RESUMEN
BACKGROUND: Atrial fibrillation (AF), the most common atrial arrhythmia, presents varied clinical manifestations. Despite the identification of genetic loci associated with AF, particularly in specific populations, research within Asian ethnicities remains limited. This study aimed to develop predictive models for AF using AF-associated single-nucleotide polymorphisms (SNPs) from a Genome-Wide Association Study (GWAS) on a substantial cohort of Taiwanese individuals, evaluating the models' predictive efficacy. METHODS: Involving 75,121 subjects, including 5,694 AF patients and 69,427 normal controls with GWAS data, the study merged polygenic risk scores (PRS) from AF-associated SNPs with Phenome-wide association study (PheWAS)-derived risk factors. Advanced statistical and machine learning techniques were employed to develop and evaluate AF predictive models for discrimination and calibration. RESULTS: The study identified the top 30 significant SNPs associated with AF, predominantly on chromosomes 10 and 16, implicating genes like NEURL1, SH3PXD2A, INA, NT5C2, STN1, and ZFHX3. Notably, INA, NT5C2, and STN1 were newly linked to AF. The GWAS predictive power using PRS-CS analysis for AF exhibited an area under the curve (AUC) of 0.600 (P < 0.001), improving to 0.855 (P < 0.001) after adjusting for age and gender. PheWAS analysis showed the top 10 diseases associated with these genes were circulatory system diseases. CONCLUSIONS: Integrating genetic and phenotypic data enhanced the accuracy and clinical relevance of AF predictive models. The findings suggest promise for refining AF risk assessment, enabling personalized interventions, and reducing AF-related morbidity and mortality burdens.