Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Genetic analysis of right heart structure and function in 40,000 people

Nature Genetics volume 54, pages 792–803 (2022)Cite this article

Subjects

Abstract

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Right heart measurement with deep learning.
Fig. 2: Right heart structures are associated with PheCode-based disease definitions.
Fig. 3: Alterations in right ventricular volume with prevalent disease.
Fig. 4: Manhattan plots of right heart traits.
Fig. 5: Right heart loci.
Fig. 6: Cumulative incidence of dilated cardiomyopathy stratified by genetic prediction of RVEF.

Data availability

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.

Code availability

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.

References

  1. Olson, E. N. Gene regulatory networks in the evolution and development of the heart. Science 313, 1922–1927 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Koshiba-Takeuchi, K. et al. Reptilian heart development and the molecular basis of cardiac chamber evolution. Nature 461, 95–98 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Farmer, C. G. Evolution of the vertebrate cardio-pulmonary system. Annu. Rev. Physiol. 61, 573–592 (1999).

    Article  CAS  PubMed  Google Scholar 

  4. 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).

    Article  CAS  PubMed  Google Scholar 

  5. Meilhac, S. M. & Buckingham, M. E. The deployment of cell lineages that form the mammalian heart. Nat. Rev. Cardiol. 15, 705–724 (2018).

    Article  PubMed  Google Scholar 

  6. 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).

    Article  CAS  PubMed  Google Scholar 

  7. 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).

    Article  CAS  PubMed  Google Scholar 

  8. 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).

    Article  CAS  PubMed  Google Scholar 

  9. 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).

    Article  CAS  PubMed  Google Scholar 

  10. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 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).

    Article  PubMed  Google Scholar 

  12. Marcus, F. I. et al. Diagnosis of arrhythmogenic right ventricular cardiomyopathy/dysplasia. Circulation 121, 1533–1541 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  13. 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).

    Article  CAS  PubMed  Google Scholar 

  14. 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).

  15. Protonotarios, N. & Tsatsopoulou, A. Naxos disease: cardiocutaneous syndrome due to cell adhesion defect. Orphanet J. Rare Dis. 1, 4 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  16. 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).

    Article  Google Scholar 

  17. 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).

    Article  CAS  PubMed  Google Scholar 

  18. 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).

    Article  CAS  PubMed  Google Scholar 

  19. 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).

    Article  PubMed  Google Scholar 

  20. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Bai, W. et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J. Cardiovasc. Magn. Reson. 20, 65 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Bai, W. et al. A population-based phenome-wide association study of cardiac and aortic structure and function. Nat. Med. 26, 1654–1662 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Petersen, S. E. UK Biobank’s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18, 8 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  25. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Howard, J. & Gugger, S. Fastai: a layered API for deep learning. Information 11, 108 (2020).

    Article  Google Scholar 

  27. 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).

  28. 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

  29. Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).

    Article  Google Scholar 

  30. 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).

  31. Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945).

    Article  Google Scholar 

  32. 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).

    Google Scholar 

  33. Pirruccello, J. P. et al. Deep learning enables genetic analysis of the human thoracic aorta. Nat. Genet. 54, 40–51 (2022).

    Article  CAS  PubMed  Google Scholar 

  34. Edwards, P. D., Bull, R. K. & Coulden, R. CT measurement of main pulmonary artery diameter. Br. J. Radiol. 71, 1018–1020 (1998).

    Article  CAS  PubMed  Google Scholar 

  35. Sanfilippo, A. J. et al. Atrial enlargement as a consequence of atrial fibrillation. A prospective echocardiographic study. Circulation 82, 792–797 (1990).

    Article  CAS  PubMed  Google Scholar 

  36. Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  41. 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).

    Article  CAS  PubMed  Google Scholar 

  42. Sahin, M. et al. The effect of cilostazol on right heart function and pulmonary pressure. Cardiovasc. Ther. 31, e88–e93 (2013).

    Article  CAS  PubMed  Google Scholar 

  43. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Vang, A., Mazer, J., Casserly, B. & Choudhary, G. Activation of endothelial BKCa channels causes pulmonary vasodilation. Vascul. Pharmacol. 53, 122–129 (2010).

    Article  CAS  PubMed  Google Scholar 

  45. Helgadottir, A. et al. Genome-wide analysis yields new loci associating with aortic valve stenosis. Nat. Commun. 9, 987 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. 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).

    Article  PubMed  CAS  Google Scholar 

  47. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  49. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lu, J. et al. FOG-2, a heart- and brain-enriched cofactor for GATA transcription factors. Mol. Cell. Biol. 19, 4495–4502 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 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).

    Article  PubMed  CAS  Google Scholar 

  53. Pu, T. et al. Identification of ZFPM2 mutations in sporadic conotruncal heart defect patients. Mol. Genet. Genomics 293, 217–223 (2018).

    Article  CAS  PubMed  Google Scholar 

  54. Qian, Y. et al. Multiple gene variations contributed to congenital heart disease via GATA family transcriptional regulation. J. Transl. Med. 15, 69 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Chang, S.-W. et al. Genetic abnormalities in FOXP1 are associated with congenital heart defects. Hum. Mutat. 34, 1226–1230 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Wang, B. et al. Foxp1 regulates cardiac outflow tract, endocardial cushion morphogenesis and myocyte proliferation and maturation. Development 131, 4477–4487 (2004).

    Article  CAS  PubMed  Google Scholar 

  58. Meyer, D. & Birchmeier, C. Multiple essential functions of neuregulin in development. Nature 378, 386–390 (1995).

    Article  CAS  PubMed  Google Scholar 

  59. Rentschler, S. et al. Neuregulin-1 promotes formation of the murine cardiac conduction system. Proc. Natl Acad. Sci. USA 99, 10464–10469 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Rupert, C. E. & Coulombe, K. L. The roles of Neuregulin-1 in cardiac development, homeostasis, and disease. Biomark Insights 10, 1–9 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 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

  62. Lonsdale, J. et al. The genotype-tissue expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Article  CAS  Google Scholar 

  63. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. 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).

    Article  PubMed Central  Google Scholar 

  65. Nagai, A. et al. Overview of the BioBank Japan project: study design and profile. J Epidemiol 27, S2–S8 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  66. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 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).

    Article  CAS  PubMed  Google Scholar 

  68. Orr, N. et al. A mutation in the atrial-specific myosin light chain gene (MYL4) causes familial atrial fibrillation. Nat. Commun. 7, 11303 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Bakker Martijn, L. et al. Transcription factor Tbx3 is required for the specification of the atrioventricular conduction system. Circ. Res. 102, 1340–1349 (2008).

    Article  CAS  PubMed  Google Scholar 

  70. Bruneau, B. G. Signaling and transcriptional networks in heart development and regeneration. Cold Spring Harb. Perspect. Biol. 5, a008292 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Boogerd, C. J. & Evans, S. M. TBX5 and NuRD divide the heart. Dev. Cell 36, 242–244 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Mori, A. D. & Bruneau, B. G. TBX5 mutations and congenital heart disease: Holt-Oram syndrome revealed. Curr. Opin. Cardiol. 19, 211–215 (2004).

    Article  PubMed  Google Scholar 

  74. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Xie, H. et al. Identification of TBX2 and TBX3 variants in patients with conotruncal heart defects by target sequencing. Human Genomics 12, 44 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 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).

    Article  PubMed  CAS  Google Scholar 

  77. 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).

    Article  PubMed  Google Scholar 

  78. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. 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).

    Article  Google Scholar 

  80. 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).

  81. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at arXiv https://doi.org/10.48550/arXiv.1412.6980 (2017).

  82. Smith, L. N. Cyclical learning rates for training neural networks. Preprint at arXiv https://doi.org/10.48550/arXiv.1506.01186 (2015).

  83. 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).

  84. 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).

  85. Cox, D. R. The regression analysis of binary sequences. J. R. Stat. Soc. B Methodol. 20, 215–232 (1958).

    Google Scholar 

  86. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Yang, J. et al. FTO genotype is associated with phenotypic variability of body mass index. Nature 490, 267–272 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Pober, B. R. Williams-Beuren syndrome. N. Engl. J. Med. 362, 239–252 (2010).

    Article  CAS  PubMed  Google Scholar 

  91. Tartaglia, M. et al. Mutations in PTPN11, encoding the protein tyrosine phosphatase SHP-2, cause Noonan syndrome. Nat. Genet. 29, 465–468 (2001).

    Article  CAS  PubMed  Google Scholar 

  92. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Boughton, A. P. et al. LocusZoom.js: interactive and embeddable visualization of genetic association study results. Bioinformatics 37, 3017–3018 (2021).

    Article  CAS  PubMed Central  Google Scholar 

  95. Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Tucker, N. R. et al. Transcriptional and cellular diversity of the human heart. Circulation 142, 466–482 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. 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).

  98. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  PubMed  CAS  Google Scholar 

  99. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Benjamin, E. J. et al. Variants in ZFHX3 are associated with atrial fibrillation in individuals of European ancestry. Nat. Genet. 41, 879–881 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. 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).

    Article  CAS  Google Scholar 

  102. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  104. 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

  105. Bellenguez, C. et al. A robust clustering algorithm for identifying problematic samples in genome-wide association studies. Bioinformatics 28, 134–135 (2012).

    Article  CAS  PubMed  Google Scholar 

  106. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

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.

Author information

Author notes

  1. These authors contributed equally: James P. Pirruccello, Paolo Di Achille, Victor Nauffal.

Authors and Affiliations

  1. Cardiology Division, Massachusetts General Hospital, Boston, MA, USA

    James P. Pirruccello, Shaan Khurshid, Pradeep Natarajan, Steven A. Lubitz, Mark E. Lindsay & Patrick T. Ellinor

  2. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    James P. Pirruccello, Shaan Khurshid, Satoshi Koyama, Pradeep Natarajan, Steven A. Lubitz, Mark E. Lindsay & Patrick T. Ellinor

  3. Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    James P. Pirruccello, Paolo Di Achille, Victor Nauffal, Mahan Nekoui, Samuel F. Friedman, Marcus D. R. Klarqvist, Mark D. Chaffin, Lu-Chen Weng, Jonathan W. Cunningham, Shaan Khurshid, Carolina Roselli, Satoshi Koyama, Sean J. Jurgens, Pradeep Natarajan, Steven A. Lubitz, Mark E. Lindsay & Patrick T. Ellinor

  4. Harvard Medical School, Boston, MA, USA

    James P. Pirruccello, Mahan Nekoui, Shaan Khurshid, Pradeep Natarajan, Steven A. Lubitz, Jennifer E. Ho, Mark E. Lindsay & Patrick T. Ellinor

  5. Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Paolo Di Achille, Samuel F. Friedman, Marcus D. R. Klarqvist, Puneet Batra & Anthony A. Philippakis

  6. Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Victor Nauffal & Jonathan W. Cunningham

  7. University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

    Carolina Roselli

  8. Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, MA, USA

    Honghuang Lin & Emelia J. Benjamin

  9. Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA

    Honghuang Lin

  10. Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan

    Satoshi Koyama & Kaoru Ito

  11. Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    Yoichiro Kamatani & Yoichiro Kamatani

  12. Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan

    Yoichiro Kamatani & Yoichiro Kamatani

  13. Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Issei Komuro

  14. Department of Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands

    Sean J. Jurgens

  15. Department of Medicine, Cardiology and Preventive Medicine Sections, Boston University School of Medicine, Boston, MA, USA

    Emelia J. Benjamin

  16. Epidemiology Department, Boston University School of Public Health, Boston, MA, USA

    Emelia J. Benjamin

  17. IBM Research, Cambridge, MA, USA

    Kenney Ng

  18. Department of Radiology, Harvard Medical School, Boston, MA, USA

    Udo Hoffmann

  19. Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA, USA

    Udo Hoffmann

  20. Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA

    Steven A. Lubitz & Patrick T. Ellinor

  21. CardioVascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Jennifer E. Ho

  22. Thoracic Aortic Center, Massachusetts General Hospital, Boston, MA, USA

    Mark E. Lindsay

  23. Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Koichi Matsuda

  24. Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    Koichi Matsuda

  25. Division of Genetics, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yuji Yamanashi

  26. Division of Clinical Genome Research, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yoichi Furukawa

  27. Division of Molecular Pathology IMSUT Hospital, Department of Internal Medicine Project Division of Genomic Medicine and Disease Prevention The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Takayuki Morisaki

  28. Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yoshinori Murakami

  29. Department of Public Policy, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Kaori Mutu & Akiko Nagai

  30. Department of Urology, Iwate Medical University, Iwate, Japan

    Wataru Obara

  31. Department of Internal Medicine and Rheumatology, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Ken Yamaji

  32. Department of Respiratory Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Kazuhisa Takahashi

  33. Division of Pharmacology, Department of Biomedical Science, Nihon University School of Medicine, Tokyo, Japan

    Satoshi Asai

  34. Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan

    Satoshi Asai & Yasuo Takahashi

  35. Tokushukai Group, Tokyo, Japan

    Takao Suzuki & Nobuaki Sinozaki

  36. Department of Hematology, Nippon Medical School, Tokyo, Japan

    Hiroki Yamaguchi

  37. Department of Bioregulation, Nippon Medical School, Kawasaki, Japan

    Shiro Minami

  38. Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan

    Shigeo Murayama

  39. Fukujuji Hospital, Tokyo, Japan

    Kozo Yoshimori

  40. Japan Anti-Tuberculosis Association, Tokyo, Japan

    Kozo Yoshimori

  41. The Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan

    Satoshi Nagayama

  42. Center for Clinical Research and Advanced Medicine, Shiga University of Medical Science, Shiga, Japan

    Daisuke Obata

  43. Department of General Thoracic Surgery, Osaka International Cancer Institute, Osaka, Japan

    Masahiko Higashiyama

  44. Aso Iizuka Hospital, Fukuoka, Japan

    Akihide Masumoto

  45. National Hospital Organization Osaka National Hospital, Osaka, Japan

    Yukihiro Koretsune

Authors
  1. James P. Pirruccello
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Di Achille
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Victor Nauffal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mahan Nekoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Samuel F. Friedman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marcus D. R. Klarqvist
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mark D. Chaffin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lu-Chen Weng
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jonathan W. Cunningham
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shaan Khurshid
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Carolina Roselli
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Honghuang Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Satoshi Koyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Kaoru Ito
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Yoichiro Kamatani
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Issei Komuro
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Sean J. Jurgens
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Emelia J. Benjamin
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Puneet Batra
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Pradeep Natarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Kenney Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Udo Hoffmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Steven A. Lubitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Jennifer E. Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Mark E. Lindsay
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Anthony A. Philippakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Patrick T. Ellinor
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

The BioBank Japan Project

  • Koichi Matsuda
  • , Yuji Yamanashi
  • , Yoichi Furukawa
  • , Takayuki Morisaki
  • , Yoshinori Murakami
  • , Yoichiro Kamatani
  • , Kaori Mutu
  • , Akiko Nagai
  • , Wataru Obara
  • , Ken Yamaji
  • , Kazuhisa Takahashi
  • , Satoshi Asai
  • , Yasuo Takahashi
  • , Takao Suzuki
  • , Nobuaki Sinozaki
  • , Hiroki Yamaguchi
  • , Shiro Minami
  • , Shigeo Murayama
  • , Kozo Yoshimori
  • , Satoshi Nagayama
  • , Daisuke Obata
  • , Masahiko Higashiyama
  • , Akihide Masumoto
  •  & Yukihiro Koretsune

Contributions

J.P.P. and P.T.E. conceived of the study. J.P.P. and V.N. annotated images. J.P.P. trained the deep learning models. P.D.A. performed surface reconstruction. J.P.P., V.N., M.D.C., P.D.A., S.F.F., and M.D.R.K. conducted bioinformatic analyses in UK Biobank. S. Koyama, K.I., Y. Kamatani, and I.K. performed replication analyses in BBJ. L.-C.W. performed replication analyses in MGB. H.L. performed replication analyses in FHS. J.P.P., M.E.L., and P.T.E. wrote the paper. M.N., J.W.C., S. Khurshid, C.R., S.J.J., E.J.B., P.B., P.N., K.N., U.H., S.A.L., J.E.H., and A.A.P. contributed to the analysis plan or provided critical revisions.

Corresponding author

Correspondence to Patrick T. Ellinor.

Ethics declarations

Competing interests

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

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–22.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Tables 1–22.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-022-01090-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing