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.

综合计算实验方法来分层单基因疾病风险项目摘要/摘要尽管大型疾病队列和国家生物库有可用性，但解释仍然是挑战稀有错义变体在既定疾病基因中的功能和临床影响。即使是像这样的基因 BRCA1和LDLR，这些基因中的大多数非同义变体很少见，因此通常太少个人在流行病学上估算风险。功能性筛选测定对解决这些错义变体的影响，但迄今为止，它们成本太高，无法扩展到许多基因和表型。人类遗传学证据的死亡和缺乏功能数据导致了许多 “不确定意义的变体”（VUS），在患者的临床管理中面临挑战。但是，我们现在处于两个方面的转折点：1）生物库已经测序了数百个成千上万的人（例如英国生物银行，盖辛格真码，我们所有人）和2）具有成本效益的变体安装评估可以一次对数千个编码变体进行功能评估。这两个方法具有完全好处和缺点。罕见的变体伯嫩分析来自Exomes 利用临床信息，但是对于绝大多数的单个编码变体，运营商数量以提供自信的风险估计。功能测定提供有关个体的强大数据但是体外表型的变异可能是临床表型的不完善的替代物。在这个建议中，我们结合这两种技术，开发一种计算方法，以整合临床表型和体外功能评估以改善编码变体风险评估。我们将使用这种方法来估计与癌症和CAD相关的100个基因中所有变体的风险。该项目将提供高质量功能和人类遗传学证据证明了广泛的表型和基因，我们将衡量通过在两个大生物库中重新评估范围来缩放基因组医学的影响和能力。我们选择了具有广泛临床影响的100个基因，具有既定的功能分析和现有支持用于生物库队列的疾病关联。首先，我们开发了一个优化的变体安装库涵盖了来自超过一百万个测序个体的所有已知稀有变体，以前的变体是在诊断环境（Clinvar）中进行评估。其次，我们使用相关的评估测量来改善筛选估计的质量，并推断这些基因内的所有可能取代。该策略将有所帮助小组扩展研究，以使用理性图书馆设计涵盖更多的变体。最后，我们整合了这些流行病学和功能数据以表征至少100个相关基因的变异的临床风险与癌症和CAD。在至少38个基因中，美国建议进行二次发现分析医学遗传学或克林根学院，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 Biobanks中的VUS分类，代表根据患者的同意和偏好，使用已建立的披露过程，超过25万个人。