Lay Abstract:
We have documented that Borzoi experience sudden unexplained death. In humans, approximately 85% of such deaths attribute to coronary artery disease, cardiomyopathies, or cardiac channelopathies. Common channelopathies in humans include long QT syndrome (LQTS) and Brugada Syndrome (BrS). BrS is diagnosed based on characteristic electrocardiogram (ECG) repolarization abnormalities of the ST segment. BrS has never been reported in dogs. Research has demonstrated that several sighthound breeds develop dilated cardiomyopathy (DCM) and ventricular arrhythmias, and that Borzoi, specifically, have repolarization abnormalities including long QT intervals and abnormal ST segment morphology, similar to humans with LQTS and BrS. However, the canine ST segment has received limited study. Our specific aims are: 1) use machine learning to identify non-invasive ECG markers of LQTS and BrS in sighthounds and 2) identify genetic variants associated with DCM, LQTS and BrS in sighthounds. We hypothesize that sighthounds have heritable DCM and repolarization abnormalities that represent natural models of human disease (LQTS and/or BrS) and increase risk of sudden cardiac death (SCD). We will utilize machine learning applied to ECG data to characterize and diagnose repolarization disorders and echocardiography to diagnose DCM across sighthounds. This phenotypic data will define cohorts for whole genome sequencing of 250 dogs, which will be applied to a pangenome-wide association study that includes reference genomes for six sighthound and 30 other breeds. A conventional GWAS approach will also be performed by combining new data with a catalog of over 10,000 dogs with whole genome sequencing to determine genetic influence on these conditions.