Genetic variant annotation scores in congenital long QT syndrome

Abstract

Abstract Background Congenital Long QT Syndrome (LQTS) is a hereditary arrhythmic disorder. We aimed to assess the performance of current genetic variant annotation scores among LQTS patients and their predictive impact. Methods We evaluated 2025 patients with unique mutations for LQT1–LQT3. A patient‐specific score was calculated for each of four established genetic variant annotation algorithms: CADD, SIFT, REVEL, and PolyPhen‐2. The scores were tested for the identification of LQTS and their predictive performance for cardiac events (CE) and life‐threatening events (LTE) and then compared with the predictive performance of LQTS categorization based on mutation location/function. Score performance was tested using Harrell's C‐index. Results A total of 917 subjects were classified as LQT1, 838 as LQT2, and 270 as LQT3. The identification of a pathogenic variant occurred in 99% with CADD, 92% with SIFT, 100% with REVEL, and 86% with PolyPhen‐2. However, none of the genetic scores correlated with the risk of CE (Harrell's C‐index: CADD = 0.50, SIFT = 0.51, REVEL = 0.50, and PolyPhen‐2 = 0.52) or LTE (Harrell's C‐index: CADD = 0.50, SIFT = 0.53, REVEL = 0.54, and PolyPhen‐2 = 0.52). In contrast, high‐risk mutation categorization based on location/function was a powerful independent predictor of CE (HR = 1.88; p < .001) and LTE (HR = 1.89, p < .001). Conclusion In congenital LQTS patients, well‐established algorithms (CADD, SIFT, REVEL, and PolyPhen‐2) were able to identify the majority of the causal variants as pathogenic. However, the scores did not predict clinical outcomes. These results indicate that mutation location/functional assays are essential for accurate interpretation of the risk associated with LQTS mutations.

Keywords

• Electrophsiology ‐ long QT syndrome
• Electrophysiology ‐ cardiac arrest/sudden death
• Molecular biology/genetics