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

项目摘要/摘要 罕见拷贝数变异(CNV)与神经精神障碍密切相关，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，否则就是微妙的。 对神经精神功能的干扰可以通过CNV的主要“打击”更清楚地辨别出来。 然而，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。 它们的稀缺性可能会阻碍对个人联想的研究。以前的研究主要集中在最常见的 CNV，使超过90%的这些变体没有记录。其次，对于CNV来说，频繁到足以 单独研究，表型变异的全谱是未知的，因为已经确定 通过神经发育和专科诊所进行，这可能代表着 表型谱。只有几项研究是在未选定的人群中进行的。最后，许多人 CNV似乎影响相同的神经精神病学领域，提示精神病学的多基因/全基因模型 症状学、RDoC领域和神经认知能力。基于这个假设，我们之前的工作已经 研究表明，遗传分数和功能注释可以准确地预测任何CNV对智商的影响，但 这些方法还没有扩展到智商以外的其他维度神经精神表型。 我们将通过一个新的、多学科的协作项目来填补这些知识空白，该项目利用现有的 档案数据(n=255,303)以估计和预测CNV(复制和缺失)对 次元神经精神表型。我们的目标包括1)表型协调；2)特征 以前在大量普通人群队列和样本中发现的精神疾病风险CNV 被确定为精神疾病；3)检查常见变量对变量表现力的贡献 罕见的CNV通过多基因风险评分(PR)在情绪、精神病、发育障碍和 一般认知能力；以及4)开发新的模型来解释任何罕见的CNV对 次元神经精神表型。最后，我们将开发数据共享工具。 波士顿儿童医院的David Glahn博士、费城儿童医院的Laura Almasy博士和Dr。 圣贾斯丁医院中心的S·雅克蒙特是这一事件的联合首席调查员 应用程序，并为项目带来互补的专业领域。由于本项目涉及分析 现有的档案数据，这是一种非常经济高效的方法，可以更好地描述罕见的基因 次元神经精神病学遗传结构的变异和一般原理的阐明 表型。我们的应用程序响应RFA-MH-19-200。

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.