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      Common and Distinct Genetic Architecture of Age at Diagnosis of Diabetes in South Indian and European Populations

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          Abstract

          OBJECTIVE

          South Asians are diagnosed with type 2 diabetes (T2D) more than a decade earlier in life than seen in European populations. We hypothesized that studying the genomics of age of diagnosis in these populations may give insight into the earlier age diagnosis of T2D among individuals of South Asian descent.

          RESEARCH DESIGN AND METHODS

          We conducted a meta-analysis of genome-wide association studies (GWAS) of age at diagnosis of T2D in 34,001 individuals from four independent cohorts of European and South Asian Indians.

          RESULTS

          We identified two signals near the TCF7L2 and CDKAL1 genes associated with age at the onset of T2D. The strongest genome-wide significant variants at chromosome 10q25.3 in TCF7L2 (rs7903146; P = 2.4 × 10 −12, β = −0.436; SE 0.02) and chromosome 6p22.3 in CDKAL1 (rs9368219; P = 2.29 × 10 −8; β = −0.053; SE 0.01) were directionally consistent across ethnic groups and present at similar frequencies; however, both loci harbored additional independent signals that were only present in the South Indian cohorts. A genome-wide signal was also obtained at chromosome 10q26.12 in WDR11 (rs3011366; P = 3.255 × 10 −8; β = 1.44; SE 0.25), specifically in the South Indian cohorts. Heritability estimates for the age at diagnosis were much stronger in South Indians than Europeans, and a polygenic risk score constructed based on South Indian GWAS explained ∼2% trait variance.

          CONCLUSIONS

          Our findings provide a better understanding of ethnic differences in the age at diagnosis and indicate the potential importance of ethnic differences in the genetic architecture underpinning T2D.

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          Most cited references38

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          The UK Biobank resource with deep phenotyping and genomic data

          The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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            LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

            Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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              METAL: fast and efficient meta-analysis of genomewide association scans

              Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: goncalo@umich.edu
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                Author and article information

                Journal
                Diabetes Care
                Diabetes Care
                diabetes care
                Diabetes Care
                American Diabetes Association
                0149-5992
                1935-5548
                August 2023
                12 June 2023
                12 June 2023
                : 46
                : 8
                : 1515-1523
                Affiliations
                [1 ]Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, U.K.
                [2 ]Madras Diabetes Research Foundation and Dr Mohan’s Diabetes Specialities Centre, Chennai, India
                Author notes
                Corresponding author: Colin N.A. Palmer, c.n.a.palmer@ 123456dundee.ac.uk
                Author information
                https://orcid.org/0000-0002-3387-9889
                https://orcid.org/0000-0001-9055-3896
                https://orcid.org/0000-0002-4843-1374
                https://orcid.org/0000-0001-9237-8585
                https://orcid.org/0000-0002-4281-0250
                https://orcid.org/0000-0002-6415-6560
                Article
                230243
                10.2337/dc23-0243
                10369131
                37308106
                68f57821-cf28-45bd-b264-b21cf2d8d1a4
                © 2023 by the American Diabetes Association

                Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.

                History
                : 10 February 2023
                : 15 May 2023
                Funding
                Funded by: National Institute for Health and Care Research, DOI 10.13039/501100000272;
                Funded by: The Wellcome Trust;
                Award ID: 072960/Z/03/Z
                Award ID: 084726/Z/08/Z
                Award ID: 084727/Z/08/Z
                Award ID: 085475/B/08/Z
                Award ID: 085475/Z/08/Z
                Funded by: INSPIRED;
                Award ID: 16/136/102
                Categories
                Original Article

                Endocrinology & Diabetes
                Endocrinology & Diabetes

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