Pathogenic Variant Discovery Across a Broad Spectrum of Human Diseases

跨多种人类疾病的致病变异发现

• 批准号: 9376872
• 负责人: FENG CHEN
• 金额: $ 55.48万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-04 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9376872
• 关键词: Adopted; Advanced Development; Affect; American; Benign; Bioinformatics; Biological Assay; Charge; Clinical; Clinical Data; Clustered Regularly Interspaced Short Palindromic Repeats; Communities; DNA; Data; Data Set; Databases; Detection; Diagnosis; Disease; Ethnic Origin; Family Study; Gene Frequency; General Population; Genetic Transcription; Genome; Genomics; Goals; Guidelines; Health; Human; Imagery; In Vitro; Individual; Knowledge; Laboratories; Medical Genetics; Methodology; Molecular Medicine; Mutagenesis; Mutation; Names; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; Nature; Ontology; Pathogenicity; Patients; Penetrance; Phenotype; Phosphorylation; Phosphotransferases; Probability; Process; Proteins; RNA; Sequence Analysis; Structure; System; Testing; Trans-Omics for Precision Medicine; Variant; Weight; base; clinical sequencing; clinically relevant; cohort; cost; data sharing; density; experimental study; falls; genetic variant; genomic data; human disease; improved; medical schools; novel; protein function; protein structure; rare variant; software systems; tool; transcription factor; transcriptome sequencing; treatment strategy; variant of unknown significance

Project Summary Falling costs of generating genomic data and computational advances in discerning health-affecting variants therein are bringing personalized molecular medicine closer to reality. Progress has also been made on establishing guidelines (e.g., by the American College of Medical Genetics and Genomics) for the interpretation of sequence variants. However, the crucial step of systematically and accurately interpreting their clinical implications remains an unsolved problem. Specifically, clinical interpretation is technically challenging for several reasons, including: 1) the enormous number of variants in individual genomes, making it difficult to pinpoint causal variants, 2) limited functional/clinical data at the gene and variant levels, 3) discovery of novel clinical variants is a tedious low-throughput process using traditional laboratory and clinical approaches, and 4) conventional bioinformatics tools tend to have insufficient precision based on limitations imposed by linear sequence analysis alone. As a result, clinical genomics is still far too costly for routine clinical use. To meet the urgent need of high precision clinical variant interpretation, our proposal aims to 1) build upon existing clinical knowledge (ClinVar) from ClinGen efforts, 2) utilize rich human variation data in public databases (e.g., ExAC and dbSNP), and 3) leverage existing and upcoming sequencing data from large disease cohorts and small family studies; all to support developing/employing a cross-cutting computational/experimental strategy for clinical variant discovery at a massive scale across a broad spectrum of human diseases. We hypothesize that variants clustering in 3D spatial proximity to known pathogenic variants have high probabilities of affecting protein function. We hypothesize further that many pathogenic variants in databases such as ExAC remain undetected/hidden due to their recessive nature or their rarity that limits statistical power for detection in association analyses. To test these hypotheses and to establish a database for functionally important variants associated with human diseases, we propose to develop a software system called ClinPath3D to detect and characterize clinically relevant pathogenic variants. Essentially, it will utilize protein structures and variant pathogenicity potential to identify 3D spatial pathogenic variant clusters (PVCs) (Aim 1). We will then apply ClinPath3D to interpret rare variants of unknown significance (VUS) from the ExAC, dbSNP, and other variant databases using pathogenic variants obtained from ClinVar as nucleation points for clustering, all with a view toward discerning disease variants in the general population (Aim 2). Finally, we will use large sequencing data sets (CCDG, TopMed, UK100K) to statistically assess variant enrichment in specific disease cohorts and will further improve positive results by experimentally characterizing 50-100 high-priority variants in kinases and 50-100 in transcription factors (Aim 3). Results from these studies will contribute to clinical advancement in two key ways: (1) methodological improvement of identifying pathogenic/functional variants in patient genomes and (2) the building of a comprehensive database of clinically relevant variants across a broad spectrum of disease types.

项目概要 生成基因组数据的成本下降以及识别影响健康的变异的计算进步 其中使个性化分子医学更接近现实。在以下方面也取得了进展 制定指南（例如，美国医学遗传学和基因组学学院） 序列变体的解释。然而，系统、准确地解释它们的关键一步 临床影响仍然是一个未解决的问题。具体来说，临床解释在技术上具有挑战性 出于多种原因，包括：1）个体基因组中存在大量变异，使得很难 查明因果变异，2) 基因和变异水平的有限功能/临床数据，3) 发现新的 使用传统实验室和临床方法，临床变异是一个乏味的低通量过程，4) 传统的生物信息学工具由于线性的限制而往往精度不足 单独进行序列分析。因此，临床基因组学对于常规临床应用来说仍然过于昂贵。为了满足 迫切需要高精度的临床变异解释，我们的建议旨在 1) 以现有的临床变异为基础 来自 ClinGen 努力的知识 (ClinVar)，2) 利用公共数据库中丰富的人类变异数据（例如 ExAC 和 dbSNP），3）利用来自大型疾病队列和小型疾病队列的现有和即将推出的测序数据 家庭研究；所有这些都是为了支持开发/采用跨领域的计算/实验策略 在广泛的人类疾病中大规模发现临床变异。我们假设 与已知致病变异在 3D 空间邻近处聚集的变异具有很高的影响概率 蛋白质功能。我们进一步假设 ExAC 等数据库中的许多致病变异仍然存在 由于其隐性性质或稀有性而未被检测到/隐藏，限制了检测的统计能力 关联分析。测试这些假设并建立功能重要变异的数据库 与人类疾病相关，我们建议开发一个名为 ClinPath3D 的软件系统来检测和 表征临床相关的致病变异。本质上，它将利用蛋白质结构和变体 识别 3D 空间致病变异簇 (PVC) 的致病潜力（目标 1）。然后我们将申请 ClinPath3D 用于解释 ExAC、dbSNP 和其他变体中具有未知意义的罕见变体 (VUS) 使用从 ClinVar 获得的致病变异作为聚类的成核点的数据库，所有这些都有一个观点 识别普通人群中的疾病变异（目标 2）。最后，我们将使用大测序数据 集（CCDG、TopMed、UK100K）用于统计评估特定疾病队列中的变异富集，并将 通过实验表征激酶中的 50-100 个高优先级变体，进一步改善阳性结果 转录因子为 50-100（目标 3）。这些研究的结果将有助于两个方面的临床进展 关键方法：（1）改进识别患者基因组致病/功能变异的方法； (2) 建立跨广泛疾病的临床相关变异的综合数据库 类型。