Connecting KCNQ1 mutants with clinical outcome.
OBJECTIVE
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.


CONCLUSIONS
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.
Yan
Shaomin
S
Guangxi Academy of Sciences, Nanning, Guangxi, China.
Wu
Guang
G
D000596:Amino Acids / Q000737:chemistry; D006801:Humans; D051657:KCNQ1 Potassium Channel / Q000737:chemistry / Q000235:genetics / Q000502:physiology*; D008133:Long QT Syndrome / Q000235:genetics* / Q000628:therapy; D009154:Mutation; D011336:Probability; D016896:Treatment Outcome
D000596:Amino Acids; D051657:KCNQ1 Potassium Channel