Identification of Novel Variants And Genes Causing Thoracic Aortic Disease

项目摘要/摘要 胸主动脉疾病新变异体和基因的鉴定 在美国，主动脉疾病是排名前20位的死亡原因之一。胸主动脉瘤很少见。 有症状，通常在解剖或破裂之前不会被发现。五分之一的胸主动脉瘤患者 和夹层(TAAD)是家族性的，其中20%-30%与已知的生殖系遗传变异有关。 另外70%-80%可能是由于尚未确定的基因所致。识别致病变异和新的风险 通过识别高危家庭成员，基因对于预防与主动脉相关的并发症或死亡非常重要。在……里面 初步研究中，我们对303名TAAD患者进行了外显子组测序，并确定了致病变异 在已知导致TAAD的3%的患者中，以及在26%的不确定意义的变异(VUS)中。我们 假设：(1)我们发现的许多VU是致病的致病变种和许多其他新的 已知的TAAD危险基因中的致病变异很可能被发现，以及(2)还有其他新的 在我们的队列中导致TAAD的基因。这项研究的目的是：(1)确定潜在的致病变异 通过整合基因组和临床数据，确定已知或候选的TAAD风险基因；(2)表征细胞类型特异性 正常发育小鼠升主动脉的基因表达谱；(3)鉴定新的TAAD危险基因 通过罕见破坏性变异的病例对照关联和遗传和转录本的综合分析 对携带新风险基因突变的患者的临床表型进行数据和特征分析。对于这个项目， 总共收集了1507名TAAD患者的DNA样本，目前正在接受外显子组 测序。我们将使用新的致病能力预测因子和家庭隔离研究来评估 已知的TAAD风险基因的变异。为了识别新的TAAD风险基因，我们将使用统计方法整合 罕见破坏性变异与基因表达数据的病例对照关联。特别是，我们将量化基因 通过E15.5小鼠发育中的升主动脉的整体和单细胞RNA-seq的表达，并鉴定 通过分析具有细胞类型特异性表达的已知和候选基因，发现与TAAD相关的细胞类型。在……里面 平行地，我们将比较病例与具有可比外显子组测序数据的一大组人口对照 病例对照关联分析。最后，我们将使用相关细胞类型中的表达水平来进一步改进 风险基因识别的能力和敏感性，假设真实的风险基因高度表达 在一种或多种与疾病相关的细胞类型中。对于新的候选风险基因，我们将表征 携带这些基因破坏性变异的患者的临床表型。该研究将在以下方面提供培训 人类遗传学、计算基因组学和精确医学，以获得大规模分析的专业知识 来自DNA和RNA测序的基因组数据，并将应用这些技术来识别新的疾病- 导致胸主动脉瘤的基因。

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.