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

• 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
• 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

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（CNVs）与神经发育异常的发生率大大升高有关。 精神疾病（NPD）。迄今为止的努力侧重于少数几个最经常发生的 CNVs。因此，CNV相关变异之间关系的指导原则， 这些CNV中包含的脑结构和基因功能尚不清楚。此外该 CNV相关脑变异介导的行为特征，以及它们与 对特发性NPD知之甚少。为了解决这些问题，我们建议利用现有的 队列，以组装迄今为止最大的神经成像基因组数据集（n~ 140，000）。最终 聚合数据集将包括约29，000个CNV ≥ 50 kb的携带者，包括编码基因。CNVs 将在所有数据集上使用相同的管道进行识别。多模态神经成像数据，包括 T1加权结构图像、T1 w/T2 w比值图像、静息态功能MRI和弥散MRI， 将使用相同的统一管道进行处理。我们的团队，在医疗和 统计遗传学、数学建模和脑成像将在4个研究中心协同工作 在美国，加拿大和挪威，以解决以下具体目标： 目的1：描述最常见和公认的复发性NPD的影响- CNVs对大脑结构和功能的影响。我们还将调查是否常见的变异 （多基因评分）调节NPD-CNV对神经成像衍生测量的影响。 目的2：使用CNV-1研究整体CNV负荷对脑结构和功能的影响。 风险评分一种将基因功能注释与CNV相关神经影像学联系起来的方法 改变。绝大多数临床相关的CNV太罕见，无法单独研究。使用 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。 所有含有编码基因的罕见CNV（n~ 29，000）的结构。 目的3：将CNV相关的神经影像学特征与RDoC结构域和精神病学联系起来 表型我们将测试上述CNV-神经影像学特征之间的关系， 在大型、深度表型化的、非典型性的 人口队列。 这项全球性的CNV神经成像计划将提高识别机制的能力， 介导缺失和复制对大脑结构的影响。这一共同奋进将 进一步了解遗传变异增加NPD脆弱性的机制 例如自闭症和精神分裂症。