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The trans-ancestral genomic architecture of glycemic traits

Subjects

Abstract

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10−8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

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Fig. 1: Summary of all 242 loci identified in this study.
Fig. 2: Trait variance explained by associated loci.
Fig. 3: Transferability of PGSs across ancestries.
Fig. 4: Trans-ancestry fine-mapping.
Fig. 5: Epigenomic landscape of trait-associated variants.
Fig. 6: Tissues and cell types that are significantly enriched in genes in loci associated with glycemic traits.
Fig. 7: Gene-set enrichment analyses.

Data availability

Ancestry-specific and overall meta-analysis summary level results are available through the MAGIC website (https://www.magicinvestigators.org/). Summary statistics are also available through the GWAS catalog (https://www.ebi.ac.uk/gwas/) with the following accession codes: GCST90002225, GCST90002226, GCST90002227, GCST90002228, GCST90002229, GCST90002230, GCST90002231, GCST90002232, GCST90002233, GCST90002234, GCST90002235, GCST90002236, GCST90002237, GCST90002238, GCST90002239, GCST90002240, GCST90002241, GCST90002242, GCST90002243, GCST90002244, GCST90002245, GCST90002246, GCST90002247 and GCST90002248.

Code availability

Source code implementing the methods described in the paper are publicly available at https://doi.org/10.5281/zenodo.4607311.

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Acknowledgements

We thank all investigators, staff members and study participants for their contribution to all participating studies. The funders had no role in study design, data collection, analysis, decision to publish or preparation of the manuscript. The authors received no specific funding for this work. A full list of funding as well as individual and study acknowledgments appears in the Supplementary Note.

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