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

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

SUBMITTER: Kwon OS 

PROVIDER: S-EPMC8796259 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature