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。在过去的十年里， 许多研究表明SV对神经精神疾病的遗传结构具有重要意义 在一些实施方案中，所述治疗包括自闭症谱系障碍（ASD）、精神分裂症、双相情感障碍和ADHD。这些研究 已经表明SV在个体疾病中的显着影响，以及跨疾病的共同遗传病因学。 一系列的NPD。然而，尽管存在这种病因学相关性，但大多数关于NPD患者SV的研究都集中在 大的典型拷贝数变异（CNV）。人口遗传学研究 这些努力，因为大多数SV数据库是由基于阵列的CNV数据为主。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个基因组计划 约2,500人。这些数据集对人类遗传研究非常宝贵;然而，它们 捕获了一小部分SV，这些SV可被WGS访问，并且在祖先多样性方面受到限制，这主要是由于 技术、算法和样本量的限制。这些挑战也降低了这些 在诊断筛查中SV的临床解释参考。这项研究将提供典型的地图， 复杂的SV的规模>50倍的1000个基因组计划，通过系统地分析聚集 基因组聚合联盟（gnomAD）中的WGS数据集。我们将整合我们完成的原型， 一个可扩展的工具，用于在通用的基因组分析工具包（GATK- SV; Aim 1）。GATK-SV将提供一个开源框架，可以捕获一系列规范和 复杂的SV，在短读WGS的能力范围内，并将包括一个模块，用于扩展到长读 WGS我们将在gnomAD（一个WGS扩展）中不同祖先的聚合中应用这些方法 Exome Aggregation Consortium（ExAC）（目的2）。gnomAD数据集目前包含85，000个WGS 到目标2结束时，这一资源将超过150，000个基因组。我们将使用这个参考 确定SV抑制的基因组区域，并提供SV抑制的系统测量。然后我们将 在患有NPD（包括ASD）的个体中进行超过60，000个基因组的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 基于微阵列的CNV聚集，这些分析将很好地量化相对风险 通过SV在每一个单独的障碍，并探讨整个NPD谱的共享风险。每个目标都将适用 创新的方法来产生新的产品，我们将免费分发这些工具，地图和分析 没有限制。重要的是，这些数据还将为诊断解释提供基准参考 在不同的祖先，和未来的人口规模的基因组医学倡议的分析框架。