Background: Polygenic risk scores (PGS) enable rapid estimation of genome-wide susceptibility for traits, which may be useful in clinical settings, such as prediction of QT interval. In this study, we sought to validate PGS for QT interval in 2 real-world cohorts of European ancestry (EA) and African ancestry (AA).
Methods and results: Two thousand nine hundred and fifteen participants of EA and 366 of AA in the MGH CAMP study (Cardiology and Metabolic Patient) were genotyped on a genome-wide array and imputed to the 1000 Genomes reference panel. An additional 820 EA and 57 AA participants in the Partners Biobank were genotyped and used for validation. PGS were created for each individual using effect estimates from association tests with QT interval obtained from prior genome-wide association studies, with variants selected based from multiple significance thresholds in the original study. In regression models, clinical variables explained ≈9% to 10% of total variation in resting QTc in EA individuals and ≈12% to 18% in AA individuals. The PGS significantly increased variation explained at most significance thresholds (P<0.001), with a trend toward increased variation explained at more stringent P value cut points in the CAMP EA cohort (P<0.05). In AA individuals, PGS provided no improvement in variation explained at any significance threshold.
Conclusions: For individuals of European descent, PGS provided a significant increase in variation in QT interval explained compared with a model with only nongenetic factors at nearly every significance level. There was no apparent benefit gained by relaxing the significance threshold from conventional genome-wide significance (P<5×10-8).
Keywords: epidemiology; genetics, bioinformatics; genome-wide association studies; prediction; repolarization.
© 2017 American Heart Association, Inc.