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.

High heritability of ascending aortic diameter and trans-ancestry prediction of thoracic aortic disease

Nature Genetics volume 54, pages 772–782 (2022)Cite this article

Subjects

Abstract

Enlargement of the aorta is an important risk factor for aortic aneurysm and dissection, a leading cause of morbidity in the developed world. Here we performed automated extraction of ascending aortic diameter from cardiac magnetic resonance images of 36,021 individuals from the UK Biobank, followed by genome-wide association. We identified lead variants across 41 loci, including genes related to cardiovascular development (HAND2, TBX20) and Mendelian forms of thoracic aortic disease (ELN, FBN1). A polygenic score significantly predicted prevalent risk of thoracic aortic aneurysm and the need for surgical intervention for patients with thoracic aneurysm across multiple ancestries within the UK Biobank, FinnGen, the Penn Medicine Biobank and the Million Veterans Program (MVP). Additionally, we highlight the primary causal role of blood pressure in reducing aortic dilation using Mendelian randomization. Overall, our findings provide a roadmap for using genetic determinants of human anatomy to understand cardiovascular development while improving prediction of diseases of the thoracic aorta.

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: AsAoD view, distribution by ancestry and Manhattan plot GWAS among individuals of European ancestry.
Fig. 2: Gene–tissue enrichment.
Fig. 3: Association between PGS for AsAoD and aortic diseases.
Fig. 4: PheWAS of PGS for AsAoD.
Fig. 5: Forest plot depicting casual effect estimate for blood pressure and lipid biomarkers.

Data availability

All imaging and genetic data of the UK Biobank are available upon request to the UK Biobank organization. Summary statistics of ancestry-specific GWAS as well as multi-ancestry meta-analysis are available for download from the GWAS catalog under accession number GCP000259. Similarly, the PGS constructed in the current paper is available for download from the PGS catalog under accession number PGS0002236.

Code availability

Code used for data analysis is available at https://github.com/priestlab/aorta_houghcircle and https://github.com/cathynes/GWAS-OAD.git.

References

  1. Stojanovska, J., Cascade, P. N., Chong, S., Quint, L. E. & Sundaram, B. Embryology and imaging review of aortic arch anomalies. J. Thorac. Imaging 27, 73–84 (2012).

    Article  PubMed  Google Scholar 

  2. Lopez, L. et al. Relationship of echocardiographic Z scores adjusted for body surface area to age, sex, race, and ethnicity: the Pediatric Heart Network Normal Echocardiogram Database. Circ. Cardiovasc. Imaging 10, e006979 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lemaire, S. A. & Russell, L. Epidemiology of thoracic aortic dissection. Nat. Rev. Cardiol. 8, 103–113 (2011).

    Article  PubMed  Google Scholar 

  4. Aday, A. W., Kreykes, S. E. & Fanola, C. L. Vascular genetics: presentations, testing, and prognostics. Curr. Treat. Options Cardiovasc. Med. 20, 103 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Saeyeldin, A. A. et al. Thoracic aortic aneurysm: unlocking the “silent killer” secrets. Gen. Thorac. Cardiovasc. Surg. 67, 1–11 (2019).

    Article  PubMed  Google Scholar 

  6. Raunsø, J. et al. Familial clustering of aortic size, aneurysms, and dissections in the community. Circulation 142, 920–928 (2020).

    Article  PubMed  Google Scholar 

  7. Wheeler, A. P., Yang, Z., Cordes, T. M., Markham, L. W. & Landis, B. J. Characterization of the rate of aortic dilation in young patients with thoracic aortic aneurysm. Pediatr. Cardiol. 42, 148–157 (2021).

    Article  PubMed  Google Scholar 

  8. 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 

  9. McBride, K. L. et al. Inheritance analysis of congenital left ventricular outflow tract obstruction malformations: segregation, multiplex relative risk, and heritability. Am. J. Med. Genet. A 134A, 180–186 (2005).

    Article  PubMed  Google Scholar 

  10. Pinard, A., Jones, G. T. & Milewicz, D. M. Genetics of thoracic and abdominal aortic diseases: aneurysms, dissections, and ruptures. Circ. Res. 124, 588–606 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Renard, M. et al. Clinical validity of genes for heritable thoracic aortic aneurysm and dissection. J. Am. Coll. Cardiol. 72, 605–615 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Tong, J. K. T. & Rabkin, S. W. The relationship between hypertension and thoracic aortic aneurysm of degenerative or atherosclerotic origin: a systematic review. Austin Hypertens. 1, 1004 (2016).

    Google Scholar 

  13. Xia, M., Luo, W., Jin, H. & Yang, Z. HAND2-mediated epithelial maintenance and integrity in cardiac outflow tract morphogenesis. Development 146, dev177477 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Strehle, E. M. et al. Genotype–phenotype analysis of 4q deletion syndrome: proposal of a critical region. Am. J. Med. Genet. A 158A, 2139–2151 (2012).

    Article  PubMed  CAS  Google Scholar 

  15. Song, K. et al. Heart repair by reprogramming non-myocytes with cardiac transcription factors. Nature 485, 599–604 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. van der Harst, P. & Verweij, N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ. Res. 122, 433–443 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Klarin, D. et al. Genetic architecture of abdominal aortic aneurysm in the Million Veteran Program. Circulation 142, 1633–1646 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ward, L. D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Watanabe, K., Taskesen, E., Van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Watanabe, K., Umićević Mirkov, M., de Leeuw, C. A., van den Heuvel, M. P. & Posthuma, D. Genetic mapping of cell type specificity for complex traits. Nat. Commun. 10, 3222 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. McInnes, G. et al. Global Biobank Engine: enabling genotype–phenotype browsing for biobank summary statistics. Bioinformatics 35, 2495–2497 (2019).

    Article  CAS  PubMed  Google Scholar 

  22. Elefteriades, J. A. et al. Indications and imaging for aortic surgery: size and other matters. J. Thorac. Cardiovasc. Surg. 149, S10–S13 (2015).

    Article  PubMed  Google Scholar 

  23. Paruchuri, V. et al. Aortic size distribution in the general population: explaining the size paradox in aortic dissection. Cardiology 131, 265–272 (2015).

    Article  PubMed  Google Scholar 

  24. Davis, A. et al. Diameters of the normal thoracic aorta measured by cardiovascular magnetic resonance imaging; correlation with gender, body surface area and body mass index. J. Cardiovasc. Magn. Reson. 15, E77 (2013).

    Article  PubMed Central  Google Scholar 

  25. Pearce, W. H. et al. Aortic diameter as a function of age, gender, and body surface area. Surgery 114, 691–697 (1993).

    CAS  PubMed  Google Scholar 

  26. Curran, M. E. et al. The elastin gene is disrupted by a translocation associated with supravalvular aortic stenosis. Cell 73, 159–168 (1993).

    Article  CAS  PubMed  Google Scholar 

  27. Angelov, S. N., Zhu, J., Hu, J. H. & Dichek, D. A. What’s the skinny on elastin deficiency and supravalvular aortic stenosis? Arterioscler. Thromb. Vasc. Biol. 37, 740–742 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Merla, G., Brunetti-Pierri, N., Piccolo, P., Micale, L. & Loviglio, M. N. Supravalvular aortic stenosis. Circ. Cardiovasc. Genet. 5, 692–696 (2012).

    Article  CAS  PubMed  Google Scholar 

  29. Earhart, B. A. et al. Phenotype of 7q11.23 duplication: a family clinical series. Am. J. Med. Genet. A 173A, 114–119 (2017).

    Article  CAS  Google Scholar 

  30. Morris, C. A. et al. 7q11.23 duplication syndrome: physical characteristics and natural history. Am. J. Med. Genet. A 167A, 2916–2935 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Kirk, E. P. et al. Mutations in cardiac T-box factor gene TBX20 are associated with diverse cardiac pathologies, including defects of septation and valvulogenesis and cardiomyopathy. Am. J. Hum. Genet. 81, 280–291 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Atik, T. et al. Novel MASP1 mutations are associated with an expanded phenotype in 3MC1 syndrome. Orphanet J. Rare Dis. 10, 128 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Sirmaci, A. et al. MASP1 mutations in patients with facial, umbilical, coccygeal, and auditory findings of Carnevale, Malpuech, OSA, and Michels syndromes. Am. J. Hum. Genet. 87, 679–686 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Holler, K. L. et al. Targeted deletion of Hand2 in cardiac neural crest-derived cells influences cardiac gene expression and outflow tract development. Dev. Biol. 341, 291–304 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Bradley, D. T. et al. A variant in LDLR is associated with abdominal aortic aneurysm. Circ. Cardiovasc. Genet. 6, 498–504 (2013).

    Article  CAS  PubMed  Google Scholar 

  36. Franceschini, N. et al. GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes. Nat. Commun. 9, 5141 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Giri, A. et al. Trans-ethnic association study of blood pressure determinants in over 750,000 individuals. Nat. Genet. 51, 51–62 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Yang, X. L. et al. Three novel loci for infant head circumference identified by a joint association analysis. Front. Genet. 10, 947 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dewey, F. E., Rosenthal, D., Murphy, D. J., Froelicher, V. F. & Ashley, E. A. Does size matter? Clinical applications of scaling cardiac size and function for body size. Circulation 117, 2279–2287 (2008).

    Article  PubMed  Google Scholar 

  40. Guo, J. et al. Global genetic differentiation of complex traits shaped by natural selection in humans. Nat. Commun. 9, 1865 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Goldfinger, J. Z. et al. Thoracic aortic aneurysm and dissection. J. Am. Coll. Cardiol. 64, 1725–1739 (2014).

    Article  PubMed  Google Scholar 

  42. Milewicz, D. M., Prakash, S. K. & Ramirez, F. Therapeutics targeting drivers of thoracic aortic aneurysms and acute aortic dissections: insights from predisposing genes and mouse models. Annu. Rev. Med. 68, 51–67 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Muiño-Mosquera, L. et al. Efficacy of losartan as add-on therapy to prevent aortic growth and ventricular dysfunction in patients with Marfan syndrome: a randomized, double-blind clinical trial. Acta Cardiol. 72, 616–624 (2017).

    Article  PubMed  Google Scholar 

  44. Taylor, A. P. et al. Statin use and aneurysm risk in patients with bicuspid aortic valve disease. Clin. Cardiol. 39, 41–47 (2016).

    Article  PubMed  Google Scholar 

  45. Toganel, R., Benedek, T. & Chitu, M. Response to statin use and aneurysm risk in patients with bicuspid aortic valve disease. Clin. Cardiol. 39, 307–308 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Inouye, M. et al. Genomic risk prediction of coronary artery disease in 480,000 adults. J. Am. Coll. Cardiol. 72, 1883–1893 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Aguirre, M., Rivas, M. A. & Priest, J. Phenome-wide burden of copy-number variation in the UK Biobank. Am. J. Hum. Genet. 105, 373–383 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Collins, R. What makes UK Biobank special? Lancet 379, 1173–1174 (2012).

    Article  PubMed  Google Scholar 

  51. 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 

  52. 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 

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

    Article  PubMed  PubMed Central  Google Scholar 

  54. Fang, H. et al. Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am. J. Hum. Genet. 105, 763–772 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Verma, S. S. et al. Imputation and quality control steps for combining multiple genome-wide datasets. Front. Genet. 5, 370 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Biasiolli, L. et al. Automated localization and quality control of the aorta in cine CMR can significantly accelerate processing of the UK Biobank population data. PLoS ONE 14, e0212272 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Warren, H. R. et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat. Genet. 49, 403–415 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1825 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Wu, P. et al. Mappings ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation. JMIR Med. Inform. 7, e14325 (2018).

    Article  Google Scholar 

  64. Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1285 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Burgess, S., Scott, R. A., Timpson, N. J., Smith, G. D. & Thompson, S. G. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur. J. Epidemiol. 30, 543–552 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Verbanck, M., Chen, C. Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Funding came from the Department of Veterans Affairs Office of Research and Development, MVP grant 1I01BX00264 and the NIH (R00HL130523) to J.R.P.; and a Stanford MCHRI Seed Grant to C.T. The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and 11 industry partners (AbbVie, AstraZeneca, Biogen, Celgene, Celgene International II Sàrl, Genentech, Merck Sharp & Dohme, Pfizer, GlaxoSmithKline, Sanofi, Maze Therapeutics, Janssen Biotech). We acknowledge the following biobanks for collecting the FinnGen project samples: Auria Biobank (https://www.auria.fi/biopankki/), THL Biobank (https://www.thl.fi/biobank), Helsinki Biobank (https://www.helsinginbiopankki.fi/), the Biobank Borealis of Northern Finland (https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki/Pages/Biobank-Borealis-briefly-in-English.aspx), Finnish Clinical Biobank Tampere (https://www.tays.fi/en-US/Research_and_development/Finnish_Clinical_Biobank_Tampere), the Biobank of Eastern Finland (https://ita-suomenbiopankki.fi/en/), Central Finland Biobank (https://www.ksshp.fi/fi-FI/Potilaalle/Biopankki), the Finnish Red Cross Blood Service Biobank (https://www.veripalvelu.fi/verenluovutus/biopankkitoiminta) and Terveystalo Biobank (https://www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/). All Finnish biobanks are members of BBMRI.fi infrastructure (https://www.bbmri.fi). We thank D. Zanetti, M. Aguirre and M. Yu for technical assistance with aspects of the analysis and R. Kajanne for assistance with administrative aspects of the FinnGen resource.

Author information

Author notes

  1. James R. Priest

    Present address: Tenaya Therapeutics, South San Francisco, CA, USA

Authors and Affiliations

  1. Department of Pediatrics, Division of Pediatric Cardiology, Stanford University School of Medicine, Stanford, CA, USA

    Catherine Tcheandjieu, Helio Tejeda & James R. Priest

  2. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Catherine Tcheandjieu, Themistocles Assimes & Philip Tsao

  3. VA Palo Alto Health Care System, Palo Alto, CA, USA

    Catherine Tcheandjieu, Themistocles Assimes & Philip Tsao

  4. MA College of Information & Computer Science, University of Massachusetts, Amherst, Amherst, MA, USA

    Ke Xiao

  5. Veterans Affairs Informatics and Computing Infrastructure, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA

    Julie A. Lynch

  6. University of Massachusetts College of Nursing & Health Sciences, Boston, MA, USA

    Julie A. Lynch

  7. Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland

    Sanni Ruotsalainen, Aarno Palotie & Mark Daly

  8. Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA

    Tiffany Bellomo, Renae Judy, Rachel L. Kember, Daniel J. Rader & Scott Damrauer

  9. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Tiffany Bellomo & Renae Judy

  10. Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Tiffany Bellomo & Scott Damrauer

  11. Center for Healthcare Organization and Implementation Research, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, MA, USA

    Madhuri Palnati

  12. VA Boston Healthcare System, Boston, MA, USA

    Derek Klarin

  13. Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Derek Klarin

  14. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Rachel L. Kember

  15. Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA

    Shefali Verma, Marylyn Ritchie & Daniel J. Rader

  16. Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA

    Manuel A. Rivas

  17. Chan–Zuckerberg Biohub, San Francisco, CA, USA

    James R. Priest

  18. Regeneron Genetics Center, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA

    Goncalo Abecasis, Aris Baras, Michael Cantor, Giovanni Coppola, Andrew Deubler, Aris Economides, Katia Karalis, Luca A. Lotta, John D. Overton, Jeffrey G. Reid, Katherine Siminovitch, Alan Shuldiner, Christina Beechert, Caitlin Forsythe, Erin D. Fuller, Zhenhua Gu, Michael Lattari, Alexander Lopez, Maria Sotiopoulos Padilla, Manasi Pradhan, Kia Manoochehri, Thomas D. Schleicher, Louis Widom, Sarah E. Wolf, Ricardo H. Ulloa, Amelia Averitt, Nilanjana Banerjee, Dadong Li, Sameer Malhotra, Deepika Sharma, Jeffrey C. Staples, Xiaodong Bai, Suganthi Balasubramanian, Suying Bao, Boris Boutkov, Siying Chen, Gisu Eom, Lukas Habegger, Alicia Hawes, Shareef Khalid, Olga Krasheninina, Rouel Lanche, Adam J. Mansfield, Evan K. Maxwell, George Mitra, Mona Nafde, Sean O’Keeffe, Max Orelus, Razvan Panea, Tommy Polanco, Ayesha Rasool, William Salerno, Kathie Sun, Jiwen Xin, Joshua Backman, Amy Damask, Lee Dobbyn, Manuel Allen Revez Ferreira, Arkopravo Ghosh, Christopher Gillies, Lauren Gurski, Eric Jorgenson, Hyun Min Kang, Michael Kessler, Jack Kosmicki, Alexander Li, Nan Lin, Daren Liu, Adam Locke, Jonathan Marchini, Anthony Marcketta, Joelle Mbatchou, Arden Moscati, Charles Paulding, Carlo Sidore, Eli Stahl, Kyoko Watanabe, Bin Ye, Blair Zhang, Andrey Ziyatdinov, Marcus B. Jones, Jason Mighty & Lyndon J. Mitnaul

Authors
  1. Catherine Tcheandjieu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ke Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Helio Tejeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Julie A. Lynch
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sanni Ruotsalainen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tiffany Bellomo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Madhuri Palnati
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Renae Judy
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Derek Klarin
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Rachel L. Kember
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Shefali Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Aarno Palotie
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Mark Daly
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Marylyn Ritchie
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Daniel J. Rader
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Manuel A. Rivas
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Themistocles Assimes
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Philip Tsao
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Scott Damrauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. James R. Priest
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

Regeneron Genetics Center

  • RGC Management and Leadership Team

    • Goncalo Abecasis
    • , Aris Baras
    • , Michael Cantor
    • , Giovanni Coppola
    • , Andrew Deubler
    • , Aris Economides
    • , Katia Karalis
    • , Luca A. Lotta
    • , John D. Overton
    • , Jeffrey G. Reid
    • , Katherine Siminovitch
    •  & Alan Shuldiner

  • Sequencing and Lab Operations

    • Christina Beechert
    • , Caitlin Forsythe
    • , Erin D. Fuller
    • , Zhenhua Gu
    • , Michael Lattari
    • , Alexander Lopez
    • , John D. Overton
    • , Maria Sotiopoulos Padilla
    • , Manasi Pradhan
    • , Kia Manoochehri
    • , Thomas D. Schleicher
    • , Louis Widom
    • , Sarah E. Wolf
    •  & Ricardo H. Ulloa

  • Clinical Informatics

    • Amelia Averitt
    • , Nilanjana Banerjee
    • , Michael Cantor
    • , Dadong Li
    • , Sameer Malhotra
    • , Deepika Sharma
    •  & Jeffrey C. Staples

  • Genome Informatics

    • Xiaodong Bai
    • , Suganthi Balasubramanian
    • , Suying Bao
    • , Boris Boutkov
    • , Siying Chen
    • , Gisu Eom
    • , Lukas Habegger
    • , Alicia Hawes
    • , Shareef Khalid
    • , Olga Krasheninina
    • , Rouel Lanche
    • , Adam J. Mansfield
    • , Evan K. Maxwell
    • , George Mitra
    • , Mona Nafde
    • , Sean O’Keeffe
    • , Max Orelus
    • , Razvan Panea
    • , Tommy Polanco
    • , Ayesha Rasool
    • , Jeffrey G. Reid
    • , William Salerno
    • , Jeffrey C. Staples
    • , Kathie Sun
    •  & Jiwen Xin

  • Analytical Genomics and Data Science

    • Goncalo Abecasis
    • , Joshua Backman
    • , Amy Damask
    • , Lee Dobbyn
    • , Manuel Allen Revez Ferreira
    • , Arkopravo Ghosh
    • , Christopher Gillies
    • , Lauren Gurski
    • , Eric Jorgenson
    • , Hyun Min Kang
    • , Michael Kessler
    • , Jack Kosmicki
    • , Alexander Li
    • , Nan Lin
    • , Daren Liu
    • , Adam Locke
    • , Jonathan Marchini
    • , Anthony Marcketta
    • , Joelle Mbatchou
    • , Arden Moscati
    • , Charles Paulding
    • , Carlo Sidore
    • , Eli Stahl
    • , Kyoko Watanabe
    • , Bin Ye
    • , Blair Zhang
    •  & Andrey Ziyatdinov

  • Research Program Management & Strategic Initiatives

    • Marcus B. Jones
    • , Jason Mighty
    •  & Lyndon J. Mitnaul

VA Million Veterans Program

  • Catherine Tcheandjieu
  • , Julie A. Lynch
  • , Madhuri Palnati
  • , Derek Klarin
  • , Daniel J. Rader
  • , Themistocles Assimes
  • , Philip Tsao
  •  & Scott Damrauer

FinnGen Project

  • Sanni Ruotsalainen
  • , Aarno Palotie
  •  & Mark Daly

Contributions

C.T. and J.R.P. conceived and designed the study. K.X. and H.T. performed image extraction derived from machine learning. R.J., T.B., R.L.K., S.V., M.R., D.J.R. and S.D. devised and performed the analyses of the Penn Medicine Biobank cohort under the auspices of the RGC consortium authorship. C.T., J.A.L., M.P., D.K., T.A. and P.T. devised and performed the analyses of the MVP cohort under the auspices of the VA–MVP consortium authorship. S.R., M.A.R., A.P. and M.D. devised and performed the analyses of the FinnGen cohort under the auspices of the FinnGen Project consortium authorship. C.T. performed the primary genetic analyses, and C.T. and J.R.P. performed the clinical analyses of the UK Biobank. C.T. and J.R.P. wrote the manuscript with valuable edits and input provided by all authors.

Corresponding author

Correspondence to James R. Priest.

Ethics declarations

Competing interests

At the time of resubmission, J.R.P. is an employee of Tenaya Therapeutics, which does not have active clinical or preclinical development programs related to the data presented here. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Matthew Bown 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 Figs. 1–12 and Note

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Tables 1–14.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tcheandjieu, C., Xiao, K., Tejeda, H. et al. High heritability of ascending aortic diameter and trans-ancestry prediction of thoracic aortic disease. Nat Genet 54, 772–782 (2022). https://doi.org/10.1038/s41588-022-01070-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-022-01070-7

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