Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.
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.
Duke Scholars
Altmetric Attention Stats
Dimensions Citation Stats
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Risk Factors
- Polymorphism, Single Nucleotide
- Humans
- Genome-Wide Association Study
- Genetic Predisposition to Disease
- Ethnicity
- Diabetes Mellitus, Type 2
- Developmental Biology
- 3105 Genetics
- 3102 Bioinformatics and computational biology
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Risk Factors
- Polymorphism, Single Nucleotide
- Humans
- Genome-Wide Association Study
- Genetic Predisposition to Disease
- Ethnicity
- Diabetes Mellitus, Type 2
- Developmental Biology
- 3105 Genetics
- 3102 Bioinformatics and computational biology