Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways

The loss-of-function PCSK9 p.R46L genetic variant does not alter glucose homeostasis

AIMS/HYPOTHESIS

Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a critical regulator of cholesterol homeostasis. PCSK9 inhibitors are being actively developed to lower LDL-cholesterol levels. However, there are conflicting data regarding the consequences of Pcsk9 deficiency on glucose homeostasis in mouse models. Here, we analysed in humans the association between the PCSK9 p.R46L loss-of-function (LOF) variant and (1) glucose homeostasis variables; (2) type 2 diabetes status; and (3) the risk of 9 year incident type 2 diabetes in a prospective study.

METHODS

PCSK9 p.R46L was genotyped in 4630 French participants from the Data from an Epidemiological Study on the Insulin Resistance Syndrome (DESIR) prospective study and in 1342 French participants with type 2 diabetes. The association between p.R46L and metabolic traits or type 2 diabetes risk was assessed through linear or logistic regression models adjusted for age, sex and BMI. The association between p.R46L and incident type 2 diabetes was assessed using a Cox regression model adjusted for sex, age and BMI at baseline.

RESULTS

Significant associations (p < 10(-6)) were found between p.R46L and lower total cholesterol (-0.394 mmol/l), LDL-cholesterol (-0.393 mmol/l) and apolipoprotein B concentrations (-0.099 g/l). However, no significant association was observed between p.R46L and markers of glucose homeostasis (including fasting glucose, fasting insulin, HbA1c, HOMA-B, HOMA-IR) or type 2 diabetes risk. Furthermore, no significant association between p.R46L variant and risk of incident type 2 diabetes was observed in DESIR.

CONCLUSIONS/INTERPRETATION

The PCSK9 p.R46L LOF variant was not associated with impaired glucose homeostasis in humans. These data are reassuring regarding the safety of PCSK9 inhibitors.

New insights from monogenic diabetes for "common" type 2 diabetes

Boundaries between monogenic and complex genetic diseases are becoming increasingly blurred, as a result of better understanding of phenotypes and their genetic determinants. This had a large impact on the way complex disease genetics is now being investigated. Starting with conventional approaches like familial linkage, positional cloning and candidate genes strategies, the scope of complex disease genetics has grown exponentially with scientific and technological advances in recent times. Despite identification of multiple loci harboring common and rare variants associated with complex diseases, interpreting and evaluating their functional role has proven to be difficult. Information from monogenic diseases, especially related to the intermediate traits associated with complex diseases comes handy. The significant overlap between traits and phenotypes of monogenic diseases with related complex diseases provides a platform to understand the disease biology better. In this review, we would discuss about one such complex disease, type 2 diabetes, which shares marked similarity of intermediate traits with different forms of monogenic diabetes.

Pancreatic islet cell development and regeneration

PURPOSE OF REVIEW

This review will discuss recent advances in understanding mouse and human pancreatic islet cell development, novel concepts related to β cell dysfunction and improved approaches for replenishing β cells to treat diabetes.

RECENT FINDINGS

Considerable knowledge about pancreatic islet development and function has been gained using model systems with subsequent validation in human tissues. Recently, several rodent studies have revealed that differentiated adult islet cells retain remarkable plasticity and can be converted to other islet cell types by perturbing their transcription factor profiles. Furthermore, significant advances have been made in the generation of β-like cells from stem cell populations. Therefore, the generation of functionally mature β cells by the in-situ conversion of non-β cell populations or by the directed differentiation of human pluripotent stem cells could represent novel mechanisms for replenishing β cells in diabetic patients.

SUMMARY

The overall conservation between mouse and human pancreatic development, islet physiology and etiology of diabetes encourages the translation of novel β cell replacement therapies to humans. Further deciphering the molecular mechanisms that direct islet cell regeneration, plasticity and function could improve and expand the β cell replacement strategies for treating diabetes.

A Mendelian Randomization Study of Circulating Uric Acid and Type 2 Diabetes

We aimed to investigate the causal effect of circulating uric acid concentrations on type 2 diabetes risk. A Mendelian randomization study was performed using a genetic score with 24 uric acid-associated loci. We used data of the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study, comprising 24,265 individuals of European ancestry from eight European countries. During a mean (SD) follow-up of 10 (4) years, 10,576 verified incident case subjects with type 2 diabetes were ascertained. Higher uric acid was associated with a higher diabetes risk after adjustment for confounders, with a hazard ratio (HR) of 1.20 (95% CI 1.11, 1.30) per 59.48 µmol/L (1 mg/dL) uric acid. The genetic score raised uric acid by 17 µmol/L (95% CI 15, 18) per SD increase and explained 4% of uric acid variation. By using the genetic score to estimate the unconfounded effect, we found that a 59.48 µmol/L higher uric acid concentration did not have a causal effect on diabetes (HR 1.01 [95% CI 0.87, 1.16]). Including data from the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) consortium, increasing our dataset to 41,508 case subjects with diabetes, the summary odds ratio estimate was 0.99 (95% CI 0.92, 1.06). In conclusion, our study does not support a causal effect of circulating uric acid on diabetes risk. Uric acid-lowering therapies may therefore not be beneficial in reducing diabetes risk.

Identification of a novel gene for diabetic traits in rats, mice, and humans

The genetic basis of type 2 diabetes remains incompletely defined despite the use of multiple genetic strategies. Multiparental populations such as heterogeneous stocks (HS) facilitate gene discovery by allowing fine mapping to only a few megabases, significantly decreasing the number of potential candidate genes compared to traditional mapping strategies. In the present work, we employed expression and sequence analysis in HS rats (Rattus norvegicus) to identify Tpcn2 as a likely causal gene underlying a 3.1-Mb locus for glucose and insulin levels. Global gene expression analysis on liver identified Tpcn2 as the only gene in the region that is differentially expressed between HS rats with glucose intolerance and those with normal glucose regulation. Tpcn2 also maps as a cis-regulating expression QTL and is negatively correlated with fasting glucose levels. We used founder sequence to identify variants within this region and assessed association between 18 variants and diabetic traits by conducting a mixed-model analysis, accounting for the complex family structure of the HS. We found that two variants were significantly associated with fasting glucose levels, including a nonsynonymous coding variant within Tpcn2. Studies in Tpcn2 knockout mice demonstrated a significant decrease in fasting glucose levels and insulin response to a glucose challenge relative to those in wild-type mice. Finally, we identified variants within Tpcn2 that are associated with fasting insulin in humans. These studies indicate that Tpcn2 is a likely causal gene that may play a role in human diabetes and demonstrate the utility of multiparental populations for positionally cloning genes within complex loci.

Directional dominance on stature and cognition in diverse human populations

Homozygosity has long been associated with rare, often devastating, Mendelian disorders, and Darwin was one of the first to recognize that inbreeding reduces evolutionary fitness. However, the effect of the more distant parental relatedness that is common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power. Here we use runs of homozygosity to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts, and find statistically significant associations between summed runs of homozygosity and four complex traits: height, forced expiratory lung volume in one second, general cognitive ability and educational attainment (P < 1 × 10(-300), 2.1 × 10(-6), 2.5 × 10(-10) and 1.8 × 10(-10), respectively). In each case, increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months' less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing evidence that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been.

Infrequent TRIB3 coding variants and coronary artery disease in type 2 diabetes

OBJECTIVE

Genes that modulate insulin sensitivity may also be involved in shaping the risk of coronary artery disease (CAD). The relatively common TRIB3 Q84R polymorphism (rs2295490) has been associated with abnormal insulin signaling, endothelial dysfunction, insulin resistance, and pro-atherogenic phenotypes. The aim of our study was to investigate the association between low-frequency TRIB3 coding variants and CAD in patients with type 2 diabetes (T2D).

METHODS

Three case-control studies for CAD from Italy and US were analyzed, for a total of 1565 individuals, all with type 2 diabetes. Infrequent variants were identified by re-sequencing TRIB3 exons in 140 "extreme cases" and 140 "super-controls" and then genotyped in all study subjects.

RESULTS

TRIB3 infrequent variants (n = 8), considered according to a collapsing rare variants framework, were significantly associated with CAD in diabetic patients from Italy (n = 700, OR = 0.43, 95% CI 0.20-0.91; p = 0.027), but not from the US (n = 865, OR = 1.22, 95% CI 0.69-2.18; p = 0.49). In the Italian sets, the association was especially strong among individuals who also carried the common R84 variant.

CONCLUSION

Although preliminary, our finding suggests a role of TRIB3 low-frequency variants on CAD among Italian patients with T2D. Further studies are needed to address the role of TRIB3 infrequent variants in other populations of both European and non-European ancestries.

Recent advances in understanding the genetic architecture of type 2 diabetes

Genome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c). At the end of 2011, established loci (P < 5 × 10(-8)) totaled 55 for T2D and 32 for glycemic traits. Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05] variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations. Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci. Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered. Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes.

Modelling the Interplay between Lifestyle Factors and Genetic Predisposition on Markers of Type 2 Diabetes Mellitus Risk

The risk of developing type 2 diabetes mellitus (T2DM) is determined by a complex interplay involving lifestyle factors and genetic predisposition. Despite this, many studies do not consider the relative contributions of this complex array of factors to identify relationships which are important in progression or prevention of complex diseases. We aimed to describe the integrated effect of a number of lifestyle changes (weight, diet and physical activity) in the context of genetic susceptibility, on changes in glycaemic traits in overweight or obese participants following 12-months of a weight management programme. A sample of 353 participants from a behavioural weight management intervention were included in this study. A graphical Markov model was used to describe the impact of the intervention, by dividing the effects into various pathways comprising changes in proportion of dietary saturated fat, physical activity and weight loss, and a genetic predisposition score (T2DM-GPS), on changes in insulin sensitivity (HOMA-IR), insulin secretion (HOMA-B) and short and long term glycaemia (glucose and HbA1c). We demonstrated the use of graphical Markov modelling to identify the importance and interrelationships of a number of possible variables changed as a result of a lifestyle intervention, whilst considering fixed factors such as genetic predisposition, on changes in traits. Paths which led to weight loss and change in dietary saturated fat were important factors in the change of all glycaemic traits, whereas the T2DM-GPS only made a significant direct contribution to changes in HOMA-IR and plasma glucose after considering the effects of lifestyle factors. This analysis shows that modifiable factors relating to body weight, diet, and physical activity are more likely to impact on glycaemic traits than genetic predisposition during a behavioural intervention.

A genetic variant in the seed region of miR-4513 shows pleiotropic effects on lipid and glucose homeostasis, blood pressure, and coronary artery disease

MicroRNAs (miRNA) play a crucial role in the regulation of diverse biological processes by post-transcriptional modulation of gene expression. Genetic polymorphisms in miRNA-related genes can potentially contribute to a wide range of phenotypes. The effect of such variants on cardiometabolic diseases has not yet been defined. We systematically investigated the association of genetic variants in the seed region of miRNAs with cardiometabolic phenotypes, using the thus far largest genome-wide association studies on 17 cardiometabolic traits/diseases. We found that rs2168518:G>A, a seed region variant of miR-4513, associates with fasting glucose, low-density lipoprotein-cholesterol, total cholesterol, systolic and diastolic blood pressure, and risk of coronary artery disease. We experimentally showed that miR-4513 expression is significantly reduced in the presence of the rs2168518 mutant allele. We sought to identify miR-4513 target genes that may mediate these associations and revealed five genes (PCSK1, BNC2, MTMR3, ANK3, and GOSR2) through which these effects might be taking place. Using luciferase reporter assays, we validated GOSR2 as a target of miR-4513 and further demonstrated that the miRNA-mediated regulation of this gene is changed by rs2168518. Our findings indicate a pleiotropic effect of miR-4513 on cardiometabolic phenotypes and may improve our understanding of the pathophysiology of cardiometabolic diseases.

Using Genetic Variants to Assess the Relationship Between Circulating Lipids and Type 2 Diabetes

The effects of dyslipidemia on the risk of type 2 diabetes (T2D) and related traits are not clear. We used regression models and 140 lipid-associated genetic variants to estimate associations between circulating HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), and triglycerides and T2D and related traits. Each genetic test was corrected for effects of variants on the other two lipid types and surrogates of adiposity. We used the largest data sets available: 34,840 T2D case and 114,981 control subjects from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium and up to 133,010 individuals without diabetes for insulin secretion and sensitivity from the MAGIC (Meta-Analyses of Glucose and Insulin-related traits Consortium) and GENESIS (GENEticS of Insulin Sensitivity) studies. Eight of 21 associations between groups of variants and diabetes traits were significant at the nominal level, including those between genetically determined lower HDL-C (β = -0.12, P = 0.03) and T2D and genetically determined lower LDL-C (β = -0.21, P = 5 × 10(-6)) and T2D. Although some of these may represent causal associations, we discuss why caution must be used when using Mendelian randomization in the context of circulating lipid levels and diabetes traits. In conclusion, we found evidence of links between genetic variants associated with lipids and T2D, but deeper knowledge of the underlying genetic mechanisms of specific lipid variants is needed before drawing definite conclusions about causality based on Mendelian randomization methodology.

Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.

Human genetic studies have demonstrated that quantitative human anthropometric and metabolic traits, including body mass index, waist-hip ratio, and plasma concentrations of glucose and insulin, are highly heritable, and are established risk factors for type 2 diabetes and cardiovascular diseases. Although many regions of the genome have been associated with these traits, the specific genes responsible have not yet been identified. By making use of advanced statistical “imputation” techniques applied to more than 87,000 individuals of European ancestry, and publicly available “reference panels” of more than 37 million genetic variants, we have been able to identify novel regions of the genome associated with these glycaemic and obesity-related traits and localise genes within these regions that are most likely to be causal. This improved understanding of the biological mechanisms underlying glycaemic and obesity-related traits is extremely important because it may advance drug development for downstream disease endpoints, ultimately leading to public health benefits.

Effect of Bile Acid Sequestrants on the Risk of Cardiovascular Events: A Mendelian Randomization Analysis

BACKGROUND

Statins lower low-density lipoprotein cholesterol (LDL-C) and risk of coronary artery disease (CAD), but they may be ineffective or not tolerated. Bile acid sequestrants (BAS) reduce LDL-C, yet their clinical efficacy on CAD remains controversial.

METHODS AND RESULTS

We conducted a systematic review and meta-analysis of randomized controlled trials to assess the effect of cholestyramine and colesevelam. We then used Mendelian randomization to estimate the effect of BAS on reducing the risk of CAD. First, we quantified the effect of rs4299376 (ABCG5/ABCG8), which affects the intestinal cholesterol absorption pathway targeted by BAS and then we used these estimates to predict the effect of BAS on CAD. Nineteen randomized controlled trials with a total of 7021 study participants were included. Cholestyramine 24 g/d was associated with a reduction in LDL-C of 23.5 mg/dL (95% confidence interval [CI] -26.8,-20.2; N=3806) and a trend toward reduced risk of CAD (odds ratio 0.81, 95% CI 0.70-1.02; P=0.07; N=3806), whereas colesevelam 3.75 g/d was associated with a reduction in LDL-C of 22.7 mg/dL (95% CI -28.3, -17.2; N=759). Based on the findings that rs4299376 was associated with a 2.75 mg/dL decrease in LDL-C and a 5% decrease in risk of CAD outcomes, we estimated that cholestyramine was associated with an odds ratio for CAD of 0.63 (95% CI 0.52-0.77; P=6.3×10(-6)) and colesevelam with an odds ratio of 0.64 (95% CI 0.52-0.79, P=4.3×10(-5)), which were not statistically different from BAS clinical trials (P>0.05).

CONCLUSIONS

The cholesterol lowering effect of BAS may translate into a clinically relevant reduction in CAD.

Associations between Potentially Modifiable Risk Factors and Alzheimer Disease: A Mendelian Randomization Study

BACKGROUND

Potentially modifiable risk factors including obesity, diabetes, hypertension, and smoking are associated with Alzheimer disease (AD) and represent promising targets for intervention. However, the causality of these associations is unclear. We sought to assess the causal nature of these associations using Mendelian randomization (MR).

METHODS AND FINDINGS

We used SNPs associated with each risk factor as instrumental variables in MR analyses. We considered type 2 diabetes (T2D, NSNPs = 49), fasting glucose (NSNPs = 36), insulin resistance (NSNPs = 10), body mass index (BMI, NSNPs = 32), total cholesterol (NSNPs = 73), HDL-cholesterol (NSNPs = 71), LDL-cholesterol (NSNPs = 57), triglycerides (NSNPs = 39), systolic blood pressure (SBP, NSNPs = 24), smoking initiation (NSNPs = 1), smoking quantity (NSNPs = 3), university completion (NSNPs = 2), and years of education (NSNPs = 1). We calculated MR estimates of associations between each exposure and AD risk using an inverse-variance weighted approach, with summary statistics of SNP-AD associations from the International Genomics of Alzheimer's Project, comprising a total of 17,008 individuals with AD and 37,154 cognitively normal elderly controls. We found that genetically predicted higher SBP was associated with lower AD risk (odds ratio [OR] per standard deviation [15.4 mm Hg] of SBP [95% CI]: 0.75 [0.62-0.91]; p = 3.4 × 10(-3)). Genetically predicted higher SBP was also associated with a higher probability of taking antihypertensive medication (p = 6.7 × 10(-8)). Genetically predicted smoking quantity was associated with lower AD risk (OR per ten cigarettes per day [95% CI]: 0.67 [0.51-0.89]; p = 6.5 × 10(-3)), although we were unable to stratify by smoking history; genetically predicted smoking initiation was not associated with AD risk (OR = 0.70 [0.37, 1.33]; p = 0.28). We saw no evidence of causal associations between glycemic traits, T2D, BMI, or educational attainment and risk of AD (all p > 0.1). Potential limitations of this study include the small proportion of intermediate trait variance explained by genetic variants and other implicit limitations of MR analyses.

CONCLUSIONS

Inherited lifetime exposure to higher SBP is associated with lower AD risk. These findings suggest that higher blood pressure--or some environmental exposure associated with higher blood pressure, such as use of antihypertensive medications--may reduce AD risk.

Triglyceride-Increasing Alleles Associated with Protection against Type-2 Diabetes

Elevated plasma triglyceride (TG) levels are an established risk factor for type-2 diabetes (T2D). However, recent studies have hinted at the possibility that genetic risk for TG may paradoxically protect against T2D. In this study, we examined the association of genetic risk for TG with incident T2D, and the interaction of baseline TG with TG genetic risk on incident T2D in 13,247 European-Americans (EA) and 3,238 African-Americans (AA) from three prospective cohort studies. A TG genetic risk score (GRS) was calculated based on 31 validated single nucleotide polymorphisms (SNPs). We considered several baseline covariates, including body- mass index (BMI) and lipid traits. Among EA and AA, we find, as expected, that baseline levels of TG are strongly positively associated with incident T2D (p<2 x 10-(10)). However, the TG GRS is negatively associated with T2D (p=0.013), upon adjusting for only race, in the full dataset. Upon additionally adjusting for age, sex, BMI, high-density lipoprotein cholesterol and TG, the TG GRS is significantly and negatively associated with T2D incidence (p=7.0 x 10(-8)), with similar trends among both EA and AA. No single SNP appears to be driving this association. We also find a significant statistical interaction of the TG GRS with TG (pi(nteraction) = 3.3 x 10-(4)), whereby the association of TG with incident T2D is strongest among those with low genetic risk for TG. Further research is needed to understand the likely pleiotropic mechanisms underlying these findings, and to clarify the causal relationship between T2D and TG.

A Mendelian randomization study of the effect of type-2 diabetes on coronary heart disease

In observational studies, type-2 diabetes (T2D) is associated with an increased risk of coronary heart disease (CHD), yet interventional trials have shown no clear effect of glucose-lowering on CHD. Confounding may have therefore influenced these observational estimates. Here we use Mendelian randomization to obtain unconfounded estimates of the influence of T2D and fasting glucose (FG) on CHD risk. Using multiple genetic variants associated with T2D and FG, we find that risk of T2D increases CHD risk (odds ratio (OR)=1.11 (1.05–1.17), per unit increase in odds of T2D, P=8.8 × 10⁻⁵; using data from 34,840/114,981 T2D cases/controls and 63,746/130,681 CHD cases/controls). FG in non-diabetic individuals tends to increase CHD risk (OR=1.15 (1.00–1.32), per mmol·per l, P=0.05; 133,010 non-diabetic individuals and 63,746/130,681 CHD cases/controls). These findings provide evidence supporting a causal relationship between T2D and CHD and suggest that long-term trials may be required to discern the effects of T2D therapies on CHD risk.

In order to effectively design interventions, it is useful to understand the complex interplay between multiple syndromes. Here, Ahmad et al. use genome-wide association study data and Mendelian randomisation to examine the influence of Type 2 diabetes and fasting glucose levels on coronary heart disease.

Genome-wide association study with 1000 genomes imputation identifies signals for nine sex hormone-related phenotypes

Genetic factors contribute strongly to sex hormone levels, yet knowledge of the regulatory mechanisms remains incomplete. Genome-wide association studies (GWAS) have identified only a small number of loci associated with sex hormone levels, with several reproductive hormones yet to be assessed. The aim of the study was to identify novel genetic variants contributing to the regulation of sex hormones. We performed GWAS using genotypes imputed from the 1000 Genomes reference panel. The study used genotype and phenotype data from a UK twin register. We included 2913 individuals (up to 294 males) from the Twins UK study, excluding individuals receiving hormone treatment. Phenotypes were standardised for age, sex, BMI, stage of menstrual cycle and menopausal status. We tested 7 879 351 autosomal SNPs for association with levels of dehydroepiandrosterone sulphate (DHEAS), oestradiol, free androgen index (FAI), follicle-stimulating hormone (FSH), luteinizing hormone (LH), prolactin, progesterone, sex hormone-binding globulin and testosterone. Eight independent genetic variants reached genome-wide significance (P<5 × 10⁻⁸), with minor allele frequencies of 1.3–23.9%. Novel signals included variants for progesterone (P=7.68 × 10⁻¹²), oestradiol (P=1.63 × 10⁻⁸) and FAI (P=1.50 × 10⁻⁸). A genetic variant near the FSHB gene was identified which influenced both FSH (P=1.74 × 10⁻⁸) and LH (P=3.94 × 10⁻⁹) levels. A separate locus on chromosome 7 was associated with both DHEAS (P=1.82 × 10⁻¹⁴) and progesterone (P=6.09 × 10⁻¹⁴). This study highlights loci that are relevant to reproductive function and suggests overlap in the genetic basis of hormone regulation.

Replication Study in a Japanese Population to Evaluate the Association between 10 SNP Loci, Identified in European Genome-Wide Association Studies, and Type 2 Diabetes

Aim

We performed a replication study in a Japanese population to evaluate the association between type 2 diabetes and 7 susceptibility loci originally identified by European genome-wide association study (GWAS) in 2012: ZMIZ1, KLHDC5, TLE1, ANKRD55, CILP2, MC4R, and BCAR1. We also examined the association of 3 additional loci: CCND2 and GIPR, identified in sex-differentiated analyses, and LAMA1, which was shown to be associated with non-obese European type 2 diabetes.

Methods

We genotyped 6,972 Japanese participants (4,280 type 2 diabetes patients and 2,692 controls) for each of the 10 single nucleotide polymorphisms (SNPs): rs12571751 in ZMIZ1, rs10842994 near KLHDC5, rs2796441 near TLE1, rs459193 near ANKRD55, rs10401969 in CILP2, rs12970134 near MC4R, rs7202877 near BCAR1, rs11063069 near CCND2, rs8108269 near GIPR, and rs8090011 in LAMA1 using a multiplex polymerase chain reaction invader assay. The association of each SNP locus with the disease was evaluated using a logistic regression analysis.

Results

All SNPs examined in this study had the same direction of effect (odds ratio > 1.0, p = 9.77 × 10^-4, binomial test), as in the original reports. Among them, rs12571751 in ZMIZ1 was significantly associated with type 2 diabetes [p = 0.0041, odds ratio = 1.123, 95% confidence interval 1.037–1.215, adjusted for sex, age and body mass index (BMI)], but we did not observe significant association of the remaining 9 SNP loci with type 2 diabetes in the present Japanese population (p ≥ 0.005). A genetic risk score, constructed from the sum of risk alleles for the 7 SNP loci identified by un-stratified analyses in the European GWAS meta-analysis were associated with type 2 diabetes in the present Japanese population (p = 2.3 × 10^-4, adjusted for sex, age and BMI).

Conclusions

ZMIZ1 locus has a significant effect on conferring susceptibility to type 2 diabetes also in the Japanese population.

Season-dependent associations of circadian rhythm-regulating loci (CRY1, CRY2 and MTNR1B) and glucose homeostasis: the GLACIER Study

AIMS/HYPOTHESIS

The association of single nucleotide polymorphisms (SNPs) proximal to CRY2 and MTNR1B with fasting glucose is well established. CRY1/2 and MTNR1B encode proteins that regulate circadian rhythmicity and influence energy metabolism. Here we tested whether season modified the relationship of these loci with blood glucose concentration.

METHODS

SNPs rs8192440 (CRY1), rs11605924 (CRY2) and rs10830963 (MTNR1B) were genotyped in a prospective cohort study from northern Sweden (n = 16,499). The number of hours of daylight exposure during the year ranged from 4.5 to 22 h daily. Owing to the non-linear distribution of daylight throughout the year, season was dichotomised based on the vernal and autumnal equinoxes. Effect modification was assessed using linear regression models fitted with a SNP × season interaction term, marginal effect terms and putative confounding variables, with fasting or 2 h glucose concentrations as outcomes.

RESULTS

The rs8192440 (CRY1) variant was only associated with fasting glucose among participants (n = 2,318) examined during the light season (β = -0.04 mmol/l per A allele, 95% CI -0.08, -0.01, p = 0.02, p interaction = 0.01). In addition to the established association with fasting glucose, the rs11605924 (CRY2) and rs10830963 (MTNR1B) loci were associated with 2 h glucose concentrations (β = 0.07 mmol/l per A allele, 95% CI 0.03, 0.12, p = 0.0008, n = 9,605, and β = -0.11 mmol/l per G allele, 95% CI -0.15, -0.06, p < 0.0001, n = 9,517, respectively), but only in participants examined during the dark season (p interaction = 0.006 and 0.04, respectively). Repeated measures analyses including data collected 10 years after baseline (n = 3,500) confirmed the results for the CRY1 locus (p interaction = 0.01).

CONCLUSIONS/INTERPRETATION

In summary, these observations suggest a biologically plausible season-dependent association between SNPs at CRY1, CRY2 and MTNR1B and glucose homeostasis.

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case.

Metabolic factors and genetic risk mediate familial type 2 diabetes risk in the Framingham Heart Study

AIMS/HYPOTHESIS

Type 2 diabetes mellitus in parents is a strong determinant of diabetes risk in their offspring. We hypothesise that offspring diabetes risk associated with parental diabetes is mediated by metabolic risk factors.

METHODS

We studied initially non-diabetic participants of the Framingham Offspring Study. Metabolic risk was estimated using beta cell corrected insulin response (CIR), HOMA-IR or a count of metabolic syndrome components (metabolic syndrome score [MSS]). Dietary risk and physical activity were estimated using questionnaire responses. Genetic risk score (GRS) was estimated as the count of 62 type 2 diabetes risk alleles. The outcome of incident diabetes in offspring was examined across levels of parental diabetes exposure, accounting for sibling correlation and adjusting for age, sex and putative mediators. The proportion mediated was estimated by comparing regression coefficients for parental diabetes with (β adj) and without (β unadj) adjustments for CIR, HOMA-IR, MSS and GRS (percentage mediated = 1 - β adj / β unadj).

RESULTS

Metabolic factors mediated 11% of offspring diabetes risk associated with parental diabetes, corresponding to a reduction in OR per diabetic parent from 2.13 to 1.96. GRS mediated 9% of risk, corresponding to a reduction in OR per diabetic parent from 2.13 to 1.99.

CONCLUSIONS/INTERPRETATION

Metabolic risk factors partially mediated offspring type 2 diabetes risk conferred by parental diabetes to a similar magnitude as genetic risk. However, a substantial proportion of offspring diabetes risk associated with parental diabetes remains unexplained by metabolic factors, genetic risk, diet and physical activity, suggesting that important familial influences on diabetes risk remain undiscovered.

Examination of the brain natriuretic peptide rs198389 single-nucleotide polymorphism on type 2 diabetes mellitus and related phenotypes in an Algerian population

BACKGROUND

In European populations, the NPPB rs198389 single nucleotide polymorphism (SNP) is associated with a reduced risk of type 2 diabetes mellitus (T2DM). We investigated the putative associations between NPPB rs198389, the T2DM risk and quantitative metabolic traits in an Algerian population.

METHODS

The association analysis was performed as a T2DM case-control study (with 78 cases and 645 controls) nested into the ISOR population-based study.

RESULTS

The NPPB rs198389 SNP was not associated with T2DM (odds ratio (OR) [95% confidence interval (CI)]=0.73 [0.51-1.04], p=0.08). However, the C allele was associated with lower fasting plasma insulin levels (p=0.05) and a lower homeostatic model assessment insulin resistance index (p=0.05) in non-diabetic individuals.

CONCLUSION

The NPPB rs198389 SNP might modulate fasting insulin levels in an Algerian population.

Future translational applications from the contemporary genomics era: a scientific statement from the American Heart Association

The field of genetics and genomics has advanced considerably with the achievement of recent milestones encompassing the identification of many loci for cardiovascular disease and variable drug responses. Despite this achievement, a gap exists in the understanding and advancement to meaningful translation that directly affects disease prevention and clinical care. The purpose of this scientific statement is to address the gap between genetic discoveries and their practical application to cardiovascular clinical care. In brief, this scientific statement assesses the current timeline for effective translation of basic discoveries to clinical advances, highlighting past successes. Current discoveries in the area of genetics and genomics are covered next, followed by future expectations, tools, and competencies for achieving the goal of improving clinical care.

Pathways targeted by antidiabetes drugs are enriched for multiple genes associated with type 2 diabetes risk

Genome-wide association studies (GWAS) have uncovered >65 common variants associated with type 2 diabetes (T2D); however, their relevance for drug development is not yet clear. Of note, the first two T2D-associated loci (PPARG and KCNJ11/ABCC8) encode known targets of antidiabetes medications. We therefore tested whether other genes/pathways targeted by antidiabetes drugs are associated with T2D. We compiled a list of 102 genes in pathways targeted by marketed antidiabetic medications and applied Gene Set Enrichment Analysis (MAGENTA [Meta-Analysis Gene-set Enrichment of variaNT Associations]) to this gene set, using available GWAS meta-analyses for T2D and seven quantitative glycemic traits. We detected a strong enrichment of drug target genes associated with T2D (P = 2 × 10(-5); 14 potential new associations), primarily driven by insulin and thiazolidinedione (TZD) targets, which was replicated in an independent meta-analysis (Metabochip). The glycemic traits yielded no enrichment. The T2D enrichment signal was largely due to multiple genes of modest effects (P = 4 × 10(-4), after removing known loci), highlighting new associations for follow-up (ACSL1, NFKB1, SLC2A2, incretin targets). Furthermore, we found that TZD targets were enriched for LDL cholesterol associations, illustrating the utility of this approach in identifying potential side effects. These results highlight the potential biomedical relevance of genes revealed by GWAS and may provide new avenues for tailored therapy and T2D treatment design.

Glucokinase regulatory protein: complexity at the crossroads of triglyceride and glucose metabolism

PURPOSE OF REVIEW

Glucokinase regulator (GCKR) encodes glucokinase regulatory protein (GKRP), a hepatocyte-specific inhibitor of the glucose-metabolizing enzyme glucokinase (GCK). Genome-wide association studies have identified a common coding variant within GCKR associated with multiple metabolic traits. This review focuses on recent insights into the critical role of GKRP in hepatic glucose metabolism that have stemmed from the study of human genetics. This knowledge has improved our understanding of glucose and lipid physiology and informed the development of targeted molecular therapeutics for diabetes.

RECENT FINDINGS

Rare GCKR variants have effects on GKRP expression, localization, and activity. These variants are collectively associated with hypertriglyceridaemia but are not causal. Crystal structures of GKRP and the GCK-GKRP complex have been solved, providing greater insight into the molecular interactions between these proteins. Finally, small molecules have been identified that directly bind GKRP and reduce blood glucose levels in rodent models of diabetes.

SUMMARY

GCKR variants across the allelic spectrum have effects on glucose and lipid homeostasis. Functional analysis has highlighted numerous molecular mechanisms for GKRP dysfunction. Hepatocyte-specific GCK activation via small molecule GKRP inhibition may be a new avenue for type 2 diabetes treatment, particularly considering evidence indicating GKRP loss-of-function alone does not cause hypertriglyceridaemia.

The genetics of diabetic complications

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.

Insm1 cooperates with Neurod1 and Foxa2 to maintain mature pancreatic β-cell function

Key transcription factors control the gene expression program in mature pancreatic β-cells, but their integration into regulatory networks is little understood. Here, we show that Insm1, Neurod1 and Foxa2 directly interact and together bind regulatory sequences in the genome of mature pancreatic β-cells. We used Insm1 ablation in mature β-cells in mice and found pronounced deficits in insulin secretion and gene expression. Insm1-dependent genes identified previously in developing β-cells markedly differ from the ones identified in the adult. In particular, adult mutant β-cells resemble immature β-cells of newborn mice in gene expression and functional properties. We defined Insm1, Neurod1 and Foxa2 binding sites associated with genes deregulated in Insm1 mutant β-cells. Remarkably, combinatorial binding of Insm1, Neurod1 and Foxa2 but not binding of Insm1 alone explained a significant fraction of gene expression changes. Human genomic sequences corresponding to the murine sites occupied by Insm1/Neurod1/Foxa2 were enriched in single nucleotide polymorphisms associated with glycolytic traits. Thus, our data explain part of the mechanisms by which β-cells maintain maturity: Combinatorial Insm1/Neurod1/Foxa2 binding identifies regulatory sequences that maintain the mature gene expression program in β-cells, and disruption of this network results in functional failure.

Genetic Variations in MicroRNA-Binding Sites Affect MicroRNA-Mediated Regulation of Several Genes Associated With Cardio-metabolic Phenotypes

BACKGROUND

Genome-wide association studies enabled us to discover a large number of variants and genomic loci contributing to cardiovascular and metabolic disorders. However, because the vast majority of the identified variants are thought to merely be proxies for other functional variants, the causal mechanisms remain to be elucidated. We hypothesized that the part of the functional variants involved in deregulating cardiometabolic genes is located in microRNA (miRNA)-binding sites.

METHODS AND RESULTS

Using the largest genome-wide association studies available on glycemic indices, lipid traits, anthropometric measures, blood pressure, coronary artery diseases, and type 2 diabetes mellitus, we identified 11,067 variants that are associated with cardiometabolic phenotypes. Of these, 230 variants are located within miRNA-binding sites in the 3'-untranslated region of 155 cardiometabolic genes. Thirty-seven of 230 variants were found to fulfill our predefined criteria for being functional in their genomic loci. Ten variants were subsequently selected for experimental validation based on genome-wide association studies results, expression quantitative trait loci (eQTL) analyses, and coexpression of their host genes and regulatory miRNAs in relevant tissues. Luciferase reporter assays revealed an allele-specific regulation of genes hosting the variants by miRNAs. These cotransfection experiments showed that rs174545 (FADS1:miR-181a-2), rs1059611 (LPL:miR-136), rs13702 (LPL:miR-410), rs1046875 (FN3KRP:miR-34a), rs7956 (MKRN2:miR-154), rs3217992 (CDKN2B:miR-138-2-3p), and rs11735092 (HSD17B13:miR-375) decrease or abrogate miRNA-dependent regulation of the genes. Conversely, 2 variants, rs6857 (PVRL2:miR-320e) and rs907091 (IKZF3:miR-326), were shown to enhance the activity of miRNAs on their host genes.

CONCLUSIONS

We provide evidence for a model in which polymorphisms in miRNA-binding sites can both positively and negatively affect miRNA-mediated regulation of cardiometabolic genes.

The study to understand the genetics of the acute response to metformin and glipizide in humans (SUGAR-MGH): design of a pharmacogenetic resource for type 2 diabetes

Objective

Genome-wide association studies have uncovered a large number of genetic variants associated with type 2 diabetes or related phenotypes. In many cases the causal gene or polymorphism has not been identified, and its impact on response to anti-hyperglycemic medications is unknown. The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH, NCT01762046) is a novel resource of genetic and biochemical data following glipizide and metformin administration. We describe recruitment, enrollment, and phenotyping procedures and preliminary results for the first 668 of our planned 1,000 participants enriched for individuals at risk of requiring anti-diabetic therapy in the future.

Methods

All individuals are challenged with 5 mg glipizide × 1; twice daily 500 mg metformin × 2 days; and 75-g oral glucose tolerance test following metformin. Genetic variants associated with glycemic traits and blood glucose, insulin, and other hormones at baseline and following each intervention are measured.

Results

Approximately 50% of the cohort is female and 30% belong to an ethnic minority group. Following glipizide administration, peak insulin occurred at 60 minutes and trough glucose at 120 minutes. Thirty percent of participants experienced non-severe symptomatic hypoglycemia and required rescue with oral glucose. Following metformin administration, fasting glucose and insulin were reduced. Common genetic variants were associated with fasting glucose levels.

Conclusions

SUGAR-MGH represents a viable pharmacogenetic resource which, when completed, will serve to characterize genetic influences on pharmacological perturbations, and help establish the functional relevance of newly discovered genetic loci to therapy of type 2 diabetes.

Trial Registration

ClinicalTrials.gov NCT01762046

I'm eating for two: parental dietary effects on offspring metabolism

It has long been understood that the pathogenesis of complex diseases such as diabetes includes both genetic and environmental components. More recently, it has become clear that not only does an individual's environment influence their own metabolism, but in some cases, the environment experienced by their parents may also contribute to their risk of metabolic disease. Here, we review the evidence that parental diet influences metabolic phenotype in offspring in mammals and provide a current survey of our mechanistic understanding of these effects.

Epigenetic modifications and long noncoding RNAs influence pancreas development and function

Insulin-producing β cells within the pancreatic islet of Langerhans are responsible for maintaining glucose homeostasis; the loss or malfunction of β cells results in diabetes mellitus. Recent advances in cell purification strategies and sequencing technologies as well as novel molecular tools have revealed that epigenetic modifications and long noncoding RNAs (lncRNAs) represent an integral part of the transcriptional mechanisms regulating pancreas development and β cell function. Importantly, these findings have uncovered a new layer of gene regulation in the pancreas that can be exploited to enhance the restoration and/or repair of β cells to treat diabetes.

Identification and validation of N-acetyltransferase 2 as an insulin sensitivity gene

Decreased insulin sensitivity, also referred to as insulin resistance (IR), is a fundamental abnormality in patients with type 2 diabetes and a risk factor for cardiovascular disease. While IR predisposition is heritable, the genetic basis remains largely unknown. The GENEticS of Insulin Sensitivity consortium conducted a genome-wide association study (GWAS) for direct measures of insulin sensitivity, such as euglycemic clamp or insulin suppression test, in 2,764 European individuals, with replication in an additional 2,860 individuals. The presence of a nonsynonymous variant of N-acetyltransferase 2 (NAT2) [rs1208 (803A>G, K268R)] was strongly associated with decreased insulin sensitivity that was independent of BMI. The rs1208 "A" allele was nominally associated with IR-related traits, including increased fasting glucose, hemoglobin A1C, total and LDL cholesterol, triglycerides, and coronary artery disease. NAT2 acetylates arylamine and hydrazine drugs and carcinogens, but predicted acetylator NAT2 phenotypes were not associated with insulin sensitivity. In a murine adipocyte cell line, silencing of NAT2 ortholog Nat1 decreased insulin-mediated glucose uptake, increased basal and isoproterenol-stimulated lipolysis, and decreased adipocyte differentiation, while Nat1 overexpression produced opposite effects. Nat1-deficient mice had elevations in fasting blood glucose, insulin, and triglycerides and decreased insulin sensitivity, as measured by glucose and insulin tolerance tests, with intermediate effects in Nat1 heterozygote mice. Our results support a role for NAT2 in insulin sensitivity.

Impact of PTBP1 rs11085226 on glucose-stimulated insulin release in adult Danes

BACKGROUND

The variant rs11085226 (G) within the gene encoding polypyrimidine tract binding protein 1 (PTBP1) was reported to associate with reduced insulin release determined by an oral glucose tolerance test (OGTT) as well as an intravenous glucose tolerance test (IVGTT). The aim of the present study was to validate the association of the rs11085226 G-allele of PTBP1 with previously investigated OGTT- and IVGTT-derived diabetes-related metabolic quantitative phenotypes, to conduct exploratory analyses of additional measures of beta-cell function, and to further investigate a potential association with type 2 diabetes.

METHODS

PTBP1 rs11085226 was genotyped in 20,911 individuals of Danish Caucasian ethnicity ascertained from 9 study samples. Case control analysis was performed on 5,634 type 2 diabetic patients and 11,319 individuals having a normal fasting glucose level as well as 4,641 glucose tolerant controls, respectively. Quantitative trait analyses were performed in up to 13,605 individuals subjected to an OGTT or blood samples obtained after an overnight fast, as well as in 596 individuals subjected to an IVGTT.

RESULTS

Analyses of fasting and OGTT-derived quantitative traits did not show any significant associations with the PTBP1 rs11085226 variant. Meta-analysis of IVGTT-derived quantitative traits showed a nominally significant association between the variant and reduced beta-cell responsiveness to glucose (β = -0.1 mmol · kg(-1) · min(-1); 95% CI: -0.200.20 - -0.024; P = 0.01) assuming a dominant model of inheritance, but failed to replicate a previously reported association with area under the curve (AUC) for insulin. Case control analysis did not show an association of the PTBP1 rs11085226 variant with type 2 diabetes.

CONCLUSIONS

Despite failure to replicate the previously reported associations of PTBP1 rs11085226 with OGTT- and IVGTT-derived measures of beta-cell function, we did find a nominally significant association with reduced beta-cell responsiveness to glucose during an IVGTT, a trait not previously investigated, leaving the potential influence of this variant in PTBP1 on glucose stimulated insulin release open for further investigation. However, the present study does not support the hypothesis that the variant confers risk of type 2 diabetes.

Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval -0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect.

Pleiotropy among common genetic loci identified for cardiometabolic disorders and C-reactive protein

Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10^-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes.

Genetics of type 2 diabetes-pitfalls and possibilities

Type 2 diabetes (T2D) is a complex disease that is caused by a complex interplay between genetic, epigenetic and environmental factors. While the major environmental factors, diet and activity level, are well known, identification of the genetic factors has been a challenge. However, recent years have seen an explosion of genetic variants in risk and protection of T2D due to the technical development that has allowed genome-wide association studies and next-generation sequencing. Today, more than 120 variants have been convincingly replicated for association with T2D and many more with diabetes-related traits. Still, these variants only explain a small proportion of the total heritability of T2D. In this review, we address the possibilities to elucidate the genetic landscape of T2D as well as discuss pitfalls with current strategies to identify the elusive unknown heritability including the possibility that our definition of diabetes and its subgroups is imprecise and thereby makes the identification of genetic causes difficult.

First genome-wide association study in an Australian aboriginal population provides insights into genetic risk factors for body mass index and type 2 diabetes

A body mass index (BMI) >22kg/m2 is a risk factor for type 2 diabetes (T2D) in Aboriginal Australians. To identify loci associated with BMI and T2D we undertook a genome-wide association study using 1,075,436 quality-controlled single nucleotide polymorphisms (SNPs) genotyped (Illumina 2.5M Duo Beadchip) in 402 individuals in extended pedigrees from a Western Australian Aboriginal community. Imputation using the thousand genomes (1000G) reference panel extended the analysis to 6,724,284 post quality-control autosomal SNPs. No associations achieved genome-wide significance, commonly accepted as P<5x10-8. Nevertheless, genes/pathways in common with other ethnicities were identified despite the arrival of Aboriginal people in Australia >45,000 years ago. The top hit (rs10868204 Pgenotyped = 1.50x10-6; rs11140653 Pimputed_1000G = 2.90x10-7) for BMI lies 5' of NTRK2, the type 2 neurotrophic tyrosine kinase receptor for brain-derived neurotrophic factor (BDNF) that regulates energy balance downstream of melanocortin-4 receptor (MC4R). PIK3C2G (rs12816270 Pgenotyped = 8.06x10-6; rs10841048 Pimputed_1000G = 6.28x10-7) was associated with BMI, but not with T2D as reported elsewhere. BMI also associated with CNTNAP2 (rs6960319 Pgenotyped = 4.65x10-5; rs13225016 Pimputed_1000G = 6.57x10-5), previously identified as the strongest gene-by-environment interaction for BMI in African-Americans. The top hit (rs11240074 Pgenotyped = 5.59x10-6, Pimputed_1000G = 5.73x10-6) for T2D lies 5' of BCL9 that, along with TCF7L2, promotes beta-catenin's transcriptional activity in the WNT signaling pathway. Additional hits occurred in genes affecting pancreatic (KCNJ6, KCNA1) and/or GABA (GABRR1, KCNA1) functions. Notable associations observed for genes previously identified at genome-wide significance in other populations included MC4R (Pgenotyped = 4.49x10-4) for BMI and IGF2BP2 Pimputed_1000G = 2.55x10-6) for T2D. Our results may provide novel functional leads in understanding disease pathogenesis in this Australian Aboriginal population.

Genetic studies of body mass index yield new insights for obesity biology

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

New genetic loci link adipose and insulin biology to body fat distribution

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.

Adjusting for heritable covariates can bias effect estimates in genome-wide association studies

In recent years, a number of large-scale genome-wide association studies have been published for human traits adjusted for other correlated traits with a genetic basis. In most studies, the motivation for such an adjustment is to discover genetic variants associated with the primary outcome independently of the correlated trait. In this report, we contend that this objective is fulfilled when the tested variants have no effect on the covariate or when the correlation between the covariate and the outcome is fully explained by a direct effect of the covariate on the outcome. For all other scenarios, an unintended bias is introduced with respect to the primary outcome as a result of the adjustment, and this bias might lead to false positives. Here, we illustrate this point by providing examples from published genome-wide association studies, including large meta-analysis of waist-to-hip ratio and waist circumference adjusted for body mass index (BMI), where genetic effects might be biased as a result of adjustment for body mass index. Using both theory and simulations, we explore this phenomenon in detail and discuss the ramifications for future genome-wide association studies of correlated traits and diseases.

Coordinated regulatory variation associated with gestational hyperglycaemia regulates expression of the novel hexokinase HKDC1

Maternal glucose levels during pregnancy impact the developing fetus, affecting metabolic health early and later in life. Both genetic and environmental factors influence maternal metabolism, but little is known about the genetic mechanisms that alter glucose metabolism during pregnancy. Here we report that haplotypes previously associated with gestational hyperglycemia in the third trimester disrupt regulatory element activity and reduce expression of the nearby HKDC1 gene. We further find that experimentally reducing or increasing HKDC1 expression reduces or increases hexokinase activity, respectively, in multiple cellular models; and that purified HKDC1 protein has hexokinase activity in vitro. Together, these results suggest a novel mechanism of gestational glucose regulation in which the effects of genetic variants in multiple regulatory elements alter glucose homeostasis by coordinately reducing expression of the novel hexokinase HKDC1.

Genetic architecture of insulin resistance in the mouse

Insulin resistance (IR) is a complex trait with multiple genetic and environmental components. Confounded by large differences between the sexes, environment, and disease pathology, the genetic basis of IR has been difficult to dissect. Here we examine IR and related traits in a diverse population of more than 100 unique male and female inbred mouse strains after feeding a diet rich in fat and refined carbohydrates. Our results show dramatic variation in IR among strains of mice and widespread differences between sexes that are dependent on genotype. We uncover more than 15 genome-wide significant loci and validate a gene, Agpat5, associated with IR. We also integrate plasma metabolite levels and global gene expression from liver and adipose tissue to identify metabolite quantitative trait loci (mQTL) and expression QTL (eQTL), respectively. Our results provide a resource for analysis of interactions between diet, sex, and genetic background in IR.

Genetic architecture of insulin resistance in the mouse

Insulin resistance (IR) is a complex trait with multiple genetic and environmental components. Confounded by large differences between the sexes, environment, and disease pathology, the genetic basis of IR has been difficult to dissect. Here we examine IR and related traits in a diverse population of more than 100 unique male and female inbred mouse strains after feeding a diet rich in fat and refined carbohydrates. Our results show dramatic variation in IR among strains of mice and widespread differences between sexes that are dependent on genotype. We uncover more than 15 genome-wide significant loci and validate a gene, Agpat5, associated with IR. We also integrate plasma metabolite levels and global gene expression from liver and adipose tissue to identify metabolite quantitative trait loci (mQTL) and expression QTL (eQTL), respectively. Our results provide a resource for analysis of interactions between diet, sex, and genetic background in IR.

Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility

Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=-0.09±0.01 mmol l(-1), P=3.4 × 10(-12)), T2D risk (OR[95%CI]=0.86[0.76-0.96], P=0.010), early insulin secretion (β=-0.07±0.035 pmolinsulin mmolglucose(-1), P=0.048), but higher 2-h glucose (β=0.16±0.05 mmol l(-1), P=4.3 × 10(-4)). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8 × 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l(-1), P=1.3 × 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.

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.

Understanding how FI and FG levels are regulated is important because their derangement is a feature of T2D. Despite recent success from GWAS in identifying regions of the genome influencing glycemic traits, collectively these loci explain only a small proportion of trait variance. Unlocking the biological mechanisms driving these associations has been challenging because the vast majority of variants map to non-coding sequence, and the genes through which they exert their impact are largely unknown. In the current study, we sought to increase our understanding of the physiological pathways influencing both traits using exome-array genotyping in up to 33,231 non-diabetic individuals to identify coding variants and consequently genes associated with either FG or FI levels. We identified novel association signals for both traits including the receptor for GLP-1 agonists which are a widely used therapy for T2D. Furthermore, we identified coding variants at several GWAS loci which point to the genes underlying these association signals. Importantly, we found that multiple coding variants in G6PC2 result in a loss of protein function and lower fasting glucose levels.

HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials

BACKGROUND

Statins increase the risk of new-onset type 2 diabetes mellitus. We aimed to assess whether this increase in risk is a consequence of inhibition of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), the intended drug target.

METHODS

We used single nucleotide polymorphisms in the HMGCR gene, rs17238484 (for the main analysis) and rs12916 (for a subsidiary analysis) as proxies for HMGCR inhibition by statins. We examined associations of these variants with plasma lipid, glucose, and insulin concentrations; bodyweight; waist circumference; and prevalent and incident type 2 diabetes. Study-specific effect estimates per copy of each LDL-lowering allele were pooled by meta-analysis. These findings were compared with a meta-analysis of new-onset type 2 diabetes and bodyweight change data from randomised trials of statin drugs. The effects of statins in each randomised trial were assessed using meta-analysis.

FINDINGS

Data were available for up to 223 463 individuals from 43 genetic studies. Each additional rs17238484-G allele was associated with a mean 0·06 mmol/L (95% CI 0·05-0·07) lower LDL cholesterol and higher body weight (0·30 kg, 0·18-0·43), waist circumference (0·32 cm, 0·16-0·47), plasma insulin concentration (1·62%, 0·53-2·72), and plasma glucose concentration (0·23%, 0·02-0·44). The rs12916 SNP had similar effects on LDL cholesterol, bodyweight, and waist circumference. The rs17238484-G allele seemed to be associated with higher risk of type 2 diabetes (odds ratio [OR] per allele 1·02, 95% CI 1·00-1·05); the rs12916-T allele association was consistent (1·06, 1·03-1·09). In 129 170 individuals in randomised trials, statins lowered LDL cholesterol by 0·92 mmol/L (95% CI 0·18-1·67) at 1-year of follow-up, increased bodyweight by 0·24 kg (95% CI 0·10-0·38 in all trials; 0·33 kg, 95% CI 0·24-0·42 in placebo or standard care controlled trials and -0·15 kg, 95% CI -0·39 to 0·08 in intensive-dose vs moderate-dose trials) at a mean of 4·2 years (range 1·9-6·7) of follow-up, and increased the odds of new-onset type 2 diabetes (OR 1·12, 95% CI 1·06-1·18 in all trials; 1·11, 95% CI 1·03-1·20 in placebo or standard care controlled trials and 1·12, 95% CI 1·04-1·22 in intensive-dose vs moderate dose trials).

INTERPRETATION

The increased risk of type 2 diabetes noted with statins is at least partially explained by HMGCR inhibition.

FUNDING

The funding sources are cited at the end of the paper.

Reduced CD300LG mRNA tissue expression, increased intramyocellular lipid content and impaired glucose metabolism in healthy male carriers of Arg82Cys in CD300LG: a novel genometabolic cross-link between CD300LG and common metabolic phenotypes

BACKGROUND

CD300LG rs72836561 (c.313C>T, p.Arg82Cys) has in genetic-epidemiological studies been associated with the lipoprotein abnormalities of the metabolic syndrome. CD300LG belongs to the CD300-family of membrane-bound molecules which have the ability to recognize and interact with extracellular lipids. We tested whether this specific polymorphism results in abnormal lipid accumulation in skeletal muscle and liver and other indices of metabolic dysfunction.

METHODS

40 healthy men with a mean age of 55 years were characterized metabolically including assessment of insulin sensitivity by the hyperinsulinemic euglycemic clamp, intrahepatic lipid content (IHLC) and intramyocellular lipid content (IMCL) by MR spectroscopy, and β-cell function by an intravenous glucose tolerance test. Changes in insulin signaling and CD300LG mRNA expression were determined by western blotting and quantitative PCR in muscle and adipose tissue.

RESULTS

Compared with the 20 controls (CC carriers), the 20 CT carriers (polymorphism carriers) had higher IMCL (p=0.045), a reduced fasting forearm glucose uptake (p=0.011), a trend toward lower M-values during the clamp; 6.0 mg/kg/min vs 7.1 (p=0.10), and higher IHLC (p=0.10). CT carriers had lower CD300LG mRNA expression and CD300LG expression in muscle correlated with IMCL (β=-0.35, p=0.046), forearm glucose uptake (β=0.37, p=0.03), and tended to correlate with the M-value (β=0.33, p=0.06), independently of CD300LG genotype. β-cell function was unaffected.

CONCLUSIONS

The CD300LG polymorphism was associated with decreased CD300LG mRNA expression in muscle and adipose tissue, increased IMCL, and abnormalities of glucose metabolism. CD300LG mRNA levels correlated with IMCL and forearm glucose uptake. These findings link a specific CD300LG polymorphism with features of the metabolic syndrome suggesting a role for CD300LG in the regulation of common metabolic traits.

TRIAL REGISTRATION NUMBER

NCT01571609.

Mendelian randomization supports causality between maternal hyperglycemia and epigenetic regulation of leptin gene in newborns

Leptin is an adipokine that acts in the central nervous system and regulates energy balance. Animal models and human observational studies have suggested that leptin surge in the perinatal period has a critical role in programming long-term risk of obesity. In utero exposure to maternal hyperglycemia has been associated with increased risk of obesity later in life. Epigenetic mechanisms are suspected to be involved in fetal programming of long term metabolic diseases. We investigated whether DNA methylation levels near LEP locus mediate the relation between maternal glycemia and neonatal leptin levels using the 2-step epigenetic Mendelian randomization approach. We used data and samples from up to 485 mother-child dyads from Gen3G, a large prospective population-based cohort. First, we built a genetic risk score to capture maternal glycemia based on 10 known glycemic genetic variants (GRS10) and showed it was an adequate instrumental variable (β = 0.046 mmol/L of maternal fasting glucose per additional risk allele; SE = 0.007; P = 7.8 × 10(-11); N = 467). A higher GRS10 was associated with lower methylation levels at cg12083122 located near LEP (β = -0.072 unit per additional risk allele; SE = 0.04; P = 0.05; N = 166). Direction and effect size of association between the instrumental variable GRS10 and methylation at cg12083122 were consistent with the negative association we observed using measured maternal glycemia. Lower DNA methylation levels at cg12083122 were associated with higher cord blood leptin levels (β = -0.17 log of cord blood leptin per unit; SE = 0.07; P = 0.01; N = 170). Our study supports that maternal glycemia is part of causal pathways influencing offspring leptin epigenetic regulation.

Temperament and depressive symptoms: what is the direction of the association?

BACKGROUND

Temperament characteristics have been suggested to be associated with mental health outcomes, especially depression, but the direction of the association is unknown. In this study, we tested whether temperament characteristics, as defined by the Buss-Plomin adulthood emotionality-activity-sociability (EAS) temperament model, predict depressive symptoms or whether depressive symptoms predict changes in temperament characteristics.

METHODS

Participants comprised a population-based sample of 719 men and 1020 women from the Young Finns study aged 20-35 years at baseline in 1997 and who responded to repeated surveys of temperament and depressive symptoms in four study phases from 1997 to 2012. The associations were tested using linear regression models, repeated cross-lagged structural equation models, parallel latent growth curve models and two-dimensional continuous-time state space model (Exact Discrete Model).

RESULTS

Both low sociability (β=-0.12, p<0.001) and high negative emotionality (β=0.34, p<0.001) predicted subsequent increased depressive symptoms, whereas earlier depressive symptoms predicted increased negative emotionality (β=0.50, p<0.001), but not low sociability.

LIMITATIONS

The depressive symptoms scale applied may not be used for measuring clinically recognized depression.

CONCLUSIONS

Our findings suggest that the direction of the association is from low sociability to depressive symptoms rather than the reverse, but the association between negative emotionality and depressive symptoms seems to be reciprocal.