Zeggini, E., Scott, L. J., Saxena, R., Voight, B. F., Marchini, J. L., Hu, T., de Bakker, P. I. et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat. Genet. 40, 638-645

Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
Nature Genetics (Impact Factor: 29.35). 06/2008; 40(5):638-45. DOI: 10.1038/ng.120
Source: PubMed


Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D)1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and 2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975. We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P = 5.0 10-
14), CDC123-CAMK1D (P = 1.2 10-
10), TSPAN8-LGR5 (P = 1.1 10-
9), THADA (P = 1.1 10-
9), ADAMTS9 (P = 1.2 10-
8) and NOTCH2 (P = 4.1 10-
8) gene regions. Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.

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    • "The association between rs864745 and T2DM varied among populations. SNP rs864745 was strongly associated with T2DM in European participants ( p = 5.0 · 10 -14 ) (Zeggini et al., 2008), whereas no significant association was observed in Han Chinese ( p > 0.05) (Hu et al., 2009) and Japanese subjects (P > 0.05) (Takeuchi et al., 2009). In our study, the OR value of rs864745 (0.48) was lower than that in Caucasians (1.50) (An et al., 2009), Han Chinese population (1.05–1.09) "
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    ABSTRACT: Substantial evidence suggests that type 2 diabetes mellitus (T2DM) is a multi-factorial disease with a strong genetic component. A list of genetic susceptibility loci in populations of European and Asian ancestry has been established in the literature. Little is known on the inter-ethnic contribution of such established functional polymorphic variants. We performed a case-control study to explore the genetic susceptibility of 16 selected T2DM-related SNPs in a cohort of 102 Uyghur objects (51 cases and 51 controls). Three of the 16 SNPs showed significant association with T2DM in the Uyghur population. There were significant differences between the T2DM and control groups in frequencies of the risk allelic distributions of rs7754840 (CDKAL1) (p=0.014), rs864745 (JAZF1) (p=0.032), and rs35767 (IGF1) (p=0.044). Carriers of rs7754840-C, rs35767-A, and rs864745-C risk alleles had a 2.32-fold [OR (95% CI): 1.19-4.54], 2.06-fold [OR (95% CI): 1.02-4.17], 0.48-fold [OR (95% CI): 0.24-0.94] increased risk for T2DM, respectively. The cumulative risk allelic scores of these 16 SNPs differed significantly between the T2DM patients and the controls [17.1±8.1 vs. 15.4±7.3; OR (95%CI): 1.27(1.07-1.50), p=0.007]. This is the first study to evaluate genomic variation at 16 SNPs in respective T2DM candidate genes for the Uyghur population compared with other ethnic groups. The SNP rs7754840 in CDKAL1, rs864745 in JAZF1, and rs35767 in IGF1 might serve as potential susceptibility loci for T2DM in Uyghurs. We suggest a broader capture and study of the world populations, including who that are hitherto understudied, are essential for a comprehensive understanding of the genetic/genomic basis of T2DM.
    Full-text · Article · Mar 2015 · Omics: a journal of integrative biology
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    • "At the CDKN2A-B locus, the strongest association signal for T2D susceptibility maps to a narrow inter-genic recombination interval spanning less than 10 kb [1, 4, 8•]. Fine mapping of the locus was undertaken by imputation into 1,000 T2D cases and 1,048 controls from the Diabetes Genetics Initiative up to a reference panel consisting of: (i) pilot data from the 1000 Genomes Project Consortium and (ii) targeted re-sequencing of 47 individuals from the same population background [45]. "
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    ABSTRACT: Genome-wide association studies of type 2 diabetes have been extremely successful in discovering loci that contribute genetic effects to susceptibility to the disease. However, at the vast majority of these loci, the variants and transcripts through which these effects on type 2 diabetes are mediated are unknown, limiting progress in defining the pathophysiological basis of the disease. In this review, we will describe available approaches for assaying genetic variation across loci and discuss statistical methods to determine the most likely causal variants in the region. We will consider the utility of trans-ethnic meta-analysis for fine mapping by leveraging the differences in the structure of linkage disequilibrium between diverse populations. Finally, we will discuss progress in fine-mapping type 2 diabetes susceptibility loci to date and consider the prospects for future efforts to localise causal variants for the disease.
    Preview · Article · Nov 2014 · Current Diabetes Reports
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    • "The biological goal of the experiment is to aid in the interpretation of the results of a Genome-Wide Association Study (GWAS) by relating metabolic traits to the Single Nucleotide Polymorphisms (SNPs) that were identified by the GWAS. GWA studies have successfully identified genomic regions that dispose individuals to diseases (see for example [26], for a review see [27]). However, the underlying biological mechanisms often remain elusive, which led the research community to evince interest in genetic association studies of metabolites levels in blood (see for example [28–30]). "
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    ABSTRACT: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows. We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as “which particular data was input to a particular workflow to test a particular hypothesis?”, and “which particular conclusions were drawn from a particular workflow?”. Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well. The Research Object is available at The Wf4Ever Research Object Model is available at
    Full-text · Article · Sep 2014 · Journal of Biomedical Semantics
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