Gene Dosage Imbalance in Neurodevelopmental Disorders

项目总结 精神健康状况每年都会对数百万人的生活质量产生负面影响。基因组学 研究挑战了精神病学、发展学和神经学之间长期存在的病因学界限 诊断，统称为发育性大脑疾病(DBD)。临床上不同的DBD，包括自闭症 和精神分裂症(SCZ)，通过常见的基因组变异，在罕见的连续体中分享病因 对大脑功能有不同的影响。采用与电子健康记录相关联的基因组学优先方法 (EHR)数据，我们将研究与DBD相关的基因组变异的完整连续体及其对 表型表达。一个新的特征是我们包含了一类基本上未被探索的与DBD相关的拷贝数 介于以下两个极端之间的中间效应大小的变体(CNV)(例如，15q11.2 BP1-2缺失) 罕见而常见的变异。我们将利用来自盖辛格大型基因组学公司DiscovEHR的现有数据 具有外显子组序列、SNP基因型和纵向EHR数据的260,000名参与者的倡议。我们会 研究整个基因组变异的相互作用如何促进临床DBD和 医疗合并症通过以下目的：1)评估DBD基因组变异的患病率 以医疗保健系统为基础的人群中的大和中等效应规模。外显子组序列数据来自 将对DiscovEHR参与者进行分析，以评估大中型DBD变种的患病率 基因组区域的效应大小和已知的对DBD风险有很大贡献的基因。变种的子集- 阳性个体将在AIMS 2和3中进行表型鉴定。2)进行追溯的电子表型鉴定，使用 现有的结构化和非结构化EHR数据，以研究DBD的临床变异性和外显性 变种。在盖辛格创新的基于EHR的数据提取和Phewas方法的基础上，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 表型将通过对1200名患有DBD变异的个体的系统图表审查来验证。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 在具有大中型效应大小的DBD变异的个体中。考虑到已知的使用 EHR数据为了捕获细粒度的认知和行为表型，我们将使用面对面来增强目标2 对大中效应变量的评估(n=1250)和额外的在线调查 中间CNV和对照(n=1000)。4)评估PGS对临床风险或弹性的影响 对于存在大或中等效果尺寸的DBD变体的DBD。我们将为添加的 PGS对大中效应变量个体的DBD风险或恢复力的影响。 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。确定SCZ和其他DBD的最高风险个人 将推动更早的检测甚至预防，这与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.