The trans-ancestral genomic architecture of glycemic traits.
Chen J, Spracklen CN, Marenne G et al.
Publication Details
Comprehensive information about this research publication
Authors
Abstract
Summary of the research findings
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.
1,431 African American individuals
Study Statistics
Key metrics and study information
Analysis
Comprehensive review of health and genetic findings
Important Disclaimer: This review has been performed semi-automatically and is provided for informational purposes only. While we strive for accuracy, this analysis may contain errors, omissions, or misinterpretations of the original research. DNA Genics disclaims all liability for any inaccuracies, errors, or consequences arising from the use of this information. Users should independently verify all information and consult original research publications before making any decisions based on this content. This analysis is not intended as a substitute for professional scientific review or medical advice.
Analysis In Progress
Our analysis of this publication is currently being prepared. Please check back soon for comprehensive insights into the health and genetic findings discussed in this research.
Summary
Key Findings
Health Insights
Disease Analysis
Genetic Trait Analysis
Clinical Relevance
Scientific Assessment
Related Publications
Other publications that may be of interest
A genome-wide association study of mass spectrometry proteomics using a nanoparticle enrichment platform.
Suhre K
Nat Genet
HV102 protein level (protein group normalized intensity)
Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models.
Cosentino J
Nat Genet
Chronic obstructive pulmonary disease liability (machine learning-based score)
A genetic map of human metabolism across the allele frequency spectrum.
Zoodsma M
Nat Genet
Free cholesterol in small VLDL (PGS-adjusted)
Large-scale genome-wide analyses of stuttering.
Polikowsky HG
Nat Genet
Stuttering
Genome-wide association study and polygenic risk prediction of hypothyroidism.
Rand SA
Nat Genet
Thyroid stimulating hormone levels
Explore More Research
Discover the latest findings in health and genetic research