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:10414009
- 负责人:
- 金额:$ 78.52万
- 依托单位:
- 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 2023-06-14
- 项目状态:已结题
- 来源:
- 关键词:AddressAlgorithmsAllelesApacheAttention deficit hyperactivity disorderBasic ScienceBenchmarkingBiological SciencesBiologyBipolar DisorderChromatinClinicalClinical ResearchCodeCommunitiesComplexCopy Number PolymorphismCountryDataData SetDatabasesDetectionDiagnosticDiseaseElementsEtiologyFree WillFreezingFutureGenesGenetic Population StudyGenetic Predisposition to DiseaseGenetic ResearchGenetsGenomeGenomic SegmentGenomic medicineHumanHuman GeneticsHuman GenomeIndividualInheritedInstitutesMapsMeasuresMethodsMicroarray AnalysisModelingMosaicismMutationPhasePopulationReference ValuesRelative RisksRepetitive SequenceResearchResourcesRiskSample SizeSamplingSchizophreniaSiteSourceSpecificityStructureTechnologyTrans-Omics for Precision MedicineUnited States National Institutes of HealthVariantautism spectrum disorderbaseclinical diagnosticscloud basedcohortdiagnostic screeningdisorder riskethnic diversityexomegenetic architecturegenome analysisgenome sequencinggenome-wideinnovationneuropsychiatric disorderneuropsychiatrynovelopen sourceopen source toolprogramsprototypepsychogeneticsrisk sharingtoolweb interfacewhole 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因素。在过去的十年里,
许多研究已经证明了 SV 对神经精神疾病遗传结构的重要性
(NPD),例如自闭症谱系障碍 (ASD)、精神分裂症、双向情感障碍和多动症。这些研究
已经表明 SV 对个体疾病以及不同疾病之间共有的遗传病因有显着影响
一系列 NPD。然而,尽管存在这种病因学相关性,大多数 NPD 中 SV 的研究都集中在
使用微阵列技术进行大规范拷贝数变异(CNV)。群体遗传学研究已
与这些努力并行的是,大多数 SV 数据库以基于阵列的 CNV 数据为主。多个全基因组
现已创建测序 (WGS) 参考来表征 SV,例如 1000 基因组计划
约 2,500 人。这些数据集对于人类基因研究具有无价的价值。然而,他们有
捕获了 WGS 可以获取的一小部分 SV,并且祖先多样性受到限制,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的价值
诊断筛查中 SV 的临床解释参考。这项研究将提供规范和
通过系统分析聚合数据,复杂的 SV 规模超过 1000 个基因组计划的 50 倍
基因组聚合联盟 (gnomAD) 中的 WGS 数据集。我们将整合我们已完成的原型
通用基因组分析工具包 (GATK-
SV;目标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,在短读长 WGS 的能力范围内,并将包括一个可扩展到长读长的模块
全基因组测序。我们将在 GnomAD(WGS 扩展)中的不同祖先的聚合中应用这些方法
我们的外显子组聚合联盟 (ExAC)(目标 2)。 gnomAD 数据集目前包含 85,000 个 WGS
样本,到目标 2 结束时,该资源将超过 150,000 个基因组。我们将使用此参考
定义对 SV 不利的基因组区域并提供 SV 约束的系统测量。我们随后将
对患有 NPD(包括自闭症谱系障碍)的个体的超过 60,000 个基因组进行 WGS 关联分析,
精神分裂症和双相情感障碍病例(目标 3)。与 gnomAD SV 地图和集成相结合
基于微阵列的 CNV 聚合,这些分析将有力地量化所赋予的相对风险
SV 在每种疾病中的作用,并探索整个 NPD 谱系的共同风险。每个目标都适用
创新方法来生产新产品,我们将免费分发这些工具、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参考
跨越不同的血统,以及未来人口规模基因组医学计划的分析框架。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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MICHAEL E TALKOWSKI其他文献
MICHAEL E TALKOWSKI的其他文献
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{{ truncateString('MICHAEL E TALKOWSKI', 18)}}的其他基金
Exploring the genetic architecture of structural birth defects
探索结构性出生缺陷的遗传结构
- 批准号:
10004116 - 财政年份:2019
- 资助金额:
$ 78.52万 - 项目类别:
Exploring the genetic architecture of structural birth defects
探索结构性出生缺陷的遗传结构
- 批准号:
9809586 - 财政年份:2019
- 资助金额:
$ 78.52万 - 项目类别:
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:
10162661 - 财政年份:2019
- 资助金额:
$ 78.52万 - 项目类别:
Molecular mechanisms and genetic drivers of reciprocal genomic disorders
相互基因组疾病的分子机制和遗传驱动因素
- 批准号:
10425331 - 财政年份:2018
- 资助金额:
$ 78.52万 - 项目类别:
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:
10737203 - 财政年份:2018
- 资助金额:
$ 78.52万 - 项目类别:
Molecular mechanisms and genetic drivers of reciprocal genomic disorders
相互基因组疾病的分子机制和遗传驱动因素
- 批准号:
9982392 - 财政年份:2018
- 资助金额:
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