Identification of Novel Variants And Genes Causing Thoracic Aortic Disease

PROJECT SUMMARY/ABSTRACT IDENTIFICATION OF NOVEL VARIANTS AND GENES CAUSING THORACIC AORTIC DISEASE Aortic diseases are among the top 20 causes of death in the US. Thoracic aortic aneurysms are rarely symptomatic and often not detected until they dissect or rupture. One fifth of all cases of thoracic aortic aneurysm and dissection (TAAD) are familial, and 20-30% of these are associated with a known, germline genetic variant. The other 70-80% may be due to genes not yet identified. Identifying disease-causing variants and novel risk genes is important for preventing aortic-related complications or deaths by identifying at risk family members. In preliminary studies, we conducted exome sequencing in 303 TAAD patients and identified pathogenic variants in genes known to cause TAAD in 3% of patients and variants of uncertain significance (VUS) in 26%. We hypothesize that: (1) Many of our identified VUS are disease-causing pathogenic variants and many other new pathogenic variants in known TAAD risk genes are likely to be discovered, and (2) There are additional novel genes causing TAAD in our cohort. The aims of the study are to: (1) Identify potential pathogenic variants in known or candidate TAAD risk genes by integrating genomic and clinical data; (2) Characterize cell-type specific gene expression patterns in the normal developing mouse ascending aorta; (3) Identify novel TAAD risk genes through case-control association of rare damaging variants and integrative analysis of genetic and transcriptomic data and characterize clinical phenotypes of patients carrying mutations in novel risk genes. For this project, DNA samples for a total of 1507 patients with TAAD have been collected and are currently undergoing exome sequencing. We will use novel in silico predictors of pathogenicity and family segregation studies to assess variants in known TAAD risk genes. To identify novel TAAD risk genes, we will use statistical methods to integrate case-control association of rare damaging variants with gene expression data. In particular, we will quantify gene expression through bulk and single cell RNA-seq of developing ascending aorta in E15.5 mice, and then identify cell types associated with TAAD by analysis of known and candidate genes with cell-type specific expression. In parallel, we will compare cases with a large set of population controls with comparable exome sequencing data in case-control association analysis. Finally, we will use expression level in relevant cell types to further improve the power and sensitivity of risk gene identification, with the hypothesis that true risk genes are highly expressed in one or multiple cell types relevant to the disease. For novel candidate risk genes, we will characterize the clinical phenotypes of patients who carry damaging variants in these genes. This study will provide training in human genetics, computational genomics, and precision medicine to gain expertise in analyzing large-scale genomic data derived from DNA- and RNA-sequencing and will apply these skills to identify novel disease- causing genes for thoracic aortic aneurysms.