Common variants in WFS1 confer risk of type 2 diabetes. Nat Genet

UK Medical Research Council (MRC) Epidemiology Unit, Strangeways Research Laboratory, Cambridge CB1 8RN, UK.
Nature Genetics (Impact Factor: 29.35). 09/2007; 39(8):951-3. DOI: 10.1038/ng2067
Source: PubMed


We studied genes involved in pancreatic beta cell function and survival, identifying associations between SNPs in WFS1 and diabetes risk in UK populations that we replicated in an Ashkenazi population and in additional UK studies. In a pooled analysis comprising 9,533 cases and 11,389 controls, SNPs in WFS1 were strongly associated with diabetes risk. Rare mutations in WFS1 cause Wolfram syndrome; using a gene-centric approach, we show that variation in WFS1 also predisposes to common type 2 diabetes.

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Available from: Colin N A Palmer, Sep 16, 2014
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    • "Our model also detected other SNPs in close proximity of different candidate genes; that is, SNP ss66397464 in peroxisome proliferator-activated receptor-γ gene (PPARG) on chromosome 3, SNP ss66402098 in the Wolfram syndrome 1 gene (WFSI) on chromosome 4, SNP ss66218814 in CDK5 regulatorysubunit-associated protein 1-like 1 gene (CDKAL1) on chromosome 6, and SNP ss66288005 in potassium inwardly-rectifying channel, subfamily J, member 11 gene (KCNJ11) on chromosome 12[Frayling (2007)]. Among these four genes, PPARG and KCNJ were found to be associated with obesity[Vidal-Puig et al. (1997);Morgan et al. (2010)], while WFSI and CDKAL1 are believed to be associated with diabetes[Sandhu et al. (2007);Scott et al. (2007);Steinthorsdottir et al. (2007)]. Therefore, all these discoveries have well validated the biological relevance of the new model. "
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    ABSTRACT: Although genome-wide association studies (GWAS) have proven powerful for comprehending the genetic architecture of complex traits, they are challenged by a high dimension of single-nucleotide polymorphisms (SNPs) as predictors, the presence of complex environmental factors, and longitudinal or functional natures of many complex traits or diseases. To address these challenges, we propose a high-dimensional varying-coefficient model for incorporating functional aspects of phenotypic traits into GWAS to formulate a so-called functional GWAS or fGWAS. The Bayesian group lasso and the associated MCMC algorithms are developed to identify significant SNPs and estimate how they affect longitudinal traits through time-varying genetic actions. The model is generalized to analyze the genetic control of complex traits using subject-specific sparse longitudinal data. The statistical properties of the new model are investigated through simulation studies. We use the new model to analyze a real GWAS data set from the Framingham Heart Study, leading to the identification of several significant SNPs associated with age-specific changes of body mass index. The fGWAS model, equipped with the Bayesian group lasso, will provide a useful tool for genetic and developmental analysis of complex traits or diseases.
    Full-text · Article · Sep 2015 · The Annals of Applied Statistics
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    • "The E23K variant in this gene demonstrated a robust association with T2D using the candidate gene approach [9]. WFS1 and HNF1B were also uncovered as established genes associated with T2D [11, 12]. WFS1 encodes wolframin, a membrane glycoprotein that maintains calcium homeostasis of the endoplasmic reticulum. "
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    ABSTRACT: With rapidly increasing prevalence, diabetes has become one of the major causes of mortality worldwide. According to the latest studies, genetic information makes substantial contributions towards the prediction of diabetes risk and individualized antidiabetic treatment. To date, approximately 70 susceptibility genes have been identified as being associated with type 2 diabetes (T2D) at a genome-wide significant level (P < 5 × 10(-8)). However, all the genetic loci identified so far account for only about 10% of the overall heritability of T2D. In addition, how these novel susceptibility loci correlate with the pathophysiology of the disease remains largely unknown. This review covers the major genetic studies on the risk of T2D based on ethnicity and briefly discusses the potential mechanisms and clinical utility of the genetic information underlying T2D.
    Full-text · Article · Apr 2014 · BioMed Research International
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    • "With the high-throughput genotyping technologies, genome-wide association studies (GWAS) not only confirmed the candidate genes such as PPARG [5], KCNJ11 [6], TCF7L2 [7] and WFS1 [8], but also identified more than 70 novel loci for T2D risk [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20]. The majority of these variants conferred T2D risk through pancreatic beta-cell dysfunction [17], [21], [22], while only a few like PPARG, FTO and IRS1 affected fat metabolism [12], [17], [22]. "
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    ABSTRACT: Recent genome-wide association studies (GWAS) identified more than 70 novel loci for type 2 diabetes (T2D), some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population. We selected 14 single nucleotide polymorphisms (SNPs) in T2D genes relating to beta-cell function validated in Asian populations and genotyped them in 5882 Chinese T2D patients and 2569 healthy controls. A combined genetic score (CGS) was calculated by summing up the number of risk alleles or weighted by the effect size for each SNP under an additive genetic model. We tested for associations by either logistic or linear regression analysis for T2D and quantitative traits, respectively. The contribution of the CGS for predicting T2D risk was evaluated by receiver operating characteristic (ROC) analysis and net reclassification improvement (NRI). We observed consistent and significant associations of IGF2BP2, WFS1, CDKAL1, SLC30A8, CDKN2A/B, HHEX, TCF7L2 and KCNQ1 (8.5×10(-18)<P<8.5×10(-3)), as well as nominal associations of NOTCH2, JAZF1, KCNJ11 and HNF1B (0.05<P<0.1) with T2D risk, which yielded odds ratios ranging from 1.07 to 2.09. The 8 significant SNPs exhibited joint effect on increasing T2D risk, fasting plasma glucose and use of insulin therapy as well as reducing HOMA-β, BMI, waist circumference and younger age of diagnosis of T2D. The addition of CGS marginally increased AUC (2%) but significantly improved the predictive ability on T2D risk by 11.2% and 11.3% for unweighted and weighted CGS, respectively using the NRI approach (P<0.001). In a Chinese population, the use of a CGS of 8 SNPs modestly but significantly improved its discriminative ability to predict T2D above and beyond that attributed to clinical risk factors (sex, age and BMI).
    Full-text · Article · Dec 2013 · PLoS ONE
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