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One hundred years have passed since the discovery of insulin—an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research’s Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health’s National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.

It has recently been suggested that the low-frequency c.136–14_136–13insC variant in high-mobility group A1 (HMGA1) may strongly contribute to insulin resistance and type 2 diabetes risk. In our study, we attempted to confirm that HMGA1 is a novel type 2 diabetes locus in French Caucasians. The gene was sequenced in 368 type 2 diabetic case subjects with a family history of type 2 diabetes and 372 normoglycemic control subjects without a family history of type 2 diabetes. None of the 41 genetic variations identified were associated with type 2 diabetes. The lack of association between the c.136–14_136–13insC variant and type 2 diabetes was confirmed in an independent French group of 4,538 case subjects and 4,015 control subjects and in a large meta-analysis of 16,605 case subjects and 46,179 control subjects. Finally, this variant had no effects on metabolic traits and was not involved in variations of HMGA1 and insulin receptor (INSR) expressions. The c.136–14_136–13insC variant was not associated with type 2 diabetes in individuals of European descent. Our study emphasizes the need to analyze a large number of subjects to reliably assess the association of low-frequency variants with the disease.

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF

Background/Aims The development of type 2 diabetes (T2D) is influenced both by environmental and by genetic determinants. Obesity is an important risk factor for T2D, mostly mediated by obesity-related insulin resistance. Obesity and insulin resistance are also modulated by the genetic milieu; thus, genes affecting risk of obesity and insulin resistance might also modulate risk of T2D. Recently, 32 loci have been associated with body mass index (BMI) by genome-wide studies, including one locus on chromosome 16p11 containing the SH2B1 gene. Animal studies have suggested that SH2B1 is a physiological enhancer of the insulin receptor and humans with rare deletions or mutations at SH2B1 are obese with a disproportionately high insulin resistance. Thus, the role of SH2B1 in both obesity and insulin resistance makes it a strong candidate for T2D. However, published data on the role of SH2B1 variability on the risk for T2D are conflicting, ranging from no effect at all to a robust association. Methods The SH2B1 tag SNP rs4788102 (SNP, single nucleotide polymorphism) was genotyped in 6978 individuals from six studies for abnormal glucose homeostasis (AGH), including impaired fasting glucose, impaired glucose tolerance or T2D, from the GENetics of Type 2 Diabetes in Italy and the United States (GENIUS T2D) consortium. Data from these studies were then meta-analyzed, in a Bayesian fashion, with those from DIAGRAM+ ( n = 47,117) and four other published studies ( n = 39,448). Results Variability at the SH2B1 obesity locus was not associated with AGH either in the GENIUS consortium (overall odds ratio (OR) = 0.96; 0.89–1.04) or in the meta-analysis (OR = 1.01; 0.98–1.05). Conclusion Our data exclude a role for the SH2B1 obesity locus in the modulation of AGH.

Background\ud Disruption of endogenous circadian rhythms has been shown to increase the risk of developing type 2 diabetes, suggesting that circadian genes might play a role in determining disease susceptibility. We present the results of a pilot study investigating the association between type 2 diabetes and selected single nucleotide polymorphisms (SNPs) in/near nine circadian genes. The variants were chosen based on their previously reported association with prostate cancer, a disease that has been suggested to have a genetic link with type 2 diabetes through a number of shared inherited risk determinants.\ud \ud Methodology/Principal Findings\ud The pilot study was performed using two genetically homogeneous Punjabi cohorts, one resident in the United Kingdom and one indigenous to Pakistan. Subjects with (N = 1732) and without (N = 1780) type 2 diabetes were genotyped for thirteen circadian variants using a competitive allele-specific polymerase chain reaction method. Associations between the SNPs and type 2 diabetes were investigated using logistic regression. The results were also combined with in silico data from other South Asian datasets (SAT2D consortium) and white European cohorts (DIAGRAM+) using meta-analysis. The rs7602358G allele near PER2 was negatively associated with type 2 diabetes in our Punjabi cohorts (combined odds ratio [OR] = 0.75 [0.66–0.86], p = 3.18×10−5), while the BMAL1 rs11022775T allele was associated with an increased risk of the disease (combined OR = 1.22 [1.07–1.39], p = 0.003). Neither of these associations was replicated in the SAT2D or DIAGRAM+ datasets, however. Meta-analysis of all the cohorts identified disease associations with two variants, rs2292912 in CRY2 and rs12315175 near CRY1, although statistical significance was nominal (combined OR = 1.05 [1.01–1.08], p = 0.008 and OR = 0.95 [0.91–0.99], p = 0.015 respectively).\ud \ud Conclusions/significance\ud None of the selected circadian gene variants was associated with type 2 diabetes with study-wide significance after meta-analysis. The nominal association observed with the CRY2 SNP, however, complements previous findings and confirms a role for this locus in disease susceptibility.

Body fat distribution, particularly centralized obesity, is associated with metabolic risk above and beyond total adiposity. We performed genome-wide association of abdominal adipose depots quantified using computed tomography (CT) to uncover novel loci for body fat distribution among participants of European ancestry. Subcutaneous and visceral fat were quantified in 5,560 women and 4,997 men from 4 population-based studies. Genome-wide genotyping was performed using standard arrays and imputed to ∼2.5 million Hapmap SNPs. Each study performed a genome-wide association analysis of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), VAT adjusted for body mass index, and VAT/SAT ratio (a metric of the propensity to store fat viscerally as compared to subcutaneously) in the overall sample and in women and men separately. A weighted z-score meta-analysis was conducted. For the VAT/SAT ratio, our most significant p-value was rs11118316 at LYPLAL1 gene (p = 3.1×10E-09), previously identified in association with waist–hip ratio. For SAT, the most significant SNP was in the FTO gene (p = 5.9×10E-08). Given the known gender differences in body fat distribution, we performed sex-specific analyses. Our most significant finding was for VAT in women, rs1659258 near THNSL2 (p = 1.6×10-08), but not men (p = 0.75). Validation of this SNP in the GIANT consortium data demonstrated a similar sex-specific pattern, with observed significance in women (p = 0.006) but not men (p = 0.24) for BMI and waist circumference (p = 0.04 [women], p = 0.49 [men]). Finally, we interrogated our data for the 14 recently published loci for body fat distribution (measured by waist–hip ratio adjusted for BMI); associations were observed at 7 of these loci. In contrast, we observed associations at only 7/32 loci previously identified in association with BMI; the majority of overlap was observed with SAT. Genome-wide association for visceral and subcutaneous fat revealed a SNP for VAT in women. More refined phenotypes for body composition and fat distribution can detect new loci not previously uncovered in large-scale GWAS of anthropometric traits., Author Summary Body fat distribution, particularly centralized obesity, is associated with metabolic risk above and beyond total adiposity. We performed genome-wide association of abdominal adipose depots quantified using computed tomography (CT) to uncover novel loci for body fat distribution among participants of European ancestry. We quantified subcutaneous and visceral fat in more than 10,000 women and men who also had genome-wide association data available. Given the known gender differences in body fat distribution, we performed sex-specific analyses. Our most significant finding was for VAT in women, near the THNSL2 gene. These findings were not observed in men. We also interrogated our data for the 14 recently published loci for body fat distribution (measured by waist–hip ratio adjusted for BMI); associations were observed for 7 of these loci, most notably for VAT/SAT ratio. We conclude that genome-wide association for visceral and subcutaneous fat revealed a SNP for VAT in women. More refined phenotypes for body composition and fat distribution can detect new loci not uncovered in large-scale GWAS of anthropometric traits.

Genome wide association studies (GWAS) and their subsequent meta-analysis have identified a large number of susceptibility variants for Type 2 diabetes (T2D) risk. However, the familial aggregation seen in this disease is not yet fully explained. The sibling relative risk (λs) due to all known variants is ~1.104 which is well below the epidemiological estimates of λs of ~3.0. There has therefore been great interest in the potential role of variants that would have been largely invisible to the initial wave of GWAS and linkage approaches. Low frequency (minor allele frequency 1-5%), incompletely penetrant (odds ratio 2-4) variants (LFIP), are one such group of potential susceptibility variants. The overall objective of this project (designed and implemented in 2007-2010) was to evaluate the contribution of LFIP variants to the inherited susceptibility to T2D. I tested the specific hypothesis that genes already-implicated in diabetes pathogenesis (due to an established role in monogenic or multifactorial disease) also harbour LFIP variants, and that those variants may contribute appreciably to the prediction of disease risk. Mutations in exons only encoding isoform-A of HNF1A have been demonstrated to lead to a later age of diagnosis of HNF1A-MODY. This region was therefore felt to be auspicious for harbouring LFIP variants impacting on T2D risk. I have demonstrated that such variants impacting on T2D risk are unlikely to be present in this region by use of Sanger sequencing in a sample enriched for young onset, familial T2D. The role in T2D risk of candidate LFIP variants across 5 genes (HNF1A, HNF4A, PDX1, KCNJ15 and LARS2), was evaluated by large scale association studies. For one variant, T130I of HNF4A, a modest association (p=5x10-4) with T2D was seen in UK samples and the strength of association was marginally improved by incorporation of all previous studies of this variant in T2D in a meta-analysis (p=2.1x10-5). This study demonstrated the difficulties encountered in confirming the association of low frequency variants to complex diseases, especially for those with modest effect sizes. At the time of project design and inception “next-generation” sequencing platforms were in their infancy and the study design I planned (that of pooled, targeted sequencing) had not been widely applied. It was therefore necessary to design and optimise protocols for sample preparation for sequencing on this platform. I used the Genome Analyzer II platform to sequence ten genes previously implicated in T2D or monogenic diabetes pathogenesis in pooled DNA samples. This approach yielded in excess of 2900 variants, a large portion being novel. As part of this project I have highlighted heuristics that can be used in the follow-up of potential susceptibility variants discovered using high throughput sequencing. I have also established protocols and pathways for sample preparation that can be utilised across several next generation sequencing platforms for future studies in the host institution and beyond.

Nonalcoholic fatty liver disease (NAFLD) clusters in families, but the only known common genetic variants influencing risk are near PNPLA3. We sought to identify additional genetic variants influencing NAFLD using genome-wide association (GWA) analysis of computed tomography (CT) measured hepatic steatosis, a non-invasive measure of NAFLD, in large population based samples. Using variance components methods, we show that CT hepatic steatosis is heritable (∼26%–27%) in family-based Amish, Family Heart, and Framingham Heart Studies (n = 880 to 3,070). By carrying out a fixed-effects meta-analysis of genome-wide association (GWA) results between CT hepatic steatosis and ∼2.4 million imputed or genotyped SNPs in 7,176 individuals from the Old Order Amish, Age, Gene/Environment Susceptibility-Reykjavik study (AGES), Family Heart, and Framingham Heart Studies, we identify variants associated at genome-wide significant levels (p, Author Summary NAFLD is a spectrum of disease that ranges from steatosis to steatohepatitis (nonalcoholic steatohepatitis or NASH: inflammation around the fat) to fibrosis/cirrhosis. Hepatic steatosis can be measured non-invasively using computed tomography (CT) whereas NASH/fibrosis is assessed histologically. The genetic underpinnings of NAFLD remain to be determined. Here we estimate that 26%–27% of the variation in CT measured hepatic steatosis is heritable or genetic. We identify three variants near PNPLAL3, NCAN, and PPP1R3B that associate with CT hepatic steatosis and show that variants in or near NCAN, GCKR, LYPLAL1, and PNPLA3, but not PPP1R3B, associate with histologic lobular inflammation/fibrosis. Variants in or near NCAN, GCKR, and PPP1R3B associate with altered serum lipid levels, whereas those in or near LYPLAL1 and PNPLA3 do not. Variants near GCKR and PPP1R3B also affect glycemic traits. Thus, we show that NAFLD is genetically influenced and expand the number of common genetic variants that associate with this trait. Our findings suggest that development of hepatic steatosis, NASH/fibrosis, or abnormalities in metabolic traits are probably influenced by different metabolic pathways that may represent distinct therapeutic targets.

Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10(-8) to P = 10(-190)). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions. We identified 69 candidate genes, including genes involved in biliary transport (ATP8B1 and ABCB11), glucose, carbohydrate and lipid metabolism (FADS1, FADS2, GCKR, JMJD1C, HNF1A, MLXIPL, PNPLA3, PPP1R3B, SLC2A2 and TRIB1), glycoprotein biosynthesis and cell surface glycobiology (ABO, ASGR1, FUT2, GPLD1 and ST3GAL4), inflammation and immunity (CD276, CDH6, GCKR, HNF1A, HPR, ITGA1, RORA and STAT4) and glutathione metabolism (GSTT1, GSTT2 and GGT), as well as several genes of uncertain or unknown function (including ABHD12, EFHD1, EFNA1, EPHA2, MICAL3 and ZNF827). Our results provide new insight into genetic mechanisms and pathways influencing markers of liver function. Major support for the work came from European Commission (FP5, FP6 and FP7); European Science Foundation; European Science Council

Genome-wide association studies have identified many genetic variants associated with complex traits. However, at only a minority of loci have the molecular mechanisms mediating these associations been characterized. In parallel, whereas cis regulatory patterns of gene expression have been extensively explored, the identification of trans regulatory effects in humans has attracted less attention. Here we show that the type 2 diabetes and high-density lipoprotein cholesterol-associated cis-acting expression quantitative trait locus (eQTL) of the maternally expressed transcription factor KLF14 acts as a master trans regulator of adipose gene expression. Expression levels of genes regulated by this trans-eQTL are highly correlated with concurrently measured metabolic traits, and a subset of the trans-regulated genes harbor variants directly associated with metabolic phenotypes. This trans-eQTL network provides a mechanistic understanding of the effect of the KLF14 locus on metabolic disease risk and offers a potential model for other complex traits. © 2011 Nature America, Inc. All rights reserved.

The let-7 tumor suppressor microRNAs are known for their regulation of oncogenes, while the RNA-binding proteins Lin28a/b promote malignancy by blocking let-7 biogenesis. In studies of the Lin28/let-7 pathway, we discovered unexpected roles in regulating metabolism. When overexpressed in mice, both Lin28a and LIN28B promoted an insulin-sensitized state that resisted high fat diet-induced diabetes, whereas muscle-specific loss of Lin28a and overexpression of let-7 resulted in insulin resistance and impaired glucose tolerance. These phenomena occurred in part through let-7-mediated repression of multiple components of the insulin-PI3K-mTOR pathway, including IGF1R, INSR, and IRS2. The mTOR inhibitor rapamycin abrogated the enhanced glucose uptake and insulin-sensitivity conferred by Lin28a in vitro and in vivo. In addition, we found that let-7 targets were enriched for genes that contain SNPs associated with type 2 diabetes and fasting glucose in human genome-wide association studies. These data establish the Lin28/let-7 pathway as a central regulator of mammalian glucose metabolism.