Pathogenic Variant Discovery Across a Broad Spectrum of Human Diseases

项目摘要 产生基因组数据的成本下降和识别影响健康的变异的计算进步 使个性化分子医学更接近现实。在以下方面也取得了进展： 建立指导方针（例如，美国医学遗传学和基因组学学院） 序列变异的解释。然而，系统和准确地解释其 临床应用仍然是一个未解决的问题。具体而言，临床解释在技术上具有挑战性 有几个原因，包括：1）个体基因组中的大量变异，使得很难 查明致病变异，2）基因和变异水平的功能/临床数据有限，3）发现新的 临床变体是使用传统实验室和临床方法的冗长的低通量过程，以及4） 传统的生物信息学工具往往基于线性生物信息学所施加的限制 单独进行序列分析因此，临床基因组学对于常规临床使用来说仍然过于昂贵。满足 迫切需要高精度的临床变异解释，我们的建议旨在1）建立在现有的临床 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），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;所有这些都是为了支持开发/采用跨领域的计算/实验策略， 在人类疾病的广泛范围内大规模发现临床变异。我们假设 在3D空间上聚集在已知致病性变体附近的变体具有高概率影响 蛋白质功能我们进一步假设，数据库中的许多致病性变异，如ExAC，仍然存在 由于其隐性性质或其罕见性而未被检测到/隐藏，这限制了检测的统计能力， 关联分析为了验证这些假设，并建立一个功能重要的变异数据库 与人类疾病相关，我们建议开发一个名为ClinPath 3D的软件系统来检测和 表征临床相关的致病性变体。本质上，它将利用蛋白质结构和变体 致病性潜力，以鉴定3D空间致病性变体簇（PVC）（目的1）。然后我们将申请 ClinPath 3D用于解释ExAC、dbSNP和其他变异中意义未知的罕见变异（VUS） 使用从ClinVar获得的致病性变体作为聚类的成核点的数据库， 在一般人群中识别疾病变异（目标2）。最后，我们将使用大型测序数据 组（CCDG、TopMed、UK 100 K），以统计学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， 50-100的转录因子（目标3）。这些研究的结果将有助于两个国家的临床进步。 关键途径：（1）改进鉴定患者基因组中致病性/功能性变异的方法， (2)建立一个广泛的疾病临床相关变异的综合数据库 类型

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.