Identification of Novel Variants And Genes Causing Thoracic Aortic Disease

项目概要/摘要 胸主动脉疾病新变异体和新基因的鉴定 主动脉疾病是美国前20大死亡原因之一。胸主动脉瘤很少 症状性的，并且通常直到它们解剖或破裂才被发现。五分之一的胸主动脉瘤病例 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。识别致病变异和新风险 基因对于通过识别有风险的家庭成员来预防与癌症相关的并发症或死亡非常重要。在 在初步研究中，我们对303名TAAD患者进行了外显子组测序，并鉴定了致病性变异体 在已知导致TAAD的基因中，3%的患者和26%的不确定意义的变异（VUS）。我们 假设：（1）我们发现的许多VUS是致病的致病变异体， 可能会发现已知TAAD风险基因中的致病性变体，以及（2）存在其他新的 导致TAAD的基因本研究的目的是：（1）确定潜在的致病性变异， 通过整合基因组和临床数据，已知或候选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模式;（3）鉴定新的TAAD风险基因 通过罕见破坏性变异的病例对照关联以及遗传和转录组学的综合分析， 数据和表征携带新风险基因突变的患者的临床表型。对于这个项目， 共收集了1507例TAAD患者的DNA样本，目前正在进行外显子组分析。 测序我们将使用新的致病性预测因子和家族分离研究来评估 已知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 罕见的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表达，然后鉴定 通过分析具有细胞类型特异性表达的已知和候选基因来确定与TAAD相关的细胞类型。在 与此同时，我们将比较病例与大量具有可比外显子组测序数据的人群对照 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提高 风险基因识别的能力和敏感性，假设真正的风险基因高度表达 与疾病相关的一种或多种细胞类型。对于新的候选风险基因，我们将描述 携带这些基因中的破坏性变体的患者的临床表型。这项研究将提供培训， 人类遗传学，计算基因组学和精准医学，以获得分析大规模 基因组数据来自DNA和RNA测序，并将应用这些技能来识别新的疾病- 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.