Cross-ancestry genome-wide analysis of atrial fibrillation unveils disease biology and enables cardioembolic risk prediction.
Kazuo MiyazawaShefali S VermaMasamichi ItoZhaonan ZouMasayuki KubotaSeitaro NomuraHiroshi MatsunagaSatoshi KoyamaHirotaka IekiMasato AkiyamaYoshinao KoikeRyo KurosawaHiroki YoshidaKouichi OzakiYoshihiro Onouchinull nullAtsushi TakahashiKoichi MatsudaYoshinori MurakamiHiroyuki AburataniMichiaki KuboYukihide MomozawaChikashi C TeraoShinya OkiHiroshi AkazawaYoichiro KamataniIssei KomuroPublished in: Nature genetics (2023)
Atrial fibrillation (AF) is a common cardiac arrhythmia resulting in increased risk of stroke. Despite highly heritable etiology, our understanding of the genetic architecture of AF remains incomplete. Here we performed a genome-wide association study in the Japanese population comprising 9,826 cases among 150,272 individuals and identified East Asian-specific rare variants associated with AF. A cross-ancestry meta-analysis of >1 million individuals, including 77,690 cases, identified 35 new susceptibility loci. Transcriptome-wide association analysis identified IL6R as a putative causal gene, suggesting the involvement of immune responses. Integrative analysis with ChIP-seq data and functional assessment using human induced pluripotent stem cell-derived cardiomyocytes demonstrated ERRg as having a key role in the transcriptional regulation of AF-associated genes. A polygenic risk score derived from the cross-ancestry meta-analysis predicted increased risks of cardiovascular and stroke mortalities and segregated individuals with cardioembolic stroke in undiagnosed AF patients. Our results provide new biological and clinical insights into AF genetics and suggest their potential for clinical applications.
Keyphrases
- atrial fibrillation
- genome wide association study
- genome wide
- catheter ablation
- oral anticoagulants
- left atrial
- left atrial appendage
- direct oral anticoagulants
- genome wide analysis
- copy number
- systematic review
- heart failure
- immune response
- dna methylation
- percutaneous coronary intervention
- end stage renal disease
- endothelial cells
- high glucose
- chronic kidney disease
- gene expression
- single cell
- randomized controlled trial
- risk assessment
- machine learning
- brain injury
- transcription factor
- electronic health record
- patient reported outcomes
- coronary artery disease
- deep learning
- diabetic rats
- pluripotent stem cells
- artificial intelligence
- case control