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

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

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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.

项目摘要拷贝数变体（CNV）与神经发育的速率大大提高有关精神疾病（NPD）。迄今为止的努力集中在少数最常经常的经常性上 CNV。结果，与CNV相关变化之间关系的指导原则的基本原则这些CNV中包含的脑结构和基因的功能尚不清楚。另外，由CNV相关的大脑变异介导的行为特征及其与发现的收敛性特发性NPD，知之甚少。为了解决这些问题，我们建议利用现有迄今（n〜140,000）组装最大的神经成像基因组数据集（N〜140,000）。决赛汇总的数据集将包括约29,000个CNV≥50KB的载体，包括编码基因。 CNV 将在所有数据集中使用相同的管道来识别。多模式神经影像学数据，包括 T1加权结构图像，T1W/T2W比图像，静止状态功能MRI和扩散MRI，将使用相同的统一管道处理。我们的团队，具有医疗方面的专业知识统计遗传学，数学建模和脑成像，将在4个站点进行协同工作在美国，加拿大和挪威，以解决以下具体目标： AIM 1：表征最频繁，最良好的复发性NPD-的效果有关大脑结构和功能的相关CNV。我们还将调查是否常见变体（多基因评分）调节NPD-CNV对神经成像衍生的测量的影响。 AIM 2：使用CNV-研究对全球CNV伯嫩的大脑结构和功能的影响风险分数。一种将基因的功能注释与CNV相关的神经影像联系起来的方法改变。绝大多数临床相关的CNV太罕见了，无法单独研究。使用 CNV风险得分基于基因注释，我们将（汇总）研究对大脑的影响所有稀有CNV（n〜29,000）的结构都包含编码基因。 AIM 3：将CNV相关的神经成像签名与RDOC领域和精神病学联系起来表型。我们将测试上面确定的CNV-NEUROIMICETION签名与大型，深层表型，未选择的认知和心理病理学的维度测量人群队。这项全球CNV神经影像学计划将增强能力，以确定调解删除和重复对大脑结构的影响。这项努力的努力促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。