Article

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

ABSTRACT

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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    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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