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

• 批准号: 10414009
• 负责人: MICHAEL E TALKOWSKI
• 金额: $ 78.52万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-02 至 2023-06-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10414009
• 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

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.

摘要 结构变异(SV)是基因组组织、内容和多样性的主要驱动力。在过去十年里， 许多研究表明，SV在神经精神障碍的遗传结构中具有重要意义 (NPDS)，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在个体疾病中的重大影响，以及共同的遗传病因 一系列NPD。然而，尽管有这种病因学上的相关性，但大多数关于NPD中SV的研究都集中在 使用微阵列技术的大规范拷贝数变异(CNV)。人口遗传学研究已经 与这些工作并行，因为大多数SV数据库由基于阵列的CNV数据主导。几个全基因组 现在已经创建了测序(WGS)参考来描述SV，例如1000基因组计划 在大约2,500个人中。这些数据集对人类基因研究来说是无价的；然而，它们 捕获了一小部分可由WGS访问的SV，其祖先多样性有限，主要是由于 在技术、算法和样本量方面的限制。这些挑战也降低了这些 为诊断筛查中SV的临床解释提供参考。这项研究将提供规范和 通过系统分析聚集体的规模是1000基因组计划的50倍的复杂SVS 基因组聚合联盟(GnomAD)中的WGS数据集。我们将集成我们完成的原型 全球可访问的基因组分析工具包(GATK-)中的可扩展工具，用于基于云的SV发现 Sv；目标1)。GATK-SV将提供一个开源框架，它可以捕获一系列规范和 复杂的服务，在短读WGS的能力内，并将包括一个可扩展到长读的模块 WGS。我们将在gnomAD(一个WGS扩展)中将这些方法应用于不同祖先的聚合 我们的Exome聚合联盟(ExAC)(目标2)。GnomAD数据集目前包括85,000个WG 样本，到目标2结束时，这个资源将超过150,000个基因组。我们将使用这个参考 确定对SV有抵抗力的基因组区域，并提供系统的SV限制措施。到时候我们会的 对包括ASD在内的NPD患者的60,000个基因组执行WGS关联分析。 精神分裂症和双相情感障碍病例(目标3)。结合gnomAD SV地图和集成 对于基于微阵列的CNV聚集，这些分析将被很好地量化所赋予的相对风险 由SV在每个个体疾病中进行，并探索NPD范围内的共同风险。每个目标都将适用 创新的方法来生产新的产品，我们将免费分发这些工具、地图和分析 没有任何限制。重要的是，这些数据还将为诊断解释提供基准参考 跨越不同的祖先，并为未来人口规模的基因组医学倡议建立一个分析框架。