Gene Dosage Imbalance in Neurodevelopmental Disorders

 描述(由申请人提供) 发育性脑疾病(DBD)包括一组病因不同的、行为定义的疾病，影响着相当大比例的美国儿童和成年人。共享的基因组基础现在直接与看似无关的DBD联系在一起，包括自闭症(ASD)、智力残疾和精神分裂症。传统上，DBD的研究使用与生物学证据不再一致的分类标准(例如ASD与非ASD)。在这里，我们提出了一种基因组优先的方法来研究DBD，检查扩展到普通人群的正态分布的数量性状。我们将通过使用EHR数据挖掘算法，在盖辛格健康系统的100,000名患者中表征与DBD相关的致病功能丧失(PLOF)变异对功能和医学影响的不同模式，目的如下：1)使用交叉无序、基因组学驱动的方法创建DBD基因和拷贝数变异(CNV)区域的全面知识库。我们将使用我们以前工作中的现有工具来扩展用于识别DBD基因座的知识库：1)人类基因组剂量图和2)基于文献注释的针对六种特定类型DBD的功能丧失、单基因预测模型。高置信度的DBD基因和CNV区域将用于目标2的基因组变异挖掘整个外显子组测序(WES)数据。2)进行大规模、基于人群的分析，以确定具有DBD pLOF变体的队列。我们将使用作为另一项盖辛格研究的一部分产生的WES数据，分析100,000名患者中DBD pLOF变异的频率，这些患者代表了我们患者群体中的一个未选定的横截面。携带DBD pLOF变异的个体(n=4,000)及其家庭成员将在AIMS 3和4中进行表型分析。3)表征具有DBD pLOF变异的个体及其家族的数量表型。在患有DBD pLOF的患者中，我们将使用链接的EHR数据来记录临床DBD诊断。我们将进行面对面和在线评估，以衡量量化的、可遗传的特征，包括认知表现、适应行为和社会功能。我们还将评估一级亲属的这些特征，以检查家庭背景对表型变异性的影响。我们将描述立体的、定量的神经发育图谱，捕捉与特定的pLOF变体相关的全部表型谱。4)评估患有DBD pLOF变异的个体的医疗合并症和医疗利用情况。我们将调查我们的EHR数据集，以比较具有和不具有pLOF变异的个体之间的医疗合并症和医疗利用情况。我们将检查一系列变量，包括与DBD相关的专科就诊和处方使用、住院和手术程序，假设携带pLOF变异的人的共病和利用率将显著更高，无论他们是否有已知的临床DBD诊断。根据基因组变异描述临床诊断、神经发育功能、医疗影响和医疗利用的不同模式，最终将为DBD的有针对性的干预铺平道路。

 DESCRIPTION (provided by applicant) Developmental brain disorders (DBD) comprise an etiologically-heterogeneous, behaviorally-defined group of conditions affecting a significant percentage of U.S. children and adults. Shared genomic underpinnings now directly connect seemingly unrelated DBD, including autism (ASD), intellectual disability and schizophrenia. DBD have traditionally been studied using categorical criteria (e.g., ASD versus no ASD) that are no longer consistent with biological evidence. Here, we propose a genome-first approach to the study of DBD, examining normally distributed quantitative traits that extend into the general population. We will characterize differential patterns of functional and medical impact of DBD-related pathogenic loss of function (pLOF) variants in 100,000 patients from Geisinger Health System through the use of EHR data mining algorithms, with the following aims: 1) Use a cross-disorder, genomics-driven approach to create a comprehensive knowledge base of DBD genes and copy number variant (CNV) regions. We will expand the knowledge base for identifying DBD loci using existing tools from our previous work: 1) a dosage map of the human genome and 2) a loss of function, single gene prediction model based on literature annotation for six specific types of DBD. High-confidence DBD genes and CNV regions will be used in Aim 2 for genomic variant mining of whole exome sequencing (WES) data. 2) Conduct large-scale, population-based analyses to identify a cohort with DBD pLOF variants. We will use WES data, generated as part of another Geisinger study, to analyze the frequency of DBD pLOF variants in 100,000 patients representing an unselected cross-section of our patient population. Individuals with a DBD pLOF variant (n=4,000) and their family members will be phenotyped in Aims 3 & 4. 3) Characterize quantitative phenotypes in individuals with DBD pLOF variants and their families. Among patients with DBD pLOF, we will use linked EHR data to document clinical DBD diagnoses. We will administer in-person and online assessments to measure quantitative, heritable traits including cognitive performance, adaptive behavior, and social function. We will also assess these traits in first-degree relatives to examine the effect of family background on phenotypic variability. We will describe dimensional, quantitative neurodevelopmental profiles that capture the full phenotypic spectrum associated with specific pLOF variants. 4) Assess medical comorbidities and healthcare utilization among individuals with DBD pLOF variants. We will survey our EHR dataset to compare medical comorbidities and healthcare utilization between individuals with and without pLOF variants. We will examine a range of variables including DBD-related specialty visits and prescription use, hospitalizations and surgical procedures, hypothesizing that comorbidities and utilization will be significantly higher among people with pLOF variants, regardless of whether they have a known clinical DBD diagnosis. Characterization of differential patterns of clinical diagnosis, neurodevelopmental function, medical impact and healthcare utilization based on genomic variants will ultimately pave the way for targeted interventions for DBD.