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.

项目总结/摘要 DNA微阵列是一种常规的临床儿科诊断测试，以确定基因组拷贝数变异（CNVs） 可能导致自闭症发育迟缓和多种先天性异常这项测试也是 在产前环境中经常使用，以找到超声发现的胎儿异常的基因组原因，或 预测出生后的潜在表型。目前解释CNV的临床指南仅关注 CNV断点内包含的基因特征。然而，最近对染色质的研究 利用Hi-C或相关技术构建的CNV也可以破坏结构， 拓扑关联域（topologically associated domains，TADs）TADs是物理DNA相互作用的“邻居”， 几种功能，包括防止异位基因增强子相互作用。这种不稳定的破坏可能导致 CNV区域外基因转录的致病性改变，这些改变最终导致 疾病该建议的中心假设是通过仅关注CNV区域内的基因用于临床治疗。 然而，在DNA微阵列解释中，关键的基因组信息被完全忽略，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）），用于在服用时协助解释CNV 考虑到潜在的干扰。据我们所知，这是同类软件中第一个尝试 将TADs整合到临床CNV解释中。虽然Clinoptosis v1.0目前可作为决策支持 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。 进一步提高Clinical的实用性激发了我们在这里提出的两个目标：1）我们的目标是优化 临床试验作为一个临床决策支持和研究工具，允许纳入临床试验的边界 从不同的数据集，使一个API的分析大型病例队列，增加解释工具， 在SNP阵列上发现的纯合性区域，并允许创建去识别数据库，其中 用户可以上传基于病毒破坏的疑似致病性病例。2)我们的目标是改善 通过机器学习确定致病性最具预测性的特征， 在一个大型的，公开可用的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，其可以预测作为CNV断点的函数的cDNAs中断。在本提案中，我们 目的是使Clinical成为解释CNVs的首要工具。我们长久以来- 我们的长期目标是建立一个用户协作网络，使我们能够识别出具有最多 有可能出现由干扰素破坏引起的临床表型。这样一个独特的病人群体， 形成了第一个同类试验的基础，以评估Hi-C作为临床试验的效用。