exome sequencing and/or exome array genotyping) has highlighted several instances where GWAS signals previously attributed to non-coding variants can be reassigned to causal coding variants (e.g., TM6SF2 [MIM: 606563] 74 ). For others, such as RREB1 (MIM: 602209), identification of T2D-associated coding variants, statistically independent of the original GWAS signal, flags the likely effector transcripts.…”

“…For others, such as RREB1 (MIM: 602209), identification of T2D-associated coding variants, statistically independent of the original GWAS signal, flags the likely effector transcripts. 74 All in all, it is possible to point to a compelling effector transcript at around one-third of the 100 T2D loci identified by GWASs. These genes represent legitimate targets for detailed empirical validation and mechanistic exploration.…”

Modeling Type 2 Diabetes GWAS Candidate Gene Function in hESCs

“…The advent of GWAS more than a decade ago has led to an ever more detailed understanding of the genetic landscape of many complex diseases, including type 2 diabetes (Fuchsberger et al, 2016). Thus, more than 90 loci have been identified, with variants in the vast majority affecting the release of insulin.…”