A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance

Insights into the genetic basis of type 2 diabetes

Type 2 diabetes is one of the most common complex diseases, of which considerable efforts have been made to unravel the pathophysiological mechanisms. Recently, large‐scale genome‐wide association (GWA) studies have successfully identified genetic loci robustly associated with type 2 diabetes by searching susceptibility variants across the entire genome in an unbiased, hypothesis‐free manner. The number of loci has climbed from just three in 2006 to approximately 70 today. For the common type 2 diabetes‐associated variants, three features have been noted. First, genetic impacts of individual variants are generally modest; mostly, allelic odds ratios range between 1.06 and 1.20. Second, most of the loci identified to date are not in or near obvious candidate genes, but some are often located in the intergenic regions. Third, although the number of loci is limited, there might be some population specificity in type 2 diabetes association. Although we can estimate a single or a few target genes for individual loci detected in GWA studies by referring to the data for experiments in vitro, biological function remains largely unknown for a substantial part of such target genes. Nevertheless, new biology is arising from GWA study discoveries; for example, genes implicated in β‐cell dysfunction are over‐represented within type 2 diabetes‐associated regions. Toward translational advances, we have just begun to face new challenges – elucidation of multifaceted (i.e., molecular, cellular and physiological) mechanistic insights into disease biology by considering interaction with the environment. The present review summarizes recent advances in the genetics of type 2 diabetes, together with its realistic potential.

Sex-specific association of the peptidase D gene rs731839 polymorphism and serum lipid levels in the Mulao and Han populations

Little is known about the association of peptidase D (PEPD) gene rs731839 single nucleotide polymorphism (SNP) and serum lipid profiles in the Chinese population. The objective of the present study was to detect the association of the PEPD rs731839 SNP and serum lipid levels in the Mulao and Han populations. Genotyping of the PEPD rs731839 SNP was performed in 751 subjects of Mulao and 762 subjects of Han using polymerase chain reaction and restriction fragment length polymorphism and then confirmed by direct sequencing. The A allele carriers had higher serum high-density lipoprotein cholesterol (HDL-C), apolipoprotein (Apo) AI levels and lower triglyceride (TG) levels in Mulao; and higher HDL-C, low-density lipoprotein cholesterol (LDL-C) and ApoAI levels in Han than the A allele non-carriers. Subgroup analyses showed that the A allele carriers had higher HDL-C, ApoAI levels and lower TG levels in Mulao males but not in females; higher total cholesterol (TC), HDL-C, LDL-C and ApoAI levels in Han males; and higher TG, HDL-C and ApoAI levels in Han females than the A allele non-carriers. Serum lipid parameters were also correlated with several environmental factors in Mulao and Han populations, or in males and females in both ethnic groups. The association of the PEPD rs731839 SNP and serum lipid levels was different between the Mulao and Han populations, and between males and females in the both ethnic groups. There may be an ethnic- and/or sex-specific association of the PEPD rs731839 SNP and serum lipid levels in our study populations.

Polygenic type 2 diabetes prediction at the limit of common variant detection

Genome-wide association studies (GWAS) may have reached their limit of detecting common type 2 diabetes (T2D)-associated genetic variation. We evaluated the performance of current polygenic T2D prediction. Using data from the Framingham Offspring (FOS) and the Coronary Artery Risk Development in Young Adults (CARDIA) studies, we tested three hypotheses: 1) a 62-locus genotype risk score (GRSt) improves T2D prediction compared with previous less inclusive GRSt; 2) separate GRS for β-cell (GRSβ) and insulin resistance (GRSIR) independently predict T2D; and 3) the relationships between T2D and GRSt, GRSβ, or GRSIR do not differ between blacks and whites. Among 1,650 young white adults in CARDIA, 820 young black adults in CARDIA, and 3,471 white middle-aged adults in FOS, cumulative T2D incidence was 5.9%, 14.4%, and 12.9%, respectively, over 25 years. The 62-locus GRSt was significantly associated with incident T2D in all three groups. In FOS but not CARDIA, the 62-locus GRSt improved the model C statistic (0.698 and 0.726 for models without and with GRSt, respectively; P < 0.001) but did not materially improve risk reclassification in either study. Results were similar among blacks compared with whites. The GRSβ but not GRSIR predicted incident T2D among FOS and CARDIA whites. At the end of the era of common variant discovery for T2D, polygenic scores can predict T2D in whites and blacks but do not outperform clinical models. Further optimization of polygenic prediction may require novel analytic methods, including less common as well as functional variants.

Association of levels of fasting glucose and insulin with rare variants at the chromosome 11p11.2-MADD locus: Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study

BACKGROUND

Common variation at the 11p11.2 locus, encompassing MADD, ACP2, NR1H3, MYBPC3, and SPI1, has been associated in genome-wide association studies with fasting glucose and insulin (FI). In the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study, we sequenced 5 gene regions at 11p11.2 to identify rare, potentially functional variants influencing fasting glucose or FI levels.

METHODS AND RESULTS

Sequencing (mean depth, 38×) across 16.1 kb in 3566 individuals without diabetes mellitus identified 653 variants, 79.9% of which were rare (minor allele frequency <1%) and novel. We analyzed rare variants in 5 gene regions with FI or fasting glucose using the sequence kernel association test. At NR1H3, 53 rare variants were jointly associated with FI (P=2.73×10(-3)); of these, 7 were predicted to have regulatory function and showed association with FI (P=1.28×10(-3)). Conditioning on 2 previously associated variants at MADD (rs7944584, rs10838687) did not attenuate this association, suggesting that there are >2 independent signals at 11p11.2. One predicted regulatory variant, chr11:47227430 (hg18; minor allele frequency=0.00068), contributed 20.6% to the overall sequence kernel association test score at NR1H3, lies in intron 2 of NR1H3, and is a predicted binding site for forkhead box A1 (FOXA1), a transcription factor associated with insulin regulation. In human HepG2 hepatoma cells, the rare chr11:47227430 A allele disrupted FOXA1 binding and reduced FOXA1-dependent transcriptional activity.

CONCLUSIONS

Sequencing at 11p11.2-NR1H3 identified rare variation associated with FI. One variant, chr11:47227430, seems to be functional, with the rare A allele reducing transcription factor FOXA1 binding and FOXA1-dependent transcriptional activity.

Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity

Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.

Routes for breaching and protecting genetic privacy

We are entering an era of ubiquitous genetic information for research, clinical care and personal curiosity. Sharing these data sets is vital for progress in biomedical research. However, a growing concern is the ability to protect the genetic privacy of the data originators. Here, we present an overview of genetic privacy breaching strategies. We outline the principles of each technique, indicate the underlying assumptions, and assess their technological complexity and maturation. We then review potential mitigation methods for privacy-preserving dissemination of sensitive data and highlight different cases that are relevant to genetic applications.

Replication of 6 obesity genes in a meta-analysis of genome-wide association studies from diverse ancestries

Obesity is a major public health problem with a significant genetic component. Multiple DNA polymorphisms/genes have been shown to be strongly associated with obesity, typically in populations of European descent. The aim of this study was to verify the extent to which 6 confirmed obesity genes (FTO, CTNNBL1, ADRB2, LEPR, PPARG and UCP2 genes) could be replicated in 8 different samples (n = 11,161) and to explore whether the same genes contribute to obesity-susceptibility in populations of different ancestries (five Caucasian, one Chinese, one African-American and one Hispanic population). GWAS-based data sets with 1000 G imputed variants were tested for association with obesity phenotypes individually in each population, and subsequently combined in a meta-analysis. Multiple variants at the FTO locus showed significant associations with BMI, fat mass (FM) and percentage of body fat (PBF) in meta-analysis. The strongest association was detected at rs7185735 (P-value = 1.01×10(-7) for BMI, 1.80×10(-6) for FM, and 5.29×10(-4) for PBF). Variants at the CTNNBL1, LEPR and PPARG loci demonstrated nominal association with obesity phenotypes (meta-analysis P-values ranging from 1.15×10(-3) to 4.94×10(-2)). There was no evidence of association with variants at ADRB2 and UCP2 genes. When stratified by sex and ethnicity, FTO variants showed sex-specific and ethnic-specific effects on obesity traits. Thus, it is likely that FTO has an important role in the sex- and ethnic-specific risk of obesity. Our data confirmed the role of FTO, CTNNBL1, LEPR and PPARG in obesity predisposition. These findings enhanced our knowledge of genetic associations between these genes and obesity-related phenotypes, and provided further justification for pursuing functional studies of these genes in the pathophysiology of obesity. Sex and ethnic differences in genetic susceptibility across populations of diverse ancestries may contribute to a more targeted prevention and customized treatment of obesity.

Dietary insulin index and insulin load in relation to endometrial cancer risk in the Nurses' Health Study

BACKGROUND

Although unopposed estrogen exposure is considered the main driver of endometrial carcinogenesis, factors associated with states of insulin resistance and hyperinsulinemia are independently associated with endometrial cancer risk. We used dietary insulin load and insulin index scores to represent the estimated insulin demand of overall diets and assessed their association with endometrial cancer risk in the prospective Nurses' Health Study.

METHODS

We estimated incidence rate ratios (RR) and 95% confidence intervals (CI) for risk of invasive endometrial cancer using Cox proportional hazards models. Between the baseline dietary questionnaire (1980) and 2010, we identified a total of 798 incident-invasive epithelial endometrial adenocarcinomas over 1,417,167 person-years of follow-up.

RESULTS

Dietary insulin scores were not associated with overall risk of endometrial cancer. Comparing women in the highest with the lowest quintile, the multivariable-adjusted RRs of endometrial cancer were 1.07 (95% CI, 0.84-1.35) for cumulative average dietary insulin load and 1.03 (95% CI, 0.82-1.31) for cumulative average dietary insulin index. Findings did not vary substantially by alcohol consumption, total dietary fiber intake, or body mass index and/or physical activity (P(heterogeneity) ≥ 0.10).

CONCLUSIONS

Intake of a diet predicted to stimulate a high postprandial insulin response was not associated with endometrial cancer risk in this large prospective study. Considering the complex interplay of diet, lifestyle, and genetic factors contributing to the hyperinsulinemic state, dietary measures alone may not sufficiently capture absolute long-term insulin exposure.

IMPACT

This study is the first to investigate dietary insulin scores in relation to endometrial cancer risk.

Systems genetics: From GWAS to disease pathways

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.

Association of adiponectin (ADIPOQ) rs2241766 polymorphism and dyslipidemia in HIV/HCV-coinfected patients

BACKGROUND

The adiponectin (ADIPOQ) rs2241766 polymorphism is related to metabolic abnormalities. The aim of this study was to evaluate the association of the ADIPOQ rs2241766 polymorphism with serum dyslipidemia and insulin resistance (IR) in human immunodeficiency virus (HIV)/hepatitis C virus (HCV)-coinfected patients.

METHODS

We carried out a cross-sectional study on 262 patients. ADIPOQ rs2241766 polymorphisms were genotyped by GoldenGate® assay. Generalized linear models (GLMs) were used to compare continuous outcome variables (total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), non-HDL-C and homeostatic model assessment (HOMA)) and categorical outcome variables (TC≥200 mg/dL, TG≥170 mg/dL, LDL-C≥100 mg/dL, HDL-C≤35 mg/dL, non-HDL-C≥120 mg/dL and HOMA≥3·8) according to ADIPOQ genotype under a dominant inheritance model.

RESULTS

Patients with the rs2241766 GG/GT genotype had significantly lower serum TC levels (P=0·038) and percentages of TC≥200 mg/dL (P=0·022) than rs2241766 TT carriers. When adjusted GLM was performed, rs2241766 GG/GT was associated with low serum TC levels (arithmetic mean ratio (AMR)=0·92 [(95% CI=0·85; 0·99) P=0·024]) and low likelihood of TC≥200 mg/dL (odds ratio (OR)=0·32 [(95% CI=0·11; 0·88) P=0·027]. When stratifying by steatosis, no significant values were found for patients without steatosis. However, for patients with steatosis, rs2241766 GG/GT genotypes were related to low TC serum values of TC (AMR=0·89; P=0·027), LDL-C (AMR=0·85; P=0·039) and non-HDL-C (AMR=0·86; P=0·015). No significant associations were found between rs2241766 and HOMA values.

CONCLUSIONS

The presence of the ADIPOQ rs2241766 G allele (GG/GT genotype) was associated with a protective effect against dyslipidemia, primarily in HIV/HCV-coinfected patients with steatosis.

The three genetics (nuclear DNA, mitochondrial DNA, and gut microbiome) of longevity in humans considered as metaorganisms

Usually the genetics of human longevity is restricted to the nuclear genome (nDNA). However it is well known that the nDNA interacts with a physically and functionally separated genome, the mitochondrial DNA (mtDNA) that, even if limited in length and number of genes encoded, plays a major role in the ageing process. The complex interplay between nDNA/mtDNA and the environment is most likely involved in phenomena such as ageing and longevity. To this scenario we have to add another level of complexity represented by the microbiota, that is, the whole set of bacteria present in the different part of our body with their whole set of genes. In particular, several studies investigated the role of gut microbiota (GM) modifications in ageing and longevity and an age-related GM signature was found. In this view, human being must be considered as “metaorganism” and a more holistic approach is necessary to grasp the complex dynamics of the interaction between the environment and nDNA-mtDNA-GM of the host during ageing. In this review, the relationship between the three genetics and human longevity is addressed to point out that a comprehensive view will allow the researchers to properly address the complex interactions that occur during human lifespan.

Quality control and conduct of genome-wide association meta-analyses

Rigorous organization and quality control (QC) are necessary to facilitate successful genome-wide association meta-analyses (GWAMAs) of statistics aggregated across multiple genome-wide association studies. This protocol provides guidelines for (i) organizational aspects of GWAMAs, and for (ii) QC at the study file level, the meta-level across studies and the meta-analysis output level. Real-world examples highlight issues experienced and solutions developed by the GIANT Consortium that has conducted meta-analyses including data from 125 studies comprising more than 330,000 individuals. We provide a general protocol for conducting GWAMAs and carrying out QC to minimize errors and to guarantee maximum use of the data. We also include details for the use of a powerful and flexible software package called EasyQC. Precise timings will be greatly influenced by consortium size. For consortia of comparable size to the GIANT Consortium, this protocol takes a minimum of about 10 months to complete.

TGFBI (βIG-H3) is a diabetes-risk gene based on mouse and human genetic studies

Transforming growth factor beta-induced (TGFBI/βIG-H3), also known as βig-H3, is a protein inducible by TGFβ1 and secreted by many cell types. It binds to collagen, forms part of the extracellular matrix and interacts with integrins on the cell surface. Recombinant TGFBI and transgenic TGFBI overexpression can promote both islet survival and function. In this study, we generated TGFBI KO mice and further assessed TGFBI function and signaling pathways in islets. Islets from KO mice were of normal size and quantity, and these animals were normoglycemic. However, KO islet survival and function was compromised in vitro. In vivo, KO donor islets became inferior to wild-type donor islets in achieving normoglycemia when transplanted into KO diabetic recipients. TGFBI KO mice were more prone to straptozotocin-induced diabetes than the wild-type counterpart. Phosphoprotein array analysis established that AKT1S1, a molecule linking the AKT and mTORC1 signaling pathways, was modulated by TGFBI in islets. Phosphorylation of four molecules in the AKT and mTORC1 signaling pathway, i.e. AKT, AKT1S1, RPS6 and EIF4EBP1, was upregulated in islets upon TGFBI stimulation. Suppression of AKT activity by a chemical inhibitor, or knockdown of AKT1S1, RPS6 and EIF4EBP1 expression by small interfering RNA, modulated islet survival, proving the relevance of these molecules in TGFBI-triggered signaling. Human genetic studies revealed that in the TGFBI gene and its vicinity, three single-nucleotide polymorphisms were significantly associated with type 1 diabetes risks, and one with type 2 diabetes risks. Our study suggests that TGFBI is a potential risk gene for human diabetes.

mtDNA mutations in human aging and longevity: controversies and new perspectives opened by high-throughput technologies

The last 30 years of research greatly contributed to shed light on the role of mitochondrial DNA (mtDNA) variability in aging, although contrasting results have been reported, mainly due to bias regarding the population size and stratification, and to the use of analysis methods (haplogroup classification) that resulted to be not sufficiently adequate to grasp the complexity of the phenomenon. A 5-years European study (the GEHA EU project) collected and analyzed data on mtDNA variability on an unprecedented number of long-living subjects (enriched for longevity genes) and a comparable number of controls (matched for gender and ethnicity) in Europe. This very large study allowed a reappraisal of the role of both the inherited and the somatic mtDNA variability in aging, as an association with longevity emerged only when mtDNA variants in OXPHOS complexes co-occurred. Moreover, the availability of data from both nuclear and mitochondrial genomes on a large number of subjects paves the way for an evaluation at a very large scale of the epistatic interactions at a higher level of complexity. This scenario is expected to be even more clarified in the next future with the use of next generation sequencing (NGS) techniques, which are becoming applicable to evaluate mtDNA variability and, then, new mathematical/bioinformatic analysis methods are urgently needed. Recent advances of association studies on age-related diseases and mtDNA variability will also be discussed in this review, taking into account the bias hidden by population stratification. Finally, very recent findings in terms of mtDNA heteroplasmy (i.e. the coexistence of wild type and mutated copies of mtDNA) and aging as well as mitochondrial epigenetic mechanisms will also be discussed.

Joint analysis of functional genomic data and genome-wide association studies of 18 human traits

Annotations of gene structures and regulatory elements can inform genome-wide association studies (GWASs). However, choosing the relevant annotations for interpreting an association study of a given trait remains challenging. I describe a statistical model that uses association statistics computed across the genome to identify classes of genomic elements that are enriched with or depleted of loci influencing a trait. The model naturally incorporates multiple types of annotations. I applied the model to GWASs of 18 human traits, including red blood cell traits, platelet traits, glucose levels, lipid levels, height, body mass index, and Crohn disease. For each trait, I used the model to evaluate the relevance of 450 different genomic annotations, including protein-coding genes, enhancers, and DNase-I hypersensitive sites in over 100 tissues and cell lines. The fraction of phenotype-associated SNPs influencing protein sequence ranged from around 2% (for platelet volume) up to around 20% (for low-density lipoprotein cholesterol), repressed chromatin was significantly depleted for SNPs associated with several traits, and cell-type-specific DNase-I hypersensitive sites were enriched with SNPs associated with several traits (for example, the spleen in platelet volume). Finally, reweighting each GWAS by using information from functional genomics increased the number of loci with high-confidence associations by around 5%.

Enhancing the power to detect low-frequency variants in genome-wide screens

In genetic association studies a conventional test statistic is proportional to the correlation coefficient between the trait and the variant, with the result that it lacks power to detect association for low-frequency variants. Considering the link between the conventional association test statistics and the linkage disequilibrium measure r(2), we propose a test statistic analogous to the standardized linkage disequilibrium D' to increase the power of detecting association for low-frequency variants. By both simulation and real data analysis we show that the proposed D' test is more powerful than the conventional methods for detecting association for low-frequency variants in a genome-wide setting. The optimal coding strategy for the D' test and its asymptotic properties are also investigated. In summary, we advocate using the D' test in a dominant model as a complementary approach to enhancing the power of detecting association for low-frequency variants with moderate to large effect sizes in case-control genome-wide association studies.

Nutrigenetics: bridging two worlds to understand type 2 diabetes

The increasing global prevalence of type 2 diabetes mellitus (T2DM) is a major public health concern. Accumulating data provides strong evidence of the shared contribution of genetic and environmental factors to T2DM risk. Genome-wide association studies have hugely improved our understanding of the genetic basis of T2DM. However, it is obvious that genetics only partly account for an individuals' predisposition to T2DM. The dietary environment has changed remarkably over the last century. Examination of individual macronutrients and more recently of foods and dietary patterns is becoming increasingly important in terms of developing public health strategies. Nutrigenetics offers the potential to improve diet-related disease prevention and therapy, but is not without its own challenges. In this review we present evidence on the dietary environment and genetics as risk factors for T2DM and bridging the 2 disciplines we highlight some key gene-nutrient interactions.

A central role for GRB10 in regulation of islet function in man

Variants in the growth factor receptor-bound protein 10 (GRB10) gene were in a GWAS meta-analysis associated with reduced glucose-stimulated insulin secretion and increased risk of type 2 diabetes (T2D) if inherited from the father, but inexplicably reduced fasting glucose when inherited from the mother. GRB10 is a negative regulator of insulin signaling and imprinted in a parent-of-origin fashion in different tissues. GRB10 knock-down in human pancreatic islets showed reduced insulin and glucagon secretion, which together with changes in insulin sensitivity may explain the paradoxical reduction of glucose despite a decrease in insulin secretion. Together, these findings suggest that tissue-specific methylation and possibly imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis. The data also emphasize the need in genetic studies to consider whether risk alleles are inherited from the mother or the father.

Genetic Testing and Type 2 Diabetes Risk Awareness

PURPOSE

The purpose of this study was to examine the motivational, attitudinal, and behavioral predictors of interest in genetic testing (GT) in those with and without awareness of their risk for type 2 diabetes (T2DM).

METHODS

A convenience sample of adults visiting emergency departments, libraries, or an online research registry was surveyed. Responses from adults without diabetes who reported 1 or more risk factors for T2DM (eg, family history, body mass index > 25) were included in the analyses (n = 265).

RESULTS

Participants were 37 ± 11 years old, white (54%), and female (69%), with some college education (53%) and an annual income below $25 000 (44%). Approximately half (52%) expressed interest in GT for T2DM. Individuals were stratified by perceived risk for T2DM (risk aware or risk unaware). Among the risk aware, younger age (P < .04) predicted greater interest in GT. Among the risk unaware, family history of T2DM (P < .008) and preference to know genetic risk (P < .0002) predicted interest in GT. Both groups identified the need for low-cost GT.

CONCLUSIONS

GT is an increasingly available and accurate tool to predict T2DM risk for patients. In this sample, GT was a salient tool for those with and without awareness of their T2DM risk. Financial accessibility is critical to use of this tool for both groups.

Cross-sectional and longitudinal replication analyses of genome-wide association loci of type 2 diabetes in Han Chinese

This study aimed to examine genomic loci of type 2 diabetes (T2D) initially identified by genome-wide association studies in populations of European ancestry for their associations with T2D and quantitative glycemic traits, as well as their effects on longitudinal change in fasting plasma glucose (FPG) and T2D development, in the Chinese population. Single nucleotide polymorphisms (SNP) from 25 loci were genotyped in a large case-control sample of 10,001 subjects (5,338 T2D cases and 4,663 controls) and a prospective cohort of 1,881 Chinese. In the case-control sample, 8 SNPs in or near WFS1, CDKAL1, CDKN2A/2B, CDC123, HHEX, TCF7L2, KCNQ1, and MTNR1B were significantly associated with T2D (P<0.05). Thirteen SNPs were associated with quantitative glycemic traits. For example, the most significant SNP, rs10811661 near CDKN2A/2B (P = 1.11×10⁻⁸ for T2D), was also associated with 2-h glucose level of an oral glucose tolerance test (P = 9.11×10⁻³) and insulinogenic index (P = 2.71×10⁻²). In the cohort study, individuals carrying more risk alleles of the replicated SNPs had greater FPG increase and T2D incidence in a 7.5-year follow-up period, with each quartile increase in the number of risk alleles being associated with a 0.06 mmol/l greater increase in FPG (P = 0.03) and 19% higher odds of developing T2D (P = 0.058). Our study identified the associations of several established T2D-loci in Europeans with T2D and quantitative glycemic traits in the Chinese population. The prospective data also suggest their potential role in the risk prediction of T2D in the Chinese population.

IL28RA polymorphism (rs10903035) is associated with insulin resistance in HIV/HCV-coinfected patients

Hepatitis C virus (HCV) infection is associated with insulin resistance (IR), although mechanisms leading to IR in these patients are not completely understood. The aim of this study was to evaluate the association of interleukin 28B (IL28B) and interleukin 28 receptor alpha (IL28RA) polymorphisms with IR among human immunodeficiency virus (HIV)/HCV-coinfected patients. We carried out a cross-sectional study on 203 patients. IL28B (rs8099917) and IL28RA (rs10903035) polymorphisms were genotyped by GoldenGate(®) assay. IR was defined as homeostatic model assessment (HOMA) values ≥3.00. Univariate and multivariate generalized linear models (GLM) were used to compare HOMA values and the percentage of patients with IR according to IL28B and IL28RA genotypes. In total, 32% (n = 65/203) of the patients had IR. IL28B rs8099917 TT was not significantly associated with HOMA values and IR. In contrast, rs10903035 AA was significantly associated with high HOMA values taking into account all patients (P = 0.024), as well as the subgroups of patients with significant fibrosis (P = 0.047) and infected with HCV genotype 3 (P = 0.024). Additionally, rs10903035 AA was significantly associated with IR (HOMA ≥3.00) in all patients (adjusted odds ratio (aOR) = 2.02; P = 0.034), in patients with significant fibrosis (aOR = 2.86; P = 0.039) and HCV genotype 3 patients (aOR = 4.89; P = 0.031). In conclusions, IL28RA polymorphism (rs10903035) seems to be implicated in the glucose homeostasis because AA genotype increases the likelihood of IR, but this association was different depending on hepatic fibrosis and HCV genotype.

Genetic insights into cardiometabolic risk factors

Many biochemical traits are recognised as risk factors, which contribute to or predict the development of disease. Only a few are in widespread use, usually to assist with treatment decisions and motivate behavioural change. The greatest effort has gone into evaluation of risk factors for cardiovascular disease and/or diabetes, with substantial overlap as 'cardiometabolic' risk. Over the past few years many genome-wide association studies (GWAS) have sought to account for variation in risk factors, with the expectation that identifying relevant polymorphisms would improve our understanding or prediction of disease; others have taken the direct approach of genomic case-control studies for the corresponding diseases. Large GWAS have been published for coronary heart disease and Type 2 diabetes, and also for associated biomarkers or risk factors including body mass index, lipids, C-reactive protein, urate, liver function tests, glucose and insulin. Results are not encouraging for personal risk prediction based on genotyping, mainly because known risk loci only account for a small proportion of risk. Overlap of allelic associations between disease and marker, as found for low density lipoprotein cholesterol and heart disease, supports a causal association, but in other cases genetic studies have cast doubt on accepted risk factors. Some loci show unexpected effects on multiple markers or diseases. An intriguing feature of risk factors is the blurring of categories shown by the correlation between them and the genetic overlap between diseases previously thought of as distinct. GWAS can provide insight into relationships between risk factors, biomarkers and diseases, with potential for new approaches to disease classification.

Epigenome-wide association study of fasting measures of glucose, insulin, and HOMA-IR in the Genetics of Lipid Lowering Drugs and Diet Network study

Known genetic susceptibility loci for type 2 diabetes (T2D) explain only a small proportion of heritable T2D risk. We hypothesize that DNA methylation patterns may contribute to variation in diabetes-related risk factors, and this epigenetic variation across the genome can contribute to the missing heritability in T2D and related metabolic traits. We conducted an epigenome-wide association study for fasting glucose, insulin, and homeostasis model assessment of insulin resistance (HOMA-IR) among 837 nondiabetic participants in the Genetics of Lipid Lowering Drugs and Diet Network study, divided into discovery (N = 544) and replication (N = 293) stages. Cytosine guanine dinucleotide (CpG) methylation at ∼470,000 CpG sites was assayed in CD4(+) T cells using the Illumina Infinium HumanMethylation 450 Beadchip. We fit a mixed model with the methylation status of each CpG as the dependent variable, adjusting for age, sex, study site, and T-cell purity as fixed-effects and family structure as a random-effect. A Bonferroni corrected P value of 1.1 × 10(-7) was considered significant in the discovery stage. Significant associations were tested in the replication stage using identical models. Methylation of a CpG site in ABCG1 on chromosome 21 was significantly associated with insulin (P = 1.83 × 10(-7)) and HOMA-IR (P = 1.60 × 10(-9)). Another site in the same gene was significant for HOMA-IR and of borderline significance for insulin (P = 1.29 × 10(-7) and P = 3.36 × 10(-6), respectively). Associations with the top two signals replicated for insulin and HOMA-IR (P = 5.75 × 10(-3) and P = 3.35 × 10(-2), respectively). Our findings suggest that methylation of a CpG site within ABCG1 is associated with fasting insulin and merits further evaluation as a novel disease risk marker.

Genetics of the first seven proprotein convertase enzymes in health and disease

Members of the substilisin/kexin like proprotein convertase (PCSK) protease family cleave and convert immature pro-proteins into their biologically active forms. By cleaving for example prohormones, cytokines and cell membrane proteins, PCSKs participate in maintaining the homeostasis in a healthy human body. Conversely, erratic enzymatic function is thought to contribute to the pathogenesis of a wide variety of diseases, including obesity and hypercholestrolemia. The first characterized seven PCSK enzymes (PCSK1-2, FURIN, PCSK4-7) process their substrates at a motif made up of paired basic amino acid residues. This feature results in a variable degree of biochemical redundancy in vitro, and consequently, shared substrate molecules between the different PCSK enzymes. This redundancy has confounded our understanding of the specific biological functions of PCSKs. The physiological roles of these enzymes have been best illustrated by the phenotypes of genetically engineered mice and patients that carry mutations in the PCSK genes. Recent developments in genome-wide methodology have generated a large amount of novel information on the genetics of the first seven proprotein convertases. In this review we summarize the reported genetic alterations and their associated phenotypes.

Two novel type 2 diabetes loci revealed through integration of TCF7L2 DNA occupancy and SNP association data

BACKGROUND

The transcription factor 7-like 2 (TCF7L2) locus is strongly implicated in the pathogenesis of type 2 diabetes (T2D). We previously mapped the genomic regions bound by TCF7L2 using ChIP (chromatin immunoprecipitation)-seq in the colorectal carcinoma cell line, HCT116, revealing an unexpected highly significant over-representation of genome-wide association studies (GWAS) loci associated primarily with endocrine (in particular T2D) and cardiovascular traits.

METHODS

In order to further explore if this observed phenomenon occurs in other cell lines, we carried out ChIP-seq in HepG2 cells and leveraged ENCODE data for five additional cell lines. Given that only a minority of the predicted genetic component to most complex traits has been identified to date, plus our GWAS-related observations with respect to TCF7L2 occupancy, we investigated if restricting association analyses to the genes yielded from this approach, in order to reduce the constraints of multiple testing, could reveal novel T2D loci.

RESULTS

We found strong evidence for the continued enrichment of endocrine and cardiovascular GWAS categories, with additional support for cancer. When investigating all the known GWAS loci bound by TCF7L2 in the shortest gene list, derived from HCT116, the coronary artery disease-associated variant, rs46522 at the UBE2Z-GIP-ATP5G1-SNF8 locus, yielded significant association with T2D within DIAGRAM. Furthermore, when we analyzed tag-SNPs (single nucleotide polymorphisms) in genes not previously implicated by GWAS but bound by TCF7L2 within 5 kb, we observed a significant association of rs4780476 within CPPED1 in DIAGRAM.

CONCLUSIONS

ChIP-seq data generated with this GWAS-implicated transcription factor provided a biologically plausible method to limit multiple testing in the assessment of genome-wide genotyping data to uncover two novel T2D-associated loci.

Current Insights into the Joint Genetic Basis of Type 2 Diabetes and Coronary Heart Disease

The large-scale genome-wide association studies conducted so far identified numerous allelic variants associated with type 2 diabetes (T2D), coronary heart disease (CHD) and related cardiometabolic traits. Many T2D- and some CHD-risk loci are also linked with metabolic traits that are hallmarks of insulin resistance (lipid profile, abdominal adiposity). Chromosome 9p21.3 and 2q36.3 are the most consistently replicated loci appearing to share genetic risk for both T2D and CHD. Although many glucose- or insulin-related trait variants are also linked with T2D risk, none of them is associated with CHD. Hence, while T2D and CHD are strongly clinically linked together, further ongoing analyses are needed to clarify the existence of a shared underlying genetic signature of these complex traits. The present review summarizes an updated picture of T2D-CHD genetics as of 2013, aiming to provide a platform for targeted studies dissecting the contribution of genetics to the phenotypic heterogeneity of T2D and CHD.

Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome

The complex aetiology of type 2 diabetes makes effective screening, diagnosis and prognosis a substantial challenge for the physician. The rapidly developing area of metabolomics, which uses analytical techniques such as mass spectrometry and nuclear magnetic resonance, has emerged as a promising approach to identify biomarkers of diabetes and the insulin-resistant state that precedes overt pathology. Initial successes with metabolomic studies have indicated potential biomarkers for insulin resistance and for identifying people at risk of developing diabetes, with particular focus on aminoacids and lipid metabolism. These biomarkers will help to improve research and management of diabetes. In particular, several biomarkers identified could be used for early identification of diabetes risk. Furthermore, changes in selected biomarkers can indicate effectiveness of therapeutic interventions for type 2 diabetes and the metabolic syndrome. Indeed, there is much promise that branched-chain aminoacids might provide a screening biomarker for type 2 diabetes risk, allowing early dietary and exercise interventions to treat or even prevent the disease.

Insights into the genetic susceptibility to type 2 diabetes from genome-wide association studies of glycaemic traits

Over the past 8 years, the genetics of complex traits have benefited from an unprecedented advancement in the identification of common variant loci for diseases such as type 2 diabetes (T2D). The ability to undertake genome-wide association studies in large population-based samples for quantitative glycaemic traits has permitted us to explore the hypothesis that models arising from studies in non-diabetic individuals may reflect mechanisms involved in the pathogenesis of diabetes. Amongst 88 T2D risk and 72 glycaemic trait loci, only 29 are shared and show disproportionate magnitudes of phenotypic effects. Important mechanistic insights have been gained regarding the physiological role of T2D loci in disease predisposition through the elucidation of their contribution to glycaemic trait variability. Further investigation is warranted to define causal variants within these loci, including functional characterisation of associated variants, to dissect their role in disease mechanisms and to enable clinical translation.

Findings from the Quebec Family Study on the Etiology of Obesity: Genetics and Environmental Highlights

The Quebec Family Study (QFS) was an observational study with three cycles of data collection between 1979 and 2002 in Quebec City, Canada. The cohort is a mixture of random sampling and ascertainment through obese individuals. The study has significantly contributed to our understanding of the determinants of obesity and associated disease risk over the past 35 years. In particular, the QFS cohort was used to investigate the contribution of familial resemblance and genetic effects on body fatness and behaviors related to energy balance. Significant familial aggregation and genetic heritability were reported for total adiposity, fat-free mass, subcutaneous fat distribution, abdominal and visceral fat, resting metabolic rate, physical activity level and other behavioral traits. The resources of QFS were also used to study the contribution of several nontraditional (non-caloric) risk factors as predictors of excess body weight and gains in weight and adiposity over time, including low calcium and micronutrient intake, high disinhibition eating behavior trait, and short sleep duration. An important finding relates to the interactions between dietary macronutrient intake and exercise intensity on body mass and adiposity.

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

The incidence of type 2 diabetes can be reduced substantially by implementing preventive measures in high-risk individuals, but this requires prior knowledge of disease risk in the individual. Various diabetes risk models have been designed, and these have all included a similar combination of factors, such as age, sex, obesity, hypertension, lifestyle factors, family history of diabetes and metabolic traits. The accuracy of prediction models is often assessed by the area under the receiver operating characteristic curve (AROC) as a measure of discrimination, but AROCs should be complemented by measures of calibration and reclassification to estimate the incremental value of novel biomarkers. This review discusses the potential of novel biomarkers to improve model accuracy. The range of molecules that serve as potential predictors of type 2 diabetes includes genetic variants, RNA transcripts, peptides and proteins, lipids and small metabolites. Some of these biomarkers lead to a statistically significant increase of model accuracy, but their incremental value currently seems too small for routine clinical use. However, only a fraction of potentially relevant biomarkers have been assessed with regard to their predictive value. Moreover, serial measurements of biomarkers may help determine individual risk. In conclusion, current risk models provide valuable tools of risk estimation, but perform suboptimally in the prediction of individual diabetes risk. Novel biomarkers still fail to have a clinically applicable impact. However, more efficient use of biomarker data and technological advances in their measurement in clinical settings may allow the development of more accurate predictive models in the future.

The architecture of risk for type 2 diabetes: understanding Asia in the context of global findings

The prevalence of Type 2 diabetes is rising rapidly in both developed and developing countries. Asia is developing as the epicentre of the escalating pandemic, reflecting rapid transitions in demography, migration, diet, and lifestyle patterns. The effective management of Type 2 diabetes in Asia may be complicated by differences in prevalence, risk factor profiles, genetic risk allele frequencies, and gene-environment interactions between different Asian countries, and between Asian and other continental populations. To reduce the worldwide burden of T2D, it will be important to understand the architecture of T2D susceptibility both within and between populations. This review will provide an overview of known genetic and nongenetic risk factors for T2D, placing the results from Asian studies in the context of broader global research. Given recent evidence from large-scale genetic studies of T2D, we place special emphasis on emerging knowledge about the genetic architecture of T2D and the potential contribution of genetic effects to population differences in risk.

Diversity of human tRNA genes from the 1000-genomes project

The sequence diversity of individual human genomes has been extensively analyzed for variations and phenotypic implications for mRNA, miRNA, and long non-coding RNA genes. TRNA (tRNA) also exhibits large sequence diversity in the human genome, but tRNA gene sequence variation and potential functional implications in individual human genomes have not been investigated. Here we capitalize on the sequencing data from the 1000-genomes project to examine the diversity of tRNA genes in the human population. Previous analysis of the reference human genome indicated an unexpected large number of diverse tRNA genes beyond the necessity of translation, suggesting that some tRNA transcripts may perform non-canonical functions. We found 24 new tRNA sequences in>1% and 76 new tRNA sequences in>0.2% of all individuals, indicating that tRNA genes are also subject to evolutionary changes in the human population. Unexpectedly, two abundant new tRNA genes contain base-pair mismatches in the anticodon stem. We experimentally determined that these two new tRNAs have altered structures in vitro; however, one new tRNA is not aminoacylated but extremely stable in HeLa cells, suggesting that this new tRNA can be used for non-canonical function. Our results show that at the scale of human population, tRNA genes are more diverse than conventionally understood, and some new tRNAs may perform non-canonical, extra-translational functions that may be linked to human health and disease.

Macrophages, fat, and the emergence of immunometabolism

Over the last century, our modern concepts and understanding of metabolism and immunology have evolved largely in parallel. Notably, during the last decade, there has been a sharpened focus on the convergence of metabolism and immune function. In part motivated by studies originally published in the JCI, we now recognize that the immune system monitors the metabolic state of tissues and organisms and responds in kind by modulating metabolic function. The complexity of these interactions, both adaptive and pathologic, continues to be studied and revealed, with the hope that harnessing the reins that control immune function may one day be used for metabolic benefit.

The SH2B1 obesity locus and abnormal glucose homeostasis: lack of evidence for association from a meta-analysis in individuals of European ancestry

BACKGROUND/AIMS

The development of type 2 diabetes (T2D) is influenced both by environmental and by genetic determinants. Obesity is an important risk factor for T2D, mostly mediated by obesity-related insulin resistance. Obesity and insulin resistance are also modulated by the genetic milieu; thus, genes affecting risk of obesity and insulin resistance might also modulate risk of T2D. Recently, 32 loci have been associated with body mass index (BMI) by genome-wide studies, including one locus on chromosome 16p11 containing the SH2B1 gene. Animal studies have suggested that SH2B1 is a physiological enhancer of the insulin receptor and humans with rare deletions or mutations at SH2B1 are obese with a disproportionately high insulin resistance. Thus, the role of SH2B1 in both obesity and insulin resistance makes it a strong candidate for T2D. However, published data on the role of SH2B1 variability on the risk for T2D are conflicting, ranging from no effect at all to a robust association.

METHODS

The SH2B1 tag SNP rs4788102 (SNP, single nucleotide polymorphism) was genotyped in 6978 individuals from six studies for abnormal glucose homeostasis (AGH), including impaired fasting glucose, impaired glucose tolerance or T2D, from the GENetics of Type 2 Diabetes in Italy and the United States (GENIUS T2D) consortium. Data from these studies were then meta-analyzed, in a Bayesian fashion, with those from DIAGRAM+ (n = 47,117) and four other published studies (n = 39,448).

RESULTS

Variability at the SH2B1 obesity locus was not associated with AGH either in the GENIUS consortium (overall odds ratio (OR) = 0.96; 0.89-1.04) or in the meta-analysis (OR = 1.01; 0.98-1.05).

CONCLUSION

Our data exclude a role for the SH2B1 obesity locus in the modulation of AGH.

Clinical neurogenetics: stroke

Understanding the genetic architecture of cerebrovascular disease holds promise of novel stroke prevention strategies and therapeutics that are both safe and effective. Apart from a few single-gene disorders associated with cerebral ischemia or intracerebral hemorrhage, stroke is a complex genetic phenotype that requires careful ascertainment and robust association testing for discovery and validation analyses. The recently uncovered shared genetic contribution between clinically manifest stroke syndromes and closely related intermediate cerebrovascular phenotypes offers effective and efficient approaches to complex trait analysis.

Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report

Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF] > 0.05) and less common (0.01 < MAF < 0.05) genetic variants associated with cancer. The marginal effects of most of these variants have been small (odds ratios: 1.1-1.4). There remain unanswered questions on how best to incorporate the joint effects of genes and environment, including gene-environment (G × E) interactions, into epidemiologic studies of cancer. To help address these questions, and to better inform research priorities and allocation of resources, the National Cancer Institute sponsored a "Gene-Environment Think Tank" on January 10-11, 2012. The objective of the Think Tank was to facilitate discussions on (1) the state of the science, (2) the goals of G × E interaction studies in cancer epidemiology, and (3) opportunities for developing novel study designs and analysis tools. This report summarizes the Think Tank discussion, with a focus on contemporary approaches to the analysis of G × E interactions. Selecting the appropriate methods requires first identifying the relevant scientific question and rationale, with an important distinction made between analyses aiming to characterize the joint effects of putative or established genetic and environmental factors and analyses aiming to discover novel risk factors or novel interaction effects. Other discussion items include measurement error, statistical power, significance, and replication. Additional designs, exposure assessments, and analytical approaches need to be considered as we move from the current small number of success stories to a fuller understanding of the interplay of genetic and environmental factors.

Using genetic prediction from known complex disease Loci to guide the design of next-generation sequencing experiments

A central focus of complex disease genetics after genome-wide association studies (GWAS) is to identify low frequency and rare risk variants, which may account for an important fraction of disease heritability unexplained by GWAS. A profusion of studies using next-generation sequencing are seeking such risk alleles. We describe how already-known complex trait loci (largely from GWAS) can be used to guide the design of these new studies by selecting cases, controls, or families who are most likely to harbor undiscovered risk alleles. We show that genetic risk prediction can select unrelated cases from large cohorts who are enriched for unknown risk factors, or multiply-affected families that are more likely to harbor high-penetrance risk alleles. We derive the frequency of an undiscovered risk allele in selected cases and controls, and show how this relates to the variance explained by the risk score, the disease prevalence and the population frequency of the risk allele. We also describe a new method for informing the design of sequencing studies using genetic risk prediction in large partially-genotyped families using an extension of the Inside-Outside algorithm for inference on trees. We explore several study design scenarios using both simulated and real data, and show that in many cases genetic risk prediction can provide significant increases in power to detect low-frequency and rare risk alleles. The same approach can also be used to aid discovery of non-genetic risk factors, suggesting possible future utility of genetic risk prediction in conventional epidemiology. Software implementing the methods in this paper is available in the R package Mangrove.

Mendelian randomization studies do not support a causal role for reduced circulating adiponectin levels in insulin resistance and type 2 diabetes

Adiponectin is strongly inversely associated with insulin resistance and type 2 diabetes, but its causal role remains controversial. We used a Mendelian randomization approach to test the hypothesis that adiponectin causally influences insulin resistance and type 2 diabetes. We used genetic variants at the ADIPOQ gene as instruments to calculate a regression slope between adiponectin levels and metabolic traits (up to 31,000 individuals) and a combination of instrumental variables and summary statistics-based genetic risk scores to test the associations with gold-standard measures of insulin sensitivity (2,969 individuals) and type 2 diabetes (15,960 case subjects and 64,731 control subjects). In conventional regression analyses, a 1-SD decrease in adiponectin levels was correlated with a 0.31-SD (95% CI 0.26-0.35) increase in fasting insulin, a 0.34-SD (0.30-0.38) decrease in insulin sensitivity, and a type 2 diabetes odds ratio (OR) of 1.75 (1.47-2.13). The instrumental variable analysis revealed no evidence of a causal association between genetically lower circulating adiponectin and higher fasting insulin (0.02 SD; 95% CI -0.07 to 0.11; N = 29,771), nominal evidence of a causal relationship with lower insulin sensitivity (-0.20 SD; 95% CI -0.38 to -0.02; N = 1,860), and no evidence of a relationship with type 2 diabetes (OR 0.94; 95% CI 0.75-1.19; N = 2,777 case subjects and 13,011 control subjects). Using the ADIPOQ summary statistics genetic risk scores, we found no evidence of an association between adiponectin-lowering alleles and insulin sensitivity (effect per weighted adiponectin-lowering allele: -0.03 SD; 95% CI -0.07 to 0.01; N = 2,969) or type 2 diabetes (OR per weighted adiponectin-lowering allele: 0.99; 95% CI 0.95-1.04; 15,960 case subjects vs. 64,731 control subjects). These results do not provide any consistent evidence that interventions aimed at increasing adiponectin levels will improve insulin sensitivity or risk of type 2 diabetes.

Gene × environment interactions in obesity: the state of the evidence

BACKGROUND/AIMS

Obesity is a pervasive and highly prevalent disease that poses substantial health risks to those it affects. The rapid emergence of obesity as a global epidemic and the patterns and distributions of the condition within and between populations suggest that interactions between inherited biological factors (e.g. genes) and relevant environmental factors (e.g. diet and physical activity) may underlie the current obesity epidemic.

METHODS

We discuss the rationale for the assertion that gene × lifestyle interactions cause obesity, systematically appraise relevant literature, and consider knowledge gaps future studies might seek to bridge.

RESULTS

We identified >200 relevant studies, of which most are relatively small scale and few provide replication data.

CONCLUSION

Although studies on gene × lifestyle interactions in obesity point toward the presence of such interactions, improved data standardization, appropriate pooling of data and resources, innovative study designs, and the application of powerful statistical methods will be required if translatable examples of gene × lifestyle interactions in obesity are to be identified. Future studies, of which most will be observational, should ideally be accompanied by appropriate replication data and, where possible, by analogous findings from experimental settings where clinically relevant traits (e.g. weight regain and weight cycling) are outcomes.

The IRS1 G972R polymorphism and glomerular filtration rate in patients with type 2 diabetes of European ancestry

BACKGROUND

In Mexican Americans, the IRS1 G972R polymorphism (rs1801278) has been associated to such a marked reduction in glomerular filtration rate (GFR) (i.e. β = -8.3 mL/min/1.73 m(2)) to be considered a major determinant of kidney function.

METHODS

This was a cross-sectional study to investigate whether a similarly strong effect can also be observed among individuals of European ancestry. We investigated a total of 3973 White patients with type 2 diabetes. Standardized serum creatinine was measured by the modified kinetic Jaffè reaction and estimated GFR (eGFR) calculated by the modification diet renal disease (MDRD) formula; rs1801278 was genotyped by TaqMan assay.

RESULTS

No significant association was observed, with R972 carriers showing only a modestly, not significant, lower eGFR level as compared with other subjects (β = -1.82 mL/min/1.73 m(2), P = 0.086).

CONCLUSIONS

Our data indicate that IRS1 G972R is not a strong determinant of GFR in diabetic patients of European ancestry as in Mexican Americans. Since we had 100% power to detect the previously reported association, the risk our finding is a false negative one is minimal.

Linkage of type 2 diabetes on chromosome 9p24 in Mexican Americans: additional evidence from the Veterans Administration Genetic Epidemiology Study (VAGES)

OBJECTIVE

Type 2 diabetes (T2DM) is a complex metabolic disease and is more prevalent in certain ethnic groups such as the Mexican Americans. The goal of our study was to perform a genome-wide linkage (GWL) analysis to localize T2DM susceptibility loci in Mexican Americans.

METHODS

We used the phenotypic and genotypic data from 1,122 Mexican-American individuals (307 families) who participated in the Veterans Administration Genetic Epidemiology Study (VAGES). GWL analysis was performed using the variance components approach. Data from 2 additional Mexican-American family studies, the San Antonio Family Heart Study (SAFHS) and the San Antonio Family Diabetes/Gallbladder Study (SAFDGS), were combined with the VAGES data to test for improved linkage evidence.

RESULTS

After adjusting for covariate effects, T2DM was found to be under significant genetic influences (h2 = 0.62, p = 2.7 × 10(-6)). The strongest evidence for linkage of T2DM occurred between markers D9S1871 and D9S2169 on chromosome 9p24.2-p24.1 (LOD = 1.8). Given that we previously reported suggestive evidence for linkage of T2DM at this region also in SAFDGS, we found the significant and increased linkage evidence (LOD = 4.3, empirical p = 1.0 × 10(-5), genome-wide p = 1.6 × 10(-3)) for T2DM at the same chromosomal region, when we performed a GWL analysis of the VAGES data combined with the SAFHS and SAFDGS data.

CONCLUSION

Significant T2DM linkage evidence was found on chromosome 9p24 in Mexican Americans. Importantly, the chromosomal region of interest in this study overlaps with several recent genome-wide association studies involving T2DM-related traits. Given its overlap with such findings and our own initial T2DM association findings in the 9p24 chromosomal region, high throughput sequencing of the linked chromosomal region could identify the potential causal T2DM genes.

Polymorphisms in Fatty Acid Desaturase (FADS) Gene Cluster: Effects on Glycemic Controls Following an Omega-3 Polyunsaturated Fatty Acids (PUFA) Supplementation

Changes in desaturase activity are associated with insulin sensitivity and may be associated with type 2 diabetes mellitus (T2DM). Polymorphisms (SNPs) in the fatty acid desaturase (FADS) gene cluster have been associated with the homeostasis model assessment of insulin sensitivity (HOMA-IS) and serum fatty acid composition. Objective: To investigate whether common genetic variations in the FADS gene cluster influence fasting glucose (FG) and fasting insulin (FI) responses following a 6-week n-3 polyunsaturated fatty acids (PUFA) supplementation. Methods: 210 subjects completed a 2-week run-in period followed by a 6-week supplementation with 5 g/d of fish oil (providing 1.9 g–2.2 g of EPA + 1.1 g of DHA). Genotyping of 18 SNPs of the FADS gene cluster covering 90% of all common genetic variations (minor allele frequency ≥ 0.03) was performed. Results: Carriers of the minor allele for rs482548 (FADS2) had increased plasma FG levels after the n-3 PUFA supplementation in a model adjusted for FG levels at baseline, age, sex, and BMI. A significant genotype*supplementation interaction effect on FG levels was observed for rs482548 (p = 0.008). For FI levels, a genotype effect was observed with one SNP (rs174456). For HOMA-IS, several genotype*supplementation interaction effects were observed for rs7394871, rs174602, rs174570, rs7482316 and rs482548 (p = 0.03, p = 0.01, p = 0.03, p = 0.05 and p = 0.07; respectively). Conclusion: Results suggest that SNPs in the FADS gene cluster may modulate plasma FG, FI and HOMA-IS levels in response to n-3 PUFA supplementation.

Identification of HKDC1 and BACE2 as genes influencing glycemic traits during pregnancy through genome-wide association studies

Maternal metabolism during pregnancy impacts the developing fetus, affecting offspring birth weight and adiposity. This has important implications for metabolic health later in life (e.g., offspring of mothers with pre-existing or gestational diabetes mellitus have an increased risk of metabolic disorders in childhood). To identify genetic loci associated with measures of maternal metabolism obtained during an oral glucose tolerance test at ∼28 weeks' gestation, we performed a genome-wide association study of 4,437 pregnant mothers of European (n = 1,367), Thai (n = 1,178), Afro-Caribbean (n = 1,075), and Hispanic (n = 817) ancestry, along with replication of top signals in three additional European ancestry cohorts. In addition to identifying associations with genes previously implicated with measures of glucose metabolism in nonpregnant populations, we identified two novel genome-wide significant associations: 2-h plasma glucose and HKDC1, and fasting C-peptide and BACE2. These results suggest that the genetic architecture underlying glucose metabolism may differ, in part, in pregnancy.

Genetics of type 2 diabetes

Type 2 diabetes (T2D) is the result of interaction between environmental factors and a strong hereditary component. We review the heritability of T2D as well as the history of genetic and genomic research in this area. Very few T2D risk genes were identified using candidate gene and linkage-based studies, but the advent of genome-wide association studies has led to the identification of multiple genes, including several that were not previously known to play any role in T2D. Highly replicated genes, for example TCF7L2, KCNQ1 and KCNJ11, are discussed in greater detail. Taken together, the genetic loci discovered to date explain only a small proportion of the observed heritability. We discuss possible explanations for this “missing heritability”, including the role of rare variants, gene-environment interactions and epigenetics. The clinical utility of current findings and avenues of future research are also discussed.

Sedentary Behavior and Health Outcomes in Children and Adolescents

The purpose of this review was to summarize findings from epidemiological studies that determined if sedentary behavior was associated with obesity, metabolic risk factors, and cardiorespiratory fitness in children and adolescents. We noted if studies adjusted for moderate-to-vigorous physical activity (MVPA), dietary intakes, and/or sleep duration. Articles were identified through PubMed using the search terms: (sedentary OR sitting OR television) AND (adiposity OR blood pressure OR body mass index OR cardiometabolic OR metabolic risk OR waist circumference). The search was limited to ages 6 to 18 years, humans, and published between January 1, 2008 and September 26, 2012. Cross-sectional and longitudinal studies observed associations between more sedentary behavior, especially screen-based sedentary behavior, and measures of obesity; and most associations were independent of MVPA and dietary intake. Cross-sectional and longitudinal studies reported associations between screen-based sedentary behavior and lower cardiorespiratory fitness, and most associations were independent of MVPA and obesity. Cross-sectional studies observed associations between more screen-based and objectively measured sedentary behavior and lower insulin sensitivity; and most associations were independent of MVPA and obesity. There was little-to-no evidence that sedentary behavior was associated with increased blood pressure and increased blood lipids.

Genetic associations with diabetes: meta-analyses of 10 candidate polymorphisms

Aims

The goal of our study is to investigate the combined contribution of 10 genetic variants to diabetes susceptibility.

Methods

Bibliographic databases were searched from 1970 to Dec 2012 for studies that reported on genetic association study of diabetes. After a comprehensive filtering procedure, 10 candidate gene variants with informative genotype information were collected for the current meta-anlayses. Using the REVMAN software, odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to evaluate the combined contribution of the selected genetic variants to diabetes.

Results

A total of 37 articles among 37,033 cases and 54,716 controls were involved in the present meta-analyses of 10 genetic variants. Three variants were found to be significantly associated with type 1 diabetes (T1D): NLRP1 rs12150220 (OR = 0.71, 95% CI = 0.55–0.92, P = 0.01), IL2RA rs11594656 (OR = 0.86, 95% CI = 0.82–0.91, P<0.00001), and CLEC16A rs725613 (OR = 0.71, 95% CI = 0.55–0.92, P = 0.01). APOA5 −1131T/C polymorphism was shown to be significantly associated with of type 2 diabetes (T2D, OR = 1.27, 95% CI = 1.03–1.57, P = 0.03). No association with diabetes was showed in the meta-analyses of other six genetic variants, including SLC2A10 rs2335491, ATF6 rs2070150, KLF11 rs35927125, CASQ1 rs2275703, GNB3 C825T, and IL12B 1188A/C.

Conclusion

Our results demonstrated that IL2RA rs11594656 and CLEC16A rs725613 are protective factors of T1D, while NLRP1 rs12150220 and APOA5 −1131T/C are risky factors of T1D and T2D, respectively.

Associations between a polymorphism in the pleiotropic GCKR and Age-related phenotypes: the HALCyon programme

BACKGROUND

The glucokinase regulatory protein encoded by GCKR plays an important role in glucose metabolism and a single nucleotide polymorphism (SNP) rs1260326 (P446L) in the gene has been associated with several age-related biomarkers, including triglycerides, glucose, insulin and apolipoproteins. However, associations between SNPs in the gene and other ageing phenotypes such as cognitive and physical capability have not been reported.

METHODS

As part of the Healthy Ageing across the Life Course (HALCyon) collaborative research programme, men and women from five UK cohorts aged between 44 and 90+ years were genotyped for rs1260326. Meta-analysis was used to pool within-study genotypic associations between the SNP and several age-related phenotypes, including body mass index (BMI), blood lipid levels, lung function, and cognitive and physical capability.

RESULTS

We confirm the associations between the minor allele of the SNP and higher triglycerides and lower glucose levels. We also observed a triglyceride-independent association between the minor allele and lower BMI (pooled beta on z-score= -0.04, p-value=0.0001, n=16,251). Furthermore, there was some evidence for gene-environment interactions, including physical activity attenuating the effects on triglycerides. However, no associations were observed with measures of cognitive and physical capability.

CONCLUSION

Findings from middle-aged to older adults confirm associations between rs1260326 GCKR and triglycerides and glucose, suggest possible gene-environment interactions, but do not provide evidence that its relevance extends to cognitive and physical capability.