Loss-of-Function Mutations in the Cell-Cycle Control Gene CDKN2A Impact on Glucose Homeostasis in Humans.

At the CDKN2A/B locus, three independent signals for type 2 diabetes risk are located in a noncoding region near CDKN2A. The disease-associated alleles have been implicated in reduced ß-cell function, but the underlying mechanism remains elusive. In mice, ß-cell-specific loss of Cdkn2a causes hyperplasia, while overexpression leads to diabetes, highlighting CDKN2A as a candidate effector transcript. Rare CDKN2A loss-of-function mutations are a cause of familial melanoma and offer the opportunity to determine the impact of CDKN2A haploinsufficiency on glucose homeostasis in humans. To test the hypothesis that such individuals have improved ß-cell function, we performed oral and intravenous glucose tolerance tests on mutation carriers and matched control subjects. Compared with control subjects, carriers displayed increased insulin secretion, impaired insulin sensitivity, and reduced hepatic insulin clearance. These results are consistent with a model whereby CDKN2A loss affects a range of different tissues, including pancreatic ß-cells and liver. To test for direct effects of CDKN2A-loss on ß-cell function, we performed knockdown in a human ß-cell line, EndoC-bH1. This revealed increased insulin secretion independent of proliferation. Overall, we demonstrated that CDKN2A is an important regulator of glucose homeostasis in humans, thus supporting its candidacy as an effector transcript for type 2 diabetes-associated alleles in the region.

Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus.

Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.

Bridging the gap between genetic associations and molecular mechanisms for type 2 diabetes.

Type 2 diabetes is a global pandemic for which there is currently no disease-modifying treatment. New and targeted therapeutics are greatly needed, but progress in identifying novel targets for therapeutic intervention is severely hampered by poor understanding of disease pathogenesis. Over the past 6 years, the success of genome-wide association studies has led to an unprecedented increase in the number of loci robustly associating with type 2 diabetes risk. Each of these signals offers the opportunity to uncover biological insights into disease pathogenesis, which, if harnessed effectively, hold the promise to deliver new pathways for therapeutic intervention, strategies for patient stratification, and potentially, biomarkers for identifying those at greatest risk of developing diabetes. We review the progress that has been made and the approaches being adopted and discuss the inherent challenges in moving from association signals, which largely map to poorly annotated sequence, to transcripts, mechanisms, and disease biology.