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 the heritability of this disease. Here, 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 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the 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 the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

DNA methylation has a great potential for understanding the aetiology of common complex traits such as Type 2 diabetes (T2D). Here we perform genome-wide methylated DNA immunoprecipitation sequencing (MeDIP-seq) in whole-blood-derived DNA from 27 monozygotic twin pairs and follow up results with replication and integrated omics analyses. We identify predominately hypermethylated T2D-related differentially methylated regions (DMRs) and replicate the top signals in 42 unrelated T2D cases and 221 controls. The strongest signal is in the promoter of the MALT1 gene, involved in insulin and glycaemic pathways, and related to taurocholate levels in blood. Integrating the DNA methylome findings with T2D GWAS meta-analysis results reveals a strong enrichment for DMRs in T2D-susceptibility loci. We also detect signals specific to T2D-discordant twins in the GPR61 and PRKCB genes. These replicated T2D associations reflect both likely causal and consequential pathways of the disease. The analysis indicates how an integrated genomics and epigenomics approach, utilizing an MZ twin design, can provide pathogenic insights as well as potential drug targets and biomarkers for T2D and other complex traits.

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.

The human gut microbiome plays a key role in human health 1 , but 16S characterization lacks quantitative functional annotation 2 . The fecal metabolome provides a functional readout of microbial activity and can be used as an intermediate phenotype mediating host-microbiome interactions 3 . In this comprehensive description of the fecal metabolome, examining 1,116 metabolites from 786 individuals from a population-based twin study (TwinsUK), the fecal metabolome was found to be only modestly influenced by host genetics (heritability (H2) = 17.9%). One replicated locus at the NAT2 gene was associated with fecal metabolic traits. The fecal metabolome largely reflects gut microbial composition, explaining on average 67.7% (±18.8%) of its variance. It is strongly associated with visceral-fat mass, thereby illustrating potential mechanisms underlying the well-established microbial influence on abdominal obesity. Fecal metabolic profiling thus is a novel tool to explore links among microbiome composition, host phenotypes, and heritable complex traits.

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

Diabetic kidney disease, or diabetic nephropathy (DN), is a major complication of diabetes and the leading cause of end-stage renal disease (ESRD) that requires dialysis treatment or kidney transplantation. In addition to the decrease in the quality of life, DN accounts for a large proportion of the excess mortality associated with type 1 diabetes (T1D). Whereas the degree of glycemia plays a pivotal role in DN, a subset of individuals with poorly controlled T1D do not develop DN. Furthermore, strong familial aggregation supports genetic susceptibility to DN. However, the genes and the molecular mechanisms behind the disease remain poorly understood, and current therapeutic strategies rarely result in reversal of DN. In the GEnetics of Nephropathy: an International Effort (GENIE) consortium, we have undertaken a meta-analysis of genome-wide association studies (GWAS) of T1D DN comprising ~2.4 million single nucleotide polymorphisms (SNPs) imputed in 6,691 individuals. After additional genotyping of 41 top ranked SNPs representing 24 independent signals in 5,873 individuals, combined meta-analysis revealed association of two SNPs with ESRD: rs7583877 in the AFF3 gene (P = 1.2 × 10(-8)) and an intergenic SNP on chromosome 15q26 between the genes RGMA and MCTP2, rs12437854 (P = 2.0 × 10(-9)). Functional data suggest that AFF3 influences renal tubule fibrosis via the transforming growth factor-beta (TGF-β1) pathway. The strongest association with DN as a primary phenotype was seen for an intronic SNP in the ERBB4 gene (rs7588550, P = 2.1 × 10(-7)), a gene with type 2 diabetes DN differential expression and in the same intron as a variant with cis-eQTL expression of ERBB4. All these detected associations represent new signals in the pathogenesis of DN.

Using an integrative approach in which genetic variation, gene expression, and clinical phenotypes are assessed in relevant tissues may help functionally characterize the contribution of genetics to disease susceptibility. We sought to identify genetic variation influencing skeletal muscle gene expression (expression quantitative trait loci [eQTLs]) as well as expression associated with measures of insulin sensitivity. We investigated associations of 3,799,401 genetic variants in expression of >7,000 genes from three cohorts (n = 104). We identified 287 genes with cis-acting eQTLs (false discovery rate [FDR] <5%; P < 1.96 × 10(-5)) and 49 expression-insulin sensitivity phenotype associations (i.e., fasting insulin, homeostasis model assessment-insulin resistance, and BMI) (FDR <5%; P = 1.34 × 10(-4)). One of these associations, fasting insulin/phosphofructokinase (PFKM), overlaps with an eQTL. Furthermore, the expression of PFKM, a rate-limiting enzyme in glycolysis, was nominally associated with glucose uptake in skeletal muscle (P = 0.026; n = 42) and overexpressed (Bonferroni-corrected P = 0.03) in skeletal muscle of patients with T2D (n = 102) compared with normoglycemic controls (n = 87). The PFKM eQTL (rs4547172; P = 7.69 × 10(-6)) was nominally associated with glucose uptake, glucose oxidation rate, intramuscular triglyceride content, and metabolic flexibility (P = 0.016-0.048; n = 178). We explored eQTL results using published data from genome-wide association studies (DIAGRAM and MAGIC), and a proxy for the PFKM eQTL (rs11168327; r(2) = 0.75) was nominally associated with T2D (DIAGRAM P = 2.7 × 10(-3)). Taken together, our analysis highlights PFKM as a potential regulator of skeletal muscle insulin sensitivity.

Epigenetic modifications such as DNA methylation play a key role in gene regulation and disease susceptibility. However, little is known about the genome-wide frequency, localization, and function of methylation variation and how it is regulated by genetic and environmental factors. We utilized the Multiple Tissue Human Expression Resource (MuTHER) and generated Illumina 450K adipose methylome data from 648 twins. We found that individual CpGs had low variance and that variability was suppressed in promoters. We noted that DNA methylation variation was highly heritable (h(2)median = 0.34) and that shared environmental effects correlated with metabolic phenotype-associated CpGs. Analysis of methylation quantitative-trait loci (metQTL) revealed that 28% of CpGs were associated with nearby SNPs, and when overlapping them with adipose expression quantitative-trait loci (eQTL) from the same individuals, we found that 6% of the loci played a role in regulating both gene expression and DNA methylation. These associations were bidirectional, but there were pronounced negative associations for promoter CpGs. Integration of metQTL with adipose reference epigenomes and disease associations revealed significant enrichment of metQTL overlapping metabolic-trait or disease loci in enhancers (the strongest effects were for high-density lipoprotein cholesterol and body mass index [BMI]). We followed up with the BMI SNP rs713586, a cg01884057 metQTL that overlaps an enhancer upstream of ADCY3, and used bisulphite sequencing to refine this region. Our results showed widespread population invariability yet sequence dependence on adipose DNA methylation but that incorporating maps of regulatory elements aid in linking CpG variation to gene regulation and disease risk in a tissue-dependent manner.

Despite their nearly identical genomes, males and females differ in risk, incidence, prevalence, severity and age-at-onset of many diseases. Sexual dimorphism is also seen in human autosomal gene expression, and has largely been explored by examining the contribution of genotype-by-sex interactions to variation in gene expression.

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.

Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes.

Current pharmacological options for type 2 diabetes do not cure the disease. Despite the availability of multiple drug classes that modulate glycemia effectively and minimize long-term complications, these agents do not reverse pathogenesis, and in practice they are not selected to correct the molecular profile specific to the patient. Pharmaceutical companies find drug development programs increasingly costly and burdensome, and many promising compounds fail before launch to market. Human genetics can help advance the therapeutic enterprise. Genomic discovery that is agnostic to preexisting knowledge has uncovered dozens of loci that influence glycemic dysregulation. Physiological investigation has begun to define disease subtypes, clarifying heterogeneity and suggesting molecular pathways for intervention. Convincing genetic associations have paved the way for the identification of effector transcripts that underlie the phenotype, and genetic or experimental proof of gain or loss of function in select cases has clarified the direction of effect to guide therapeutic development. Genetic studies can also examine off-target effects and furnish causal inference. As this information is curated and made widely available to all stakeholders, it is hoped that it will enhance therapeutic development pipelines by accelerating efficiency, maximizing cost-effectiveness, and raising ultimate success rates.

Small RNAs are functional molecules that modulate mRNA transcripts and have been implicated in the aetiology of several common diseases. However, little is known about the extent of their variability within the human population. Here, we characterise the extent, causes, and effects of naturally occurring variation in expression and sequence of small RNAs from adipose tissue in relation to genotype, gene expression, and metabolic traits in the MuTHER reference cohort. We profiled the expression of 15 to 30 base pair RNA molecules in subcutaneous adipose tissue from 131 individuals using high-throughput sequencing, and quantified levels of 591 microRNAs and small nucleolar RNAs. We identified three genetic variants and three RNA editing events. Highly expressed small RNAs are more conserved within mammals than average, as are those with highly variable expression. We identified 14 genetic loci significantly associated with nearby small RNA expression levels, seven of which also regulate an mRNA transcript level in the same region. In addition, these loci are enriched for variants significant in genome-wide association studies for body mass index. Contrary to expectation, we found no evidence for negative correlation between expression level of a microRNA and its target mRNAs. Trunk fat mass, body mass index, and fasting insulin were associated with more than twenty small RNA expression levels each, while fasting glucose had no significant associations. This study highlights the similar genetic complexity and shared genetic control of small RNA and mRNA transcripts, and gives a quantitative picture of small RNA expression variation in the human population.

OBJECTIVE Recent genome-wide association studies have revealed loci associated with glucose and insulin-related traits. We aimed to characterize 19 such loci using detailed measures of insulin processing, secretion, and sensitivity to help elucidate their role in regulation of glucose control, insulin secretion and/or action. RESEARCH DESIGN AND METHODS We investigated associations of loci identified by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) with circulating proinsulin, measures of insulin secretion and sensitivity from oral glucose tolerance tests (OGTTs), euglycemic clamps, insulin suppression tests, or frequently sampled intravenous glucose tolerance tests in nondiabetic humans (n = 29,084). RESULTS The glucose-raising allele in MADD was associated with abnormal insulin processing (a dramatic effect on higher proinsulin levels, but no association with insulinogenic index) at extremely persuasive levels of statistical significance (P = 2.1 x 10(-71)). Defects in insulin processing and insulin secretion were seen in glucose-raising allele carriers at TCF7L2, SCL30A8, GIPR, and C2CD4B. Abnormalities in early insulin secretion were suggested in glucose-raising allele carriers at MTNR1B, GCK, FADS1, DGKB, and PROX1 (lower insulinogenic index; no association with proinsulin or insulin sensitivity). Two loci previously associated with fasting insulin (GCKR and IGF1) were associated with OGTT-derived insulin sensitivity indices in a consistent direction. CONCLUSIONS Genetic loci identified through their effect on hyperglycemia and/or hyperinsulinemia demonstrate considerable heterogeneity in associations with measures of insulin processing, secretion, and sensitivity. Our findings emphasize the importance of detailed physiological characterization of such loci for improved understanding of pathways associated with alterations in glucose homeostasis and eventually type 2 diabetes.

The RETN gene encodes the adipokine resistin. Associations of RETN with plasma resistin levels, type 2 diabetes, and related metabolic traits have been inconsistent. Using comprehensive linkage disequilibrium mapping, we genotyped tag single nucleotide polymorphisms (SNPs) in RETN and tested associations with plasma resistin levels, risk of diabetes, and glycemic traits.

Ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) inhibits insulin-receptor (IR) signaling and, when over-expressed, induces insulin resistance in vitro and in vivo. Understanding the regulation of ENPP1 expression may, thus, unravel new molecular mechanisms of insulin resistance. Recent data point to a pivotal role of the ENPP1 3'UTR, in modulating ENPP1 mRNA stability and expression. We sought to identify trans-acting proteins binding the ENPP1-3'UTR and to investigate their role on ENPP1 expression and on IR signaling. By RNA electrophoresis mobility shift analysis and tandem mass spectrometry, we demonstrated the binding of heat shock protein 70 (HSP70) to ENPP1-3'UTR. Through this binding, HSP70 stabilizes ENPP1 mRNA and increases ENPP1 transcript and protein levels. This positive modulation of ENPP1 expression is paralleled by a reduced insulin-induced IR and IRS-1 phosphorylation. Taken together these data suggest that HSP70, by affecting ENPP1 expression, may be a novel mediator of altered insulin signaling.

The sequence of Ciona savignyi was determined using a whole-genome shotgun strategy, but a high degree of polymorphism resulted in a fractured assembly wherein allelic sequences from the same genomic region assembled separately. We designed a multistep strategy to generate a nonredundant reference sequence from the original assembly by reconstructing and aligning the two 'haplomes' (haploid genomes). In the resultant 174 megabase reference sequence, each locus is represented once, misassemblies are corrected, and contiguity and continuity are dramatically improved.

A recent meta-analysis demonstrated a nominal association of the ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) K-->Q missense single nucleotide polymorphism (SNP) at position 121 with type 2 diabetes. We set out to confirm the association of ENPP1 K121Q with hyperglycemia, expand this association to insulin resistance traits, and determine whether the association stems from K121Q or another variant in linkage disequilibrium with it.

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662057, associated with a twofold increased risk for T2D in males. rs146662057 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches.