Gene Dosage Imbalance in Neurodevelopmental Disorders

项目摘要 心理健康状况每年对数百万人的生活质量产生负面影响。Genomics 研究已经挑战了精神病学、发育学和神经学之间长期存在的病因学界限 诊断，统称为发育性脑障碍（DBD）。临床上不同的DBD，包括自闭症 和精神分裂症（SCZ），在一系列罕见的常见基因组变异中共享病因， 对大脑功能产生不同的影响。采用与电子健康记录相关的基因组学优先方法 (EHR)数据，我们将研究DBD相关基因组变异的完整连续体及其对 表型表达一个新的特点是我们纳入了一个很大程度上未开发的DBD相关拷贝数类 中等效应量的变异体（CNV）（例如，15q11.2 BP 1 -2缺失），其落在以下两个极端之间： 罕见和常见的变异。我们将利用盖辛格的大规模基因组学DiscovEHR的现有数据 超过260，000名参与者的主动性，包括外显子组序列、SNP基因型和纵向EHR数据。我们将 研究基因组变异的整个连续体之间的相互作用如何促成临床DBD， 通过以下目的评估医学共病：1）评估DBD基因组变异的患病率， 在基于医疗保健系统的人群中的大和中等效应量。外显子序列数据来自 将对DiscovEHR参与者进行分析，以评估大型和中型DBD变体的患病率。 在基因组区域和已知是DBD风险强贡献者的基因中的效应大小。变种的一个子集- 将在目的2和3中对阳性个体进行表型分析。2)进行回顾性e表型分析，使用 现有的结构化和非结构化EHR数据，以调查DBD的临床变异性和重复率 变体。基于Geisinger创新的基于EHR的数据提取和PheWAS方法，我们将 开发分层的、可复制的策略，以高度准确地捕获DBD和医学表型。这些 将通过对1200名DBD变异个体的系统图表审查来验证表型。3)执行 使用面对面和在线评估比较数量性状的前瞻性直接表型分析 在具有大和中等效应量的DBD变体的个体中。考虑到已知的使用 EHR数据捕获细粒度的认知和行为表型，我们将增加目标2与人 大效应量和中等效应量变量的评估（共n=1250）和额外的在线调查， 中间CNV和对照组（每组n=1000）。4)评估PGS对临床风险或恢复力的影响 对于存在大或中等效应量的DBD变体的DBD。我们将为添加的 PGS对个体DBD风险或弹性的影响具有较大和中等效应量的变体。 这些研究可能最终导致个体水平的DBD风险预测，类似于基因组算法 用于治疗癌症和心血管疾病。识别SCZ和其他DBD风险最高的个体 这将推动早期检测甚至预防，与NIMH 2020年战略计划的目标一致。

PROJECT SUMMARY Mental health conditions negatively impact the quality of life of millions of individuals every year. Genomics research has challenged long-held etiological boundaries among psychiatric, developmental, and neurological diagnoses, collectively known as developmental brain disorders (DBD). Clinically distinct DBD, including autism and schizophrenia (SCZ), share etiologies across a continuum of rare through common genomic variants that confer varying impacts on brain function. Employing a genomics-first approach linked to electronic health record (EHR) data, we will study the full continuum of DBD-related genomic variants and their combined effects on phenotypic expression. A novel feature is our inclusion of a largely unexplored class of DBD-related copy number variants (CNVs) of intermediate effect size (e.g., 15q11.2 BP1-2 deletions) that fall between the two extremes of rare and common variation. We will leverage existing data from DiscovEHR, Geisinger’s large-scale genomics initiative of >260,000 participants with exome sequence, SNP genotype, and longitudinal EHR data. We will investigate how the interplay across the full continuum of genomic variants contributes to clinical DBD and medical comorbidities through the following aims: 1) Evaluate the prevalence of DBD genomic variants of large and intermediate effect size in a healthcare system-based population. Exome sequence data from DiscovEHR participants will be analyzed to assess the prevalence of DBD variants of large and intermediate effect size in genomic regions and genes known to be strong contributors to DBD risk. A subset of variant- positive individuals will be phenotyped in Aims 2 and 3. 2) Conduct retrospective e-phenotyping, using existing structured and unstructured EHR data, to investigate clinical variability and penetrance of DBD variants. Building on Geisinger’s innovative EHR-based data extraction and PheWAS methodologies, we will develop tiered, replicable strategies for highly accurate capture of DBD and medical phenotypes. These phenotypes will be validated through systematic chart review of 1200 individuals with DBD variants. 3) Perform prospective direct phenotyping using in-person and online assessments to compare quantitative traits in individuals with DBD variants of large and intermediate effect size. Given the known limitations of using EHR data to capture fine-grained cognitive and behavioral phenotypes, we will augment Aim 2 with in-person assessments for variants of large and intermediate effect size (n=1250 total) and additional online surveys for intermediate CNVs and controls (n=1000 each). 4) Evaluate the impact of PGS on clinical risk or resilience for DBD in the presence of a DBD variant of large or intermediate effect size. We will model the added impact of PGS on risk or resilience for DBD in individuals with variants of large and intermediate effect sizes. These investigations may ultimately lead to individual-level DBD risk predictions, similar to genomic algorithms in place for cancer and cardiovascular disease. Identification of individuals at highest risk for SCZ and other DBD will drive earlier detection and even prevention, consistent with the goals of NIMH’s 2020 strategic plan.