Scalable tool and comprehensive maps to interpret structural variation across the neuropsychiatric spectrum

可扩展的工具和综合图谱可解释整个神经精神谱系的结构变化

基本信息

批准号：
10162661
负责人：
MICHAEL E TALKOWSKI
金额：
$ 79.35万
依托单位：
BROAD INSTITUTE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-02 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10162661
关键词：
Address Algorithms Alleles Apache Attention deficit hyperactivity disorder Basic Science Benchmarking Biological Sciences Biology Bipolar Disorder Chromatin Clinical Clinical Research Code Communities Complex Copy Number Polymorphism Country Data Data Set Databases Detection Diagnostic Disease Elements Etiology Free Will Freezing Future Genes Genetic Population Study Genetic Predisposition to Disease Genetic Research Genets Genome Genomic Segment Genomic medicine Human Human Genetics Human Genome Individual Inherited Institutes Maps Measures Methods Microarray Analysis Modeling Mosaicism Mutation Phase Population Reference Values Relative Risks Repetitive Sequence Research Resources Risk Sample Size Sampling Schizophrenia Site Source Specificity Structure Technology Trans-Omics for Precision Medicine United States National Institutes of Health Variant autism spectrum disorder base clinical diagnostics cloud based cohort diagnostic screening disorder risk ethnic diversity exome genetic architecture genome analysis genome sequencing genome-wide innovation neuropsychiatric disorder neuropsychiatry novel open source open source tool programs prototype psychogenetics risk sharing tool web interface whole genome

项目摘要

ABSTRACT Structural variation (SV) is a major driver of genome organization, content, and diversity. Over the last decade, many studies have demonstrated the significance of SV to the genetic architecture of neuropsychiatric disorders (NPDs) such as autism spectrum disorder (ASD), schizophrenia, bipolar disorder, and ADHD. These studies have suggested a significant impact of SV within individual disorders, as well as shared genetic etiology across a spectrum of NPDs. However, despite this etiological relevance, most studies of SV in NPDs have focused on large canonical copy number variation (CNV) using microarray technologies. Population genetic studies have paralleled these efforts, as most SV databases are dominated by array-based CNV data. Several whole-genome sequencing (WGS) references have now been created to characterize SV, such as the 1000 Genomes Project in ~2,500 individuals. These datasets have been invaluable to human genetic research; however, they have captured a small fraction of SV that is accessible to WGS and are limited in ancestral diversity, primarily due to limitations in technologies, algorithms, and sample sizes. These challenges have also reduced the value of these reference for clinical interpretation of SV in diagnostic screening. This study will provide maps of canonical and complex SVs on a scale >50-fold that of the 1000 Genomes Project by systematically analyzing aggregated WGS datasets in the genome aggregation consortium (gnomAD). We will integrate our completed prototype of a scalable tool for cloud-based SV discovery within the universally accessible Genome Analysis Toolkit (GATK- SV; Aim 1). GATK-SV will provide an open source framework that can capture a spectrum of canonical and complex SV, within the capabilities of short-read WGS, and will include a module for extensibility to long-read WGS. We will apply these methods across the aggregation of diverse ancestries in gnomAD, a WGS extension of our Exome Aggregation Consortium (ExAC) (Aim 2). The gnomAD dataset currently includes 85,000 WGS samples, and this resource will exceed 150,000 genomes by the conclusion of Aim 2. We will use this reference to define genomic regions recalcitrant to SV and provide systematic measures of SV constraint. We will then perform WGS association analyses across >60,000 genomes in individuals with NPDs, including ASD, schizophrenia, and bipolar disorder cases (Aim 3). In combination with the gnomAD SV maps and the integration of microarray-based CNV aggregation, these analyses will be well powered to quantify the relative risk conferred by SV in each individual disorder, and to explore shared risk across the NPD spectrum. Each aim will apply innovative approaches to yield novel products, and we will freely distribute these tools, maps, and analyses without restriction. Importantly, these data will also provide benchmarked references for diagnostic interpretation across diverse ancestries, and an analytical framework for future population-scale genomic medicine initiatives.

抽象的结构变异（SV）是基因组组织，内容和多样性的主要驱动力。在过去的十年中，许多研究表明，SV对神经精神疾病的遗传结构的重要性（NPD），例如自闭症谱系障碍（ASD），精神分裂症，双相情感障碍和ADHD。这些研究已经提出了SV对个体疾病的重大影响，以及跨越共享的遗传病因 NPD频谱。然而，尽管存在这种病因，但大多数NPD中的SV研究都集中在使用微阵列技术的大规范拷贝数变化（CNV）。人口遗传研究有与这些工作相似，因为大多数SV数据库都由基于数组的CNV数据主导。几个全基因组现在已经创建了测序（WGS）参考来表征SV，例如1000个基因组项目在约2500个人中。这些数据集对人类的遗传研究非常宝贵。但是，他们有捕获了一小部分SV，WGS可访问，并且在祖先多样性方面受到限制，主要是由于技术，算法和样本量的局限性。这些挑战也降低了这些挑战的价值参考诊断筛查中SV的临床解释。这项研究将提供规范和复杂的SVS在1000个基因组项目中的比例> 50倍，通过系统地分析聚合基因组聚集联盟（GNOMAD）中的WGS数据集。我们将整合我们完成的原型在普遍可访问的基因组分析工具包中基于云的SV发现的可扩展工具（gatk-- SV;目标1）。 GATK-SV将提供一个开源框架，该框架可以捕获一系列规范和复杂的SV，在短阅读WGS的功能中，并将包括一个可扩展性的模块 WGS。我们将在gnomad的不同祖先的聚集中应用这些方法，WGS扩展我们的外显着聚集财团（EXAC）（AIM 2）。 GNOMAD数据集目前包括85,000 WGS 样本，通过AIM 2的结论，该资源将超过150,000个基因组。我们将使用此参考为了定义基因组区域的重电，并提供了SV约束的系统测量方法。然后我们会在包括ASD，包括ASD的个体中，对60,000个基因组进行WGS关联分析精神分裂症和躁郁症病例（AIM 3）。结合GNOMAD SV地图和集成在基于微阵列的CNV聚合中，这些分析将有充分的动力来量化相对风险通过SV在每种单个疾病中，并探索整个NPD频谱中的共同风险。每个目标都将适用创新的方法来生产新产品，我们将自由分发这些工具，地图和分析无限制。重要的是，这些数据还将为诊断解释提供基准的参考跨越各种祖先，也是未来人口规模基因组医学计划的分析框架。