Integrative computational-experimental approaches to stratify monogenic disease risk

综合计算实验方法对单基因疾病风险进行分层

基本信息

批准号：
10889297
负责人：
Christopher Cassa
金额：
$ 30万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-20 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10889297
关键词：
Acceleration American BRCA1 gene Behavioral Benign Biological Assay Cardiovascular Diseases Cell Line Classification ClinVar Clinical Clinical Data Clinical Management Clustered Regularly Interspaced Short Palindromic Repeats Code Codon Nucleotides Communication Computing Methodologies Consent Data Development Diagnostic Disclosure Disease Epidemiology Family Genes Genomic medicine Human Human Genetics In Vitro Individual Inherited Libraries Life Style Malignant Neoplasms Measurement Measures Medical Genetics Mendelian disorder Modeling Mutagenesis Mutation Noise Outcome Pathogenicity Patients Persons Phenotype Population Preventive Medicine Process Process Assessment Recommendation Reporting Resolution Risk Risk Assessment Risk Estimate Risk Factors Source Statistical Models Structure Syndrome Techniques Therapeutic Intervention Time Translating Update Variant base editing biobank clinical phenotype clinical risk cohort cost cost effective data modeling design disorder risk epidemiologic data exome genomic data high dimensionality improved medical schools mortality novel strategies patient-clinician communication phenotypic data population health predict clinical outcome preference rare variant risk prediction risk variant screening variant of unknown significance

项目摘要

Integrative computational-experimental approaches to stratify monogenic disease risk Project Summary/Abstract Despite the availability of large disease cohorts and national biobanks, it remains challenging to interpret the functional and clinical impact of rare missense variants in established disease genes. Even in genes like BRCA1 and LDLR, most nonsynonymous variants in these genes are rare, and so there are often too few individuals to estimate risk epidemiologically. Functional screening assays have shown great promise toward resolving the impact of these missense variants, but to date, they have been too costly to scale to many genes and phenotypes. This dearth of human genetics evidence and absence of functional data has resulted in many ‘Variants of Uncertain Significance’ (VUS), which poses challenges in clinical management for patients. However, we are now at a turning point on two fronts: 1) biobanks have sequenced the exomes of hundreds of thousands of individuals (e.g. UK Biobank, Geisinger MyCode, All of Us) and 2) cost effective variant installation assays enable the functional assessment of thousands of coding variants at a time. These two approaches have complementary benefits and drawbacks. Rare variant burden analyses from exomes leverage clinical information, yet for the vast majority of individual coding variants there are insufficient numbers of carriers to provide confident estimates of risk. Functional assays provide robust data on individual variants, yet in vitro phenotypes may be imperfect surrogates for clinical phenotypes. In this proposal, we combine both techniques, developing a computational method that integrates clinical phenotyping and in vitro functional assessment to improve coding variant risk assessment. We will use this approach to estimate the risk for all variants in 100 genes associated with cancer and CAD. This project will provide high quality functional and human genetics evidence for a broad set of phenotypes and genes, and we will measure its impact and ability to scale genomic medicine by reassessing VUSs in two large biobanks. We selected 100 genes with broad clinical impact, with an established functional assay, and existing support for disease association from biobank cohorts. First, we develop an optimized variant installation library that covers all known rare variants from over one million sequenced individuals, and variants previously been assessed in the diagnostic setting (ClinVar). Second, we use related assay measurements to improve the quality of screening estimates, and infer every possible substitution within these genes. This strategy will help groups scale their studies to cover many more variants using rational library design. Finally, we integrate these epidemiological and functional data to characterize the clinical risk of variants in at least 100 genes associated with cancer and CAD. In at least 38 genes recommended for secondary findings analysis by the American College of Medical Genetics or ClinGen, we will apply evidence in the form of functional impact and case data to update VUS classifications in the Geisinger MyCode and Mass General Brigham biobanks, representing over 250,000 individuals, using established disclosure processes, based on patient consent and preferences.

综合计算实验方法对单基因疾病风险进行分层项目概要/摘要尽管有大型疾病队列和国家生物库，但解释仍然具有挑战性已确定的疾病基因中罕见错义变异的功能和临床影响，甚至在基因等中。 BRCA1 和 LDLR，这些基因中的大多数非同义变异都很罕见，因此通常太少从流行病学角度估计风险的个人已显示出巨大的前景。解决了这些错义变异的影响，但迄今为止，它们的成本太高，无法扩展到许多基因人类遗传学证据和功能数据的缺乏导致了许多问题。 “意义不确定的变异”(VUS)，这给患者的临床管理带来了挑战。然而，我们现在正处于两个方面的转折点：1）生物银行已经对数百个样本的外显子组进行了测序。数千个人（例如 UK Biobank、Geisinger MyCode、All of Us）和 2) 具有成本效益的变体安装分析可以同时对数千个编码变体进行功能评估。外显子组的罕见变异负荷分析具有互补的优点和缺点。利用临床信息，但对于绝大多数个体编码变体来说，没有足够的信息数量的携带者提供可靠的风险估计，功能分析提供了关于个体的可靠数据。变异体，但体外表型可能是临床表型的不完美替代品。将这两种技术结合起来，开发一种集成临床表型分析和体外分析的计算方法功能评估以改进编码变异风险评估我们将使用这种方法来估计。该项目将提供高质量的 100 个与癌症和 CAD 相关的基因的所有变异的风险。一系列广泛的表型和基因的功能和人类遗传学证据，我们将测量其通过重新评估两个大型生物库中的 VUS 来扩大基因组医学的影响和能力。我们选择了 100 个具有广泛临床影响的基因，具有成熟的功能测定和现有支持首先，我们开发了一个优化的变异安装库。涵盖来自超过一百万个已测序个体的所有已知罕见变异，并且这些变异之前已被其次，我们使用相关的测定测量来改进诊断设置（ClinVar）。筛选估计的质量，并推断这些基因内的每个可能的替代，该策略将有所帮助。小组使用合理的库设计扩展他们的研究以涵盖更多变体，最后，我们将这些进行整合。流行病学和功能数据可描述至少 100 个相关基因变异的临床风险美国建议对至少 38 个基因进行二次发现分析。医学遗传学学院或 ClinGen，我们将以功能影响和病例数据的形式应用证据更新 Geisinger MyCode 和 Mass General Brigham 生物库中的 VUS 分类，代表超过 250,000 名个人，根据患者同意和偏好，使用既定的披露流程。