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：
10213798
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/10213798
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 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！项目概要/摘要 All of Us 和 Million Veteran Project 等大型生物库现已收集来自数百万患者，其他人口健康研究正在迅速扩大。中变体的解释临床上可操作的疾病基因在此类项目中变得越来越常见。美国学院医学遗传学和基因组学建议序列解释包括最少的一组 59 个基因，无论测序指示如何 (ACMG 59)。这些基因负责多种临床综合征并已得到广泛研究。然而，即使在经过充分研究的疾病基因中，大多数变异仅在一两个家族中观察到。这使得确定他们的角色变得具有挑战性在疾病的因果关系中。此外，虽然可能存在关于变体的现有证据，但通常不足以证明解释，因为数据库中的许多变异最初是在小型、有症状的群体中发现的，而没有匹配的对照组，因此它们的关联可能会受到对显着性或效应大小的错误估计的影响，并且一个不平凡的分数可能是假的。由于这些原因，临床基因组学的一个中心挑战是解释临床上可行的变异测序过程中识别出的基因。由于 ACMG 59 基因已被深入研究就其临床适用性而言，有大量独特的功能和结构数据可用于改善预测。在这里，我们建议开发可用于临床评估的新数据变体包括结构后果的新颖预测、区域和结构信息选择性约束以及来自临床诊断和流行病学健康数据的临床风险。使用这些数据，我们将开发贝叶斯统计模型来预测可以补充现有突变的影响由联盟和临床实验室进行的评估。这将具体包括努力大力改进结构和结构的计算预测利用广泛的科学和医学知识对这些基因的功能产生影响。接下来，我们将结构和功能洞察力与大规模人口数据结合起来。我们将测量统计数据相关错义变体组的变异畸变，并且还识别了哪些变体位点组富含与癌症相关的复发性体细胞或种系变异。最后，我们将开发一个贝叶斯预测框架集成了全套变量观察和特征以改进预测个体变异的临床风险，并前瞻性地衡量其在临床中的表现诊断实验室。！！