Understanding Rare Genetic Variation and Disease Risk: A Global Neurogenetics Initiative

了解罕见的遗传变异和疾病风险：全球神经遗传学倡议

基本信息

批准号：
10660098
负责人：
CARRIE E BEARDEN
金额：
$ 62.97万
依托单位：
SAINTE-JUSTINE UNIVERSITY HOSPITAL CTR
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-07 至 2028-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10660098
关键词：
Address Algorithms Architecture Attention deficit hyperactivity disorder Behavioral Brain Brain Diseases Brain imaging Canada Clinic Clinical Code Cognition Copy Number Polymorphism DNA DNA Sequence Alteration Data Data Aggregation Data Set Development Diffusion Magnetic Resonance Imaging Dimensions Functional Magnetic Resonance Imaging General Population Genes Genetic Genetic Diseases Genetic Predisposition to Disease Genetic Variation Heterogeneity Image Individual Intelligence Knowledge Link Loneliness Magnetic Resonance Imaging Maps Measures Mediating Medical Mental disorders Methods Modality Molecular Neurotic Disorders Norway Participant Pattern Pediatric Hospitals Personality Traits Phenotype Population Process Psychopathology Psychoses Recurrence Research Domain Criteria Rest Risk Sample Size Schizophrenia Site Statistical Models Structure Surface Symptoms Testing Training Variant Work autism spectrum disorder brain behavior case control clinically relevant cohort disorder risk gene function genetic variant genome-wide analysis genomic data mathematical model multidisciplinary multimodal neuroimaging neural neurodevelopmental effect neurogenetics neuroimaging patient subsets polygenic risk score population based postnatal prenatal psychotic risk variant social structural imaging trait

项目摘要

Project Abstract Copy number variants (CNVs) are associated with greatly elevated rates of neurodevelopmental psychiatric disorders (NPDs). Efforts to date have focused on a handful of the most frequent recurrent CNVs. As a result, the guiding principles underlying relationships between CNV-related variations in brain architecture and the function of genes encompassed in these CNVs are unknown. In addition, the behavioral features mediated by CNV-related brain variations, and their convergence with findings in idiopathic NPDs, are poorly understood. To address these questions, we propose to utilize existing cohorts to assemble the largest neuroimaging genomic dataset to date (n~140,000). The final aggregated dataset will include ~29,000 carriers of CNVs ≥ 50kb, encompassing coding genes. CNVs will be identified using the same pipeline across all datasets. Multimodal neuroimaging data, including T1-weighted structural images, T1w/T2w ratio images, resting-state functional MRI, and diffusion MRI, will be processed using the same harmonized pipelines. Our team, with expertise in medical and statistical genetics, mathematical modeling, and brain imaging, will work collaboratively across 4 sites in the USA, Canada, and Norway to address the following Specific Aims: Aim 1: Characterize the effect of the most frequent and well-established recurrent NPD- associated CNVs on brain structure and function. We will also investigate if common variants (polygenic scores) modulate the effects of NPD-CNVs on neuroimaging-derived measures. Aim 2: Investigate effects on brain structure and function of global CNV burden using CNV- risk scores. A method to link functional annotations of genes to CNV-associated neuroimaging alterations. The vast majority of clinically relevant CNVs are too rare to be studied individually. Using CNV-risk scores based on gene annotations, we will investigate (in aggregate) the effects on brain architecture of all rare CNVs (n~29,000) containing coding genes. Aim 3: Linking CNV-associated neuroimaging signatures to RDoC domains and psychiatric phenotypes. We will test the relationship between CNV-neuroimaging signatures identified above and dimensional measures of cognition and psychopathology in large, deeply phenotyped, unselected population cohorts. This worldwide CNV neuroimaging initiative will boost power to identify mechanisms that mediate the effects of deletions and duplications on brain architecture. This concerted endeavor will advance our understanding of mechanisms by which genetic variants increase vulnerability for NPDs such as autism and schizophrenia.

项目摘要拷贝数变异 (CNV) 与神经发育率大幅升高相关迄今为止，我们的努力主要集中在少数最常见的复发性疾病上。因此，CNV 相关变异之间关系的指导原则。这些 CNV 中包含的大脑结构和基因功能尚不清楚。由 CNV 相关的大脑变异介导的行为特征及其与研究结果的趋同为了解决这些问题，我们建议利用现有的方法。队列组装迄今为止最大的神经影像基因组数据集（n~140,000）。聚合数据集将包括约 29,000 个 CNV ≥ 50kb 的载体，包括编码基因。将使用所有数据集的相同管道进行识别，包括多模态神经影像数据。 T1 加权结构图像、T1w/T2w 比率图像、静息态功能 MRI 和扩散 MRI，将使用相同的协调管道进行处理，我们的团队拥有医疗和专业知识。统计遗传学、数学建模和脑成像将在 4 个地点协同工作在美国、加拿大和挪威，旨在实现以下具体目标：目标 1：描述最常见且公认的复发性 NPD 的影响我们还将研究常见的变异是否与大脑结构和功能相关。（多基因评分）调节 NPD-CNV 对神经影像衍生指标的影响。目标 2：使用 CNV- 研究整体 CNV 负担对大脑结构和功能的影响一种将基因功能注释与 CNV 相关神经影像联系起来的方法。绝大多数临床相关的 CNV 都非常罕见，无法单独研究。基于基因注释的 CNV 风险评分，我们将研究（总体）对大脑的影响所有包含编码基因的罕见 CNV（n~29,000）的结构。目标 3：将 CNV 相关神经影像特征与 RDoC 领域和精神病学联系起来我们将测试上面确定的 CNV 神经影像特征之间的关系。认知和精神病理学的维度测量在大的、深层表型的、未经选择的情况下人群群体。这项全球 CNV 神经影像计划将增强识别机制的能力介导删除和重复对大脑结构的影响。增进我们对遗传变异增加 NPD 脆弱性的机制的理解例如自闭症和精神分裂症。