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.

项目摘要 基因组数据生成成本的下降和识别影响健康的变异的计算进展 这使得个性化的分子医学更接近现实。在以下方面也取得了进展 建立指南(例如，由美国医学遗传学和基因组学学院制定) 序列变异的解释。然而，系统和准确地解释他们的 临床影响仍然是一个未解决的问题。具体地说，临床解释在技术上是具有挑战性的 有几个原因，包括：1)个体基因组中的大量变异，使得很难 精确定位因果变异，2)基因和变异水平上有限的功能/临床数据，3)新发现 临床变异是一个使用传统实验室和临床方法的繁琐的低通量过程，以及4) 传统的生物信息学工具往往没有足够的精确度，基于线性的限制 单独进行序列分析。因此，临床基因组学对于常规的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体解释，我们的建议旨在1)建立在现有临床的基础上 来自Clingen努力的知识(ClinVar)，2)利用公共数据库(例如Exac)中丰富的人类变异数据 和数据库SNP)，以及3)利用来自大型疾病队列和小型疾病队列的现有和即将到来的测序数据 家庭研究；都是为了支持制定/采用贯穿各领域的计算/实验战略 在广泛的人类疾病中大规模的临床变异发现。我们假设 在3D空间中聚集在已知致病变异体附近的变异体具有很高的影响概率 蛋白质的功能。我们进一步假设，数据库中的许多致病变种，如Exac，仍然存在 未被发现/隐藏，因为它们的隐性性质或它们的稀有性限制了对 关联性分析。为了检验这些假说并建立一个具有重要功能的变体数据库 结合人类疾病，我们建议开发一个名为ClinPath3D的软件系统来检测和 描述临床相关的致病变异体。从本质上讲，它将利用蛋白质结构和变体 识别3D空间致病变异簇(PVCs)的致病潜力(目标1)。然后我们会申请 ClinPath3D用于解释ExAC、DBSNP和其他变体中具有未知意义(VU)的罕见变体 使用从ClinVar获得的致病变异体作为聚类点的数据库，都有一个视图 在普通人群中识别疾病变种(目标2)。最后，我们将使用大量的测序数据 SET(CCDG、TopMed、UK100K)以统计评估特定疾病队列和意愿中的变异丰富 通过实验表征50-100个高优先级的激酶和 转录因子50-100(目标3)。这些研究的结果将有助于在两个方面的临床进步 主要方法：(1)改进鉴定患者基因组中致病/功能变异的方法 (2)建立广泛疾病的临床相关变异的全面数据库 类型。