Using Genomics to Identify Novel Therapeutic Targets for Aortic Disease.
Arterioscler Thromb Vasc Biol
; 44(2): 334-351, 2024 02.
Article
em En
| MEDLINE
| ID: mdl-38095107
ABSTRACT
Aortic disease, including dissection, aneurysm, and rupture, carries significant morbidity and mortality and is a notable cause of sudden cardiac death. Much of our knowledge regarding the genetic basis of aortic disease has relied on the study of individuals with Mendelian aortopathies and, until recently, the genetic determinants of population-level variance in aortic phenotypes remained unclear. However, the application of machine learning methodologies to large imaging datasets has enabled researchers to rapidly define aortic traits and mine dozens of novel genetic associations for phenotypes such as aortic diameter and distensibility. In this review, we highlight the emerging potential of genomics for identifying causal genes and candidate drug targets for aortic disease. We describe how deep learning technologies have accelerated the pace of genetic discovery in this field. We then provide a blueprint for translating genetic associations to biological insights, reviewing techniques for locus and cell type prioritization, high-throughput functional screening, and disease modeling using cellular and animal models of aortic disease.
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Base de dados:
MEDLINE
Assunto principal:
Doenças da Aorta
/
Aneurisma da Aorta Torácica
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Dissecção Aórtica
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article