The electrocardiogram (ECG) is one of the most useful non-invasive diagnostic tests for a wide array of cardiac disorders. Traditional approaches to analyzing ECGs focus on individual segments. Here, we performed comprehensive deep phenotyping of 77,190 ECGs in the UK Biobank across the complete cycle of cardiac conduction, resulting in 500 spatial-temporal datapoints, across 10 million genetic variants. In addition to characterizing polygenic risk scores for the traditional ECG segments, we identified over 300 genetic loci that are statistically associated with the high-dimensional representation of the ECG. We established the genetic ECG signature for dilated cardiomyopathy, associated the BAG3, HSPB7/CLCNKA, PRKCA, TMEM43, and OBSCN loci with disease risk and confirmed this association in an independent cohort. In total, our work demonstrates that a high-dimensional analysis of the entire ECG provides unique opportunities for studying cardiac biology and disease and furthering drug development.
Multi-institutional 1000G GWAS study to identify heart rate-associated loci and their effects
Resting heart rate is associated with increased incidence of cardiovascular disease as well as with other cardiovascular all-cause mortality. The aim of this study is to explore new insights into the mechanisms regulating heart rate and identify new therapeutic targets. The purpose of UK Biobank is to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society. To gain more insight into the genetic regulation of heart rate, we are now performing a GWAS meta-analysis with imputation on the 1000G (March 2012) reference panel. The goal is to get fresh insights into the mechanisms regulating heart rate, its relevance for clinical outcomes, and identify new therapeutic targets. We will perform a genome-wide association study meta-analysis on the phenotype heart rate. We will combine GWAS data from different cohorts to find significant single nucleotide polymorphism (SNPS). From the loci associated with heart rate we will then create a genetic predisposition score (GPS). Additionally we will test whether the meta-analysis identified loci show evidence of association with heart conditions registered via hospital in-patient data (including atrial fibrillation, conduction-related disorders, myocardial infarction and heart failure) and mortality via death-register. We will also test candidate genes in in silico and in vitro follow-up. Major GWAS meta-analysis consortium, aimed at including >200,000 subjects from >100 sites.
|Professor Pim van der Harst
|University Medical Center Groningen