| Title: | Cross-population GWAS and proteomics improve risk prediction and reveal mechanisms in atrial fibrillation |
| Journal: | Nature Communications |
| Published: | 11 Jul 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40645996/ |
| DOI: | https://doi.org/10.1038/s41467-025-61720-2 |
Publication 16910
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Abstract
Atrial fibrillation (AF) is a common cardiac arrhythmia with strong genetic components, yet its underlying molecular mechanisms and potential therapeutic targets remain incompletely understood. We conducted a cross-population genome-wide meta-analysis of 252,438 AF cases and identified 525 loci that met genome-wide significance. Two loci of PITX2 and ZFHX3 genes were identified as shared across populations of different ancestries. Comprehensive gene prioritization approaches reinforced the role of muscle development and heart contraction while also uncovering additional pathways, including cellular response to transforming growth factor-beta. Population-specific genetic correlations uncovered common and unique circulatory comorbidities between Europeans and Africans. Mendelian randomization identified modifiable risk factors and circulating proteins, informing disease prevention and drug development. Integrating genomic data from this cross-population genome-wide meta-analysis with proteomic profiling significantly enhanced AF risk prediction. This study advances our understanding of the genetic etiology of AF while also enhancing risk prediction, prevention strategies, and therapeutic development.</p>
15 Keywords
- African People
- Atrial Fibrillation
- European People
- Female
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Homeobox Protein PITX2
- Homeodomain Proteins
- Humans
- Male
- Mendelian Randomization Analysis
- Polymorphism, Single Nucleotide
- Proteomics
- Risk Factors
- Transcription Factors
17 Authors
- Shuai Yuan
- Jie Chen
- Xixin Ruan
- Yuying Li
- Sarah A. Abramowitz
- Lijuan Wang
- Fangyuan Jiang
- Ying Xiong
- Michael G. Levin
- Benjamin F. Voight
- Dipender Gill
- Stephen Burgess
- Agneta Åkesson
- Karl Michaëlsson
- Xue Li
- Scott M. Damrauer
- Susanna C. Larsson
1 Application
| Application ID | Title |
|---|---|
| 72723 | Improving causal inference in rheumatic diseases research by integrating observational and genetic epidemiology |
Enabling scientific discoveries that improve human health