Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence
- 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
- Issue Date
- Jan-2022
- Publisher
- BMJ Publishing Group
- Keywords
- Atrial fibrillation; Genetics; Genome-wide association study
- Citation
- Open Heart, v.9, no.1
- Indexed
- SCOPUS
ESCI
- Journal Title
- Open Heart
- Volume
- 9
- Number
- 1
- URI
- https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/55240
- DOI
- 10.1136/openhrt-2021-001898
- ISSN
- 2053-3624
- Abstract
- Objective
We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nudeotide 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 dass 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.
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- Appears in
Collections - 2. Clinical Science > Department of Cardiology > 1. Journal Articles
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