Journal Article

Genetics and mechanisms of thoracic aortic disease

Ellinor, Patrick T, Chou, Elizabeth, Pirruccello, James P, Lindsay, Mark E

2023

Overview

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.Advances in machine learning technology in the past decade have accelerated the discovery of genetic loci associated with aortic disease. In this Review, Lindsay and colleagues discuss how emerging insights into the genetic architecture of aortic disease can improve the accuracy of disease prediction and facilitate the discovery of new therapeutic targets.

• Publisher: Nature Publishing Group
• Subject: Aortic aneurysms / Aortic dissection / Cardiovascular disease / Congenital diseases / Coronary vessels / Datasets / Genes / Health risk assessment / Hospitals / Machine learning / Surgery