Connecting KCNQ1 mutants with clinical outcome.

PURPOSE Mutations in KCNQ1 are linked to long QT and other syndromes. This study reports a method to predict clinical outcome when a mutation at KCNQ1 is found. METHODS We used amino-acid distribution probability to measure KCNQ1 mutants, and cross-impact analysis to couple KCNQ1 mutants with clinical outcome. Then, Bayesian equation was used to calculate the probability of occurrence of long-QT syndrome in the presence of a mutation. RESULTS Seventy-six mutations were classified into two groups according to whether a mutation increased or decreased amino-acid distribution probability. Cross-impact analysis showed that a mutation that increases the distribution probability has a greater chance of causing long-QT syndrome than a mutation that decreases the distribution probability. Bayesian calculation suggested that a patient would have a 90% chance of developing long-QT syndrome when a mutation is found at KCNQ1. CONCLUSION This study details the process of building a quantitative relationship between KCNQ1 mutations and clinical outcome and provides the probability of LQT1 in the presence of a mutation.