Congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, right ventricle and pulmonary artery, measuring right heart structures in 40,000 individuals from the UK Biobank with magnetic resonance imaging. Genome-wide association studies identified 130 distinct loci associated with at least one right heart measurement, of which 72 were not associated with left heart structures. Loci were found near genes previously linked with congenital heart disease, including NKX2-5, TBX5/TBX3, WNT9B and GATA4. A genome-wide polygenic predictor of right ventricular ejection fraction was associated with incident dilated cardiomyopathy (hazard ratio, 1.33 per standard deviation; P = 7.1 × 10−13) and remained significant after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic determinants of right heart structure and function.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Cardiovascular Research Open Access 09 October 2023
Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score
Arthritis Research & Therapy Open Access 12 June 2023
Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure
Nature Communications Open Access 14 November 2022
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
UK Biobank data are made available to researchers from research institutions with genuine research inquiries, following IRB and UK Biobank approval. GWAS summary statistics are available at the Broad Institute Cardiovascular Disease Knowledge Portal (http://www.broadcvdi.org). Single nucleus RNA sequencing data are publicly available at the Single Cell Portal (https://singlecell.broadinstitute.org/single_cell accession no. SCP498). The dbGAP study accession numbers used for FHS replication were phs000007.v32.p13 for PA diameter measurement and phs000342.v20.p13 for genotyping. BBJ data are available to bona fide researchers for approved research by application to the Japanese Genotype-phenotype Archive. MGB data are available to MGB investigators. All other data are contained within the article and its Supplementary information, or are available upon reasonable request to the corresponding author.
The code used to perform Poisson surface reconstruction from segmentation output is located at https://github.com/broadinstitute/ml4h and is available under an open-source BSD license. The code used to perform permutation testing to assess enrichment of disease-related genes near GWAS loci is located at https://github.com/carbocation/genomisc and is available under an open-source BSD license. The code used to annotate magnetic resonance images is located at https://github.com/carbocation/traceoverlay and is available under an open-source BSD license.
Olson, E. N. Gene regulatory networks in the evolution and development of the heart. Science 313, 1922–1927 (2006).
Koshiba-Takeuchi, K. et al. Reptilian heart development and the molecular basis of cardiac chamber evolution. Nature 461, 95–98 (2009).
Farmer, C. G. Evolution of the vertebrate cardio-pulmonary system. Annu. Rev. Physiol. 61, 573–592 (1999).
Galli, D. et al. Atrial myocardium derives from the posterior region of the second heart field, which acquires left-right identity as Pitx2c is expressed. Development 135, 1157–1167 (2008).
Meilhac, S. M. & Buckingham, M. E. The deployment of cell lineages that form the mammalian heart. Nat. Rev. Cardiol. 15, 705–724 (2018).
Verzi, M. P., McCulley, D. J., De Val, S., Dodou, E. & Black, B. L. The right ventricle, outflow tract, and ventricular septum comprise a restricted expression domain within the secondary/anterior heart field. Dev. Biol. 287, 134–145 (2005).
Zaffran, S., Kelly, R. G., Meilhac, S. M., Buckingham, M. E. & Brown, N. A. Right ventricular myocardium derives from the anterior heart field. Circ. Res. 95, 261–268 (2004).
Jiang, X., Rowitch, D. H., Soriano, P., McMahon, A. P. & Sucov, H. M. Fate of the mammalian cardiac neural crest. Development 127, 1607–1616 (2000).
Li, J., Chen, F. & Epstein, J. A. Neural crest expression of Cre recombinase directed by the proximal Pax3 promoter in transgenic mice. Genesis 26, 162–164 (2000).
Lin, C.-J., Lin, C.-Y., Chen, C.-H., Zhou, B. & Chang, C.-P. Partitioning the heart: mechanisms of cardiac septation and valve development. Development 139, 3277–3299 (2012).
Gotschy, A. et al. Right ventricular outflow tract dimensions in arrhythmogenic right ventricular cardiomyopathy/dysplasia-a multicentre study comparing echocardiography and cardiovascular magnetic resonance. Eur. Heart J. Cardiovasc. Imaging 19, 516–523 (2018).
Marcus, F. I. et al. Diagnosis of arrhythmogenic right ventricular cardiomyopathy/dysplasia. Circulation 121, 1533–1541 (2010).
McKoy, G. et al. Identification of a deletion in plakoglobin in arrhythmogenic right ventricular cardiomyopathy with palmoplantar keratoderma and woolly hair (Naxos disease). Lancet 355, 2119–2124 (2000).
McNally, E., et al. Arrhythmogenic right ventricular cardiomyopathy. In: GeneReviews [Internet] Seattle, WA: University of Washington, Seattle, 1993–2002. 18 April 2005 (updated 25 May 2017).
Protonotarios, N. & Tsatsopoulou, A. Naxos disease: cardiocutaneous syndrome due to cell adhesion defect. Orphanet J. Rare Dis. 1, 4 (2006).
Romero, J., Mejia-Lopez, E., Manrique, C. & Lucariello, R. Arrhythmogenic right ventricular cardiomyopathy (ARVC/D): a systematic literature review. Clin Med Insights Cardiol 7, CMC.S10940 (2013).
Behr, E. R., Ben-Haim, Y., Ackerman, M. J., Krahn, A. D. & Wilde, A. A. M. Brugada syndrome and reduced right ventricular outflow tract conduction reserve: a final common pathway? Eur. Heart J. 42, 1073–1081 (2021).
Ghio, S. et al. Independent and additive prognostic value of right ventricular systolic function and pulmonary artery pressure in patients with chronic heart failure. J. Am. Coll. Cardiol. 37, 183–188 (2001).
Kjaergaard, J. et al. Right ventricular dysfunction as an independent predictor of short- and long-term mortality in patients with heart failure. Eur. J. Heart Fail. 9, 610–616 (2007).
Melenovsky, V., Hwang, S.-J., Lin, G., Redfield, M. M. & Borlaug, B. A. Right heart dysfunction in heart failure with preserved ejection fraction. Eur. Heart J. 35, 3452–3462 (2014).
Bai, W. et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J. Cardiovasc. Magn. Reson. 20, 65 (2018).
Bai, W. et al. A population-based phenome-wide association study of cardiac and aortic structure and function. Nat. Med. 26, 1654–1662 (2020).
Petersen, S. E. et al. Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank—rationale, challenges and approaches. J. Cardiovasc. Magn. Reson. 15, 46 (2013).
Petersen, S. E. UK Biobank’s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18, 8 (2016).
Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
Howard, J. & Gugger, S. Fastai: a layered API for deep learning. Information 11, 108 (2020).
Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. Preprint at arXiv https://doi.org/10.48550/arXiv.1912.01703 (2019).
Deng, J. et al. ImageNet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255 (IEEE, 2009). https://doi.org/10.1109/CVPR.2009.5206848
Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).
Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. Preprint at arXiv https://doi.org/10.48550/arXiv.1505.04597 (2015).
Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945).
Sørensen, T. J. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. K. Dan. Vidensk. Selsk. Biol. Skr. 5, 1–34 (1948).
Pirruccello, J. P. et al. Deep learning enables genetic analysis of the human thoracic aorta. Nat. Genet. 54, 40–51 (2022).
Edwards, P. D., Bull, R. K. & Coulden, R. CT measurement of main pulmonary artery diameter. Br. J. Radiol. 71, 1018–1020 (1998).
Sanfilippo, A. J. et al. Atrial enlargement as a consequence of atrial fibrillation. A prospective echocardiographic study. Circulation 82, 792–797 (1990).
Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).
Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906–908 (2018).
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Chen, Y.-Z. Autosomal dominant familial dyskinesia and facial myokymia: single exome sequencing identifies a mutation in adenylyl cyclase 5. Arch. Neurol. 69, 630 (2012).
Givertz, M. M., Hare, J. M., Loh, E., Gauthier, D. F. & Colucci, W. S. Effect of bolus milrinone on hemodynamic variables and pulmonary vascular resistance in patients with severe left ventricular dysfunction: a rapid test for reversibility of pulmonary hypertension. J. Am. Coll. Cardiol. 28, 1775–1780 (1996).
Sahin, M. et al. The effect of cilostazol on right heart function and pulmonary pressure. Cardiovasc. Ther. 31, e88–e93 (2013).
Singh, H. et al. mitoBKCa is encoded by the Kcnma1 gene, and a splicing sequence defines its mitochondrial location. Proc. Natl Acad. Sci. USA 110, 10836–10841 (2013).
Vang, A., Mazer, J., Casserly, B. & Choudhary, G. Activation of endothelial BKCa channels causes pulmonary vasodilation. Vascul. Pharmacol. 53, 122–129 (2010).
Helgadottir, A. et al. Genome-wide analysis yields new loci associating with aortic valve stenosis. Nat. Commun. 9, 987 (2018).
Córdova-Palomera, A. et al. Cardiac imaging of aortic valve area from 34 287 UK Biobank participants reveals novel genetic associations and shared genetic comorbidity with multiple disease phenotypes. Circ. Genom. Precis. Med. 13, e003014 (2020).
Thériault, S. et al. A transcriptome-wide association study identifies PALMD as a susceptibility gene for calcific aortic valve stenosis. Nat. Commun. 9, 988 (2018).
Wild, P. S. et al. Large-scale genome-wide analysis identifies genetic variants associated with cardiac structure and function. J. Clin. Invest. 127, 1798–1812 (2017).
Machiela, M. J. & Chanock, S. J. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555–3557 (2015).
Lu, J. et al. FOG-2, a heart- and brain-enriched cofactor for GATA transcription factors. Mol. Cell. Biol. 19, 4495–4502 (1999).
Svensson, E. C., Tufts, R. L., Polk, C. E. & Leiden, J. M. Molecular cloning of FOG-2: A modulator of transcription factor GATA-4 in cardiomyocytes. Proc. Natl Acad. Sci. USA 96, 956–961 (1999).
D’Alessandro, L. C. A. et al. Exome sequencing identifies rare variants in multiple genes in atrioventricular septal defect. Genet Med 18, 189–198 (2016).
Pu, T. et al. Identification of ZFPM2 mutations in sporadic conotruncal heart defect patients. Mol. Genet. Genomics 293, 217–223 (2018).
Qian, Y. et al. Multiple gene variations contributed to congenital heart disease via GATA family transcriptional regulation. J. Transl. Med. 15, 69 (2017).
Chang, S.-W. et al. Genetic abnormalities in FOXP1 are associated with congenital heart defects. Hum. Mutat. 34, 1226–1230 (2013).
Lozano, R. et al. FOXP1 syndrome: a review of the literature and practice parameters for medical assessment and monitoring. J. Neurodev. Disord. 13, 18 (2021).
Wang, B. et al. Foxp1 regulates cardiac outflow tract, endocardial cushion morphogenesis and myocyte proliferation and maturation. Development 131, 4477–4487 (2004).
Meyer, D. & Birchmeier, C. Multiple essential functions of neuregulin in development. Nature 378, 386–390 (1995).
Rentschler, S. et al. Neuregulin-1 promotes formation of the murine cardiac conduction system. Proc. Natl Acad. Sci. USA 99, 10464–10469 (2002).
Rupert, C. E. & Coulombe, K. L. The roles of Neuregulin-1 in cardiac development, homeostasis, and disease. Biomark Insights 10, 1–9 (2015).
Evaluate the effect of injectable neucardin on the cardiac function of subjects with chronic systolic heart failure (Zensun Sci. & Tech. Co., Ltd, accessed June 24, 2021); https://clinicaltrials.gov/ct2/show/NCT04468529
Lonsdale, J. et al. The genotype-tissue expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).
Karlson, E. W., Boutin, N. T., Hoffnagle, A. G. & Allen, N. L. Building the Partners HealthCare Biobank at Partners Personalized Medicine: informed consent, return of research results, recruitment lessons and operational considerations. J. Pers. Med. 6, 2 (2016).
Nagai, A. et al. Overview of the BioBank Japan project: study design and profile. J Epidemiol 27, S2–S8 (2017).
Sakaue, S. et al. Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction. Nat. Commun. 11, 1569 (2020).
McElhinney, D. B., Geiger, E., Blinder, J., Benson, D. W. & Goldmuntz, E. NKX2.5 mutations in patients with congenital heart disease. J. Am. Coll. Cardiol. 42, 1650–1655 (2003).
Orr, N. et al. A mutation in the atrial-specific myosin light chain gene (MYL4) causes familial atrial fibrillation. Nat. Commun. 7, 11303 (2016).
Bakker Martijn, L. et al. Transcription factor Tbx3 is required for the specification of the atrioventricular conduction system. Circ. Res. 102, 1340–1349 (2008).
Bruneau, B. G. Signaling and transcriptional networks in heart development and regeneration. Cold Spring Harb. Perspect. Biol. 5, a008292 (2013).
Hoogaars, W. M. H. et al. Tbx3 controls the sinoatrial node gene program and imposes pacemaker function on the atria. Genes Dev. 21, 1098–1112 (2007).
Boogerd, C. J. & Evans, S. M. TBX5 and NuRD divide the heart. Dev. Cell 36, 242–244 (2016).
Mori, A. D. & Bruneau, B. G. TBX5 mutations and congenital heart disease: Holt-Oram syndrome revealed. Curr. Opin. Cardiol. 19, 211–215 (2004).
Mesbah, K., Harrelson, Z., Théveniau-Ruissy, M., Papaioannou, V. E. & Kelly, R. G. Tbx3 is required for outflow tract development. Circ. Res. 103, 743–750 (2008).
Xie, H. et al. Identification of TBX2 and TBX3 variants in patients with conotruncal heart defects by target sequencing. Human Genomics 12, 44 (2018).
van Eif, V. W. W., Devalla, H. D., Boink, G. J. J. & Christoffels, V. M. Transcriptional regulation of the cardiac conduction system. Nat. Rev. Cardiol. 15, 617–630 (2018).
Juillière, Y. et al. Additional predictive value of both left and right ventricular ejection fractions on long-term survival in idiopathic dilated cardiomyopathy. Eur. Heart J. 18, 276–280 (1997).
Udler, M. S. et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLoS Med. 15, e1002654 (2018).
Is, R. et al. Distribution, determinants, and normal reference values of thoracic and abdominal aortic diameters by computed tomography (from the Framingham Heart Study). Am J Cardiol 111, 1510–1516 (2013).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. Preprint at arXiv https://doi.org/10.48550/arXiv.1512.03385 (2015).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at arXiv https://doi.org/10.48550/arXiv.1412.6980 (2017).
Smith, L. N. Cyclical learning rates for training neural networks. Preprint at arXiv https://doi.org/10.48550/arXiv.1506.01186 (2015).
Smith, L. N. A disciplined approach to neural network hyper-parameters: Part 1–learning rate, batch size, momentum, and weight decay. Preprint at arXiv https://doi.org/10.48550/arXiv.1803.09820 (2018).
Lin, T.-Y., Goyal, P., Girshick, R., He, K. & Dollár, P. Focal loss for dense object detection. Preprint at arXiv https://doi.org/10.48550/arXiv.1708.02002 (2018).
Cox, D. R. The regression analysis of binary sequences. J. R. Stat. Soc. B Methodol. 20, 215–232 (1958).
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
Yang, J. et al. FTO genotype is associated with phenotypic variability of body mass index. Nature 490, 267–272 (2012).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Osborne, L. R. & Mervis, C. B. Rearrangements of the Williams–Beuren syndrome locus: molecular basis and implications for speech and language development. Expert Rev. Mol. Med. 9, 1–16 (2007).
Pober, B. R. Williams-Beuren syndrome. N. Engl. J. Med. 362, 239–252 (2010).
Tartaglia, M. et al. Mutations in PTPN11, encoding the protein tyrosine phosphatase SHP-2, cause Noonan syndrome. Nat. Genet. 29, 465–468 (2001).
Wigginton, J. E., Cutler, D. J. & Abecasis, G. R. A note on exact tests of Hardy-Weinberg equilibrium. Am. J. Hum. Genet. 76, 887–893 (2005).
Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Boughton, A. P. et al. LocusZoom.js: interactive and embeddable visualization of genetic association study results. Bioinformatics 37, 3017–3018 (2021).
Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).
Tucker, N. R. et al. Transcriptional and cellular diversity of the human heart. Circulation 142, 466–482 (2020).
Law, C. W., Chen, Y., Shi, W., & Smyth. G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts Genome Biol. 15 R29 (2014).
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Benjamin, E. J. et al. Variants in ZFHX3 are associated with atrial fibrillation in individuals of European ancestry. Nat. Genet. 41, 879–881 (2009).
Hong, H. et al. Assessing batch effects of genotype calling algorithm BRLMM for the Affymetrix GeneChip Human Mapping 500 K array set using 270 HapMap samples. BMC Bioinf. 9, S17 (2008).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
Qazi, S. et al. Increased aortic diameters on multidetector computed tomographic scan are independent predictors of incident adverse cardiovascular events: the Framingham Heart Study. Circ. Cardiovasc. Imaging 10, e006776 (2017).
Therneau, T. M. & Grambsch, P. M. Modeling Survival Data: Extending the Cox Model. (Springer-Verlag, 2000). https://doi.org/10.1007/978-1-4757-3294-8
Bellenguez, C. et al. A robust clustering algorithm for identifying problematic samples in genome-wide association studies. Bioinformatics 28, 134–135 (2012).
Pirruccello, J. P. et al. Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy. Nat. Commun. 11, 2254 (2020).
We thank all participants of UK Biobank, MGB, BBJ and FHS. We acknowledge the staff of BBJ for their assistance. Cardiac magnetic resonance images in Fig. 1 are reproduced by kind permission of UK Biobank. We acknowledge Servier Medical Art (smart.servier.com) for the right heart illustration in Fig. 1, which is licensed under a Creative Commons Attribution 3.0 Unported License (CC-BY-3.0). We also acknowledge M. O’Reilly, from Pattern at the Broad Institute, for modifying the right heart illustration and for creating the remaining graphical illustrations in Fig. 1. This work was supported by grants from the National Institutes of Health K08HL159346 (J.P.P.), R01HL092577 (P.T.E.), K24HL105780 (P.T.E.), R01HL134893 (J.E.H.), R01HL140224 (J.E.H.), K24HL153669 (J.E.H.), 5T32HL007604-35 (V.N.), T32HL007208 (S. Khurshid), R01HL128914 (E.J.B.), R01HL092577 (E.J.B.), R01HL141434 (E.J.B.), U54HL120163 (E.J.B.) and R01HL139731 (S.A.L.). This work was supported by the Fondation Leducq 14CVD01 (P.T.E.). This work was supported by a John S LaDue Memorial Fellowship (J.P.P.) and the Sarnoff Cardiovascular Research Foundation Scholar Award (J.P.P.). This work was supported by the Tailor-Made Medical Treatment Program of the Ministry of Education, Culture, Sports, Science and Technology (BBJ). This work was supported by the Japan Agency for Medical Research via JP17km0305002 (BBJ), JP17km0305001 (BBJ), JP20km0405209 (BBJ, S. Koyama, K.I., I.K.) and JP20ek0109487 (BBJ, S. Koyama, K.I., I.K.). This work was supported by student scholarships from the Dutch Heart Foundation (S.J.) and the Amsterdams Universiteitsfonds (S.J.). This work was supported by grants from the NIH/NHLBI R01HL148050 (P.N.) and R01HL127564 (P.N.), NIH/NHGRI U01HG011719 (P.N.), and Massachusetts General Hospital Fireman Chair (P.N.). This work was supported by American Heart Association grants 18SFRN34110082 (E.J.B.), 18SFRN34250007 (S.A.L.) and a Strategically Focused Research Networks grant (P.T.E.). This work was supported by the Fredman Fellowship for Aortic Disease (M.E.L.) and the Toomey Fund for Aortic Dissection Research (M.E.L.). This work was funded by a collaboration between the Broad Institute and IBM Research.
J.P.P. has served as a consultant for Maze Therapeutics. P.B. is supported by grants from Bayer AG and IBM applying machine learning in cardiovascular disease. S.A.L. receives sponsored research support from Bristol Myers Squibb/Pfizer, Bayer AG, Boehringer Ingelheim and Fitbit, has consulted for Bristol Myers Squibb/Pfizer and Bayer AG and participates in a research collaboration with IBM. K.N. is employed by IBM Research. J.E.H. is supported by a grant from Bayer AG focused on machine learning and cardiovascular disease and a research grant from Gilead Sciences. J.E.H. has received research supplies from EcoNugenics. A.A.P. is employed as a Venture Partner at GV; he is also supported by a grant from Bayer AG to the Broad Institute focused on machine learning for clinical trial design. P.T.E. received sponsored research support from Bayer AG and IBM Research. P.T.E. has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia and Novartis. P.N. reports investigator-initated grants from Amgen, Apple, AstraZeneca, Boston Scientific, and Novartis, personal fees from Apple, AstraZeneca, Blackstone Life Sciences, Foresite Labs, Novartis, Roche / Genentech, is a co-founder of TenSixteen Bio, is a shareholder of geneXwell and TenSixteen Bio, and spousal employment at Vertex, all unrelated to the present work. The Broad Institute has filed for a patent on an invention from P.T.E., M.E.L. and J.P.P. related to a genetic risk predictor for aortic disease. All remaining authors report no competing interests.
Peer review information
Nature Genetics thanks Heribert Schunkert, Eric Villard and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Pirruccello, J.P., Di Achille, P., Nauffal, V. et al. Genetic analysis of right heart structure and function in 40,000 people. Nat Genet 54, 792–803 (2022). https://doi.org/10.1038/s41588-022-01090-3
This article is cited by
Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score
Arthritis Research & Therapy (2023)
Nature Cardiovascular Research (2023)
Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure
Nature Communications (2022)