Shared genetic architecture of specific learning disorders at behavioral, functional genomic and molecular genetic levels of analysis

在行为、功能基因组和分子遗传分析水平上特定学习障碍的共享遗传结构

• 批准号: 10758089
• 负责人: JEFFREY R GRUEN
• 金额: $ 20.57万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-12-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10758089
• 关键词: Achievement; Anxiety; Architecture; Attention; Behavioral; Biologic Characteristic; Biological; Brain; Brain imaging; Brain region; Categories; Chromatin Loop; Classification; Clinical; Collaborations; Comprehension; Data; Data Analyses; Data Set; Diagnostic; Differential Diagnosis; Disease; Epigenetic Process; Equation; Factor Analysis; Functional Magnetic Resonance Imaging; Gene Expression; Genes; Genetic; Genetic Diseases; Genetic Drift; Genetic Research; Genetic Risk; Genetic Variation; Genomics; Goals; Grouping; Growth; Heterogeneity; High Prevalence; Image; Individual; Knowledge; Learning Disabilities; Learning Disorders; Letters; Linkage Disequilibrium; Methods; Methylation; Modeling; Molecular Genetics; Nature; Neurons; Organoids; Pathway interactions; Persons; Phenotype; Process; Property; Reading; Regulatory Pathway; Risk Factors; Sampling; System; Testing; Time; Transcend; Variant; Vocabulary; brain tissue; clinical diagnostics; clinically relevant; comorbidity; connectome; disease classification; epigenome; functional genomics; genetic architecture; genetic variant; genome wide association study; genome-wide; improved; indexing; longitudinal analysis; neurogenesis; open source; pleiotropism; polygenic risk score; risk sharing; skills; sound; statistics; success; trait; transcriptome

The high prevalence of comorbidity between specific learning disabilities (SLDs), with other conditions, and their associations with genes in common with all of them, strongly suggest substantial pleiotropic effects that transcend diagnostic boundaries. While several of these genes with associations across SLDs are known, the nature, scope, and mechanisms of these pleiotropic effects remain unclear. The goal of this project is to examine genetic convergence and divergence that transcend clinical diagnostic categories for SLDs that could account for the high prevalence of comorbidity. Based on our track record of genetic research in comorbid SLDs, we hypothesize that elucidating the biological significance of cross-disorder genetic influences will reveal fundamental properties of pleiotropic loci that differentiate them from disorder-specific loci and help to clarify the biological substrates shared across the diagnostic landscape of SLDs. To test this hypothesis, we have assembled data for genome-wide association study (GWAS) and summary statistics for SLD-related traits in 17 samples that we utilize to examine shared genetic effects. In Aim 1 we propose to apply genomic structural equation modeling (Genomic SEM) to model the multivariate, genome-wide architecture of SLD-related traits by bringing together the largest available GWAS summary statistics within the learning disorder space. This will describe how different SLD components and disorders cluster together at the genomic level of analysis and identify clinically relevant external traits, for example anxiety and attention, that are associated with these clusters. Next, in Aim 2 we propose to apply Stratified Genomic SEM to identify mechanistic correlates of shared genetic variance for SLD-traits that frequently co- occur, implicating transdiagnostic genetic risk pathways. These mechanistic correlates are with functional data developed in our lab, in collaboration with other labs, and from open-source consortiums (e.g., GETex, ENCODE). These include gene-expression datasets from brain tissue, developing neurons, and organoids (transcriptome), methylation datasets (epigenome) and regulatory pathways (regulome), and fMRI imaging data (brain connectome). Mechanistic correlates will identify pleiotropic effects that transcend diagnostic categories and offer new ways to understand SLDs and their neurodevelopmental underpinnings. In Aim 3 we propose to perform a multivariate GWAS of SLDs to identify pleiotropic genetic variants. This will categorize SLD-associated genetic variants as either pleiotropic or disorder-specific. SLD-associated genetic variants that are pleiotropic could partially account for comorbidity. We will also analyze longitudinal data to show whether the genetic components that underlie achievement at a single time point are distinct from those important for growth over time, both of which are vital aspects of academic success. Completion of this project will identify mechanisms that improve our understanding of comorbidity, inform bottom-up revisions of the classification system for SLDs, and enhance the current nosology.

特定学习障碍（SLD）与其他疾病之间的共病率很高， 它们与所有这些基因共同的基因相关，强烈表明了大量的多效性效应， 超越诊断界限虽然已知这些基因中有几个与SLD之间的关联， 这些多效性作用的性质、范围和机制仍不清楚。该项目的目标是 检查超越SLD临床诊断类别的遗传趋同和趋异， 导致了合并症的高患病率。根据我们对共病基因研究的记录， SLD，我们假设阐明交叉疾病遗传影响的生物学意义将 揭示了多效性基因座的基本特性，这些特性将它们与疾病特异性基因座区分开来，并有助于 阐明SLD诊断领域共有的生物学底物。为了验证这个假设，我们 已经收集了全基因组关联研究（GWAS）的数据和SLD相关的汇总统计数据。 我们利用17个样本中的性状来检查共享的遗传效应。 在目标1中，我们提出应用基因组结构方程模型（基因组SEM）来模拟多变量， SLD相关性状的全基因组架构，汇集了最大的可用GWAS摘要 学习障碍空间中的统计学。这将描述不同的SLD成分和疾病 在基因组水平上进行聚类分析，并确定临床相关的外部特征，例如 焦虑和注意力，这些都与这些集群有关。接下来，在目标2中，我们建议应用分层 基因组扫描电镜，以确定机制相关的共享遗传变异的SLD性状，经常共同 发生，涉及转诊断遗传风险途径。这些机械相关性与功能数据 在我们的实验室开发，与其他实验室合作，并从开源联盟（例如，GETex， ENCODE）。其中包括来自脑组织、发育中的神经元和类器官的基因表达数据集 （转录组），甲基化数据集（表观基因组）和调控途径（调节组），以及功能磁共振成像 数据（脑连接体）。机制相关性将确定超越诊断的多效性效应 分类，并提供新的方法来了解SLD及其神经发育基础。在目标3中， 建议对SLD进行多变量GWAS以鉴定多效性遗传变体。这将分类 SLD相关的遗传变异是多效性的或疾病特异性的。SLD相关的遗传变异， 是多效性的可以部分解释合并症。我们还将分析纵向数据，以显示 在一个时间点上决定成就的遗传成分与那些重要的 随着时间的推移，这两者都是学术成功的重要方面。 该项目的完成将确定机制，提高我们对科摩罗的了解， 自下而上修订SLD的分类系统，并加强目前的疾病分类。