Genetic architecture of cardiac dynamic flow volumes
Bruna Gomes, Aditya Singh, Jack W. O’Sullivan, Theresia M. Schnurr, Pagé C. Goddard, Shaun Loong, David Amar, J. Weston Hughes, Mykhailo Kostur, François Haddad, Michael Salerno, Roger Foo, Stephen B. Montgomery, Victoria N. Parikh, Benjamin Meder, Euan A. Ashley
Abstract
Cardiac blood flow is a critical determinant of human health. However, the definition of its genetic architecture is limited by the technical challenge of capturing dynamic flow volumes from cardiac imaging at scale. We present DeepFlow, a deep-learning system to extract cardiac flow and volumes from phase-contrast cardiac magnetic resonance imaging. A mixed-linear model applied to 37,653 individuals from the UK Biobank reveals genome-wide significant associations across cardiac dynamic flow volumes spanning from aortic forward velocity to aortic regurgitation fraction. Mendelian randomization reveals a causal role for aortic root size in aortic valve regurgitation. Among the most significant contributing variants, localizing genes (near ELN, PRDM6 and ADAMTS7) are implicated in connective tissue and blood pressure pathways. Here we show that DeepFlow cardiac flow phenotyping at scale, combined with genotyping data, reinforces the contribution of connective tissue genes, blood pressure and root size to aortic valve function. DeepFlow uses deep learning to extract cardiac flow phenotypes from phase-contrast magnetic resonance images. Genome-wide analyses of cardiac flow traits in UK Biobank highlight the contribution of connective tissue genes to aortic valve function.