| Title: | Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms |
| Journal: | Circulation Genomic and Precision Medicine |
| Published: | 6 Jun 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37278238/ |
| DOI: | https://doi.org/10.1161/circgen.122.003808 |
| URL: | https://www.ahajournals.org/doi/pdf/10.1161/CIRCGEN.122.003808 |
Publication 11536
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Abstract
BACKGROUND: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well understood. We hypothesized that there might be a genetic basis for an AI algorithm for predicting the 5-year risk of new-onset AF using 12-lead ECGs (ECG-AI)-based risk estimates.</p>
METHODS: We applied a validated ECG-AI model for predicting incident AF to ECGs from 39 986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk and compared it with an AF GWAS and a GWAS of risk estimates from a clinical variable model.</p>
RESULTS: In the ECG-AI GWAS, we identified 3 signals (P<5×10-8) at established AF susceptibility loci marked by the sarcomeric gene TTN and sodium channel genes SCN5A and SCN10A. We also identified 2 novel loci near the genes VGLL2 and EXT1. In contrast, the clinical variable model prediction GWAS indicated a different genetic profile. In genetic correlation analysis, the prediction from the ECG-AI model was estimated to have a higher correlation with AF than that from the clinical variable model.</p>
CONCLUSIONS: Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways.</p>
18 Authors
- Xin Wang
- Shaan Khurshid
- Seung Hoan Choi
- Samuel Friedman
- Lu-Chen Weng
- Christopher Reeder
- James P. Pirruccello
- Pulkit Singh
- Emily S. Lau
- Rachael Venn
- Nate Diamant
- Paolo Di Achille
- Anthony Philippakis
- Christopher D. Anderson
- Jennifer E. Ho
- Patrick T. Ellinor
- Puneet Batra
- Steven A. Lubitz
1 Application
| Application ID | Title |
|---|---|
| 7089 | Exome Sequencing of All Premature Coronary Artery Disease Participants in UK Biobank |
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