Adiposity-related heterogeneity in patterns of type 2 diabetes susceptibility observed in genome-wide association data

The impact of family history of type 2 diabetes on clinical heterogeneity in idiopathic type 1 diabetes

AIM

To investigate the impact of family history of type 2 diabetes (T2D) on the clinical phenotypes of patients with idiopathic type 1 diabetes (T1D).

METHODS

In clinically diagnosed T1D cases, a total of 335 idopathic T1D patients were included in the study, after excluding autoimmune T1D using islet autoantibody testing and monogenic diabetes using a custom monogenic diabetes gene panel obtained from clinically diagnosed T1D cases. A semi-structured questionnaire was used to collect information on the presence of T2D in first-degree relatives. The demographic and metabolic markers of idiopathic T1D patients were analysed. Subgroup analysis was performed to investigate potential interactions between T2D family history and human leukocyte antigen (HLA) genotypes.

RESULTS

A total of 18.2% of individuals with idiopathic T1D had a T2D family history, and these individuals were more likely to have features associated with T2D, such as older age of onset, higher body mass index at diagnosis, lower insulin dosage and better beta-cell function, as indicated by higher levels of fasting C-peptide and 2-hour postprandial C-peptide (all P < 0.05). Additionally, regardless of HLA susceptible genotypes, the impact of family history of T2D was consistently observed in idiopathic T1D patients. Multivariable analyses showed that T2D family history was negatively correlated with the risk of beta-cell function failure in idiopathic T1D patients (P < 0.05).

CONCLUSIONS

Family history of T2D may be implicated in the heterogeneity of idiopathic T1D patients.

Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes

We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, P_FDR = 3.40 × 10^-5), CCR5 rs333 (OR = 1.99, P_FDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, P_FDR = 2.64 × 10^-4), TCF7L2 rs114758349 (OR = 1.77, P_FDR = 9.37 × 10^-5), and CCL2 rs1024611 (OR = 1.38, P_FDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7-87.8%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10^-6). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = -17.86, p = 0.093). The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and elucidate the molecular mechanisms of the disease development.

Association between TCF7L2 polymorphism and type 2 diabetes mellitus susceptibility: a case-control study among the Bangladeshi population

BACKGROUND

Diabetes is a severe health burden for Bangladesh. Genetic polymorphism has been reported to be one of the major risk factors for diabetes in various studies. TCF7L2 (transcription factor 7 like 2) transcripts in the human β-cell have effects on β-cell survival, function, and Wnt signaling activation. This study aimed to evaluate the frequency and association of various polymorphisms namely TCF7L2 rs12255372 and rs7903146 among Bangladeshi patients with T2DM (Type 2 Diabetes Mellitus).

METHODS

This case-control study included 300 patients with T2DM and 234 healthy individuals from two health facilities in the Chattogram Division of Bangladesh. Anthropometric measurements were assessed using a self-reported, structured, eight-item questionnaire. The polymorphisms were identified by PCR-RFLP and sequencing method.

RESULTS

A strong association of T2DM with polymorphisms was observed, including rs12255372 (p = 0.0004) and rs7903146 (p = 0.005). It was observed that the risk genotype at rs12255372 was associated with age (p = 0.009), a family history of diabetes (p < 0.0001), and HbA1C (p < 0.0001). Furthermore, it was found that rs12255372 was substantially associated with hypertension (p = 0.03), eye problems (p = 0.01), and neurological abnormalities (p = 0.02).

CONCLUSION

This study postulates that TCF7L2 genetic polymorphism is associated with the risk of T2DM among the studied Bangladeshi population. The findings should be replicated through more studies with a large number of samples and in different populations.

The common rs13266634 C > T variant in SLC30A8 contributes to the heterogeneity of phenotype and clinical features of both type 1 and type 2 diabetic subtypes

AIMS

T2D and T1D are phenotypically heterogeneous. This study aims to reveal the relationship between the common SLC30A8 rs13266634 variant and subgroups of T2D and T1D and their clinical characteristics.

METHODS

We included 3158 OGTT-based healthy controls, unrelated 1754 T2D, and 1675 autoantibody-positive T1D individuals. The associations between rs13266634 and subtypes of T2D, T1D, autoantibody status and glycemic-related quantitative traits were performed by binary logistic regression analysis under the additive model and multiple linear regression with appropriate adjustment.

RESULTS

We found that the T allele of rs13266634 was protectively associated with lean (OR = 0.810, P = 6.91E-04) but not obese T2D with considerable heterogeneity (P = 0.018). This allele also conferred significant protection with T1D of single (OR = 0.847, P = 9.76E-03), but not multi autoantibodies with substantial heterogeneity (P = 0.005). This variant significantly affected OGTT-related insulin release in lean (P = 2.66E-03, 3.88E-03 for CIR and DI, respectively) but not obese healthy individuals. Furthermore, rs13266634 T allele correlated with the risk of ZnT8A (OR = 1.440, P = 3.31E-05) and IA-2A (OR = 1.219, P = 1.32E-03) positivity, with more effect size in children/adolescents compared with adult-onset T1D subtypes.

CONCLUSIONS

These suggested that the SLC30A8 rs13266634 variant might be put into genetic risk scores to assess the risk of the subtypes of T1D and T2D and their related clinical features.

Challenges and future directions for studying effects of host genetics on the gut microbiome

The human gut microbiome is a complex ecosystem that is involved in its host's metabolism, immunity and health. Although interindividual variations in gut microbial composition are mainly driven by environmental factors, some gut microorganisms are heritable and thus can be influenced by host genetics. In the past 5 years, 12 microbial genome-wide association studies (mbGWAS) with >1,000 participants have been published, yet only a few genetic loci have been consistently confirmed across multiple studies. Here we discuss the state of the art for mbGWAS, focusing on current challenges such as the heterogeneity of microbiome measurements and power issues, and we elaborate on potential future directions for genetic analysis of the microbiome.

Toward an Improved Classification of Type 2 Diabetes: Lessons From Research into the Heterogeneity of a Complex Disease

CONTEXT

Accumulating evidence indicates that type 2 diabetes (T2D) is phenotypically heterogeneous. Defining and classifying variant forms of T2D are priorities to better understand its pathophysiology and usher clinical practice into an era of "precision diabetes."

EVIDENCE ACQUISITION AND METHODS

We reviewed literature related to heterogeneity of T2D over the past 5 decades and identified a range of phenotypic variants of T2D. Their descriptions expose inadequacies in current classification systems. We attempt to link phenotypically diverse forms to pathophysiology, explore investigative methods that have characterized "atypical" forms of T2D on an etiological basis, and review conceptual frameworks for an improved taxonomy. Finally, we propose future directions to achieve the goal of an etiological classification of T2D.

EVIDENCE SYNTHESIS

Differences among ethnic and racial groups were early observations of phenotypic heterogeneity. Investigations that uncover complex interactions of pathophysiologic pathways leading to T2D are supported by epidemiological and clinical differences between the sexes and between adult and youth-onset T2D. Approaches to an etiological classification are illustrated by investigations of atypical forms of T2D, such as monogenic diabetes and syndromes of ketosis-prone diabetes. Conceptual frameworks that accommodate heterogeneity in T2D include an overlap between known diabetes types, a "palette" model integrated with a "threshold hypothesis," and a spectrum model of atypical diabetes.

CONCLUSION

The heterogeneity of T2D demands an improved, etiological classification scheme. Excellent phenotypic descriptions of emerging syndromes in different populations, continued clinical and molecular investigations of atypical forms of diabetes, and useful conceptual models can be utilized to achieve this important goal.

Association of Genetic Variants Affecting microRNAs and Pancreatic Cancer Risk

Genetic factors play an important role in the susceptibility to pancreatic cancer (PC). However, established loci explain a small proportion of genetic heritability for PC; therefore, more progress is needed to find the missing ones. We aimed at identifying single nucleotide polymorphisms (SNPs) affecting PC risk through effects on micro-RNA (miRNA) function. We searched in silico the genome for SNPs in miRNA seed sequences or 3 prime untranslated regions (3'UTRs) of miRNA target genes. Genome-wide association data of PC cases and controls from the Pancreatic Cancer Cohort (PanScan) Consortium and the Pancreatic Cancer Case-Control (PanC4) Consortium were re-analyzed for discovery, and genotyping data from two additional consortia (PanGenEU and PANDoRA) were used for replication, for a total of 14,062 cases and 11,261 controls. None of the SNPs reached genome-wide significance in the meta-analysis, but for three of them the associations were in the same direction in all the study populations and showed lower value of p in the meta-analyses than in the discovery phase. Specifically, rs7985480 was consistently associated with PC risk (OR = 1.12, 95% CI 1.07-1.17, p = 3.03 × 10-6 in the meta-analysis). This SNP is in linkage disequilibrium (LD) with rs2274048, which modulates binding of various miRNAs to the 3'UTR of UCHL3, a gene involved in PC progression. In conclusion, our results expand the knowledge of the genetic PC risk through miRNA-related SNPs and show the usefulness of functional prioritization to identify genetic polymorphisms associated with PC risk.

Exome Chip Analysis of 14,026 Koreans Reveals Known and Newly Discovered Genetic Loci Associated with Type 2 Diabetes Mellitus

BACKGROUND

Most loci associated with type 2 diabetes mellitus (T2DM) discovered to date are within noncoding regions of unknown functional significance. By contrast, exonic regions have advantages for biological interpretation.

METHODS

We analyzed the association of exome array data from 14,026 Koreans to identify susceptible exonic loci for T2DM. We used genotype information of 50,543 variants using the Illumina exome array platform.

RESULTS

In total, 7 loci were significant with a Bonferroni adjusted P=1.03×10-6. rs2233580 in paired box gene 4 (PAX4) showed the highest odds ratio of 1.48 (P=1.60×10-10). rs11960799 in membrane associated ring-CH-type finger 3 (MARCH3) and rs75680863 in transcobalamin 2 (TCN2) were newly identified loci. When we built a model to predict the incidence of diabetes with the 7 loci and clinical variables, area under the curve (AUC) of the model improved significantly (AUC=0.72, P<0.05), but marginally in its magnitude, compared with the model using clinical variables (AUC=0.71, P<0.05). When we divided the entire population into three groups-normal body mass index (BMI; <25 kg/m2), overweight (25≤ BMI <30 kg/m2), and obese (BMI ≥30 kg/m2) individuals-the predictive performance of the 7 loci was greatest in the group of obese individuals, where the net reclassification improvement was highly significant (0.51; P=8.00×10-5).

CONCLUSION

We found exonic loci having a susceptibility for T2DM. We found that such genetic information is advantageous for predicting T2DM in a subgroup of obese individuals.

Disentangling the heterogeneity of adulthood-onset non-autoimmune diabetes: a little closer but lot more to do

Diabetes diagnosed in adults is a highly heterogeneous disorder. It mostly consists of what is referred to as type 2 diabetes but also comprises other entities (i.e. different diseases), including latent autoimmune diabetes, late onset forms of monogenic diabetes and familial diabetes of the adulthood, which has recently been the source of new diabetogenes discovery. Notably, type 2 diabetes is itself heterogeneous as it includes subtypes with onset at the extremes of age and/or weight distributions characterized by different degree of hyperglycemia and cardiovascular risk as compared to common forms of type 2 diabetes occurring in middle-aged, overweight/obese individuals. Understanding whether these are different presentations of one, highly heterogeneous disease or separate nosological entities with different clinical trajectories and requiring different treatments is essential to effectively pursue the path of precision medicine.

Defining and Classifying New Subgroups of Diabetes

An etiologically based classification of diabetes is needed to account for the heterogeneity of type 1 and type 2 diabetes (T1D and T2D) and emerging forms of diabetes worldwide. It may be productive for both classification and clinical discovery to consider variant forms of diabetes as a spectrum. Maturity onset diabetes of youth and neonatal diabetes serve as models for etiologically defined, rare forms of diabetes in the spectrum. Ketosis-prone diabetes is a model for more complex forms, amenable to phenotypic dissection. Bioinformatic approaches such as clustering analyses of large datasets and multi-omics investigations of rare and atypical phenotypes are promising avenues to explore and define new subgroups of diabetes.

Associations of alcohol consumption and physical activity with lean type 2 diabetes mellitus among Korean adults: A prospective cohort study

Data on the association between alcohol consumption and the risk of type 2 diabetes mellitus (T2DM) have accumulated, but little has been reported about this association in terms of lean T2DM. The present study analyzed 10-year longitudinal data to investigate the association between alcohol consumption and T2DM risk among lean individuals. This prospective study included 2,366 male and female Koreans aged 40–69 years who were free of DM and had a body mass index (BMI) <23 kg/m² during the baseline period between 2001 and 2012. Information on alcohol consumption, BMI, and incident cases of T2DM were identified by interviews and health examinations. To analyze the association between alcohol consumption and T2DM risk, Cox proportional hazard regression analysis was used. Alcohol drinkers consuming at least 16 g/day of alcohol (2 units/day) who maintained a BMI <23 kg/m² over 10 years had a significantly higher T2DM risk even after controlling for BMI and potential risk factors. Compared with lifetime abstainers, multivariate hazard ratios (HR) [95% confidence interval] of T2DM were 1.74 [1.02, 2.95] for 16–30 g/day, 2.09 [1.16, 3.77] for 31–60 g/day, and 1.94 [1.07, 3.51] for >60g/day among alcohol drinkers. No protective effect of moderate alcohol consumption <16 g/day on T2DM risk was observed. Age, parental history of DM, and physical inactivity were also significant risk factors for lean T2DM. Alcohol consumption of at least 2 units/day increased T2DM risk among lean individuals. Abstaining from alcohol and physical activity may be beneficial for the prevention of lean T2DM.

The Influence of Type 2 Diabetes-Associated Factors on Type 1 Diabetes

Current efforts to prevent progression from islet autoimmunity to type 1 diabetes largely focus on immunomodulatory approaches. However, emerging data suggest that the development of diabetes in islet autoantibody-positive individuals may also involve factors such as obesity and genetic variants associated with type 2 diabetes, and the influence of these factors increases with age at diagnosis. Although these factors have been linked with metabolic outcomes, particularly through their impact on β-cell function and insulin sensitivity, growing evidence suggests that they might also interact with the immune system to amplify the autoimmune response. The presence of factors shared by both forms of diabetes contributes to disease heterogeneity and thus has important implications. Characteristics that are typically considered to be nonimmune should be incorporated into predictive algorithms that seek to identify at-risk individuals and into the designs of trials for disease prevention. The heterogeneity of diabetes also poses a challenge in diagnostic classification. Finally, after clinically diagnosing type 1 diabetes, addressing nonimmune elements may help to prevent further deterioration of β-cell function and thus improve clinical outcomes. This Perspectives in Care article highlights the role of type 2 diabetes-associated genetic factors (e.g., gene variants at transcription factor 7-like 2 [TCF7L2]) and obesity (via insulin resistance, inflammation, β-cell stress, or all three) in the pathogenesis of type 1 diabetes and their impacts on age at diagnosis. Recognizing that type 1 diabetes might result from the sum of effects from islet autoimmunity and type 2 diabetes-associated factors, their interactions, or both affects disease prediction, prevention, diagnosis, and treatment.

Identification of novel population clusters with different susceptibilities to type 2 diabetes and their impact on the prediction of diabetes

Type 2 diabetes is one of the subtypes of diabetes. However, previous studies have revealed its heterogeneous features. Here, we hypothesized that there would be heterogeneity in its development, resulting in higher susceptibility in some populations. We performed risk-factor based clustering (RFC), which is a hierarchical clustering of the population with profiles of five known risk factors for type 2 diabetes (age, gender, body mass index, hypertension, and family history of diabetes). The RFC identified six population clusters with significantly different prevalence rates of type 2 diabetes in the discovery data (N = 10,023), ranging from 0.09 to 0.44 (Chi-square test, P < 0.001). The machine learning method identified six clusters in the validation data (N = 215,083), which also showed the heterogeneity of prevalence between the clusters (P < 0.001). In addition to the prevalence of type 2 diabetes, the clusters showed different clinical features including biochemical profiles and prediction performance with the risk factors. SOur results seem to implicate a heterogeneous mechanism in the development of type 2 diabetes. These results will provide new insights for the development of more precise management strategy for type 2 diabetes.

Transcription Factor 7-Like 2 (TCF7L2) Gene Polymorphism and Progression From Single to Multiple Autoantibody Positivity in Individuals at Risk for Type 1 Diabetes

OBJECTIVE

The type 2 diabetes-associated alleles at the TCF7L2 locus mark a type 1 diabetes phenotype characterized by single islet autoantibody positivity as well as lower glucose and higher C-peptide measures. Here, we studied whether the TCF7L2 locus influences progression of islet autoimmunity, from single to multiple (≥2) autoantibody positivity, in relatives of patients with type 1 diabetes.

RESEARCH DESIGN AND METHODS

We evaluated 244 participants in the Type 1 Diabetes TrialNet Pathway to Prevention study with confirmed single autoantibody positivity at screening and Immunochip single nucleotide polymorphism data (47.5% male; median age 12.8 years, range 1.2-45.9; 90.2% white). We analyzed risk allele frequency at TCF7L2 rs4506565 (in linkage disequilibrium with rs7903146). Altogether, 62.6% participants carried ≥1 risk allele. Univariate and multivariable Cox proportional hazards models and Kaplan-Meier statistical methods were used.

RESULTS

During follow-up (median 5.2 years, range 0.2-12.6), 62% of the single autoantibody-positive participants developed multiple autoantibody positivity. In the overall cohort, the TCF7L2 locus did not significantly predict progression to multiple autoantibody positivity. However, among single GAD65 autoantibody-positive participants (n = 158), those who carried ≥1 risk allele had a lower rate of progression to multiple autoantibody positivity (hazard ratio [HR] 0.65, P = 0.033) than those who did not, after adjustment for HLA risk haplotypes and age. Among subjects who were either IA-2 or insulin autoantibody positive only, carrying ≥1 TCF7L2 risk allele was not a significant factor overall, but in overweight or obese participants, it increased the risk of progression to multiple autoantibody positivity (HR 3.02, P = 0.016) even with adjustment for age.

CONCLUSIONS

The type 2 diabetes-associated TCF7L2 locus influences progression of islet autoimmunity, with differential effects by autoantibody specificity and interaction by obesity/overweight.

An in vivo screen for neuronal genes involved in obesity identifies Diacylglycerol kinase as a regulator of insulin secretion

Objective

Obesity is a complex disorder involving many genetic and environmental factors that are required to maintain energy homeostasis. While studies in human populations have led to significant progress in the generation of an obesity gene map and broadened our understanding of the genetic basis of common obesity, there is still a large portion of heritability and etiology that remains unknown. Here, we have used the genetically tractable fruit fly, Drosophila melanogaster, to identify genes/pathways that function in the nervous system to regulate energy balance.

Methods

We performed an in vivo RNAi screen in Drosophila neurons and assayed for obese or lean phenotypes by measuring changes in levels of stored fats (in the form of triacylglycerides or TAG). Three rounds of screening were performed to verify the reproducibility and specificity of the adiposity phenotypes. Genes that produced >25% increase in TAG (206 in total) underwent a second round of screening to verify their effect on TAG levels by retesting the same RNAi line to validate the phenotype. All remaining hits were screened a third time by testing the TAG levels of additional RNAi lines against the genes of interest to rule out any off-target effects.

Results

We identified 24 genes including 20 genes that have not been previously associated with energy homeostasis. One identified hit, Diacylglycerol kinase (Dgk), has mammalian homologues that have been implicated in genome-wide association studies for metabolic defects. Downregulation of neuronal Dgk levels increases TAG and carbohydrate levels and these phenotypes can be recapitulated by reducing Dgk levels specifically within the insulin-producing cells that secrete Drosophila insulin-like peptides (dILPs). Conversely, overexpression of kinase-dead Dgk, but not wild-type, decreased circulating dILP2 and dILP5 levels resulting in lower insulin signalling activity. Despite having higher circulating dILP levels, Dgk RNAi flies have decreased pathway activity suggesting that they are insulin-resistant.

Conclusion

Altogether, we have identified several genes that act within the CNS to regulate energy homeostasis. One of these, Dgk, acts within the insulin-producing cells to regulate the secretion of dILPs and energy homeostasis in Drosophila.

•

RNAi screen in neurons identifies 24 regulators of energy homeostasis.

•

One of the hits, Dgk, affects lipid and carbohydrate homeostasis.

•

Dgk acts within the IPCs to regulate dILP secretion and insulin signalling activity.

Complex patterns of direct and indirect association between the transcription Factor-7 like 2 gene, body mass index and type 2 diabetes diagnosis in adulthood in the Hispanic Community Health Study/Study of Latinos

Background

Genome-wide association studies have implicated the transcription factor 7-like 2 (TCF7L2) gene in type 2 diabetes risk, and more recently, in decreased body mass index. Given the contrary direction of genetic effects on these two traits, it has been suggested that the observed association with body mass index may reflect either selection bias or a complex underlying biology at TCF7L2.

Methods

Using 9031 Hispanic/Latino adults (21–76 years) with complete weight history and genetic data from the community-based Hispanic Community Health Study/Study of Latinos (HCHS/SOL, Baseline 2008–2011), we estimated the multivariable association between the additive number of type 2 diabetes increasing-alleles at TCF7L2 (rs7903146-T) and body mass index. We then used structural equation models to simultaneously model the genetic association on changes in body mass index across the life course and estimate the odds of type 2 diabetes per TCF7L2 risk allele.

Results

We observed both significant increases in type 2 diabetes prevalence at examination (independent of body mass index) and decreases in mean body mass index and waist circumference across genotypes at rs7903146. We observed a significant multivariable association between the additive number of type 2 diabetes-risk alleles and lower body mass index at examination. In our structured modeling, we observed non-significant inverse direct associations between rs7903146-T and body mass index at ages 21 and 45 years, and a significant positive association between rs7903146-T and type 2 diabetes onset in both middle and late adulthood.

Conclusions

Herein, we replicated the protective effect of rs7930146-T on body mass index at multiple time points in the life course, and observed that these effects were not explained by past type 2 diabetes status in our structured modeling. The robust replication of the negative effects of TCF7L2 on body mass index in multiple samples, including in our diverse Hispanic/Latino community-based sample, supports a growing body of literature on the complex biologic mechanism underlying the functional consequences of TCF7L2 on obesity and type 2 diabetes across the life course.

Electronic supplementary material

The online version of this article (10.1186/s40608-018-0200-x) contains supplementary material, which is available to authorized users.

Replicability and Prediction: Lessons and Challenges from GWAS

Since the publication of the Wellcome Trust Case Control Consortium (WTCCC) landmark study a decade ago, genome-wide association studies (GWAS) have led to the discovery of thousands of risk variants involved in disease etiology. This success story has two angles that are often overlooked. First, GWAS findings are highly replicable. This is an unprecedented phenomenon in complex trait genetics, and indeed in many areas of science, which in past decades have been plagued by false positives. At a time of increasing concerns about the lack of reproducibility, we examine the biological and methodological reasons that account for the replicability of GWAS and identify the challenges ahead. In contrast to the exemplary success of disease gene discovery, at present GWAS findings are not useful for predicting phenotypes. We close with an overview of the prospects for individualized prediction of disease risk and its foreseeable impact in clinical practice.

Variants of CDKAL1 rs7754840 (G/C) and CDKN2A/2B rs10811661 (C/T) with gestational diabetes: insignificant association

Objectives

Pathophysiological similarity exists between gestational diabetes mellitus (GDM) and type 2 diabetes mellitus with common genetic origin. Genetic liability for GDM in our population is still not researched. The goal was to reveal the genotypic and allele frequency differences of 2 single nucleotide polymorphisms (SNPs) namely, CDKAL1 (rs7754840) and CDKN2A/2B (rs10811661) between GDM pregnancies and normal pregnancies. We assessed them by real time polymerase chain reaction using Taqman^® allelic discrimination assays. We included 47 GDM pregnant subjects and 51 normal glucose tolerance (NGT) pregnant women as controls.

Results

The genotype frequencies in the GDM group and the NGT group of rs7754840-GG/GC/CC were 6.4/15.7% (3/8), 55.3/45.1% (26/23) and 38.3/39.2% (18/20) respectively. Also, those of rs10811661-CC/CT/TT were 74.5/14.9/4.3% (38/7/2) and 80.9/19.6/5.9% (38/10/3) respectively. The allele frequencies in the GDM group and the NGT group of C/G and T/C were 66/34% (62/32), 61.8/38.2% (63/39) and 11.7/88.3% (11/83), 15.7/84.3% (16/86) respectively. There were no statistical differences between the two groups in allele frequencies and genotype frequencies (all P > 0.05). Non-significant association was seen in the two SNPs of CDKAL1 and CDKN2A/B genes with GDM. Further studies are essential to validate data.

Multiple genetic variations confer risks for obesity and type 2 diabetes mellitus in arab descendants from UAE.Int J Obes (Lond). 2018;42:1345-1353. [PMID: 29717269 DOI: 10.1038/s41366-018-0057-6]

BACKGROUND

The United Arab Emirates (UAE) is one of the countries most threatened with obesity. Here we investigated associations between hundreds of single-nucleotide polymorphisms (SNPs) and the following obesity indicators: body mass index (BMI), waist circumference (WC), and height. We also investigated the associations between obesity-related genes with type 2 diabetes mellitus (T2DM).

METHODS

We tested 87, 58, and 586 SNPs in a previous genome-wide significance level for associations with BMI (n = 880), WC (n = 455), and height (n = 897), respectively. For each trait, we used normally transformed Z scores and tested them with SNPs using linear regression models that incorporated age and gender as covariates. The weighted polygenic risk scores for significant SNPs for each trait were tested with the corresponding Z scores using linear regression models with the same covariates. We further tested 145 obesity loci with T2DM (464 cases, 415 controls) using a logistic regression model including age, gender, and BMI Z scores as covariates.

RESULTS

The Mean BMI was 29.39 kg/m², and mean WC was 103.66 cm. Hypertension and dyslipidemia were common obesity comorbidities (>60%). The best associations for BMI was in FTO, LOC284260 and USP37, and for WC in RFX7 and MYEOV. For height, the best association was in NSD1 followed by MFAP2 and seven other loci. The polygenic scores revealed stronger associations for each trait than individual SNPs; although they could only explain <1% of the traits' Z scores variations. For T2DM, the strongest associations were with the TCF7L2 and MC4R loci (P < 0.01, OR ~1.70), with novel associations detected with KCNK3 and RARB.

CONCLUSIONS

In this first study of Arab descendants, we confirmed several known obesity (FTO, USP37, and RFX7), height (NSD1, MFAP2), and T2DM (TCF7L2, MC4R) associations; and report novel associations, like KCNK3 and RARB for T2DM.

Interrelationship of the rs7903146 TCF7L2 gene variant with measures of glucose metabolism and adiposity: The NEO study

BACKGROUND AND AIMS

We investigated the interrelationship of rs7903146-T in TCF7L2 with measures of glucose metabolism and measures of adiposity.

METHODS AND RESULTS

This cross-sectional analysis was conducted in 5744 middle-aged participants (mean (standard deviation [SD]) age is 55.9 (6.0) years) from the Netherlands Epidemiology of Obesity (NEO) Study. Associations between rs7903146-T and Type 2 diabetes mellitus (T2D) were assessed with logistic regression. Additive (per-allele) associations with measures of glucose metabolism (e.g., fasting insulin) and adiposity (e.g., body mass index [BMI]) were examined with multivariable linear regression. In the total study population, rs7903146-T was associated with a higher risk of T2D (additive odds ratio: 1.42; 95% confidence interval: 1.17; 1.72), and specifically with T2D treated with insulin analogs (2.31 [1.19; 4.46]). After exclusion of participants treated with glucose-lowering medication, rs7903146-T was associated with lower mean insulin concentration (additive mean difference: -0.07 SD [-0.14; 0.00]), but not with higher mean glucose concentration (0.03 SD [-0.01; 0.07]). Furthermore, rs7903146-T was associated with, among other measures of adiposity, a lower mean BMI (-0.04 SD [-0.09; -0.00]), and a lower mean total body fat (-0.04 SD [-0.08; -0.00]). The association between rs7903146-T and T2D increased after adjustment for BMI (odds ratio: 1.51 [1.24; 1.86]); the association between rs7903146-T and fasting insulin diminished after adjustment (-0.05 SD [-0.11; 0.02]).

CONCLUSION

rs7903146-T is associated with a decreased insulin concentration and increased risk of T2D with opposing effects of adjustment for adiposity.

A prospective study of dietary and supplemental zinc intake and risk of type 2 diabetes depending on genetic variation in SLC30A8

Background

The solute carrier family 30 member 8 gene (SLC30A8) encodes a zinc transporter in the pancreatic beta cells and the major C-allele of a missense variant (rs13266634; C/T; R325W) in SLC30A8 is associated with an increased risk of type 2 diabetes (T2D). We hypothesized that the association between zinc intake and T2D may be modified by the SLC30A8 genotype.

Results

We carried out a prospective study among subjects with no history cardio-metabolic diseases in the Malmö Diet and Cancer Study cohort (N = 26,132, 38% men; 86% with genotype data). Zinc intake was assessed using a diet questionnaire and food record. During a median follow-up of 19 years, 3676 T2D cases occurred. A BMI-stratified Cox proportional hazards regression model with attained age as the time scale was used to model the association between total and dietary zinc intake, zinc supplement use, zinc to iron ratio, and risk of T2D adjusting for putative confounding factors.

The median total zinc intake was 11.4 mg/day, and the median dietary zinc intake was 10.7 mg/day. Zinc supplement users (17%) had a median total zinc intake of 22.4 mg/day. Dietary zinc intake was associated with increased risk of T2D (P_trend < 0.0001). In contrast, we observed a lower risk of T2D among zinc supplement users (HR = 0.79, 95% CI 0.70–0.89). The SLC30A8 CC genotype was associated with a higher risk of T2D (HR = 1.16, 95% CI 1.07–1.24), and the effect was stronger among subjects with higher BMI (P_interaction = 0.007). We observed no significant modification of the zinc-T2D associations by SLC30A8 genotype. However, a three-way interaction between SLC30A8 genotype, BMI, and zinc to iron ratio was observed (P_interaction = 0.007). A high zinc to iron ratio conferred a protective associated effect on T2D risk among obese subjects, and the effect was significantly more pronounced among T-allele carriers.

Conclusions

Zinc supplementation and a high zinc to iron intake ratio may lower the risk of T2D, but these associations could be modified by obesity and the SLC30A8 genotype. The findings implicate that when considering zinc supplementation for T2D prevention, both obesity status and SLC30A8 genotype may need to be accounted for.

Electronic supplementary material

The online version of this article (10.1186/s12263-017-0586-y) contains supplementary material, which is available to authorized users.

Linking Alzheimer's disease and type 2 diabetes: Novel shared susceptibility genes detected by cFDR approach

BACKGROUND

Both type 2 diabetes (T2D) and Alzheimer's disease (AD) occur commonly in the aging populations and T2D has been considered as an important risk factor for AD. The heritability of both diseases is estimated to be over 50%. However, common pleiotropic single-nucleotide polymorphisms (SNPs)/loci have not been well-defined. The aim of this study is to analyze two large public accessible GWAS datasets to identify novel common genetic loci for T2D and/or AD.

METHODS AND MATERIALS

The recently developed novel conditional false discovery rate (cFDR) approach was used to analyze the summary GWAS datasets from International Genomics of Alzheimer's Project (IGAP) and Diabetes Genetics Replication And Meta-analysis (DIAGRAM) to identify novel susceptibility genes for AD and T2D.

RESULTS

We identified 78 SNPs (including 58 novel SNPs) that were associated with AD in Europeans conditional on T2D (cFDR<0.05). 66 T2D SNPs (including 40 novel SNPs) were identified by conditioning on SNPs association with AD (cFDR<0.05). A conjunction-cFDR (ccFDR) analysis detected 8 pleiotropic SNPs with a significance threshold of ccFDR<0.05 for both AD and T2D, of which 5 SNPs (rs6982393, rs4734295, rs7812465, rs10510109, rs2421016) were novel findings. Furthermore, among the 8 SNPs annotated at 6 different genes, 3 corresponding genes TP53INP1, TOMM40 and C8orf38 were related to mitochondrial dysfunction, critically involved in oxidative stress, which potentially contribute to the etiology of both AD and T2D.

CONCLUSION

Our study provided evidence for shared genetic loci between T2D and AD in European subjects by using cFDR and ccFDR analyses. These results may provide novel insight into the etiology and potential therapeutic targets of T2D and/or AD.

Association between 28 single nucleotide polymorphisms and type 2 diabetes mellitus in the Kazakh population: a case-control study

Background

We evaluated the associations between single nucleotide polymorphisms and different clinical parameters related to type 2 diabetes mellitus (T2DM), obesity risk, and metabolic syndrome (MS) in a Kazakh cohort.

Methods

A total of 1336 subjects, including 408 T2DM patients and 928 control subjects, were recruited from an outpatient clinic and genotyped for 32 polymorphisms previously associated with T2DM and obesity-related phenotypes in other ethnic groups. For association studies, the chi-squared test or Fisher’s exact test for binomial variables were used. Logistic regression was conducted to explore associations between the studied SNPs and the risk of developing T2DM, obesity, and MS, after adjustments for age and sex.

Results

After excluding four SNPs due to Hardy-Weinberg disequilibrium, significant associations in age-matched cohorts were found betweenT2DM and the following SNPs: rs9939609 (FTO), rs13266634 (SLC30A8), rs7961581 (TSPAN8/LGR5), and rs1799883 (FABP2). In addition, examination of general unmatched T2DM and control cohorts revealed significant associations between T2DM and SNPsrs1799883 (FABP2) and rs9939609 (FTO). Furthermore, polymorphisms in the FTO gene were associated with increased obesity risk, whereas polymorphisms in the FTO and FABP2 genes were also associated with the risk of developing MS in general unmatched cohorts.

Conclusion

We confirmed associations between polymorphisms within the SLC30A8, TSPAN8/LGR5, FABP2, and FTO genes and susceptibility to T2DM in a Kazakh cohort, and revealed significant associations with anthropometric and metabolic traits. In particular, FTO and FABP2 gene polymorphisms were significantly associated with susceptibility to MS and obesity in this cohort.

Electronic supplementary material

The online version of this article (doi:10.1186/s12881-017-0443-2) contains supplementary material, which is available to authorized users.

Association of CDKAL1, CDKN2A/B & HHEX gene polymorphisms with type 2 diabetes mellitus in the population of Hyderabad, India

Background & objectives:

The genome-wide association studies (GWAS) have shown an association of type 2 diabetes mellitus (T2DM) with several novel genes. We report here the findings on the pattern of genetic association of three genes (CDKAL1, CDKN2A/B and HHEX) with T2DM in the population of Hyderabad, south India.

Methods:

A sample of 1379 individuals (758 T2DM cases and 621 controls) from Hyderabad, India, were genotyped for five single nucleotide polymorphisms (SNPs) of CDKAL1 (rs7754840, rs7756992) CDKN2A/B (rs10811661) and HHEX (rs1111875, rs7923837) genes on Sequenom Mass Array platform.

Results:

The risk allele frequencies of the CDKAL1 and CDKN2A/B SNPs were relatively higher in cases than in the controls and the logistic regression analysis yielded significant odds ratios suggesting that the variant alleles conferred risk for developing T2DM in this population. The HHEX gene did not show either allelic or genotypic association with T2DM. The multivariate logistic regression analysis with reference to both alleles and genotypes of CDKAL1 SNPs showed significant association, suggesting an important role for this gene in the T2DM pathophysiology.

Interpretation & conclusions:

A significant association was seen of all the three SNPs of CDKAL1 and CDKN2A/B genes with T2DM but none of the two SNPs of HHEX. Further studies are required to cross-validate our findings in a relatively larger sample. It is also necessary to explore other SNPs of HHEX gene to unequivocally establish the pattern of association of this gene with T2DM in this population.

Quantifying the extent to which index event biases influence large genetic association studies

As genetic association studies increase in size to 100 000s of individuals, subtle biases may influence conclusions. One possible bias is 'index event bias' (IEB) that appears due to the stratification by, or enrichment for, disease status when testing associations between genetic variants and a disease-associated trait. We aimed to test the extent to which IEB influences some known trait associations in a range of study designs and provide a statistical framework for assessing future associations. Analyzing data from 113 203 non-diabetic UK Biobank participants, we observed three (near TCF7L2, CDKN2AB and CDKAL1) overestimated (body mass index (BMI) decreasing) and one (near MTNR1B) underestimated (BMI increasing) associations among 11 type 2 diabetes risk alleles (at P  <  0.05). IEB became even stronger when we tested a type 2 diabetes genetic risk score composed of these 11 variants (-0.010 standard deviations BMI per allele, P  =  5 × 10- 4), which was confirmed in four additional independent studies. Similar results emerged when examining the effect of blood pressure increasing alleles on BMI in normotensive UK Biobank samples. Furthermore, we demonstrated that, under realistic scenarios, common disease alleles would become associated at P <  5 × 10- 8 with disease-related traits through IEB alone, if disease prevalence in the sample differs appreciably from the background population prevalence. For example, some hypertension and type 2 diabetes alleles will be associated with BMI in sample sizes of  >500 000 if the prevalence of those diseases differs by >10% from the background population. In conclusion, IEB may result in false positive or negative genetic associations in very large studies stratified or strongly enriched for/against disease cases.

Fat mass and obesity-associated gene variations are related to fatty liver disease in HIV-infected patients

OBJECTIVES

Fatty liver disease (FLD) is frequently observed in HIV-infected patients. Obesity and type 2 diabetes mellitus (T2DM) are strongly associated with FLD. Because genetic variants within the fat mass and obesity-associated (FTO) gene have been associated with both pathologies, our aim was to evaluate the association of single nucleotide polymorphisms (SNPs) within the FTO, previously related to obesity or T2DM, with FLD in HIV-infected patients.

METHODS

FLD was defined as a value of the controlled attenuation parameter (CAP) ≥ 238 dB/m, obtained by transient elastography. Four SNPs within FTO intron 1 (rs11642841, rs8050136, rs9939609 and rs9940128) were genotyped in 421 individuals using a custom Golden Gate protocol. The results were replicated in a validation sample consisting of a further 206 HIV-infected patients. Multivariate logistic regression analyses were conducted in the entire population.

RESULTS

Three SNPs (rs8050136, rs9939609 and rs9940128) were associated with FLD, with rs9940128 showing the strongest association. This polymorphism also showed an association with FLD in the validation sample. In total, rs9940128 was genotyped in 627 HIV-infected patients, including 267 (42.6%) FLD-diagnosed individuals. The frequency of FLD among rs9940128 AA carriers was 55.7% (63 of 113 individuals) and that in patients without this genotype was 39.7% (204 of 514 individuals) [P = 0.009; adjusted odds ratio 1.88; 95% confidence interval (CI) 1.17-3.01].

CONCLUSIONS

Variations within FTO may be predictors of FLD in HIV-infected patients independently of metabolic factors.

Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the “large p and small n” problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

Polymorphism of the Transcription Factor 7-Like 2 Gene (TCF7L2) Interacts with Obesity on Type-2 Diabetes in the PREDIMED Study Emphasizing the Heterogeneity of Genetic Variants in Type-2 Diabetes Risk Prediction: Time for Obesity-Specific Genetic Risk Scores

Nutrigenetic studies analyzing gene–diet interactions of the TCF7L2-rs7903146 C > T polymorphism on type-2 diabetes (T2D) have shown controversial results. A reason contributing to this may be the additional modulation by obesity. Moreover, TCF7L2-rs7903146 is one of the most influential variants in T2D-genetic risk scores (GRS). Therefore, to increase the predictive value (PV) of GRS it is necessary to first see whether the included polymorphisms have heterogeneous effects. We comprehensively investigated gene-obesity interactions between the TCF7L2-rs7903146 C > T polymorphism on T2D (prevalence and incidence) and analyzed other T2D-polymorphisms in a sub-sample. We studied 7018 PREDIMED participants at baseline and longitudinally (8.7 years maximum follow-up). Obesity significantly interacted with the TCF7L2-rs7903146 on T2D prevalence, associations being greater in non-obese subjects. Accordingly, we prospectively observed in non-T2D subjects (n = 3607) that its association with T2D incidence was stronger in non-obese (HR: 1.81; 95% CI: 1.13–2.92, p = 0.013 for TT versus CC) than in obese subjects (HR: 1.01; 95% CI: 0.61–1.66; p = 0.979; p-interaction = 0.048). Accordingly, TCF7L2-PV was higher in non-obese subjects. Additionally, we created obesity-specific GRS with ten T2D-polymorphisms and demonstrated for the first time their higher strata-specific PV. In conclusion, we provide strong evidence supporting the need for considering obesity when analyzing the TCF7L2 effects and propose the use of obesity-specific GRS for T2D.

Replication of genome-wide association signals in Asian Indians with early-onset type 2 diabetes

AIMS

To evaluate the association of 87 genetic variants previously associated with type 2 diabetes mellitus (T2DM) in genome-wide association studies of populations of European ancestry in an Asian Indian population with early-onset type 2 diabetes mellitus (EOT2DM).

METHODS

The study groups comprised of 877 type 2 diabetes individuals, 436 individuals with EOT2DM (age at diagnosis below 35 years), 441 individuals with older T2DM (diagnosis at 35 years or greater) and controls with normal glucose tolerance (NGT) (n = 400 younger than 35 years; n = 438 older than 35 years). The participants were genotyped for 87 SNPs from 44 genes and 27 intergenic loci. Associations were tested using logistic regression.

RESULTS

All the variants in TCF7L2 and CDKN2A/2B showed study-wide significance (p < 1.4 × 10^-4) with T2DM, but only rs7903146, rs12243326, rs12255372 of TCF7L2 and rs7020996 of CDKN2A/2B showed study-wide significance (p < 1.4 × 10^-4) with EOT2DM in this population. In addition, an intergenic SNP on chromosome 1 (rs10493685) was also shown to be study-wide significant (p = 7.1 × 10^-6). Several additional SNPs previously associated with T2DM reached borderline significance in this study, but may have been limited by relatively low sample numbers. Various other SNPs of T2DM were not associated with EOT2DM.

CONCLUSIONS

Some of the variants in TCF7L2 and CDKN2A/2B associated with T2DM are associated with EOT2DM as well. An intergenic SNP on chromosome 1p31 showed association only with early-onset T2DM in this Asian Indian population. The lack of association with many other SNPs of T2DM may be a reflection of the lack of power of the study, sample size, differences in the frequencies of genetic polymorphisms in different ethnic groups, effect sizes, as well as ancestral differences in pattern of LD between the genetic variants involved in early- and late-onset T2DM.

Predictive utility of a genetic risk score of common variants associated with type 2 diabetes in a black South African population

AIMS

To determine the predictive utility of polygenic risk scores of common variants associated with type 2 diabetes derived from the European and Asian ethnicities among a black South African population.

METHOD

Our study was a case-control study nested within the Prospective Urban and Rural Epidemiological (PURE) study of 178 male and female cases, matched for age and gender with 178 controls. Four types of genetic risk scores (GRS) were developed from 66 selected SNPs. These comprised of beta cell related variants (GRSb), variants which had significant associations with T2D in our study (GRSn), variants from the trans-ethnic meta-analysis (GRStrans) and all the 66 selected SNPs (GRSt).

RESULTS

Of the GRS's, only GRSn was associated with increased risk of T2D as indicated by an OR (95CI) of 1.21 (1.02-1.43) p-value=0.015. Stratified analysis of age and BMI, indicated the GRSn to be significantly associated with T2D among the non-obese and participants less than 50years. The area under the ROC of the T2D risk factors only was 0.652 (p value<0.001) and with the addition of GRSn it was 0.665 (p value<0.001).

CONCLUSIONS

The GRS of European and Asian derived variants have limited clinical utility in the black South African population. The inclusion of population specific variants in the GRS is pivotal.

eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants

BACKGROUND

We explored premature stop-gain variants to test the hypothesis that variants, which are likely to have a consequence on protein structure and function, will reveal important insights with respect to the phenotypes associated with them. We performed a phenome-wide association study (PheWAS) exploring the association between a selected list of functional stop-gain genetic variants (variation resulting in truncated proteins or in nonsense-mediated decay) and an extensive group of diagnoses to identify novel associations and uncover potential pleiotropy.

RESULTS

In this study, we selected 25 stop-gain variants: 5 stop-gain variants with previously reported phenotypic associations, and a set of 20 putative stop-gain variants identified using dbSNP. For the PheWAS, we used data from the electronic MEdical Records and GEnomics (eMERGE) Network across 9 sites with a total of 41,057 unrelated patients. We divided all these samples into two datasets by equal proportion of eMERGE site, sex, race, and genotyping platform. We calculated single effect associations between these 25 stop-gain variants and ICD-9 defined case-control diagnoses. We also performed stratified analyses for samples of European and African ancestry. Associations were adjusted for sex, site, genotyping platform and the first three principal components to account for global ancestry. We identified previously known associations, such as variants in LPL associated with hyperglyceridemia indicating that our approach was robust. We also found a total of three significant associations with p < 0.01 in both datasets, with the most significant replicating result being LPL SNP rs328 and ICD-9 code 272.1 "Disorder of Lipoid metabolism" (pdiscovery = 2.59x10-6, preplicating = 2.7x10-4). The other two significant replicated associations identified by this study are: variant rs1137617 in KCNH2 gene associated with ICD-9 code category 244 "Acquired Hypothyroidism" (pdiscovery = 5.31x103, preplicating = 1.15x10-3) and variant rs12060879 in DPT gene associated with ICD-9 code category 996 "Complications peculiar to certain specified procedures" (pdiscovery = 8.65x103, preplicating = 4.16x10-3).

CONCLUSION

In conclusion, this PheWAS revealed novel associations of stop-gained variants with interesting phenotypes (ICD-9 codes) along with pleiotropic effects.

Association of SLC30A8 gene polymorphism with type 2 diabetes, evidence from 46 studies: a meta-analysis

The solute carrier family 30 member 8 (SLC30A8) gene may be involved in the development of type 2 diabetes mellitus (T2DM) through disrupting β-cell function. The aim of this study was to assess the association between SLC30A8 rs13266634 polymorphism and susceptibility to T2DM. We searched all reports regarding the association between SLC30A8 rs13266634 polymorphism and T2DM risk through Pubmed, Embase, and the Cochrane Library for English language reports and Chongqing VIP database, Wanfang data, CBMDisc, and CNKI for Chinese language studies. Allelic and genotype comparisons between cases and controls were evaluated, and odds ratios with 95 % confidence intervals were used to assess the strength of their association. A random effects model was selected. Publication bias was estimated using Begg's test. Forty-six studies were included in the analysis with a total of 71,890 cases and 96,753 controls. This meta-analysis suggests that SLC30A8 (rs13266634) polymorphism was associated with T2DM risk. Although previous meta-analyses have shown that this association was only found in Asian and European groups, and not in African populations, our analysis revealed the deleterious effect of SLC30A8 rs13266634 on T2DM in an African population when stratified by ethnicity under additive model even with a small number of studies.

Genetic variants associated with lean and obese type 2 diabetes in a Han Chinese population: A case-control study

Type 2 diabetes (T2D) is highly phenotypically heterogeneous. Genetics of the heterogeneity of lean and obese T2D is not clear. The aim of the present study was to identify the associations of T2D-related genetic variants with the risks for lean and obese T2D among the Chinese Han population. A case-control study consisting of 5338 T2D patients and 4663 normal glycemic controls of Chinese Han recruited in the Chinese National Diabetes and Metabolic Disorders Study was conducted. T2D cases were identified according to the 1999 World Health Organization criteria. Lean T2D was defined as T2D patient with a body mass index (BMI) <23 kg/m, whereas obese T2D was defined as T2D patient with a BMI ≥28 kg/m. Twenty-five genome-wide association studies previously validated T2D-related single-nucleotide polymorphisms (SNPs) were genotyped. A genotype risk score (GRS) based on the 25 SNPs was created. After adjusting for multiple covariates, SNPs in or near CDKAL1, CDKN2BAS, KCNQ1, TCF7L2, CDC123/CAMK1D, HHEX, and TCF2 were associated with the risk for lean T2D, and SNPs in or near KCNQ1 and FTO were associated with the risk for obese T2D. The results showed that the GRS for 25 T2D-related SNPs was more strongly associated with the risk for lean T2D (Ptrend = 2.66 × 10) than for obese T2D (Ptrend = 2.91 × 10) in our study population. Notably, the T2D GRS contributed to lower obesity-related measurements and greater β-cell dysfunction, including lower insulin levels in oral glucose tolerance test, decreased insulinogenic index, and Homeostasis Model Assessment for β-cell Function. In conclusion, our findings identified T2D-related genetic loci that contribute to the risk of lean and obese T2D individually and additively in a Chinese Han population. Moreover, the study highlights the contribution of known T2D genomic loci to the heterogeneity of lean and obese T2D in Chinese Hans.

Accumulating evidence for a role of TCF7L2 variants in bipolar disorder with elevated body mass index

OBJECTIVES

Bipolar disorder (BD) is a complex disease associated with various hereditary traits, including a higher body mass index (BMI). In a prior genome-wide association study, we found that BMI modified the association of rs12772424 - a common variant in the gene encoding transcription factor 7-like 2 (TCF7L2) - with risk for BD. TCF7L2 is a transcription factor in the canonical Wnt pathway, involved in multiple disorders, including diabetes, cancer and psychiatric conditions. Here, using an independent sample, we evaluated 26 TCF7L2 single nucleotide polymorphisms (SNPs) to explore further the association of BD with the TCF7L2-BMI interaction.

METHODS

Using a sample of 662 BD cases and 616 controls, we conducted SNP-level and gene-level tests to assess the evidence for an association between BD and the interaction of BMI and genetic variation in TCF7L2. We also explored the potential mechanism behind the detected associations using human brain expression quantitative trait loci (eQTL) analysis.

RESULTS

The analysis provided independent evidence of an rs12772424-BMI interaction (p = 0.011). Furthermore, while overall there was no evidence for SNP marginal effects on BD, the TCF7L2-BMI interaction was significant at the gene level (p = 0.042), with seven of the 26 SNPs showing SNP-BMI interaction effects with p < 0.05. The strongest evidence of interaction was observed for rs7895307 (p = 0.006). TCF7L2 expression showed a significant enrichment of association with the expression of other genes in the Wnt canonical pathway.

CONCLUSIONS

The current study provides further evidence suggesting that TCF7L2 involvement in BD risk may be regulated by BMI. Detailed, prospective assessment of BMI, comorbidity, and other possible contributing factors is necessary to explain fully the mechanisms underlying this association.

JBASE: Joint Bayesian Analysis of Subphenotypes and Epistasis

MOTIVATION

Rapid advances in genotyping and genome-wide association studies have enabled the discovery of many new genotype-phenotype associations at the resolution of individual markers. However, these associations explain only a small proportion of theoretically estimated heritability of most diseases. In this work, we propose an integrative mixture model called JBASE: joint Bayesian analysis of subphenotypes and epistasis. JBASE explores two major reasons of missing heritability: interactions between genetic variants, a phenomenon known as epistasis and phenotypic heterogeneity, addressed via subphenotyping.

RESULTS

Our extensive simulations in a wide range of scenarios repeatedly demonstrate that JBASE can identify true underlying subphenotypes, including their associated variants and their interactions, with high precision. In the presence of phenotypic heterogeneity, JBASE has higher Power and lower Type 1 Error than five state-of-the-art approaches. We applied our method to a sample of individuals from Mexico with Type 2 diabetes and discovered two novel epistatic modules, including two loci each, that define two subphenotypes characterized by differences in body mass index and waist-to-hip ratio. We successfully replicated these subphenotypes and epistatic modules in an independent dataset from Mexico genotyped with a different platform.

AVAILABILITY AND IMPLEMENTATION

JBASE is implemented in C++, supported on Linux and is available at http://www.cs.toronto.edu/∼goldenberg/JBASE/jbase.tar.gz. The genotype data underlying this study are available upon approval by the ethics review board of the Medical Centre Siglo XXI. Please contact Dr Miguel Cruz at mcruzl@yahoo.com for assistance with the application.

CONTACT

anna.goldenberg@utoronto.ca

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comparative Genome of GK and Wistar Rats Reveals Genetic Basis of Type 2 Diabetes

The Goto-Kakizaki (GK) rat, which has been developed by repeated inbreeding of glucose-intolerant Wistar rats, is the most widely studied rat model for Type 2 diabetes (T2D). However, the detailed genetic background of T2D phenotype in GK rats is still largely unknown. We report a survey of T2D susceptible variations based on high-quality whole genome sequencing of GK and Wistar rats, which have generated a list of GK-specific variations (228 structural variations, 2660 CNV amplification and 2834 CNV deletion, 1796 protein affecting SNVs or indels) by comparative genome analysis and identified 192 potential T2D-associated genes. The genes with variants are further refined with prior knowledge and public resource including variant polymorphism of rat strains, protein-protein interactions and differential gene expression. Finally we have identified 15 genetic mutant genes which include seven known T2D related genes (Tnfrsf1b, Scg5, Fgb, Sell, Dpp4, Icam1, and Pkd2l1) and eight high-confidence new candidate genes (Ldlr, Ccl2, Erbb3, Akr1b1, Pik3c2a, Cd5, Eef2k, and Cpd). Our result reveals that the T2D phenotype may be caused by the accumulation of multiple variations in GK rat, and that the mutated genes may affect biological functions including adipocytokine signaling, glycerolipid metabolism, PPAR signaling, T cell receptor signaling and insulin signaling pathways. We present the genomic difference between two closely related rat strains (GK and Wistar) and narrow down the scope of susceptible loci. It also requires further experimental study to understand and validate the relationship between our candidate variants and T2D phenotype. Our findings highlight the importance of sequenced-based comparative genomics for investigating disease susceptibility loci in inbreeding animal models.

Functional mechanisms for type 2 diabetes-associated genetic variants

AIMS

Type 2 diabetes (T2D) is a complex endocrine and metabolic disorder, characterized by hyperglycemia due to insulin resistance and relative lack of insulin. Several recent studies have identified a large number of genetic loci associated with T2D without exploring functional mechanisms underlying the associations. This study established integrative analyses to detect the functional mechanisms for T2D-related associations.

METHODS

Based on the public available datasets and resources, this study performed integrative analyses (gene relationships among implicated loci (GRAIL), expression quantitative trait loci (eQTL) analysis, differential gene expression analysis and functional prediction analysis) to detect the molecular functional mechanisms underlying the associations.

RESULTS

Two single nucleotide polymorphisms (SNPs) (rs7593730, rs2439312) have been found to act as cis-effect regulators of two corresponding eQTL genes (RBMS1, NRG1) among 252 selected (P<E-4) genetic associations that were archived in the public databases. These two non-HLA genes were also differentially expressed in T2D-related cell groups. The two SNPs were predicted as regulatory sites by utilizing online prediction tools.

CONCLUSIONS

This study detected potential regulatory mechanisms underlying the associations between T2D and two identified SNPs. Integrative analysis can be used to provide suggestive clues for the molecular functional mechanisms in T2D.

Can data science inform environmental justice and community risk screening for type 2 diabetes?

Background

Having the ability to scan the entire country for potential “hotspots” with increased risk of developing chronic diseases due to various environmental, demographic, and genetic susceptibility factors may inform risk management decisions and enable better environmental public health policies.

Objectives

Develop an approach for community-level risk screening focused on identifying potential genetic susceptibility hotpots.

Methods

Our approach combines analyses of phenotype-genotype data, genetic prevalence of single nucleotide polymorphisms, and census/geographic information to estimate census tract-level population attributable risks among various ethnicities and total population for the state of California.

Results

We estimate that the rs13266634 single nucleotide polymorphism, a type 2 diabetes susceptibility genotype, has a genetic prevalence of 56.3%, 47.4% and 37.0% in Mexican Mestizo, Caucasian, and Asian populations. Looking at the top quintile for total population attributable risk, 16 California counties have greater than 25% of their population living in hotspots of genetic susceptibility for developing type 2 diabetes due to this single genotypic susceptibility factor.

Conclusions

This study identified counties in California where large portions of the population may bear additional type 2 diabetes risk due to increased genetic prevalence of a susceptibility genotype. This type of screening can easily be extended to include information on environmental contaminants of interest and other related diseases, and potentially enables the rapid identification of potential environmental justice communities. Other potential uses of this approach include problem formulation in support of risk assessments, land use planning, and prioritization of site cleanup and remediation actions.

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.

Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression

Accounting for gene-environment (G×E) interactions in complex trait association studies can facilitate our understanding of genetic heterogeneity under different environmental exposures, improve the ability to discover susceptible genes that exhibit little marginal effect, provide insight into the biological mechanisms of complex diseases, help to identify high-risk subgroups in the population, and uncover hidden heritability. However, significant G×E interactions can be difficult to find. The sample sizes required for sufficient power to detect association are much larger than those needed for genetic main effects, and interactions are sensitive to misspecification of the main-effects model. These issues are exacerbated when working with binary phenotypes and rare variants, which bear less information on association. In this work, we present a similarity-based regression method for evaluating G×E interactions for rare variants with binary traits. The proposed model aggregates the genetic and G×E information across markers, using genetic similarity, thus increasing the ability to detect G×E signals. The model has a random effects interpretation, which leads to robustness against main-effect misspecifications when evaluating G×E interactions. We construct score tests to examine G×E interactions and a computationally efficient EM algorithm to estimate the nuisance variance components. Using simulations and data applications, we show that the proposed method is a flexible and powerful tool to study the G×E effect in common or rare variant studies with binary traits.

Association study of polymorphisms in miRNAs with T2DM in Chinese population

Accumulated evidence indicates that microRNA (miRNA or miR) is involved in the development of type 2 diabetes (T2DM). Several studies have shown that single nucleotide polymorphisms (SNPs) located in miRNAs are associated with T2DM in Caucasian populations. The association studies of miRNA's SNPs with T2DM in Asian are rarely reported, and there are distinct genetic differences between Caucasian and Asian populations. The focus of this study, therefore, is the association of T2DM with five SNPs (rs895819 in miR-27a, rs531564 in miR-124a, rs11888095 in miR-128a, rs3820455 in miR-194a and rs2910164 in miR-146a) located in five miRNAs in a Han Chinese population. A total of 738 subjects with T2DM and 610 non-diabetic subjects were genotyped using the TaqMan method. Next, the associations between the five SNPs with T2DM and individual metabolic traits were evaluated. Our data showed that the C allele of rs531564 in miR-124a may protect against T2DM (P=0.009, OR=0.758; 95%CI: 0.616-0.933). Conversely, the C allele of rs2910164 in miR-146a may increase the risk of developing T2DM (P<0.001, OR=1.459; 95%CI: 1.244-1.712). However, these five SNPs did not exhibit significant associations with individual metabolic traits in either the T2DM or non-diabetic groups. Our results revealed that genetic variations in miRNAs were associated with T2DM susceptibility in a Han Chinese population, and these results highlight the need to study the functional effects of these variants in miRNAs on the risk of developing T2DM.

Associations of genetic variants in/near body mass index-associated genes with type 2 diabetes: a systematic meta-analysis

OBJECTIVE

Genome-wide association studies have identified many obesity/body mass index (BMI)-associated loci in Europeans and East Asians. Since then, a large number of studies have investigated the role of BMI-associated loci in the development of type 2 diabetes (T2D). However, the results have been inconsistent. The objective of this study was to investigate the associations of eleven obesity/BMI loci with T2D risk and explore how BMI influences this risk.

METHODS

We retrieved published literature from PubMed and Embase. The pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated using fixed- or random-effect models.

RESULTS

In the meta-analysis of 42 studies for 11 obesity/BMI-associated loci, we observed a statistically significant association of the FTO rs9939609 polymorphism (66 425 T2D cases/239 689 normoglycaemic subjects; P = 1·00 × 10(-41) ) and six other variants with T2D risk (17 915 T2D cases/27 531 normoglycaemic individuals: n = 40 629-130 001; all P < 0·001 for SH2B1 rs7498665, FAIM2 rs7138803, TMEM18 rs7561317, GNPDA2 rs10938397, BDNF rs925946 and NEGR1 rs2568958). After adjustment for BMI, the association remained statistically significant for four of the seven variants (all P < 0·05 for FTO rs9939609, SH2B1 rs7498665, FAIM2 rs7138803, GNPDA2 rs10938397). Subgroup analysis by ethnicity demonstrated similar results.

CONCLUSIONS

This meta-analysis indicates that several BMI-associated variants are significantly associated with T2D risk. Some variants increase the T2D risk independent of obesity, while others mediate this risk through obesity.

A comparison of type 2 diabetes risk allele load between African Americans and European Americans

The prevalence of type 2 diabetes (T2D) is greater in populations of African descent compared to European-descent populations. Genetic risk factors may underlie the disparity in disease prevalence. Genome-wide association studies (GWAS) have identified >60 common genetic variants that contribute to T2D risk in populations of European, Asian, African and Hispanic descent. These studies have not comprehensively examined population differences in cumulative risk allele load. To investigate the relationship between risk allele load and T2D risk, 46 T2D single nucleotide polymorphisms (SNPs) in 43 loci from GWAS in European, Asian, and African-derived populations were genotyped in 1,990 African Americans (n = 963 T2D cases, n = 1,027 controls) and 1,644 European Americans (n = 719 T2D cases, n = 925 controls) ascertained and recruited using a common protocol in the southeast United States. A genetic risk score (GRS) was constructed from the cumulative risk alleles for each individual. In African American subjects, risk allele frequencies ranged from 0.024 to 0.964. Risk alleles from 26 SNPs demonstrated directional consistency with previous studies, and 3 SNPs from ADAMTS9, TCF7L2, and ZFAND6 showed nominal evidence of association (p < 0.05). African American individuals carried 38-67 (53.7 ± 4.0, mean ± SD) risk alleles. In European American subjects, risk allele frequencies ranged from 0.084 to 0.996. Risk alleles from 36 SNPs demonstrated directional consistency, and 10 SNPs from BCL11A, PSMD6, ADAMTS9, ZFAND3, ANK1, CDKN2A/B, TCF7L2, PRC1, FTO, and BCAR1 showed evidence of association (p < 0.05). European American individuals carried 38-65 (50.9 ± 4.4) risk alleles. African Americans have a significantly greater burden of 2.8 risk alleles (p = 3.97 × 10(-89)) compared to European Americans. However, GRS modeling showed that cumulative risk allele load was associated with risk of T2D in European Americans, but only marginally in African Americans. This result suggests that there are ethnic-specific differences in genetic architecture underlying T2D, and that these differences complicate our understanding of how risk allele load impacts disease susceptibility.

Genome-wide association study of height-adjusted BMI in childhood identifies functional variant in ADCY3

OBJECTIVE

Genome-wide association studies (GWAS) of BMI are mostly undertaken under the assumption that "kg/m(2) " is an index of weight fully adjusted for height, but in general this is not true. The aim here was to assess the contribution of common genetic variation to a adjusted version of that phenotype which appropriately accounts for covariation in height in children.

METHODS

A GWAS of height-adjusted BMI (BMI[x] = weight/height(x) ), calculated to be uncorrelated with height, in 5809 participants (mean age 9.9 years) from the Avon Longitudinal Study of Parents and Children (ALSPAC) was performed.

RESULTS

GWAS based on BMI[x] yielded marked differences in genomewide results profile. SNPs in ADCY3 (adenylate cyclase 3) were associated at genome-wide significance level (rs11676272 (0.28 kg/m(3.1) change per allele G (0.19, 0.38), P = 6 × 10(-9) ). In contrast, they showed marginal evidence of association with conventional BMI [rs11676272 (0.25 kg/m(2) (0.15, 0.35), P = 6 × 10(-7) )]. Results were replicated in an independent sample, the Generation R study.

CONCLUSIONS

Analysis of BMI[x] showed differences to that of conventional BMI. The association signal at ADCY3 appeared to be driven by a missense variant and it was strongly correlated with expression of this gene. Our work highlights the importance of well understood phenotype use (and the danger of convention) in characterising genetic contributions to complex traits.

Rs7206790 and rs11644943 in FTO gene are associated with risk of obesity in Chinese school-age population

To evaluate the associations between candidate FTO single nucleotide polymorphisms (SNPs) and obesity, a case-control study was conducted among Chinese school-age children, which included 500 obese cases and 500 matched controls (age, gender and location). We selected 24 candidate FTO tag-SNPs via bio-informatics analysis and performed genotyping using SNPScan technology. Results indicated that rs7206790 and rs11644943 were significantly associated with obesity among school-age children in both additive and recessive models (P<0.05) after adjusting confounders. Comparing rs7206790 CC and CG genotype of carriers, those carrying the GG genotype had an increased risk of obesity (adjusted odds ratio [OR], 3.76; 95% Confidence interval [CI], 1.24–11.43). Carriers of the AA allele of rs11644943 had a lower risk of obesity (adjusted OR, 0.16; 95% CI, 0.04–0.72) compared with those of the T allele (TT and TA). These two SNPs (rs7206790 and rs11644943) were not Linkage Disequilibrium (LD) with previous reported obesity-associated SNPs. Under the recessive model adjusted for age and gender and location, rs7206790 GG allele carriers had significantly increased BMIs (P = 0.012), weight (P = 0.012), waist circumferences (WC) (P = 0.045) and hip circumferences (HC) (P = 0.033). Conversely, rs11644943 AA allele carriers had significantly decreased BMIs (P = 0.006), WC (P = 0.037) and Waist-to-height ratios (WHtR) (P = 0.012). A dose-response relationship was found between the number of risk alleles in rs7206790, rs11644943 and rs9939609 and the risk of obesity. The Genetic Risk Score (GRS) of the reference group was 3; in comparison, those of 2, 4, and ≥5 had ORs for obesity of 0.24 (95%CI, 0.05–1.13), 1.49 (95%CI, 1.10–2.01), and 5.20 (95%CI, 1.75–15.44), respectively. This study confirmed the role of FTO variation on genetic susceptibility to obesity. We reported two new obesity-related FTO SNPs (rs7206790 and rs11644943) among Chinese school-age children.

Genome-wide association study of bipolar disorder accounting for effect of body mass index identifies a new risk allele in TCF7L2

Bipolar disorder (BD) is associated with higher body mass index (BMI) and increased metabolic comorbidity. Considering the associated phenotypic traits in genetic studies of complex diseases, either by adjusting for covariates or by investigating interactions between genetic variants and covariates, may help to uncover the missing heritability. However, obesity-related traits have not been incorporated in prior genome-wide analyses of BD as covariates or potential interacting factors. To investigate the genetic factors underlying BD while considering BMI, we conducted genome-wide analyses using data from the Genetic Association Information Network BD study. We analyzed 729,454 genotyped single-nucleotide polymorphism (SNP) markers on 388 European-American BD cases and 1020 healthy controls with available data for maximum BMI. We performed genome-wide association analyses of the genetic effects while accounting for the effect of maximum BMI, and also evaluated SNP-BMI interactions. A joint test of main and interaction effects demonstrated significant evidence of association at the genome-wide level with rs12772424 in an intron of TCF7L2 (P=2.85E-8). This SNP exhibited interaction effects, indicating that the bipolar susceptibility risk of this SNP is dependent on BMI. TCF7L2 codes for the transcription factor TCF/LF, part of the Wnt canonical pathway, and is one of the strongest genetic risk variants for type 2 diabetes (T2D). This is consistent with BD pathophysiology, as the Wnt pathway has crucial implications in neurodevelopment, neurogenesis and neuroplasticity, and is involved in the mechanisms of action of BD and depression treatments. We hypothesize that genetic risk for BD is BMI dependent, possibly related to common genetic risk with T2D.

Genetic association study with metabolic syndrome and metabolic-related traits in a cross-sectional sample and a 10-year longitudinal sample of chinese elderly population

Background

The metabolic syndrome (MetS) has been known as partly heritable, while the number of genetic studies on MetS and metabolic-related traits among Chinese elderly was limited.

Methods

A cross-sectional analysis was performed among 2 014 aged participants from September 2009 to June 2010 in Beijing, China. An additional longitudinal study was carried out among the same study population from 2001 to 2010. Biochemical profile and anthropometric parameters of all the participants were measured. The associations of 23 SNPs located within 17 candidate genes (MTHFR, PPARγ, LPL, INSIG, TCF7L2, FTO, KCNJ11, JAZF1, CDKN2A/B, ADIPOQ, WFS1, CDKAL1, IGF2BP2, KCNQ1, MTNR1B, IRS1, ACE) with overweight and obesity, diabetes, metabolic phenotypes, and MetS were examined in both studies.

Results

In this Chinese elderly population, prevalence of overweight, central obesity, diabetes, dyslipidemia, hypertension, and MetS were 48.3%, 71.0%, 32.4%, 75.7%, 68.3% and 54.5%, respectively. In the cross-sectional analyses, no SNP was found to be associated with MetS. Genotype TT of SNP rs4402960 within the gene IGF2BP2 was associated with overweight (odds ratio (OR)  = 0.479, 95% confidence interval (CI): 0.316-0.724, p = 0.001) and genotype CA of SNP rs1801131 within the gene MTHFR was associated with hypertension (OR = 1.560, 95% CI: 1.194–2.240, p = 0.001). However, these associations were not observed in the longitudinal analyses.

Conclusions

The associations of SNP rs4402960 with overweight as well as the association of SNP rs1801131 with hypertension were found to be statistically significant. No SNP was identified to be associated with MetS in our study with statistical significance.