bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429046; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Genetic Analysis of Right Heart Structure and Function in 40,000 People James P. Pirruccello*1,2,3,4, Paolo Di Achille*3,5, Victor Nauffal*3,6, Mahan Nekoui3,4, Samuel N. Friedman3,5, Marcus D. R. Klarqvist3,5, Mark D. Chaffin3, Shaan Khurshid1,2,3, Carolina Roselli3,7, Puneet Batra5, Kenney Ng8, Steven A. Lubitz1,2,3,4, Jennifer E. Ho1,2,4, Mark E. Lindsay1,2,3,4,9, Anthony A. Philippakis5,10, Patrick T. Ellinor1,2,3,4 1 Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA 2 Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA 3 Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA 4 Harvard Medical School, Boston, Massachusetts, USA 5 Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA 6 Cardiovascular Division, Brigham and Women's Hospital, Boston, Massachusetts, USA 7 University Medical Center Groningen, University of Groningen, Groningen, NL 8 IBM Research, Cambridge, Massachusetts, USA 9 Thoracic Aortic Center, Massachusetts General Hospital, Boston, Massachusetts, USA 10 GV, Mountain View, California, USA * = These authors contributed equally to this work. Running Title: Genetics of the right heart Keywords: Right ventricle, right atrium, pulmonary artery, deep learning, Poisson surface reconstruction, cardiovascular disease, genetics Corresponding Author: Patrick T. Ellinor, MD, PhD Cardiovascular Disease Initiative The Broad Institute of MIT and Harvard 75 Ames Street Cambridge, MA 02142 [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429046; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The heart evolved hundreds of millions of years ago. During mammalian evolution, the cardiovascular system developed with complete separation between pulmonary and systemic circulations incorporated into a single pump with chambers dedicated to each circulation. A lower pressure right heart chamber supplies deoxygenated blood to the lungs, while a high pressure left heart chamber supplies oxygenated blood to the rest of the body. Due to the complexity of morphogenic cardiac looping and septation required to form these two chambers, congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. Additionally, some diseases predominantly affect structures of the right heart, including arrhythmogenic right ventricular cardiomyopathy (ARVC) and pulmonary hypertension. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, the right ventricle, and the pulmonary artery, and then used those models to measure right heart structures in over 40,000 individuals from the UK Biobank with magnetic resonance imaging. We found associations between these measurements and clinical disease including pulmonary hypertension and dilated cardiomyopathy. We then conducted genome-wide association studies, identifying 104 distinct loci associated with at least one right heart measurement. Several of these loci were found near genes previously linked with congenital heart disease, such as NKX2-5, TBX3, WNT9B, and GATA4. We also observed interesting commonalities and differences in association patterns at genetic loci linked with both right and left ventricular measurements. Finally, we found that a polygenic predictor of right ventricular end systolic volume was associated with incident dilated cardiomyopathy (HR 1.28 per standard deviation; P = 2.4E-10), and remained a significant predictor of disease even after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic and clinical determinants of right heart structure and function. bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429046; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The heart evolved hundreds of millions of years ago as a tubular organ1. Septation of the main pumping chamber of the heart into distinct left and right ventricles evolved later in birds, mammals, and some reptiles, and is under the control of conserved transcription factors such as TBX52. Substantially greater delivery of oxygen to the systemic circulation—and to the heart itself—is the putative advantage of this separation of the circulatory system into a left heart- driven systemic circuit and a right heart-driven pulmonary circuit3. The structures of the left and right heart are derived from different progenitor cell populations and operate under different pressure regimes: the left heart operates against high pressure, while the right heart generally faces little afterload. During embryogenesis, the left ventricle forms from the first heart field, while the right ventricle, the outflow tract, and portions of the atria form from the second heart field4–7. Septation of the outflow tract also requires neuroectodermal neural crest cells8–10. The distinct embryological origins of the right and left ventricles likely explain, in part, the existence of right heart-predominant pathologies. These include arrhythmogenic right ventricular cardiomyopathy (ARVC)11–14, Brugada syndrome, and pulmonary hypertension. In addition, right ventricular dysfunction can play a role in other heart failure syndromes. The function of the right heart is an important determinant of outcomes in people who have heart failure with either reduced (HFrEF) or preserved left ventricular ejection fraction (HFpEF)15–17. HFpEF represents a heterogeneous set of diseases for which very few disease-modifying therapies exist. Consequently, there is substantial interest in identifying new therapies for conditions such as right ventricular dysfunction18–21. The distinct pathologies, embryology, and physiology of the right heart motivated our efforts to quantify right heart structure and function, and to probe the common genetic basis for human variation in these measurements. bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429046; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Results In this work, we developed deep learning models to determine the dimensions and function of the right atrium (RA), the right ventricle (RV), and the pulmonary artery (PA) in up to 45,000 UK Biobank participants. We then evaluated the epidemiologic associations, pathologic outcomes, and the common genetic basis of variation in these right heart structures. Reconstruction of right heart structures from cardiovascular magnetic resonance images We first derived right heart measurements in the UK Biobank imaging substudy of over 45,000 people22–24 using deep learning models. To do so, a cardiologist created training data for deep learning models by manually tracing the right atrium and right ventricle in the four-chamber long axis view, and the right ventricle and pulmonary artery in the short axis view (Figure 1). This process, called semantic segmentation, yielded anatomical labels identifying the pixels belonging to cardiac structures in 714 short axis images and 445 four-chamber long axis images. Two U-Net derived deep learning models, containing long-range skip connections that allow for pixel-accurate segmentation, were then trained from these data: one for the four- chamber long axis view and another for the short axis views25,26. The deep learning models were then used to produce pixel labels for the remainder of the images. Quality assessment is detailed in the Online Methods and Supplementary Note. The deep learning model output was then post-processed to extract measurements of the right atrium, the right ventricle, and the pulmonary artery. The right atrium was only consistently visible in one view (the four-chamber long axis view), and therefore a 2-dimensional area was computed by summing the pixels and multiplying by their width and height. We computed the maximum and minimum area during the cardiac cycle, as well as the fractional area change (RA FAC), which is the ratio of the change in area between the maximum and minimum area divided by the maximum area. The right ventricle has a complex 3-dimensional geometry; to estimate right ventricular structure, we integrated data from the short axis views and the four-chamber long axis view with a Poisson surface reconstruction approach, detailed in the Online Methods. We measured the maximum volume (right ventricular end diastolic volume; RVEDV), the minimum volume (right ventricular end systolic volume; RVESV), the difference between those two volumes (stroke volume), and the ejection fraction (RVEF). The pulmonary trunk’s elliptical minor axis (diameter) was computed from short axis images at end-systole. For participants whose pulmonary trunk was visible in multiple short-axis slices, we refer to the component closest