Genetic variant annotation scores in congenital long QT syndrome

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.