Genetics, genealogy and pathophysiology in The Long QT Syndrome (LQTS)
Research project
The Long QT Syndrome (LQTS) is an inherited potentially life-threatening condition caused by mutations in genes encoding cardiac ion channels.
The defect ion channel function causes delayed repolarization associated with Torsade de Pointes that can deteriorate into ventricular fibrillation and ultimately sudden cardiac death.
The prevalence of LQTS has earlier been estimated to 1:4000 but more recent studies and our ongoing study indicate that the frequency of gene carriers seems to be much higher, 1:1000-2000. In the most common form of LQTS, arrhythmia is often associated with exercise or excitement. However, in spite of intensive research efforts, much of the phenotypic variability remains unexplained.
The general aim of this project is to prevent sudden cardiovascular death by investigating pathophysiological mechanisms and correlating genetic findings (genotype) to the observable characteristics (phenotype) of patients with LQTS.
Our present research has a multimodel approach .
We are identifying new disease causing genetic variants and SNPs of significance for clinical course and prognosis in LQTS using genome-wide association studies.
Genotype-phenotype correlations are also described and high/ low-risk mutations assessed.
We analyze the T-vector loop using VCG and heart rate variability to investigate phenotype and gene- and mutation specific findings.
With genealogical investigations of LQTS patients, we stratify mortality rates in pedigrees of LQTS founder populations using the Family Tree Mortality Ratio Method.
By studying the myocardial appearance and response to triggers, like exercise, in symptomatic and asymptomatic patients with LQTS and in relation to normal individuals, we aim to identify risk factors for sudden cardiac death.
To model the disorder in vitro, we generate iPS cell lines from patients with LQTS mutations with the aim to study disease mechanisms, genetic modifiers, aid in the development of new therapies, and ultimately optimize patient management.