Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population

Comprehensive overview of disease models for Wolfram syndrome: toward effective treatments

Wolfram syndrome (OMIM 222300) is a rare autosomal recessive disease with a devastating array of symptoms, including diabetes mellitus, optic nerve atrophy, diabetes insipidus, hearing loss, and neurological dysfunction. The discovery of the causative gene, WFS1, has propelled research on this disease. However, a comprehensive understanding of the function of WFS1 remains unknown, making the development of effective treatment a pressing challenge. To bridge these knowledge gaps, disease models for Wolfram syndrome are indispensable, and understanding the characteristics of each model is critical. This review will provide a summary of the current knowledge regarding WFS1 function and offer a comprehensive overview of established disease models for Wolfram syndrome, covering animal models such as mice, rats, flies, and zebrafish, along with induced pluripotent stem cell (iPSC)-derived human cellular models. These models replicate key aspects of Wolfram syndrome, contributing to a deeper understanding of its pathogenesis and providing a platform for discovering potential therapeutic approaches.

Risk of type 2 diabetes and KCNJ11 gene polymorphisms: a nested case-control study and meta-analysis

Due to the central role in insulin secretion, the potassium inwardly-rectifying channel subfamily J member 11 (KCNJ11) gene is one of the essential genes for type 2 diabetes (T2D) predisposition. However, the relevance of this gene to T2D development is not consistent among diverse populations. In the current study, we aim to capture the possible association of common KCNJ11 variants across Iranian adults, followed by a meta-analysis. We found that the tested variants of KCNJ11 have not contributed to T2D incidence in Iranian adults, consistent with similar insulin secretion levels among individuals with different genotypes. The integration of our results with 72 eligible published case-control studies (41,372 cases and 47,570 controls) as a meta-analysis demonstrated rs5219 and rs5215 are significantly associated with the increased T2D susceptibility under different genetic models. Nevertheless, the stratified analysis according to ethnicity showed rs5219 is involved in the T2D risk among disparate populations, including American, East Asian, European, and Greater Middle Eastern, but not South Asian. Additionally, the meta-regression analysis demonstrated that the sample size of both case and control groups was significantly associated with the magnitude of pooled genetic effect size. The present study can expand our knowledge about the KCNJ11 common variant's contributions to T2D incidence, which is valuable for designing SNP-based panels for potential clinical applications in precision medicine. It also highlights the importance of similar sample sizes for avoiding high heterogeneity and conducting a more precise meta-analysis.

PPARG, TMEM163, UBE2E2 and WFS1 Gene Polymorphisms Are Not Significant Risk Factors for Gestational Diabetes in the Polish Population

Gestational diabetes mellitus (GDM) is a common disorder that occurs in pregnant women, leading to many maternal and neonatal complications. The pathogenesis of GDM is complex and includes risk factors, such as: age, obesity, and family history of diabetes. Studies have shown that genetic factors also play a role in the pathogenesis of GDM. The present study investigated whether polymorphisms in the PPARG (rs1801282), TMEM163 (rs6723108 and rs998451), UBE2E2 (rs6780569), and WFS1 (rs4689388) genes are risk factors for the development of GDM and whether they affect selected clinical parameters in women with GDM. This study included 204 pregnant women with GDM and 207 pregnant women with normal glucose tolerance (NGT). The diagnosis of GDM was based on a 75 g oral glucose tolerance test (OGTT) at 24–28 weeks gestation, according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. There were no statistically significant differences in the distribution of polymorphisms studied between women with GDM and pregnant women with normal carbohydrate tolerance, which suggests that these polymorphisms are not risk factors for GDM. We also examined the associations between studied gene polymorphisms and clinical parameters: fasting glucose, daily insulin requirement, body mass before pregnancy, body mass at birth, body mass increase during pregnancy, BMI before pregnancy, BMI at birth, BMI increase during pregnancy, new-born body mass, and APGAR score in women with GDM. We observed lower BMI values before pregnancy and at birth in women with PPARG rs17036160 TT genotype. The results of this study suggest that the PPARG (rs1801282), TMEM163 (rs6723108 and rs998451), UBE2E2 (rs6780569), and WFS1 (rs4689388) gene polymorphisms are not significant risk factors for GDM development in the Polish population and do not affect the clinical parameters in women with GDM; only rs1801282 of the PPARG gene may influence BMI values in women with GDM.

p53 p.Pro72Arg (rs1042522) and Mouse Double Minute 2 (MDM2) Single-Nucleotide Polymorphism (SNP) 309 Variants and Their Interaction in Chronic Lymphocytic Leukemia(CLL): A Survey in CLL Patients from Western Iran

Background: Chronic lymphocytic leukemia (CLL) is the most common leukemia in adults. The MDM2 and p53 are interacting proteins that play crucial roles in cell biology. Genetic variations of p53 and MDM2 have been identified in many cancers including CLL; among which are SNP309 in the promoter of MDM2 and SNP codon72 in p53. Materials and Methods: In this study, we sought to find the impact of two SNPs of p53 and MDM2 in the pathogenesis of CLL. A total of 100 CLL patients and 102 healthy controls were recruited. Genomic DNA was extracted, and genotyping was performed using the PCR-RFLP method. The allele and genotype associations were analyzed using the χ2 test. The gene-gene interaction analysis was studied using GMDR v0.9. Results: Our study found the absence of a significant difference between CLL patients and controls related to the allelic frequencies or genotypic distributions for both MDM2 SNP309 and p53 codon72. A significantly higher frequency of p53 C allele was found in patients with disease duration of more than 36 compared to those less than 36 months. However, GMDR analysis suggests genetic interaction between the genes under study. Conclusion: Our findings indicated each polymorphism of p53 codon72 and MDM2 (SNP309) was not a risk factor for CLL but the p53 C allele could be associated with the disease duration. Besides, the interaction between p53/MDM2 genotypes may confer susceptibility to CLL. Our study could be useful in genetic association studies of CLL and the role of gene-gene interactions in the susceptibility to the disease.

A computational structural biology study to understand the impact of mutation on structure-function relationship of inward-rectifier potassium ion channel Kir6.2 in human

Type 2 diabetes (T2D) is clinically characterized via hyperglycemia. Polymorphism rs5219 in the KCNJ11 gene is a risk factor for developing T2D in humans. KCNJ11 encodes the 'inward-rectifier potassium ion channel (Kir6.2)'. However, because of the absence of the complete crystal/NMR structures of Kir6.2 proteins, insight into its structure and function and its interaction with diverse ligands remain elusive to date. Therefore, a computational approach was employed for predicting the best plausible 'three-dimensional' structure of Kir6.2 as well as for studying the influence of mutation (p. GLU23LYS) on both architectures as well as the function of Kir6.2 employing simulation studies. Results obtained revealed that though, with increased time, 'Gibbs free energy' becomes positive, residues in wild type Kir6.2 experiences less random movement as compared to mutant Kir6.2. The less random movement of residues in wild type Kir6.2 represents the standard coupling between open and closing of 'K_ATP channel' and thus the normal secretion of insulin. The more dispersed motion of mutant Kir6.2 residues represents 'overactivity' of the 'K_ATP channel' and thus insulin 'under-secretion'. Further, molecular docking and simulation studies identified two phytochemicals/drugs, namely, A-348441 and chushizisin I, which retains the wild type property of Kir6.2 after binding with mutant protein. Unlike A-348441, this is for the first time, the present study is reporting about the plausible anti-diabetic property of chushizisin I. As these two phytochemicals/drugs, namely, A-348441 and chushizisin I, have passed ADMET test, in the near future, they may be utilized as anti-diabetic drugs after further investigation.Communicated by Ramaswamy H. Sarma.

Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: An application to maize flowering

When handling a structured population in association mapping, group-specific allele effects may be observed at quantitative trait loci (QTLs) for several reasons: (i) a different linkage disequilibrium (LD) between SNPs and QTLs across groups, (ii) group-specific genetic mutations in QTL regions, and/or (iii) epistatic interactions between QTLs and other loci that have differentiated allele frequencies between groups. We present here a new genome-wide association (GWAS) approach to identify QTLs exhibiting such group-specific allele effects. We developed genetic materials including admixed progeny from different genetic groups with known genome-wide ancestries (local admixture). A dedicated statistical methodology was developed to analyze pure and admixed individuals jointly, allowing one to disentangle the factors causing the heterogeneity of allele effects across groups. This approach was applied to maize by developing an inbred "Flint-Dent" panel including admixed individuals that was evaluated for flowering time. Several associations were detected revealing a wide range of configurations of allele effects, both at known flowering QTLs (Vgt1, Vgt2 and Vgt3) and new loci. We found several QTLs whose effect depended on the group ancestry of alleles while others interacted with the genetic background. Our GWAS approach provides useful information on the stability of QTL effects across genetic groups and can be applied to a wide range of species.

Association Analysis of the HNF4A Common Genetic Variants with Type 2 Diabetes Mellitus Risk

Type 2 diabetes mellitus (T2DM) is a complex disease that involves a wide range of genetic and environmental factors. The hepatocyte nuclear factor (HNF4A) carries out hepatic gluconeogenesis regulation and insulin secretion crucially, and the corresponding gene was shown to be linked to T2DM in several studies. The aim of the present study was to evaluate the association between HNF4A genetic variants (rs1884613 and rs1884614) and T2DM risk in a group of Iranian patients. This case-control study included 100 patients with T2DM and 100 control subjects. Genotyping of two single nucleotide polymorphisms (SNPs) (rs1884613 and rs1884614) of HNF4A was performed using the sequencing method. There was no statistically significant difference for allele and genotype distribution of the HNF4A common variants (rs1884613 and rs1884614) between subjects with and without T2DM (P=0.9 and P=0.9, respectively). Regarding diabetic complications, although the presence of mentioned polymorphisms increased the odds of developing ophthalmic complications and reduction of the odds of renal complications among diabetic patients, the mentioned risk was non- significant and cannot be generalized to the whole population.  It seems that rs1884613 and rs1884614 polymorphisms are not associated with T2DM or its renal and ophthalmic complications. To investigate the precise influence of these polymorphisms, prospective cohorts with larger sample sizes are required.

Targeted deletion of Tcf7l2 in adipocytes promotes adipocyte hypertrophy and impaired glucose metabolism

OBJECTIVE

Activation of the Wnt-signaling pathway is known to inhibit differentiation in adipocytes. However, there is a gap in our understanding of the transcriptional network regulated by components of the Wnt-signaling pathway during adipogenesis and in adipocytes during postnatal life. The key intracellular effectors of the Wnt-signaling pathway occur through TCF transcription factors such as TCF7L2 (transcription factor-7-like 2). Several genetic variants in proximity to TCF7L2 have been linked to type 2 diabetes through genome-wide association studies in various human populations. Our work aims to functionally characterize the adipocyte specific gene program regulated by TCF7L2 and understand how this program regulates metabolism.

METHODS

We generated Tcf7l2^F/F mice and assessed TCF7L2 function in isolated adipocytes and adipose specific knockout mice. ChIP-sequencing and RNA-sequencing was performed on the isolated adipocytes with control and TCF7L2 knockout cells. Adipose specific TCF7L2 knockout mice were challenged with high fat diet and assessed for body weight, glucose tolerance, and lipolysis.

RESULTS

Here we report that TCF7L2 regulates adipocyte size, endocrine function, and glucose metabolism. Tcf7l2 is highly expressed in white adipose tissue, and its expression is suppressed in genetic and diet-induced models of obesity. Genome-wide distribution of TCF7L2 binding and gene expression analysis in adipocytes suggests that TCF7L2 directly regulates genes implicated in cellular metabolism and cell cycle control. When challenged with a high-fat diet, conditional deletion of TCF7L2 in adipocytes led to impaired glucose tolerance, impaired insulin sensitivity, promoted weight gain, and increased adipose tissue mass. This was accompanied by reduced expression of triglyceride hydrolase, reduced fasting-induced free fatty acid release, and adipocyte hypertrophy in subcutaneous adipose tissue.

CONCLUSIONS

Together our studies support that TCF7L2 is a central transcriptional regulator of the adipocyte metabolic program by directly regulating the expression of genes involved in lipid and glucose metabolism.

Genetic variants in promoter regions associated with type 2 diabetes mellitus: A large-scale meta-analysis and subgroup analysis

BACKGROUND

Promoter plays important roles in regulating transcription of genes. Association studies of genetic variants in promoter region with type 2 diabetes (T2D) risk have been reported, but most were limited to small number of individual genetic variants and insufficient sample sizes. In addition, the effect of study populations and demographic characteristics were often neglected.

METHODS

In this study, we conducted a large-scale meta-analysis and subgroup analysis of T2D associated genetic variants in the promoter regions to evaluate their contribution to the susceptibility in T2D. Alleles and genotypes from cohort or case-controlled studies were extracted for future study. Total 41 742 cases and 50 493 controls for three loci were involved in 70 articles.

RESULTS

Seventy case-controlled studies of three genes with 41 742 cases and 50 493 controls were included. Meta-analysis showed only rs266729 and rs17300539 of ADIPOQ, and rs1884613, rs2144908, and rs4810424 of HNF4A were significantly associated with T2D risk. Subgroup analysis showed that both rs266729 and rs17300539 of ADIPOQ were associated with the risk of T2D in Caucasian population, but only rs266729 of ADIPOQ in Asian population and rs2144908 in other population including multinational North American. For diagnostic criteria, rs266729 of ADIPOQ and rs2144908 of HNF4A were associated with T2D risk when WHO/ADA diagnostic criteria were used. For genotyping methods, both rs266729 of ADIPOQ and rs2144908 of HNF4A were associated with T2D risk when other than Taqman and Sequencing methods were used.

CONCLUSIONS

T2D was significantly associated with promoter rs266729, rs17300539, rs1884613, rs2144908, and rs4810424, and the association of T2D risk were affected by study population, diagnostic criteria, and genotype methods.

Association of Kir6.2 gene rs5219 variation with type 2 diabetes: A meta-analysis of 21,464 individuals

AIMS

rs5219 is in Potassium inwardly-rectifying channel, subfamily J, member 11 (KCNJ11) E23K gene, located at 11p15.1. Researches on the association between rs5219 gene polymorphism with type 2 diabetes mellitus (T2DM) were performed extensively, but the results remain controversial. To investigate the relationship, a meta-analysis involving 21,464 individuals was conducted.

METHODS

Odds ratios (OR) and 95% confidence intervals (CI) were used to assess the strength of this association. Publication bias was evaluated with Begg's test. Our research includes three gene models: allelic genetic model (K-allele vs. E-allele), recessive genetic model (KK vs. EK+EE) and dominant genetic model (EE vs. EK+KK).

RESULTS

In allelic genetic model, subgroup analysis demonstrated rs5219 K-allele was relevant to T2DM risk in Caucasian (OR: 1.16, 95% CI: 1.09-1.24, P=0.000) and East Asian (OR: 1.19, 95% CI: 1.13-1.26, P=0.000), recessive genetic model indicated rs5219 KK genotype was related to T2DM risk in Caucasian, East Asian, South Asian, and North African (OR: 1.27, 95% CI: 1.17-1.38, P=0.000), dominant genetic model pointed out rs5219 EE genotype was an opposite association with T2DM risk in Caucasian (OR: 0.86, 95% CI: 0.78-0.94, P=0.001). No obvious evidence of publication bias was found.

CONCLUSIONS

There was a believable evidence to verify that rs5219 variation was associated with T2DM.

A polygenic score for schizophrenia predicts glycemic control

Schizophrenia is substantially comorbid with type 2 diabetes (T2D), but the molecular basis of this effect is incompletely understood. Here, we show that a cortical schizophrenia expression score predicts glycemic control from pancreatic islet cell expression. We used machine learning to identify a cortical expression signature in 212 schizophrenia patients and controls, which explained ~25% of the illness-associated variance. The algorithm was predicted in expression data from 51 subjects (9 with T2D), explained up to 26.3% of the variance in the glycemic control indicator HbA_1c and could significantly differentiate T2D patients from controls. The cross-tissue prediction was driven by processes previously linked to diabetes. Genes contributing to this prediction were involved in the electron transport chain as well as kidney development and support oxidative stress as a molecular process underlying the comorbidity between both conditions. Together, the present results suggest a molecular commonality between schizophrenia and glycemic markers of type 2 diabetes.

Polymorphisms in MIR499A and MIR125A gene are associated with autoimmune thyroid diseases

BACKGROUND

Single nucleotide polymorphisms (SNPs) of the miR-146a, miR-499a and miR-125a have been shown to be associated with the susceptibility to several autoimmune diseases. This study was conducted to identify the association of SNPs rs2910164, rs57095329, rs3746444 and rs12976445 with autoimmune thyroid diseases (AITDs) in a Chinese Han population.

METHODS

We enrolled 1061 patients with AITDs, including 701 patients with Graves' disease (GD) and 360 patients with Hashimoto's thyroiditis (HT), and 938 healthy individuals for a case-control genetic association study. Four SNPs were selected for genotyping by multiplex polymerase chain reaction and ligase detection reaction.

RESULTS

The frequencies of rs3746444 genotypes in patients with AITD and GD differed significantly from those in the controls. The frequencies of rs12976445 genotypes in patients with HT differed significantly from those in the controls. The frequencies of allele C in HT groups were significantly higher than those in control group. For the rs3746444 polymorphism, genetic associations between the combinational genotype and AITD/GD risk were observed in the dominant model, recessive model, and overdominant model. For the rs12976445 polymorphism, genetic associations between the combinational genotype and HT risk were also found in the dominant model and overdominant model. Moreover, gene-sex interactions were identified by GMDR and 2 × 2 crossover analysis.

CONCLUSIONS

Our results suggest rs3746444 (miR-499a) and rs12976445 (miR-125a) associated with AITD susceptibility and potential gene-sex interactions between the four polymorphisms and AITD.

Genetic variants in KCNJ11, TCF7L2 and HNF4A are associated with type 2 diabetes, BMI and dyslipidemia in families of Northeastern Mexico: A pilot study

The aim of the present study was to investigate whether genetic markers considered risk factors for metabolic syndromes, including dyslipidemia, obesity and type 2 diabetes mellitus (T2DM), can be applied to a Northeastern Mexican population. A total of 37 families were analyzed for 63 single nucleotide polymorphisms (SNPs), and the age, body mass index (BMI), glucose tolerance values and blood lipid levels, including those of cholesterol, low-density lipoprotein (LDL), very LDL (VLDL), high-density lipoprotein (HDL) and triglycerides were evaluated. Three genetic markers previously associated with metabolic syndromes were identified in the sample population, including KCNJ11, TCF7L2 and HNF4A. The KCNJ11 SNP rs5210 was associated with T2DM, the TCF7L2 SNP rs11196175 was associated with BMI and cholesterol and LDL levels, the TCF7L2 SNP rs12255372 was associated with BMI and HDL, VLDL and triglyceride levels, and the HNF4A SNP rs1885088 was associated with LDL levels (P<0.05).

Gene-gene and gene-sex epistatic interactions of DNMT1, DNMT3A and DNMT3B in autoimmune thyroid disease

The aim of this study was to investigate the associations of DNA methyltransferases (DNMTs) polymorphisms with susceptibility to autoimmune thyroid diseases (AITDs) and to test gene-gene/gene-sex epistasis interactions. Eight single-nucleotide polymorphisms (SNPs) in DNMT1, DNMT3A and DNMT3B were selected and genotyped by multiplex polymerase chain reaction combined with ligase detection reaction method (PCR-LDR). A total of 685 Graves' disease (GD) patients, 353 Hashimoto's thyroiditis (HT) patients and 909 healthy controls were included in the final analysis. Epistasis was tested by additive model, multiplicative model and general multifactor dimensionality reduction (general MDR). Rs2424913 (DNMT3B) and rs2228611 (DNMT1) were associated with susceptibility to AITD and GD in the dominant and overdominant model, respectively (rs2424913: P=0.009 for AITD, P=0.0041 for GD; rs2228611: P=0.035 for AITD, P=0.043 for GD). Multiplicative and multiple high dimensional gene-gene or gene-sex interactions were also observed in this study. We have found evidence for a potential role of rs2424913 (DNMT3B) and rs2228611 (DNMT1) in AITD susceptibility and identified novel gene-gene/gene-sex interactions in AITD. Our study may highlight sex and genes of DNMTs family as contributors to the pathogenesis of AITD.

Association of HNF4α gene polymorphisms with susceptibility to type 2 diabetes

The present study aimed to explore the association between single nucleotide polymorphisms (SNPs) in the hepatocyte nuclear factor‑4α (HNF‑4α) gene and the incidence of type 2 diabetes in the Chinese Bai population in Dali city, China. The polymerase chain reaction‑restriction fragment length polymorphism method was used to analyze four SNPs (rs4810424, rs1884613, rs1884614 and rs2144908) in the HNF‑4α gene in 44 patients with type 2 diabetes and 87 healthy controls in Chinese Bai individuals. The haploid type was subsequently built to assess its association with the incidence of type 2 diabetes in the Bai population in Dali city. No significant differences were observed between the genotype and allele frequencies of the four SNPs in the HNF‑4α gene and type 2 diabetes mellitus (P>0.05). However, the frequency of haplotype, CCTA, built by rs4810424, rs1884613, rs1884614 and rs2144908 was significantly higher in the type 2 diabetes mellitus group compared with the control group (χ2=8.34, P=0.004). The four polymorphisms, rs4810424, rs1884613, rs1884614 and rs2144908, in the HNF‑4α gene were not the susceptible loci for type 2 diabetes in the Bai population of Dali city, however, the haplotype, CCTA, built from the four SNPs may increase the risk of type 2 diabetes in this population.

Association of Angiotensin Converting Enzyme Insertion-Deletion Polymorphism with Hypertension in Emiratis with Type 2 Diabetes Mellitus and Its Interaction with Obesity Status

The association of Angiotensin Converting Enzyme (ACE) insertion-deletion (I/D) polymorphism with Type 2 Diabetes Mellitus (T2DM) and hypertension has been extensively studied throughout various ethnic populations but largely with inconsistent findings. We investigated these associations in Emirati population and their interaction with obesity status. Saliva samples were collected from a total of 564 Emiratis (277 T2DM and 297 healthy). DNA was extracted and the samples were genotyped for ACE I/D polymorphism by a PCR based method followed by gel electrophoresis. Upon evaluation of the ACE I/D polymorphism amongst all T2DM, hypertensive patients, and respective controls regardless of obesity status, ACE DD genotype was not found to be associated with either T2DM [odds ratio (OR) = 1.34, p = 0.086] or hypertension [odd ratio (OR) = 1.02, p = 0.93]. When the genetic variants amongst the nonobese and obese population were analyzed separately, the risk genotype ACE DD conferred significantly increased risk of hypertension in nonobese population [odds ratio (OR) = 1.80, p = 0.02] but was found to be protective against the hypertension in the obese group ((OR) = 0.54, p = 0.01). However, there was no effect of obesity status on the association of ACE genotypes with T2DM. The risk of hypertension associated with ACE DD is modulated by obesity status and hence future genetic association studies should take obesity into account for the interpretation of data. We also confirmed that ACE I/D polymorphism is not associated with T2DM risk in Emirati population.

Association of DRD3, COMT, and SLC6A4 Gene Polymorphisms with Type 2 Diabetes in Southern Chinese: A Hospital-Based Case-Control Study

AIM

The aim of this study was to assess the associations of six single nucleotide polymorphisms (SNPs) of three genes (DRD3, COMT, and SCL6A4) with type 2 diabetes mellitus (T2DM) in Southern Chinese.

SUBJECTS AND METHODS

Five hundred ninety-five cases with T2DM and 725 healthy controls of Han origin were recruited from six hospitals in Guangdong Province, Southern China. Fasting serum concentrations of markers of interest (total cholesterol, triglyceride, plasma glucose, etc.) were measured in hospitals. SNP genotyping was performed using a custom-by-design 2-×48-Plex SNPscan™ kit (Genesky Biotechnologies Inc., Shanghai, China). Single-point SNP analysis, haplotype analysis, and SNP-SNP interactions were carried out.

RESULTS

SNP rs4646312 in COMT achieved statistical significance in both allelic association and genotypic association and even after adjusting covariates (odds ratio [OR]=1.26; 95% confidence interval [CI], 1.04-1.53; P=0.021). Two haplotypes consisting of rs4646312 and rs4680 were also significantly associated with T2DM, of which C-G was a protective haplotype for T2DM (OR=0.83; 95% CI, 0.70-0.98; P=0.029), whereas T-A was a risk one (OR=1.23, 95% CI, 1.03-1.46; P=0.022). Interaction analysis identified a significant epistatic effect between rs4680 in COMT and rs2066713 in SCL6A4 after adjusting for covariates (OR=3.59, 95% CI, 1.72-7.48; P=0.001 for dominant-dominant model). However, only the interaction between rs4680 and rs2066713 was significant, and haplotype T-A showed a marginally increased risk after Bonferroni correction.

CONCLUSIONS

The genetic polymorphisms in COMT and SCL6A4 confer significant effects in joint actions to T2DM in Southern Chinese.

A roadmap to multifactor dimensionality reduction methods

Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statistical regression models. Given the known limitations of classical methods, approaches from the machine-learning community have also become attractive. From this latter family, a fast-growing collection of methods emerged that are based on the Multifactor Dimensionality Reduction (MDR) approach. Since its first introduction, MDR has enjoyed great popularity in applications and has been extended and modified multiple times. Based on a literature search, we here provide a systematic and comprehensive overview of these suggested methods. The methods are described in detail, and the availability of implementations is listed. Most recent approaches offer to deal with large-scale data sets and rare variants, which is why we expect these methods to even gain in popularity.

Replication of KCNJ11 (p.E23K) and ABCC8 (p.S1369A) Association in Russian Diabetes Mellitus 2 Type Cohort and Meta-Analysis

The genes ABCC8 and KCNJ11 have received intense focus in type 2 diabetes mellitus (T2DM) research over the past two decades. It has been hypothesized that the p.E23K (KCNJ11) mutation in the 11p15.1 region may play an important role in the development of T2DM. In 2009, Hamming et al. found that the p.1369A (ABCC8) variant may be a causal factor in the disease; therefore, in this study we performed a meta-analysis to evaluate the association between these single nucleotide polymorphisms (SNPs), including our original data on the Siberian population (1384 T2DM and 414 controls). We found rs5219 and rs757110 were not associated with T2DM in this population, and that there was linkage disequilibrium in Siberians (D’=0.766, r²= 0.5633). In addition, the haplotype rs757110[T]-rs5219[C] (p.23K/p.S1369) was associated with T2DM (OR = 1.52, 95% CI: 1.04-2.24). We included 44 original studies published by June 2014 in a meta-analysis of the p.E23K association with T2DM. The total OR was 1.14 (95% CI: 1.11-1.17) for p.E23K for a total sample size of 137,298. For p.S1369A, a meta-analysis was conducted on a total of 10 studies with a total sample size of 14,136 and pooled OR of 1.14 [95% CI (1.08-1.19); p = 2 x 10^-6]. Our calculations identified causal genetic variation within the ABCC8/KCNJ11 region for T2DM with an OR of approximately 1.15 in Caucasians and Asians. Moreover, the OR value was not dependent on the frequency of p.E23K or p.S1369A in the populations.

KCNJ11: Genetic Polymorphisms and Risk of Diabetes Mellitus

Diabetes mellitus (DM) is a major worldwide health problem and its prevalence has been rapidly increasing in the last century. It is caused by defects in insulin secretion or insulin action or both, leading to hyperglycemia. Of the various types of DM, type 2 occurs most frequently. Multiple genes and their interactions are involved in the insulin secretion pathway. Insulin secretion is mediated through the ATP-sensitive potassium (KATP) channel in pancreatic beta cells. This channel is a heteromeric protein, composed of four inward-rectifier potassium ion channel (Kir6.2) tetramers, which form the pore of the KATP channel, as well as sulfonylurea receptor 1 subunits surrounding the pore. Kir6.2 is encoded by the potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) gene, a member of the potassium channel genes. Numerous studies have reported the involvement of single nucleotide polymorphisms of the KCNJ11 gene and their interactions in the susceptibility to DM. This review discusses the current evidence for the contribution of common KCNJ11 genetic variants to the development of DM. Future studies should concentrate on understanding the exact role played by these risk variants in the development of DM.

Genetic Dissection of the Physiological Role of Skeletal Muscle in Metabolic Syndrome

The primary deficiency underlying metabolic syndrome is insulin resistance, in which insulin-responsive peripheral tissues fail to maintain glucose homeostasis. Because skeletal muscle is the major site for insulin-induced glucose uptake, impairments in skeletal muscle’s insulin responsiveness play a major role in the development of insulin resistance and type 2 diabetes. For example, skeletal muscle of type 2 diabetes patients and their offspring exhibit reduced ratios of slow oxidative muscle. These observations suggest the possibility of applying muscle remodeling to recover insulin sensitivity in metabolic syndrome. Skeletal muscle is highly adaptive to external stimulations such as exercise; however, in practice it is often not practical or possible to enforce the necessary intensity to obtain measurable benefits to the metabolic syndrome patient population. Therefore, identifying molecular targets for inducing muscle remodeling would provide new approaches to treat metabolic syndrome. In this review, the physiological properties of skeletal muscle, genetic analysis of metabolic syndrome in human populations and model organisms, and genetically engineered mouse models will be discussed in regard to the prospect of applying skeletal muscle remodeling as possible therapy for metabolic syndrome.

Association and interaction analyses of 5-HT3 receptor and serotonin transporter genes with alcohol, cocaine, and nicotine dependence using the SAGE data

Previous studies have implicated genes encoding the 5-HT3AB receptors (HTR3A and HTR3B) and the serotonin transporter (SLC6A4), both independently and interactively, in alcohol (AD), cocaine (CD), and nicotine dependence (ND). However, whether these genetic effects also exist in subjects with comorbidities remains largely unknown. We used 1,136 African-American (AA) and 2,428 European-American (EA) subjects from the Study of Addiction: Genetics and Environment (SAGE) to determine associations between 88 genotyped or imputed variants within HTR3A, HTR3B, and SLC6A4 and three types of addictions, which were measured by DSM-IV diagnoses of AD, CD, and ND and the Fagerström Test for Nicotine Dependence (FTND), an independent measure of ND commonly used in tobacco research. Individual SNP-based association analysis revealed a significant association of rs2066713 in SLC6A4 with FTND in AA (β = -1.39; P = 1.6E - 04). Haplotype-based association analysis found one major haplotype formed by SNPs rs3891484 and rs3758987 in HTR3B that was significantly associated with AD in the AA sample, and another major haplotype T-T-G, formed by SNPs rs7118530, rs12221649, and rs2085421 in HTR3A, which showed significant association with FTND in the EA sample. Considering the biologic roles of the three genes and their functional relations, we used the GPU-based Generalized Multifactor Dimensionality Reduction (GMDR-GPU) program to test SNP-by-SNP interactions within the three genes and discovered two- to five-variant models that have significant impacts on AD, CD, ND, or FTND. Interestingly, most of the SNPs included in the genetic interaction model(s) for each addictive phenotype are either overlapped or in high linkage disequilibrium for both AA and EA samples, suggesting these detected variants in HTR3A, HTR3B, and SLC6A4 are interactively contributing to etiology of the three addictive phenotypes examined in this study.

Four pairs of gene-gene interactions associated with increased risk for type 2 diabetes (CDKN2BAS-KCNJ11), obesity (SLC2A9-IGF2BP2, FTO-APOA5), and hypertension (MC4R-IGF2BP2) in Chinese women

Metabolic disorders including type 2 diabetes, obesity and hypertension have growing prevalence globally every year. Genome-wide association studies have successfully identified many genetic markers associated to these diseases, but few studied their interaction effects. In this study, twenty candidate SNPs from sixteen genes are selected, and a lasso-multiple regression approach is implemented to consider the SNP–SNP interactions among them in an Asian population. It is found out that the main effects of the markers are weak but the interactions among the candidates showed a significant association to diseases. SNPs from genes CDKN2BAS and KCNJ11 are significantly associated to risk for developing diabetes, and SNPs from FTO and APOA5 might interact to play an important role for the onset of hypertension.

Quantitative assessment of the effect of KCNJ11 gene polymorphism on the risk of type 2 diabetes

To clarify the role of potassium inwardly-rectifying-channel, subfamily-J, member 11 (KCNJ11) variation in susceptibility to type 2 diabetes (T2D), we performed a systematic meta-analysis to investigate the association between the KCNJ11 E23K polymorphism (rs5219) and the T2D in different genetic models. Databases including PubMed, Medline, EMBASE, and ISI Web of Science were searched to identify relevant studies. A total of 48 published studies involving 56,349 T2D cases, 81,800 controls, and 483 family trios were included in this meta-analysis. Overall, the E23K polymorphism was significantly associated with increased T2D risk with per-allele odds ratio (OR) of 1.12 (95% CI: 1.09-1.16; P<10-5). The summary OR for T2D was 1.09 (95% CI: 1.03-1.14; P<10-5), and 1.26 (95% CI: 1.17-1.35; P<10-5), for heterozygous and homozygous, respectively. Similar results were also detected under dominant and recessive genetic models. When stratified by ethnicity, significantly increased risks were found for the polymorphism in Caucasians and East Asians. However, no such associations were detected among Indian and other ethnic populations. Significant associations were also observed in the stratified analyses according to different mean BMI of cases and sample size. Although significant between study heterogeneity was identified, meta-regression analysis suggested that the BMI of controls significantly correlated with the magnitude of the genetic effect. The current meta-analysis demonstrated that a modest but statistically significant effect of the 23K allele of rs5219 polymorphism in susceptibility to T2D. But the contribution of its genetic variants to the epidemic of T2D in Indian and other ethnic populations appears to be relatively low.

Introduction to personalized medicine in diabetes mellitus

The world is facing an epidemic rise in diabetes mellitus (DM) incidence, which is challenging health funders, health systems, clinicians, and patients to understand and respond to a flood of research and knowledge. Evidence-based guidelines provide uniform management recommendations for "average" patients that rarely take into account individual variation in susceptibility to DM, to its complications, and responses to pharmacological and lifestyle interventions. Personalized medicine combines bioinformatics with genomic, proteomic, metabolomic, pharmacogenomic ("omics") and other new technologies to explore pathophysiology and to characterize more precisely an individual's risk for disease, as well as response to interventions. In this review we will introduce readers to personalized medicine as applied to DM, in particular the use of clinical, genetic, metabolic, and other markers of risk for DM and its chronic microvascular and macrovascular complications, as well as insights into variations in response to and tolerance of commonly used medications, dietary changes, and exercise. These advances in "omic" information and techniques also provide clues to potential pathophysiological mechanisms underlying DM and its complications.

Chromanol 293B, an inhibitor of KCNQ1 channels, enhances glucose-stimulated insulin secretion and increases glucagon-like peptide-1 level in mice

Glucose-stimulated insulin secretion (GSIS) is a highly regulated process involving complex interaction of multiple factors. Potassium voltage-gated channel subfamily KQT member 1 (KCNQ1) is a susceptibility gene for type 2 diabetes (T2D) and the risk alleles of the KCNQ1 gene appear to be associated with impaired insulin secretion. The role of KCNQ1 channel in insulin secretion has been explored by previous work in clonal pancreatic β-cells but has yet to be investigated in the context of primary islets as well as intact animals. Genetic studies suggest that altered incretin glucagon-like peptide-1 (GLP-1) secretion might be a potential link between KCNQ1 variants and impaired insulin secretion, but this hypothesis has not been verified so far. In the current study, we examined KCNQ1 expression in pancreas and intestine from normal mice and then investigated the effects of chromanol 293B, a KCNQ1 channel inhibitor, on insulin secretion in vitro and in vivo. By double-immunofluorescence staining, KCNQ1 was detected in insulin-positive β-cells and GLP-1-positive L-cells. Administration of chromanol 293B enhanced GSIS in cultured islets and intact animals. Along with the potentiated insulin secretion during oral glucose tolerance tests (OGTT), plasma GLP-1 level after gastric glucose load was increased in 293B treated mice. These data not only provided new evidence for the participation of KCNQ1 in GSIS at the level of pancreatic islet and intact animal but also indicated the potential linking role of GLP-1 between KCNQ1 and insulin secretion.

Minireview: intraislet regulation of insulin secretion in humans

The higher organization of β-cells into spheroid structures termed islets of Langerhans is critical for the proper regulation of insulin secretion. Thus, rodent β-cells form a functional syncytium that integrates and propagates information encoded by secretagogues, producing a "gain-of-function" in hormone release through the generation of coordinated cell-cell activity. By contrast, human islets possess divergent topology, and this may have repercussions for the cell-cell communication pathways that mediate the population dynamics underlying the intraislet regulation of insulin secretion. This is pertinent for type 2 diabetes mellitus pathogenesis, and its study in rodent models, because environmental and genetic factors may converge on these processes in a species-specific manner to precipitate the defective insulin secretion associated with glucose intolerance. The aim of the present minireview is therefore to discuss the structural and functional underpinnings that influence insulin secretion from human islets, and the possibility that dyscoordination between individual β-cells may play an important role in some forms of type 2 diabetes mellitus.

Development of GMDR-GPU for gene-gene interaction analysis and its application to WTCCC GWAS data for type 2 diabetes

Although genome-wide association studies (GWAS) have identified a significant number of single-nucleotide polymorphisms (SNPs) associated with many complex human traits, the susceptibility loci identified so far can explain only a small fraction of the genetic risk. Among other possible explanations, the lack of a comprehensive examination of gene–gene interaction (G×G) is often considered a source of the missing heritability. Previously, we reported a model-free Generalized Multifactor Dimensionality Reduction (GMDR) approach for detecting G×G in both dichotomous and quantitative phenotypes. However, the computational burden and less efficient implementation of the original programs make them impossible to use for GWAS. In this study, we developed a graphics processing unit (GPU)-based GMDR program (named GWAS-GPU), which is able not only to analyze GWAS data but also to run much faster than the earlier version of the GMDR program. As a demonstration of the program, we used the GMDR-GPU software to analyze a publicly available GWAS dataset on type 2 diabetes (T2D) from the Wellcome Trust Case Control Consortium. Through an exhaustive search of pair-wise interactions and a selected search of three- to five-way interactions conditioned on significant pair-wise results, we identified 24 core SNPs in six genes (FTO: rs9939973, rs9940128, rs9922047, rs1121980, rs9939609, rs9930506; TSPAN8: rs1495377; TCF7L2: rs4074720, rs7901695, rs4506565, rs4132670, rs10787472, rs11196205, rs10885409, rs11196208; L3MBTL3: rs10485400, rs4897366; CELF4: rs2852373, rs608489; RUNX1: rs445984, rs1040328, rs990074, rs2223046, rs2834970) that appear to be important for T2D. Of these core SNPs, 11 in FTO, TSPAN8, and TCF7L2 have been reported to be associated with T2D, obesity, or both, providing an independent replication of previously reported SNPs. Importantly, we identified three new susceptibility genes; i.e., L3MBTL3, CELF4, and RUNX1, for T2D, a finding that warrants further investigation with independent samples.

ACE I/D and MTHFR C677T polymorphisms are significantly associated with type 2 diabetes in Arab ethnicity: a meta-analysis

In this meta-analysis study, SNPs were investigated for their association with type 2 diabetes (T2D) in both Arab and Caucasian ethnicities. A total of 55 SNPs were analyzed, of which 11 fulfilled the selection criteria, and were used for analysis. It was found that TCF7L2 rs7903146 was significantly associated with a pooled OR of 1.155 (95%C.I.=1.059-1.259), p<0.0001 and I(2)=78.30% among the Arab population, whereas among Caucasians, the pooled OR was 1.45 (95%C.I.=1.386-1.516), p<0.0001 and I(2)=77.20%. KCNJ11 rs5219 was significantly associated in both the populations with a pooled OR of 1.176(1.092-1.268), p<0.0001 and I(2)=32.40% in Caucasians and a pooled OR of 1.28(1.111-1.475), p=0.001 among Arabs. The ACE I/D polymorphism was found to be significantly associated with a pooled OR of 1.992 (95%C.I.=1.774-2.236), p<0.0001 and I(2)=83.20% among the Arab population, whereas among Caucasians, the pooled OR was 1.078 (95%C.I.=0.993-1.17), p=0.073 and I(2)=0%. Similarly, MTHFR C677T polymorphism was also found to be significantly associated among Arabs with a pooled OR of 1.924 (95%C.I.=1.606-2.304), p<0.0001 and I(2)=27.20%, whereas among Caucasians, the pooled OR was 0.986 (95%C.I.=0.868-1.122), p=0.835 and I(2)=0%. Meanwhile PPARG-2 Pro12Ala, CDKN2A/2B rs10811661, IGF2BP2 rs4402960, HHEX rs7923837, CDKAL1 rs7754840, EXT2 rs1113132 and SLC30A8 rs13266634 were found to have no significant association with T2D among Arabs. In conclusion, it seems from this study that both Arabs and Caucasians have different SNPs associated with T2D. Moreover, this study sheds light on the profound necessity for further investigations addressing the question of the genetic components of T2D in Arabs.

Network graph analysis of gene-gene interactions in genome-wide association study data

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

Gene-gene interaction and functional impact of polymorphisms on innate immune genes in controlling Plasmodium falciparum blood infection level

Genetic variations in toll-like receptors and cytokine genes of the innate immune pathways have been implicated in controlling parasite growth and the pathogenesis of Plasmodium falciparum mediated malaria. We previously published genetic association of TLR4 non-synonymous and TNF-α promoter polymorphisms with P.falciparum blood infection level and here we extend the study considerably by (i) investigating genetic dependence of parasite-load on interleukin-12B polymorphisms, (ii) reconstructing gene-gene interactions among candidate TLRs and cytokine loci, (iii) exploring genetic and functional impact of epistatic models and (iv) providing mechanistic insights into functionality of disease-associated regulatory polymorphisms. Our data revealed that carriage of AA (P = 0.0001) and AC (P = 0.01) genotypes of IL12B 3′UTR polymorphism was associated with a significant increase of mean log-parasitemia relative to rare homozygous genotype CC. Presence of IL12B+1188 polymorphism in five of six multifactor models reinforced its strong genetic impact on malaria phenotype. Elevation of genetic risk in two-component models compared to the corresponding single locus and reduction of IL12B (2.2 fold) and lymphotoxin-α (1.7 fold) expressions in patients'peripheral-blood-mononuclear-cells under TLR4Thr399Ile risk genotype background substantiated the role of Multifactor Dimensionality Reduction derived models. Marked reduction of promoter activity of TNF-α risk haplotype (C-C-G-G) compared to wild-type haplotype (T-C-G-G) with (84%) and without (78%) LPS stimulation and the loss of binding of transcription factors detected in-silico supported a causal role of TNF-1031. Significantly lower expression of IL12B+1188 AA (5 fold) and AC (9 fold) genotypes compared to CC and under-representation (P = 0.0048) of allele A in transcripts of patients' PBMCs suggested an Allele-Expression-Imbalance. Allele (A+1188C) dependent differential stability (2 fold) of IL12B-transcripts upon actinomycin-D treatment and observed structural modulation (P = 0.013) of RNA-ensemble were the plausible explanations for AEI. In conclusion, our data provides functional support to the hypothesis that de-regulated receptor-cytokine axis of innate immune pathway influences blood infection level in P. falciparum malaria.

The effect of KCNJ11 polymorphism on the risk of type 2 diabetes: a global meta-analysis based on 49 case-control studies

Potassium inwardly rectifying channel, subfamily-J, member 11 (KCNJ11) gene encodes Kir6.2 subunits of the adenosine triphosphate (ATP)-sensitive potassium channel involved in glucose-mediated metabolic signaling pathway and has attracted considerable attention as a candidate gene for type 2 diabetes (T2D) based on its function in glucose-stimulated insulin secretion. In the past decade, a number of case-control studies have been conducted to investigate the relationship between the KCNJ11 polymorphisms and T2D. However, these studies have yielded contradictory results. To investigate this inconsistency and derive a more precise estimation of the relationship, we conducted a comprehensive meta-analysis of 64,403 cases and 122,945 controls from 49 published studies. Using the random-effects model, we found a significant association between E23K (rs5219) polymorphism and T2D risk with per-allele odds ratio of 1.13 (95% confidence interval: 1.10-1.15; p<10(-5)). Significant results were found in East Asians and Caucasians when stratified by ethnicity; whereas no significant associations were found among South Asians and other ethnic populations. In subgroup analysis by sample size, mean age and body mass index (BMI) of cases, mean BMI of controls and diagnostic criterion, significantly increased risks were found in all genetic models. This meta-analysis suggests that the E23K polymorphism in KCNJ11 is associated with elevated T2D risk, but these associations vary in different ethnic populations.

Practical and theoretical considerations in study design for detecting gene-gene interactions using MDR and GMDR approaches

Detection of interacting risk factors for complex traits is challenging. The choice of an appropriate method, sample size, and allocation of cases and controls are serious concerns. To provide empirical guidelines for planning such studies and data analyses, we investigated the performance of the multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) methods under various experimental scenarios. We developed the mathematical expectation of accuracy and used it as an indicator parameter to perform a gene-gene interaction study. We then examined the statistical power of GMDR and MDR within the plausible range of accuracy (0.50∼0.65) reported in the literature. The GMDR with covariate adjustment had a power of>80% in a case-control design with a sample size of≥2000, with theoretical accuracy ranging from 0.56 to 0.62. However, when the accuracy was<0.56, a sample size of≥4000 was required to have sufficient power. In our simulations, the GMDR outperformed the MDR under all models with accuracy ranging from 0.56∼0.62 for a sample size of 1000–2000. However, the two methods performed similarly when the accuracy was outside this range or the sample was significantly larger. We conclude that with adjustment of a covariate, GMDR performs better than MDR and a sample size of 1000∼2000 is reasonably large for detecting gene-gene interactions in the range of effect size reported by the current literature; whereas larger sample size is required for more subtle interactions with accuracy<0.56.