Background: Thoracic aortic aneurysms progressively enlarge and predispose to acute aortic dissections. Up to 25% of individuals with thoracic aortic disease harbor an underlying Mendelian pathogenic variant. An evidence-based strategy for selection of genes to test in hereditary thoracic aortic aneurysm and dissection (HTAAD) helps inform family screening and intervention to prevent life-threatening thoracic aortic events.
Objectives: The purpose of this study was to accurately identify genes that predispose to HTAAD using the Clinical Genome Resource (ClinGen) framework.
Methods: We applied the semiquantitative ClinGen framework to assess presumed gene-disease relationships between 53 candidate genes and HTAAD. Genes were classified as causative for HTAAD if they were associated with isolated thoracic aortic disease and were clinically actionable, triggering routine aortic surveillance, intervention, and family cascade screening. All gene-disease assertions were evaluated by a pre-defined curator-expert pair and subsequently discussed with an expert panel.
Results: Genes were classified based on the strength of association with HTAAD into 5 categories: definitive (n = 9), strong (n = 2), moderate (n = 4), limited (n = 15), and no reported evidence (n = 23). They were further categorized by severity of associated aortic disease and risk of progression. Eleven genes in the definitive and strong groups were designated as "HTAAD genes" (category A). Eight genes were classified as unlikely to be progressive (category B) and 4 as low risk (category C). The remaining genes were recent genes with an uncertain classification or genes with no evidence of association with HTAAD.
Conclusions: The ClinGen framework is useful to semiquantitatively assess the strength of gene-disease relationships for HTAAD. Gene categories resulting from the curation may inform clinical laboratories in the development, interpretation, and subsequent clinical implications of genetic testing for patients with aortic disease.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6378369 | PMC |
| http://dx.doi.org/10.1016/j.jacc.2018.04.089 | DOI Listing |
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