Cross-population GWAS and proteomics improve risk prediction and reveal mechanisms in atrial fibrillation
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 168,007 AF cases and identified 525...
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61720-2 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Shuai Yuan |
| collection | DOAJ |
| description | 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 168,007 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. |
| format | Article |
| id | doaj-art-9282d775296f45a3bf19ae59eade2cc8 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9282d775296f45a3bf19ae59eade2cc82025-08-20T03:43:14ZengNature PortfolioNature Communications2041-17232025-07-0116111310.1038/s41467-025-61720-2Cross-population GWAS and proteomics improve risk prediction and reveal mechanisms in atrial fibrillationShuai Yuan0Jie Chen1Xixin Ruan2Yuying Li3Sarah A. Abramowitz4Lijuan Wang5Fangyuan Jiang6Ying Xiong7Michael G. Levin8Benjamin F. Voight9Dipender Gill10Stephen Burgess11Agneta Åkesson12Karl Michaëlsson13Xue Li14Scott M. Damrauer15Susanna C. Larsson16Department of Surgery, University of Pennsylvania Perelman School of MedicineDepartment of Gastroenterology, Central South University Third Xiangya HospitalDepartment of Gastroenterology, Central South University Third Xiangya HospitalDepartment of Medical Epidemiology and Biostatistics, Karolinska InstitutetDepartment of Surgery, University of Pennsylvania Perelman School of MedicineSchool of Public Health, Zhejiang University School of MedicineSchool of Public Health, Zhejiang University School of MedicineDepartment of Medical Epidemiology and Biostatistics, Karolinska InstitutetCorporal Michael J. Crescenz VA Medical CenterCorporal Michael J. Crescenz VA Medical CenterDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College LondonMRC Biostatistics Unit, University of CambridgeUnit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental MedicineMedical Epidemiology, Department of Surgical Sciences, Uppsala UniversitySchool of Public Health, Zhejiang University School of MedicineDepartment of Surgery, University of Pennsylvania Perelman School of MedicineUnit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental MedicineAbstract 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 168,007 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.https://doi.org/10.1038/s41467-025-61720-2 |
| spellingShingle | 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 Cross-population GWAS and proteomics improve risk prediction and reveal mechanisms in atrial fibrillation Nature Communications |
| title | Cross-population GWAS and proteomics improve risk prediction and reveal mechanisms in atrial fibrillation |
| title_full | Cross-population GWAS and proteomics improve risk prediction and reveal mechanisms in atrial fibrillation |
| title_fullStr | Cross-population GWAS and proteomics improve risk prediction and reveal mechanisms in atrial fibrillation |
| title_full_unstemmed | Cross-population GWAS and proteomics improve risk prediction and reveal mechanisms in atrial fibrillation |
| title_short | Cross-population GWAS and proteomics improve risk prediction and reveal mechanisms in atrial fibrillation |
| title_sort | cross population gwas and proteomics improve risk prediction and reveal mechanisms in atrial fibrillation |
| url | https://doi.org/10.1038/s41467-025-61720-2 |
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