ClinTAD: A Tool for Improving Clinical CNV Interpretation

ClinTAD：改善临床 CNV 解读的工具

• 批准号: 10286951
• 负责人: Arun P. Wiita
• 金额: $ 8.08万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-03 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10286951
• 关键词: Address; Architecture; Area; Award; Benign; Birth; Characteristics; Child; Childhood; Chromatin; Chromosome Mapping; Clinical; Clinical Research; Computer software; Copy Number Polymorphism; DNA; DNA Microarray Chip; DNA copy number; Data; Data Set; Databases; Developmental Delay Disorders; Diagnosis; Diagnostic tests; Disease; Enhancers; Fireflies; Future; Genes; Genetic Transcription; Genets; Genomics; Goals; Guidelines; Hi-C; Interruption; Lead; Machine Learning; Medical; Methods; Modeling; Molecular; Molecular Cytogenetics; Nature; Neighborhoods; Network-based; Neural Network Simulation; Output; Pathogenicity; Pathologist; Patients; Phenotype; Prevention; Probability; Protocols documentation; Recessive Genes; Research; Research Personnel; SNP array; Scientist; Signal Transduction; Software Tools; Structure; Techniques; Testing; Tissues; Ultrasonography; Variant; Visualization; Weight; application programming interface; autism spectrum disorder; autistic children; base; chromosome conformation capture; clinical decision support; clinical diagnostics; clinical phenotype; clinical practice; cohort; congenital anomaly; convolutional neural network; expectation; experience; fetal; human embryonic stem cell; improved; interest; learning strategy; postnatal; prenatal; research clinical testing; support tools; tool; uptake; usability; web site

PROJECT SUMMARY/ABSTRACT DNA microarray is a routine clinical pediatric diagnostic test to identify genomic copy number variants (CNVs) that could be causative of autism, developmental delay, and multiple congenital anomalies. This test is also used frequently in the prenatal setting to find genomic causes of fetal anomalies found by ultrasound, or to predict potential phenotypes postnatally. Current clinical guidelines for interpretation of CNVs focus solely on the characteristics of genes contained within the CNV breakpoints. However, recent studies on chromatin architecture, utilizing Hi-C or related techniques, have demonstrated that CNVs can also disrupt the structure of topologically associated domains (TADs). TADs are “neighborhoods” of physical DNA interactions that serve several functions, including the prevention of ectopic gene-enhancer interactions. This TAD disruption can lead to pathogenic alterations in transcription of genes outside the CNV region that are ultimately causative of disease. The central hypothesis of this proposal is by only focusing on genes within the CNV region for clinical interpretation, critical genomic information is being entirely ignored in DNA microarray interpretation, ultimately leading to missed diagnoses for patients. To address this issue, we have recently developed the free-to-use software ClinTAD (www.clintad.com; J Hum Genet (2019)) to assist in the interpretation of CNVs while taking potential TAD disruption into account. To our knowledge, this is the first software of its kind to attempt to integrate TADs into clinical CNV interpretation. While ClinTAD v1.0 is currently available as a decision support tool to assist in clinical practice, it is currently limited both in its ease-of-use as well as its predictive power. Further enhancing the utility of ClinTAD motivates the two Aims of our proposal here: 1) We aim to optimize ClinTAD as both a clinical decision support and research tool by allowing incorporation of TAD boundaries from different datasets, enabling an API for analysis of large case cohorts, adding interpretation tools for Regions of Homozygosity found on SNP array, and allowing for creation of a de-identified database where users can upload cases with suspicion for pathogenicity based on TAD disruption. 2) We aim to improve the predictive power of ClinTAD through machine learning to identify the most predictive features of pathogenicity in a large, publicly available CNV cohorts, as well as by incorporating a recently-described convolutional neural network-based model which can predict TAD disruption as a function of CNV breakpoints. In this proposal we aim to make ClinTAD the premier tool for the interpretation of CNVs in the context of TAD disruption. Our long- term goal is to build a collaborative network of users that will enable us to identify patients with the most probability of having clinical phenotypes caused by TAD disruption. Such a unique patient cohort could then form the basis of a first-of-its kind trial to evaluate the utility of Hi-C as a clinical test.

项目概要/摘要 DNA 微阵列是一种常规临床儿科诊断测试，用于识别基因组拷贝数变异 (CNV) 这也可能导致自闭症、发育迟缓和多种先天性异常。 经常在产前环境中使用，以查找超声波发现的胎儿异常的基因组原因，或 目前解释 CNV 的临床指南仅关注于预测出生后的潜在表型。 然而，最近对染色质的研究。 利用 Hi-C 或相关技术的架构已证明 CNV 也可以破坏结构 拓扑相关域 (TAD) 是服务于物理 DNA 相互作用的“邻域”。 这种 TAD 破坏可能会导致多种功能，包括防止异位基因增强子相互作用。 CNV 区域外基因转录的致病性改变最终导致 该提案的中心假设是仅关注 CNV 区域内的基因用于临床。 解释，关键的基因组信息在 DNA 微阵列解释中被完全忽略，最终 导致患者漏诊为了解决这个问题，我们最近开发了免费使用的。 软件 ClinTAD（www.clintad.com；J Hum Genet (2019)）在服用药物时协助解释 CNV 据我们所知，这是同类软件中第一个尝试这样做的软件。 将 TAD 纳入临床 CNV 解释，而 ClinTAD v1.0 目前可用作决策支持。 作为辅助临床实践的工具，它目前在易用性和预测能力方面都受到限制。 进一步增强 ClinTAD 的实用性激发了我们在此提出的两个目标：1）我们的目标是优化 ClinTAD 通过允许合并 TAD 边界，既作为临床决策支持又作为研究工具 来自不同的数据集，启用用于分析大型病例组的 API，添加解释工具 在 SNP 阵列上发现纯合性区域，并允许创建去识别化数据库，其中 用户可以根据 TAD 破坏上传疑似致病性的病例 2) 我们的目标是改进 ClinTAD 通过机器学习的预测能力来识别最具预测性的致病性特征 在一个大型的、公开的 CNV 队列中，以及通过结合最近描述的卷积神经网络 基于网络的模型，可以预测 TAD 破坏作为 CNV 断点的函数。 我们的目标是使 ClinTAD 成为在 TAD 颠覆背景下解释 CNV 的首要工具。 长期目标是建立一个用户协作网络，使我们能够识别最有帮助的患者。 这样一个独特的患者群体就有可能出现由 TAD 破坏引起的临床表型。 构成首次此类试验的基础，以评估 Hi-C 作为临床测试的效用。