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Genome-wide association studies (GWAS) have long relied on proposed statistical significance thresholds to be able to differentiate true positives from false positives. Although the genome-wide significance P-value threshold of 5 × 10(-8) has become a standard for common-variant GWAS, it has not been updated to cope with the lower allele frequency spectrum used in many recent array-based GWAS studies and sequencing studies. Using a whole-genome- and -exome-sequencing data set of 2875 individuals of European ancestry from the Genetics of Type 2 Diabetes (GoT2D) project and a whole-exome-sequencing data set of 13 000 individuals from five ancestries from the GoT2D and T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples) projects, we describe guidelines for genome- and exome-wide association P-value thresholds needed to correct for multiple testing, explaining the impact of linkage disequilibrium thresholds for distinguishing independent variants, minor allele frequency and ancestry characteristics. We emphasize the advantage of studying recent genetic isolate populations when performing rare and low-frequency genetic association analyses, as the multiple testing burden is diminished due to higher genetic homogeneity.

Key Points The majority of the morbidity and mortality associated with diabetes mellitus is due to complications, such as diabetic kidney disease A limited understanding of the underlying mechanisms responsible for the development of diabetic complications compromises efforts to develop novel strategies for treatment and prevention Studies of human genetics offer powerful tools for delivering robust mechanistic insights into complex traits, such as diabetic complications, which have a substantial genetic component Scientific progress to date has been limited by small sample sizes and/or phenotypic imprecision; however, several major discovery efforts are underway that have been designed to address these issues The identification of genetic variants with robust effects on the risk of developing diabetic complications will accelerate efforts to develop more effective strategies for treatment and prevention Diabetes mellitus is often accompanied by numerous microvascular and/or macrovascular complications. Identification of risk alleles for diabetic complications could lead to improved understanding of the underlying mechanistic processes, but has so far been limited. Here, McCarthy and colleagues discuss the utility of human genetic studies in identifying novel risk variants for diabetic complications, the challenges faced in identifying robust genetic associations for diabetic kidney disease and the benefits that genome-wide association studies can offer. The rising global prevalence of diabetes mellitus is accompanied by an increasing burden of morbidity and mortality that is attributable to the complications of chronic hyperglycaemia. These complications include blindness, renal failure and cardiovascular disease. Current therapeutic options for chronic hyperglycaemia reduce, but do not eradicate, the risk of these complications. Success in defining new preventative and therapeutic strategies hinges on an improved understanding of the molecular processes involved in the development of these complications. This Review explores the role of human genetics in delivering such insights, and describes progress in characterizing the sequence variants that influence individual predisposition to diabetic kidney disease, retinopathy, neuropathy and accelerated cardiovascular disease. Numerous risk variants for microvascular complications of diabetes have been reported, but very few have shown robust replication. Furthermore, only limited evidence exists of a difference in the repertoire of risk variants influencing macrovascular disease between those with and those without diabetes. Here, we outline the challenges associated with the genetic analysis of diabetic complications and highlight ongoing efforts to deliver biological insights that can drive translational benefits.

Diabetes is a much more heterogeneous disease than the present subdivision into types 1 and 2 assumes; type 1 and type 2 diabetes probably represent extremes on a range of diabetic disorders. Both type 1 and type 2 diabetes seem to result from a collision between genes and environment. Although genetic predisposition establishes susceptibility, rapid changes in the environment (ie, lifestyle factors) are the most probable explanation for the increase in incidence of both forms of diabetes. Many patients have genetic predispositions to both forms of diabetes, resulting in hybrid forms of diabetes (eg, latent autoimmune diabetes in adults). Obesity is a strong modifier of diabetes risk, and can account for not only a large proportion of the epidemic of type 2 diabetes in Asia but also the ever-increasing number of adolescents with type 2 diabetes. With improved characterisation of patients with diabetes, the range of diabetic subgroups will become even more diverse in the future.

To explore novel genetic loci for diabetic nephropathy, we performed genome-wide association studies (GWAS) for diabetic nephropathy in Japanese patients with type 2 diabetes. We analyzed the association of 5,768,242 single nucleotide polymorphisms (SNPs) in Japanese patients with type 2 diabetes, 2,380 nephropathy cases and 5,234 controls. We further performed GWAS for diabetic nephropathy using independent Japanese patients with type 2 diabetes, 429 cases and 358 controls and the results of these two GWAS were combined with an inverse variance meta-analysis (stage-1), followed by a de novo genotyping for the candidate SNP loci (p < 1.0 × 10(-4)) in an independent case-control study (Stage-2; 1,213 cases and 1,298 controls). After integrating stage-1 and stage-2 data, we identified one SNP locus, significantly associated with diabetic nephropathy; rs56094641 in FTO, P = 7.74 × 10(-10). We further examined the association of rs56094641 with diabetic nephropathy in independent Japanese patients with type 2 diabetes (902 cases and 1,221 controls), and found that the association of this locus with diabetic nephropathy remained significant after integrating all association data (P = 7.62 × 10(-10)). We have identified FTO locus as a novel locus for conferring susceptibility to diabetic nephropathy in Japanese patients with type 2 diabetes.

About 10% of people older than 70 years of age carry one or more mutations in their hematopoietic cells, and these persons have a higher relative risk of a hematologic cancer (by a factor of 11) and of death from cardiovascular disease (by a factor of 2.0 to 2.6). Cancer is thought to arise through the stepwise acquisition of genetic or epigenetic changes that transform a normal cell. 1 Hence, the existence of a premalignant state bearing only the initiating lesions may be detectable in some persons who have no other signs of disease. For example, multiple myeloma is frequently preceded by monoclonal gammopathy of unknown significance, 2 and chronic lymphocytic leukemia is commonly preceded by monoclonal B-cell lymphocytosis. 3 Several lines of evidence have suggested that clonal hematopoiesis resulting from an expansion of cells that harbor an initiating driver mutation might be an aspect of the aging hematopoietic system. Clonal hematopoiesis . . .

Aims/hypothesis The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers. Methods We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study. Results Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong’s p  = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p  = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors. Conclusions/interpretation This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.

Aging associates with impaired pancreatic islet function and increased type 2 diabetes (T2D) risk. Here we examine whether age-related epigenetic changes affect human islet function and if blood-based epigenetic biomarkers reflect these changes and associate with future T2D. We analyse DNA methylation genome-wide in islets from 87 non-diabetic donors, aged 26–74 years. Aging associates with increased DNA methylation of 241 sites. These sites cover loci previously associated with T2D, for example, KLF14 . Blood-based epigenetic biomarkers reflect age-related methylation changes in 83 genes identified in human islets (for example, KLF14, FHL2, ZNF518B and FAM123C ) and some associate with insulin secretion and T2D. DNA methylation correlates with islet expression of multiple genes, including FHL2 , ZNF518B, GNPNAT1 and HLTF. Silencing these genes in β-cells alter insulin secretion. Together, we demonstrate that blood-based epigenetic biomarkers reflect age-related DNA methylation changes in human islets, and associate with insulin secretion in vivo and T2D. Aging is associated with impaired pancreatic islet function, increased risk of type 2 diabetes, and changes in DNA methylation. Here the authors find blood-based biomarkers that reflect age-associated DNA methylation changes in human pancreatic islets associated with insulin secretion and diabetes.

Type 2 diabetes (T2D) is a complex disorder that is affected by multiple genetic and environmental factors. Extensive efforts have been made to identify the disease-affecting genes to better understand the disease pathogenesis, find new targets for clinical therapy, and allow prediction of disease. Our knowledge about the genes involved in disease pathogenesis has increased substantially in recent years, thanks to genomewide association studies and international collaborations joining efforts to collect the huge numbers of individuals needed to study complex diseases on a population level. We have summarized what we have learned so far about the genes that affect T2D risk and their functions. Although more than 40 loci associated with T2D or glycemic traits have been reported and reproduced, only a minor part of the genetic component of the disease has been explained, and the causative variants and affected genes are unknown for many of the loci. Great advances have recently occurred in our understanding of the genetics of T2D, but much remains to be learned about the disease etiology. The genetics of T2D has so far been driven by technology, and we now hope that next-generation sequencing will provide important information on rare variants with stronger effects. Even when variants are known, however, great effort will be required to discover how they affect disease risk.

Aim/hypothesisFive subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian individuals with young-onset type 2 diabetes.MethodsWe applied the European-derived centroids to Indian individuals with type 2 diabetes diagnosed before 45 years of age from the WellGen cohort (n = 1612). We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts, ANDIS (n = 962) and DIREVA (n = 420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205).ResultsBoth Indian and European young-onset type 2 diabetes patients were predominantly classified into severe insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severe insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to findings in Europeans (Swedish 26% vs 68%, Finnish 24% vs 71%, respectively). A higher proportion of SIDD compared with MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterised by insulin deficiency and hyperglycaemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared with MOD while the latter had higher prevalence of neuropathy.Conclusions /interpretationOur data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European individuals in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in type 2 diabetes in the young Indian population and suggest a need to review management strategies.