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

Authors
 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 
Citation
 OPEN HEART, Vol.9(1) : e001898, 2022-01 
Journal Title
OPEN HEART
Issue Date
2022-01
MeSH
Artificial Intelligence* ; Atrial Fibrillation / diagnosis* ; Atrial Fibrillation / epidemiology ; Atrial Fibrillation / genetics ; DNA / genetics* ; Female ; Genetic Predisposition to Disease* ; Genome-Wide Association Study ; Homeodomain Proteins / genetics* ; Homeodomain Proteins / metabolism ; Humans ; Male ; Middle Aged ; Morbidity / trends ; Polymorphism, Single Nucleotide* ; Republic of Korea / epidemiology ; Transcription Factors / genetics* ; Transcription Factors / metabolism
Keywords
atrial fibrillation ; genetics ; genome-wide association study
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.
Files in This Item:
T202201859.pdf Download
DOI
10.1136/openhrt-2021-001898
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kwon, Oh-Seok(권오석)
Kim, Tae-Hoon(김태훈) ORCID logo https://orcid.org/0000-0003-4200-3456
Pak, Hui Nam(박희남) ORCID logo https://orcid.org/0000-0002-3256-3620
Uhm, Jae Sun(엄재선) ORCID logo https://orcid.org/0000-0002-1611-8172
Yu, Hee Tae(유희태) ORCID logo https://orcid.org/0000-0002-6835-4759
Lee, Moon-Hyoung(이문형) ORCID logo https://orcid.org/0000-0002-7268-0741
Joung, Bo Young(정보영) ORCID logo https://orcid.org/0000-0001-9036-7225
Hong, Myunghee(홍명희)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/188710
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