Gene Dosage Imbalance in Neurodevelopmental Disorders

 描述（由申请人提供） 发育性脑障碍（DBD）包括一组病因异质性，行为定义的条件，影响了相当大比例的美国儿童和成人。共享的基因组基础现在直接连接看似无关的DBD，包括自闭症（ASD），智力残疾和精神分裂症。传统上使用分类标准（例如，ASD与非ASD）不再与生物学证据一致。在这里，我们提出了一个基因组优先的方法来研究DBD，检查正态分布的数量性状，扩展到一般人群。我们将通过使用EHR数据挖掘算法来表征来自Geisinger Health System的100，000名患者中DBD相关致病性功能丧失（pLOF）变体的功能和医学影响的差异模式，其目的如下：1）使用交叉疾病，基因组学驱动的方法来创建DBD基因和拷贝数变异（CNV）区域的综合知识库。我们将使用我们以前工作中的现有工具扩展用于识别DBD基因座的知识基础：1）人类基因组的剂量图和2）基于六种特定类型DBD的文献注释的功能丧失，单基因预测模型。高置信度DBD基因和CNV区域将用于Aim 2中，用于全外显子组测序（WES）数据的基因组变体挖掘。2)进行大规模、基于人群的分析，以识别具有DBD pLOF变体的队列。我们将使用作为另一项Geisinger研究的一部分生成的WES数据来分析100，000例患者中DBD pLOF变体的频率，这些患者代表了我们患者人群的一个横截面。具有DBD pLOF变体的个体（n= 4，000）及其家族成员将在目的3和4中进行表型分型。3)表征具有DBD pLOF变体的个体及其家族的定量表型。在DBD pLOF患者中，我们将使用相关EHR数据记录临床DBD诊断。我们将进行面对面和在线评估，以测量定量的遗传特征，包括认知表现，适应行为和社会功能。我们还将评估这些性状的一级亲属，以研究家庭背景对表型变异的影响。我们将描述维度，定量神经发育概况，捕捉与特定pLOF变体相关的全表型谱。4)评估DBD pLOF变异个体的医学合并症和医疗保健利用。我们将调查我们的EHR数据集，以比较有和没有pLOF变异的个体之间的医疗合并症和医疗保健利用。我们将检查一系列变量，包括DBD相关的专业访问和处方使用，住院和外科手术，假设合并症和利用率在pLOF变异体人群中显着更高，无论他们是否有已知的临床DBD诊断。基于基因组变异的临床诊断、神经发育功能、医学影响和医疗保健利用的差异模式的表征将最终为DBD的靶向干预铺平道路。

 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.