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：
10085103
负责人：
Laura A. Almasy
金额：
$ 17.84万
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 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10085103
关键词：
Algorithms Boston Clinic Communities Complex Copy Number Polymorphism Data Set Developmental Disabilities Diagnosis Diagnostic Dimensions Evaluation Gene Mutation General Population Genes Genetic Genomics Glass Individual Knowledge Locales Location Maps Measurement Medical Genetics Mental Depression Mental disorders Modeling Moods Nature Neurocognitive Outcome Patient Self-Report Patients Pediatric Hospitals Phenotype Philadelphia Population Principal Investigator Psyche structure Psychopathology Psychotic Disorders Recurrence Regulatory Element Relative Risks Research Domain Criteria Research Personnel Resources Risk Sampling Schizophrenia Variant Work autism spectrum disorder base cognitive ability cognitive testing cohort cost effective data archive data sharing data tools gene function genetic architecture genetic variant genome-wide 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.

项目摘要/摘要罕见的拷贝数变体（CNV）与神经精神疾病密切相关，表明它们可能是研究精神病理学的一般机制的放大镜通过CNV的主要“命中”，可以更清楚地发现对神经精神病功能的扰动。但是，我们对CNV对精神症状，RDOC领域和神经认知能力（称为“维度神经精神表型”）至少在三种方面受到限制。第一的，绝大多数CNV的影响大小对神经精神型表型的理解不足，并且他们的稀有性可能会阻止个别关联研究。先前的研究集中于最复发的 CNV，这些变体中有90％以上没有证件。其次，对于CNV，经常足够单独研究，全部表型变化尚不清楚，因为确定已有通过神经发育和专科诊所进行的，大概代表了严重的末端表型频谱。在未选择的人群中仅进行了少量研究。最后，很多 CNV似乎会影响相同的神经精神域，这暗示症状学，RDOC领域和神经认知能力。基于这一假设，我们以前的工作有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，但是这些方法尚未扩展到智商超出其他维度神经精神型表型。我们将通过一个小说，多学科的协作项目来填补这些知识差距，该项目利用现有档案数据（n = 255,303）以估计和预测CNV（重复和删除）对维度神经精神型表型。我们的目标包括1）表型协调； 2）表征先前在一般人群中的大型人群中和样本中识别出的精神疾病风险CNV 确定精神疾病； 3）检查常见变体对可变表达的贡献情绪，精神病，发育障碍和一般认知能力； 4）开发新型模型来解释任何罕见CNV对维度神经精神型表型。最后，我们将开发用于数据共享的工具。波士顿儿童医院，费城儿童医院劳拉·阿尔马斯西博士和博士塞巴斯蒂安·雅克蒙特（SébastienJacquemont 应用并为该项目带来完整的专业领域。由于该项目涉及分析现有的档案数据，这是一种具有异常成本效益的方法，可以更好地表征罕见的遗传关于维度神经精神病学的遗传结构的变体和一般原则表型。我们的应用程序对RFA-MH-19-200响应。