Genetic mapping of cardiac conduction disorders - Genetic architecture of ECG

Project: Research Project

Project Details


Complex disorders show non-mendelian inheritance patterns due to the interaction of multiple factors, with cause and mechanisms remaining unknown. Heart failure (HF), coronary heart disease and cardiovascular disease (CVD) are complex disorders that represent a public health problem, affecting more than 23 million people worldwide. According to the America Heart Association, mortality caused by cardiovascular diseases (CVD) in 2009 was approximately 40.6%, an average of 1 death every 40 seconds (236.1 per 100000).
Diagnoses of heart attacks were described by Herrick in 1912 and six years later he encouraged the use of the electrocardiogram (ECG) to diagnose myocardial infarction more expensive than cancer and benign neoplasm in 2008. Since this moment, the (ECG) has proven to be a suitable diagnostic tool for CVD, heart failure, arrhythmias and sudden cardiac death (SCD). ECG provides heart polarization and repolarization information of the myocardial cells, reflecting the electrical activity of the heart. Electrical activity abnormalities can indicate an evolving myocardial infarction, cardiac arrhythmias, hypertension effects, cardiac exercise and cardiac rehabilitation. The overall rhythm of the heart and weaknesses in heart muscle is shown in the ECG. Measurements of ECG include P wave, QRS interval, T wave and QT interval. The P wave shows conduction of the cardiac impulse transmitting from the atria. The QRS complex amplitude is larger than the P wave and is produced by the ventricular contraction after the ventricular myocardial cells depolarizing. T wave corresponds to the repolarization of the ventricle, while the QT interval is the time between the onset of ventricular depolarization and the end of ventricular repolarization, while PR interval measures atrial and atrioventricular conduction from the sinoatrial node to the ventricular myocardium, primarily through the atrioventricular node.
Since common variants among different genes are associated with common complex traits, the genetic architecture of common complex traits has been explored through genome wide association studies (GWAs). It was stablished through GWAs strong association between 58 loci intra e intergenic and ECG variability. These loci are related with the PR, QRS and QT intervals, building endophenotypes for each trait. Surprisingly, only two of the novel loci include genes with established electrophysiological function (ATP1B1 and PLN) and only a few have been confirmed through functional analysis (NDRG4, SCN5A). These loci typically have small effects, individually accounting for only a small proportion of the variance seen with these traits. To date, no studies have directly estimated the extent to which these loci explain the trait heritabilities. Understanding these estimations are important if we are to understand their genetic influence and establish the genetic architecture of ECG traits. One cause of CVD is arrhythmias and genes encoding ion channels are the most important genetic defects responsible for these cardiac electrical disorders [22]. Among cardiac electrical conduction problems, four different entities have emerged: congenital long-QT syndrome (LQTS), Brugada syndrome (BrS), catecholaminergic polymorphic ventricular tachycardia (CPVT) and short-QT syndrome (SQTS).
Fewer studies have investigated the genetic component for left ventricular hypertrophy (LVH). To date, four genetic loci (PTGES3, NMB, IGF1R, and SCN5) have been identified through GWAS of ECGs-measured LVH. It is well known that LVH is a risk factor for cardiovascular morbidity and mortality. A major limitation of ECG for the detection of LVH is its low sensitivity as compared with other more accurate techniques such as echocardiography, computerized tomography, magnetic resonance, and, more recently, three-dimensional echocardiography. The Sokolow–Lyon voltage (SLV), one of the established ECG criteria for detecting LVH, had a low sensitivity (21%) and a relatively high specificity (89%). This is despite the improvement of ECG sensitivity as the LVH severity increases. The Cornell voltage (CV) showed a greater diagnostic accuracy and a closer correlation with LV mass than the SLV; Combining the CV with the 12-lead sum, which has a sensitivity of 76%, improves LVH detection.
The present project has two principal parts: genetic epidemiological analysis and functional analysis. Since genetic components of ECG variability remains unknown, we want to explore rare and common variants responsible for this variability. Linkage analysis, heritability and exon sequencing have been useful tools for uncovering genetic variants.

Commitments / Obligations

Doctoral thesis at Erasmus University and Universidad del Rosario
Effective start/end date7/1/1112/1/17


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