权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Large-Scale Evaluation of the Effect of Rare Genetic Variants on Psychiatric Symptoms and Cognitive Ability

大规模评估罕见遗传变异对精神症状和认知能力的影响

基本信息

批准号：
10610393
负责人：
Laura A. Almasy
金额：
$ 116.98万
依托单位：
BOSTON CHILDREN'S HOSPITAL
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：
2019-05-06 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10610393
关键词：
Algorithms Boston Clinic Communities Complex Copy Number Polymorphism Data Set Developmental Disabilities Diagnosis Diagnostic Dimensions Disparate Evaluation General Population Genes Genetic Genomics Glass Individual Knowledge Locales Maps Measurement Medical Genetics Mental Depression Mental disorders Modeling Moods Mutation Nature Neurocognitive Outcome Patient Self-Report Patients Pediatric Hospitals Phenotype Philadelphia Population Principal Investigator Psychopathology Psychoses Recurrence Regulatory Element Relative Risks Research Domain Criteria Research Personnel Resources Risk Sampling Schizophrenia Variant Work archived data autism spectrum disorder cognitive ability cognitive testing cohort cost effective data sharing data tools gene function gene network genetic architecture genetic variant genome-wide genomic locus individual variation insight instrument interest medical specialties multidisciplinary neurodevelopment neuropsychiatric disorder neuropsychiatry novel phenotypic data polygenic risk score prevent psychiatric symptom rare genetic disorder repository symptomatology trait web-based tool

项目摘要

PROJECT SUMMARY/ABSTRACT Rare copy number variants (CNVs) are strongly associated with neuropsychiatric disorders, suggesting that they might serve as a magnifying glass to study general mechanisms of psychopathology as otherwise subtle perturbations to neuropsychiatric functions may be more clearly discerned through the major `hit' of the CNV. However, our understanding of the impact of CNVs on psychiatric symptomatology, RDoC domains and neurocognitive ability (termed `dimensional neuropsychiatric phenotypes') is limited in at least three ways. First, the effects sizes of the vast majority of CNVs on neuropsychiatric phenotypes remain poorly understood and their rarity will likely to prevent individual association studies. Prior studies concentrated on the most recurrent CNVs, leaving more than 90% of these variants undocumented. Second, for CNVs frequent enough to be studied individually, the full spectrum of phenotypic variation is unknown because ascertainment has been performed through neurodevelopmental and specialty clinics, which presumably represent the severe end of the phenotypic spectrum. Only a few studies have been conducted in unselected populations. Finally, many CNVs seem to impact the same neuropsychiatric domains, suggesting a poly/omnigenic model for psychiatric symptomatology, RDoC domains and neurocognitive ability. Based on this hypothesis, our previous work has shown that genetic scores and functional annotations can accurately predict the effect of any CNV on IQ but these approaches have not yet been extended beyond IQ to other dimensional neuropsychiatric phenotypes. We will fill these knowledge gaps with a novel, multidisciplinary, collaborative project that leverages existing archival data (n=255,303) to estimate and predict the effect sizes of CNVs (duplications and deletions) on dimensional neuropsychiatric phenotypes. Our aims include 1) phenotypic harmonization; 2) characterizing previously identified risk CNVs for mental illness in a large in general population cohorts and in samples ascertained for mental illnesses; 3) examine the contribution of common variants to variable expressivity of rare CNVs via polygenic risk scores (PRS) in the domains of mood, psychosis, developmental disability, and general cognitive ability; and 4) develop novel models to explain the effect size of any rare CNVs on dimensional neuropsychiatric phenotypes. Finally, we will develop tools for data sharing. Dr. David Glahn, Boston Children's Hospital, Dr. Laura Almasy, Children's Hospital of Philadelphia, and Dr. Sébastien Jacquemont, Centre Hospitalier Universitaire Sainte-Justine, are co-principal investigators on this application and bring complementary domains of expertise to the project. As this project involves analysis of existing archival data, it is an exceptionally cost-effective approach to better characterizing the rare genetic variants and elucidating general principles regarding the genetic architecture of dimensional neuropsychiatric phenotypes. Our application is responsive to RFA-MH-19-200.

项目总结/摘要罕见的拷贝数变异（CNV）与神经精神疾病密切相关，这表明，它们可以作为一个放大镜来研究精神病理学的一般机制，通过CNV的主要“打击”，可以更清楚地辨别对神经精神功能的干扰。然而，我们对CNVs对精神病学、RDoC领域和神经认知能力（称为“维度神经精神表型”）至少在三个方面受到限制。第一、绝大多数CNV对神经精神表型的影响大小仍然知之甚少，它们的稀有性可能会妨碍个体关联研究。以前的研究集中在最经常发生的 CNVs，使超过90%的这些变体没有记录。第二，对于CNVs频率足够高的情况，单独研究，全谱的表型变异是未知的，因为确定已通过神经发育和专业诊所进行，这大概代表了严重的结束，表型谱只有少数研究是在少数人群中进行的。最后，许多 CNV似乎影响相同的神经精神领域，提示精神疾病的多基因/全基因模型。神经病学、RDoC领域和神经认知能力。基于这一假设，我们以前的工作表明遗传评分和功能注释可以准确预测任何CNV对智商的影响，这些方法还没有从IQ扩展到其他维度的神经精神表型。我们将填补这些知识空白，一个新颖的，多学科的，合作项目，利用现有的档案数据（n= 255，303），以估计和预测CNV（重复和缺失）对维度神经精神表型。我们的目标包括1）表型协调; 2）表征先前在大规模一般人群队列和样本中确定的精神疾病风险CNV 确定的精神疾病; 3）检查常见变量的贡献变量的表达能力，罕见的CNV通过多基因风险评分（PRS）在情绪，精神病，发育障碍，一般认知能力;和4）开发新的模型来解释任何罕见的CNV对维度神经精神表型。最后，我们将开发数据共享工具。博士波士顿儿童医院的大卫格拉恩，费城儿童医院的劳拉·阿尔马西博士和 Sébastien Jacquemont，Centre Hospitalier Universiael Sainte-Justine，是该研究的共同主要研究者。应用程序，并为项目带来互补的专业知识领域。由于该项目涉及分析现有的档案数据，这是一个非常具有成本效益的方法，以更好地表征罕见的遗传变异和阐明有关维神经精神病的遗传结构的一般原则表型我们的应用程序响应RFA-MH-19-200。