Predicting atrial fibrillation using a combination of genetic risk score and clinical risk factors

Atrial fibrillation (AF) has a genetic basis, and environmental factors can modify its actual pathogenesis.

The purpose of this study was to construct a combined risk assessment method including both genetic and clinical factors in the Japanese population.

We screened a cohort of 540 AF patients and 520 non-AF controls for single nucleotide polymorphisms (SNPs) previously associated with AF by genome-wide association studies. The most strongly associated SNPs after propensity score analysis were then used to calculate a weighted genetic risk score (WGRS). We also enrolled 1018 non-AF Japanese subjects as a validation cohort and monitored AF emergence over several years. Finally, we constructed a logistic model for AF prediction combining WGRS and clinical risk factors.

We identified 5 SNPs (in PRRX1, ZFHX3, PITX2, HAND2, and NEURL1) associated with AF after Bonferroni correction. There was a 4.92-fold difference in AF risk between the highest and lowest WGRS calculated using these 5 SNPs (P = 2.32 × 10^-10). Receiver operating characteristic analysis of WGRS yielded an area under the curve (AUC) of 0.73 for the screening cohort and 0.72 for the validation cohort. The predictive logistic model constructed using a combination of WGRS and AF clinical risk factors (age, body mass index, sex, and hypertension) demonstrated better discrimination of AF than WGRS alone (AUC = 0.84; sensitivity 75.4%; specificity 80.2%).

This novel predictive model of combined AF-associated SNPs and known clinical risk factors can accurately stratify AF risk in the Japanese population.