Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence. Issue 1 (27th January 2022)

• Record Type: Journal Article
• Authors: Kwon, Oh-Seok; Hong, Myunghee; Kim, Tae-Hoon; Hwang, Inseok; Shim, Jaemin; Choi, Eue-Keun; Lim, Hong Euy; Yu, Hee Tae; Uhm, Jae-Sun; Joung, Boyoung; Oh, Seil; Lee, Moon-Hyoung; Kim, Young-Hoon; Pak, Hui-Nam
• Is Part Of: Open heart. Volume 9:Issue 1(2022)
• Publication Date: 2022-01-27
• Subjects: genome-wide association study -- atrial fibrillation -- genetics; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; Heart -- Diseases -- Patients -- Periodicals; 616.12005
• DOI: 10.1136/openhrt-2021-001898
• Languages: English
• ISSNs: 2398-595X

Abstract

Abstract : 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 .