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Genetics and mechanisms of thoracic aortic disease

Abstract

Aortic disease has many forms including aortic aneurysm and dissection, aortic coarctation or abnormalities in aortic function, such as loss of aortic distensibility. Genetic analysis in humans is one of the most important experimental approaches in uncovering disease mechanisms, but the relative infrequency of thoracic aortic disease compared with other cardiovascular conditions such as coronary artery disease has hindered large-scale identification of genetic associations. In the past decade, advances in machine learning technology coupled with large imaging datasets from biobank repositories have facilitated a rapid expansion in our capacity to measure and genotype aortic traits, resulting in the identification of dozens of genetic associations. In this Review, we describe the history of technological advances in genetic discovery and explain how newer technologies such as deep learning can rapidly define aortic traits at scale. Furthermore, we integrate novel genetic observations provided by these advances into our current biological understanding of thoracic aortic disease and describe how these new findings can contribute to strategies to prevent and treat aortic disease.

Key points

  • Historically, familial studies provided the first footholds to understanding the genetic basis of aortic disease.

  • Approaches using machine learning to analyse millions of images within large datasets are accelerating the discovery of genetic loci associated with aortic disease phenotypes.

  • The genetic loci associated with aortic disease phenotypes highlight the importance of extracellular matrix and vascular smooth muscle cell function in the pathophysiology of aortic disease.

  • The highly heritable nature of aortic diameter, distensibility and strain raises the possibility that polygenic scores for quantitative aortic phenotypes will guide the identification of individuals at risk of sporadic aortopathy.

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Fig. 1: Aortic pathology in humans.
Fig. 2: Deep learning technology applied to aortic imaging facilitates genetic discovery.
Fig. 3: Pathways related to genetic loci associated with aortic disease.

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Acknowledgements

E.C. is supported by the National Institutes of Health (T32HL007208) and the Vascular and Endovascular Surgery Society Resident Research Award. J.P.P. is supported by the National Institutes of Health (K08HL159346) and a Sarnoff Cardiovascular Research Foundation Scholar Award. P.T.E. is supported by the Fondation Leducq (14CVD01), the National Institutes of Health (1RO1HL092577, K24HL105780) and the AHA (18SFRN34110082). M.E.L. is supported by the National Institutes of Health (1RO1HL130113) and the Toomey Fund for Aortic Research.

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Correspondence to Mark E. Lindsay.

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J.P.P. has consulted for Maze Therapeutics. P.T.E. receives sponsored research support from Bayer AG and IBM Health, and has served on advisory boards or as a consulted for Bayer AG, MyoKardia, Novartis and Quest Diagnostics. M.E.L. has received support from Bayer AG. E.C. declares no competing interests.

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Nature Reviews Cardiology thanks Kim Eagle; John Elefteriades; and Dianna Milewicz, who co-reviewed with Alan Cecchi, for their contribution to the peer review of this work.

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Chou, E., Pirruccello, J.P., Ellinor, P.T. et al. Genetics and mechanisms of thoracic aortic disease. Nat Rev Cardiol (2022). https://doi.org/10.1038/s41569-022-00763-0

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