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

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 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.

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Fig. 1: Comparison of fine-mapping resolution for distinct association signals for T2D obtained from ancestry-specific meta-analysis and multi-ancestry meta-regression.
Fig. 2: T2D-association signal at the BCAR1 locus colocalizes with multiple circulating plasma pQTL.
Fig. 3: Defining causal molecular mechanisms at the PROX1 locus.
Fig. 4: Transferability of multi-ancestry and ancestry-specific GRS into GWAS across diverse population groups.
Fig. 5: Positive selection acting on T2D index SNVs.

Data availability

Association summary statistics from the multi-ancestry meta-analysis and annotation-informed fine-mapping are available through the AMP T2D Knowledge Portal (http://www.type2diabetesgenetics.org/) and the DIAGRAM Consortium data download website (http://diagram-consortium.org/downloads.html). Source data are provided with this paper.

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Acknowledgements

A complete list of acknowledgements and funding appears in the Supplementary Note. This research was funded in part by the Wellcome Trust (grant numbers 064890, 072960, 083948, 084723, 085475, 086113, 088158, 090367, 090532, 095101, 098017, 098051, 098381, 098395, 101033, 101630, 104085, 106130, 200186, 200837, 202922, 203141, 206194, 212259, 212284, 212946 and 220457). For the purpose of open access, the authors have applied a CC-BY public copyright licence to any author accepted manuscript version arising from this submission.

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