Integrated pathogenicity assessment of clinically actionable genetic variants

临床可行的遗传变异的综合致病性评估

基本信息

批准号：
9976565
负责人：
Christopher Cassa
金额：
$ 69.24万
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：
2018
资助国家：
美国
起止时间：
2018-09-24 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9976565
关键词：
Age Algorithms American Bayesian Modeling Bayesian Prediction Binding Proteins Biochemical Characteristics Classification ClinVar Clinical Clinical Data Clinical assessments Code Complement Control Groups Coupling Crystallization Data Databases Daughter Development Disease Epidemiology Etiology Evaluation Family Fathers Genes Genetic Genomics Hypertrophic Cardiomyopathy Individual Knowledge Laboratories Malignant Neoplasms Measures Medical Medical Genetics Methods Modeling Molecular Conformation Mutation Participant Pathogenicity Patients Pattern Penetrance Performance Phenotype Population Positioning Attribute Predisposition Protein Region Proteins Recurrence Risk Role Screening procedure Single Nucleotide Polymorphism Site Structure Syndrome Training Trans-Omics for Precision Medicine Variant Veterans biobank clinical application clinical diagnostics clinical risk clinically actionable cohort exhaustion genetic pedigree genetic variant health data improved insight medical schools novel population health prospective protein structure segregation standard of care structured data variant of unknown significance

项目摘要

Integrated pathogenicity assessment of clinically actionable genetic variants ! Project Summary/Abstract Large biobanks such as All of Us and the Million Veteran Project have now collected genetic data from millions of patients, and other population health studies are expanding rapidly. The interpretation of variants in clinically actionable disease genes is becoming increasingly common in such projects. The American College of Medical Genetics and Genomics has recommended that sequence interpretation include a minimum set of 59 genes regardless of the indication for sequencing (ACMG 59). These genes are responsible for a variety of clinical syndromes and have been extensively studied. However, even in well-studied disease genes, the majority of variants are only observed in one or two families. which makes it challenging to be sure of their role in causation of disease. Further, while there may be existing evidence about a variant, it is often inadequate for interpretation, as many variants in databases were originally identified in small, symptomatic cohorts without matched control groups, so their associations can suffer from incorrect estimates of significance or effect size, and a non-trivial fraction are likely to be spurious. For these reasons, a central challenge in clinical genomics is to interpret variants in clinically actionable genes that are identified during sequencing. Because the ACMG 59 genes have been studied intensively due to their clinical applicability, there is a unique abundance of functional and structural data that can be used to improve predictions. Here, we propose to develop new data that can be leveraged in the clinical assessment of variants including novel predictions of structural consequences, regional and structurally-informed selective constraint, and clinical risk from clinical diagnostic and epidemiologic health data. Using these data, we will develop a Bayesian statistical model to predict the effects of mutations that can complement existing assessments made by consortia and clinical laboratories. This will specifically include efforts to intensively improve computational predictions of structural and functional impact using the extensive scientific and medical knowledge in each of these genes. Next, we combine that structural and functional insight with large-scale population data. We will measure statistical aberration of variation for related groups of missense variants, and also identify groups of variant sites which are enriched in recurrent somatic or germline variation associated with cancer. Finally, we will develop a Bayesian prediction framework that integrates the full set of variant observations and characteristics to improve predictions of clinical risk for individual variants, and prospectively measure its performance in a clinical diagnostic laboratory. ! !

临床可行的遗传变异的综合致病性评估呢项目摘要/摘要我们所有人和百万退伍军人项目等大型生物库现已从数以百万计的患者和其他人口健康研究正在迅速扩大。对变体的解释临床上可行的疾病基因在此类项目中变得越来越普遍。美国学院医学遗传学和基因组学已建议序列解释包括一组最小 59个基因，无论测序的指示如何（ACMG 59）。这些基因负责各种临床综合征，已经进行了广泛的研究。但是，即使在研究良好的疾病基因中，仅在一个或两个家庭中观察到大多数变体。这使得确保自己的角色具有挑战性在疾病因果关系中。此外，虽然可能有关于变体的现有证据，但通常不足解释，因为数据库中的许多变体最初都是在小的，有症状的同类中鉴定的匹配的对照组，因此他们的关联可能会遭受意义或影响大小的不正确估计，而且非平凡的部分可能是虚假的。由于这些原因，临床基因组学中的核心挑战是解释临床可行的变体在测序过程中鉴定出的基因。因为已深入研究了ACMG 59个基因对于它们的临床适用性，有独特的功能和结构数据可用于改善预测。在这里，我们建议开发可以在临床评估中利用的新数据变体包括对结构后果，区域和结构信息的新预测的变体临床诊断和流行病学健康数据的约束和临床风险。使用这些数据，我们将开发一个贝叶斯统计模型，以预测突变的影响，以补充现有的财团和临床实验室的评估。这将特别包括在结构和使用这些基因中的广泛科学和医学知识的功能影响。接下来，我们将该结构和功能性洞察力与大规模人口数据相结合。我们将衡量统计相关的错义变体的变异差，还确定了变体站点组的差异富含与癌症相关的复发体或种系变异。最后，我们将发展一个贝叶斯预测框架，集成了完整的变体观测和特征以改进预测单个变体的临床风险，并前瞻性地衡量其在临床上的表现诊断实验室。呢呢