Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence

Oh Seok Kwon, Myunghee Hong, Tae Hoon Kim, Inseok Hwang, Jaemin Shim, Eue Keun Choi, Hong Euy Lim, Hee Tae Yu, Jae Sun Uhm, Boyoung Joung, Seil Oh, Moon Hyoung Lee, Young Hoon Kim, Hui Nam Pak

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4 Citations (Scopus)


Objective We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GWAS) of an external large cohort has a prediction power for AF in Korean population through a convolutional neural network (CNN). Methods This study included 6358 subjects (872 cases, 5486 controls) from the Korean population GWAS data. We extracted the lists of SNPs at each p value threshold of the association statistics from three different previously reported ethnical-specific GWASs. The Korean GWAS data were divided into training (64%), validation (16%) and test (20%) sets, and a stratified K-fold cross-validation was performed and repeated five times after data shuffling. Results The CNN-GWAS predictive power for AF had an area under the curve (AUC) of 0.78±0.01 based on the Japanese GWAS, AUC of 0.79±0.01 based on the European GWAS, and AUC of 0.82±0.01 based on the multiethnic GWAS, respectively. Gradient-weighted class activation mapping assigned high saliency scores for AF associated SNPs, and the PITX2 obtained the highest saliency score. The CNN-GWAS did not show AF prediction power by SNPs with non-significant p value subset (AUC 0.56±0.01) despite larger numbers of SNPs. The CNN-GWAS had no prediction power for odd-even registration numbers (AUC 0.51±0.01). Conclusions AF can be predicted by genetic information alone with moderate accuracy. The CNN-GWAS can be a robust and useful tool for detecting polygenic diseases by capturing the cumulative effects and genetic interactions of moderately associated but statistically significant SNPs. Trial registration number NCT02138695.

Original languageEnglish
Article numbere001898
JournalOpen Heart
Issue number1
Publication statusPublished - 2022 Jan 27

Bibliographical note

Publisher Copyright:
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

All Science Journal Classification (ASJC) codes

  • Cardiology and Cardiovascular Medicine


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