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Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

Mahajan, A. ; Lyssenko, V. LU ; Groop, L. LU and Morris, Andrew P. (2022) In Nature Genetics 54(5). p.560-572
Abstract
We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10−9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled... (More)
We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10−9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background. © 2022, The Author(s), under exclusive licence to Springer Nature America, Inc. (Less)
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Contribution to journal
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keywords
ancestry group, Article, comparative study, controlled study, disease risk assessment, gene mapping, genetic association, genetic background, genetic heterogeneity, genetic risk score, genome-wide association study, human, major clinical study, missense mutation, multiomics, non insulin dependent diabetes mellitus, population genetics, quantitative trait locus, single nucleotide polymorphism, ethnicity, genetic predisposition, genetics, meta analysis, risk factor, Diabetes Mellitus, Type 2, Ethnicity, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Risk Factors
in
Nature Genetics
volume
54
issue
5
pages
13 pages
publisher
Nature Publishing Group
external identifiers
  • scopus:85130637871
  • pmid:35551307
ISSN
1061-4036
DOI
10.1038/s41588-022-01058-3
language
English
LU publication?
yes
id
37523452-75a1-4050-9619-c056acc96e31
date added to LUP
2022-09-14 15:45:33
date last changed
2024-04-04 12:04:37
@article{37523452-75a1-4050-9619-c056acc96e31,
  abstract     = {{We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P &lt; 5 × 10−9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with &gt;50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background. © 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.}},
  author       = {{Mahajan, A. and Lyssenko, V. and Groop, L. and Morris, Andrew P.}},
  issn         = {{1061-4036}},
  keywords     = {{ancestry group; Article; comparative study; controlled study; disease risk assessment; gene mapping; genetic association; genetic background; genetic heterogeneity; genetic risk score; genome-wide association study; human; major clinical study; missense mutation; multiomics; non insulin dependent diabetes mellitus; population genetics; quantitative trait locus; single nucleotide polymorphism; ethnicity; genetic predisposition; genetics; meta analysis; risk factor; Diabetes Mellitus, Type 2; Ethnicity; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Polymorphism, Single Nucleotide; Risk Factors}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{560--572}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Genetics}},
  title        = {{Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation}},
  url          = {{http://dx.doi.org/10.1038/s41588-022-01058-3}},
  doi          = {{10.1038/s41588-022-01058-3}},
  volume       = {{54}},
  year         = {{2022}},
}