Genotype-Phenotype Associations in Reading Disorders

阅读障碍中的基因型-表型关联

• 批准号: 9396406
• 负责人: Hope Sparks Lancaster
• 金额: $ 6.01万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9396406
• 关键词: Accounting; Adoption; Affect; Age; Algorithmic Analysis; Behavioral; Behavioral Symptoms; Big Data; Biological; Biology; Characteristics; Child; Complex; Comprehension; Data; Data Analyses; Data Set; Data Sources; Databases; Demographic Accounting; Development; Diagnosis; Dyslexia; Early Intervention; Early identification; Etiology; Factor Analysis; Failure; Future; General Population; Genes; Genetic; Genetic Markers; Genetic Predisposition to Disease; Genetic study; Genotype; Goals; Heritability; High-Throughput Nucleotide Sequencing; Interdisciplinary Study; Intervention; Joints; Language; Learning; Link; Literature; Longitudinal Studies; Machine Learning; Mental Health; Methods; Modeling; Modernization; Outcome; Parents; Pathway Analysis; Pathway interactions; Phenotype; Population Study; Prevention; Preventive measure; Reading Disorder; Reproducibility; Research; Risk; Running; School-Age Population; Schools; Services; Socioeconomic Status; Special Education; Techniques; Testing; Text; Time; Twin Studies; Underachievement; Underemployment; Validation; base; behavior measurement; boys; data modeling; gene discovery; gene interaction; genetic approach; girls; high risk; improved; personalized intervention; phonological awareness; population based; predictive modeling; public health relevance; reading ability; reading difficulties; sex

Project Summary/Abstract Dyslexia and other language-based reading disorders (RD) account for nearly 85% of children receiving special education services in the U.S. RD affect between 5% and 17% of school-age children with boys at higher risk than girls. Children with RD are at high risk for academic failure and future underemployment. The current diagnosis of RD relies on behavioral symptoms. This means that RD cannot be identified until after the child has begun to learn to read. By this time, a potentially opportune window for intervention has been missed. Early identification and prevention is possible by using genetic markers because RD have a high heritability rate. The existing genetic studies about RD are limited in the sense that the gene-RD association was evaluated on a one-gene-at-a-time basis. This approach is not efficient because hundreds of thousands of genes need to be evaluated; nor is it effective because it runs a high risk of miss-detecting important genes due to overly-strict multiple test corrections applied to too many genes. Also, the existing one-gene-at-a-time approach only examines the marginal association of each gene with RD, without accounting for the joint effect of multiple genes and their interaction in relation to RD. For a complex phenotype like RD, a multi-gene- interactive mechanism is more plausible and has been supported by recent studies. Despite this, little research has been done to discover the genes simultaneously and characterize their interactions. This is partially because this field has not been able to take advantage of modern machine learning developments that provide effective and efficient approaches for genetic big data modeling and analysis. Another limitation of the existing studies is that they have been focused on single behavioral deficits and did not account for demographic difference. The short-term goals for this proposed project are to identify the gene sets associated with RD, link the gene sets to enriched functional biological pathways, characterize the gene-RD associations across the behavioral deficits in multiple reading abilities and accounting for demographic differences, and validate the findings using existing population-based datasets. These goals will be achieved using a combination of advanced machine learning algorithms and pathway analyses that are applied to existing population-based RD data sources. There are two specific aims: Aim 1 focuses on identification of significant genes and their interactions in relation to RD using sparse machine learning models and pathway analysis; Aim 2 focuses on validation for the models and findings in Aim 1 using the Avon Longitudinal Study of Parents and Children (ALSPAC) dataset and another independent dataset. The long-term goal of this research is to contribute to the development of personalized early identification methods and early intervention for children at risk of RD.

项目摘要/摘要 阅读障碍和其他基于语言的阅读障碍（RD）占接收儿童的近85％ 美国RD的特殊教育服务影响5％至17％的男孩学龄儿童 风险高于女孩。 RD的儿童有学术失败和未来就业不足的高风险。这 RD的当前诊断依赖于行为症状。这意味着直到之后才能识别RD 孩子已经开始学习阅读。到这个时候，已经错过了一个潜在的有机干预窗口。 通过使用遗传标记，可以使用遗传标记，因为RD具有很高的遗传力，这是可能的 速度。关于RD的现有遗传研究是有限的，因为基因-RD关联是 以一次性的方式进行评估。这种方法不是有效的，因为成千上万 基因需要评估；这也不是有效的，因为它具有错过检测重要基因的高风险 由于过度分解的多个测试校正，应用于太多基因。另外，现有的一次性 方法仅检查每个基因与RD的边际关联，而无需考虑关节效应 多个基因及其相对于RD的相互作用。对于像RD这样的复杂表型，多基因 - 交互式机制更为合理，并得到了最近的研究支持。尽管如此，很少的研究 已经完成了同时发现基因并表征其相互作用的基因。这是部分 因为该领域无法利用现代机器学习发展的优势 遗传大数据建模和分析的有效和有效方法。现有的另一个限制 研究是，他们一直专注于单个行为缺陷，但没有考虑人口统计 不同之处。该拟议项目的短期目标是确定与RD相关的基因集 该基因设置为富集功能性生物学途径，表征了整个基因-RD的关联 多次阅读能力的行为缺陷以及人口统计学差异的核算，并验证 使用现有基于人群的数据集的发现。这些目标将通过结合 高级机器学习算法和途径分析应用于现有的基于人群的RD 数据源。有两个具体的目的：目标1专注于识别重要基因及其其 使用稀疏的机器学习模型和途径分析与RD相关的相互作用； AIM 2专注于 使用父母和孩子的Avon纵向研究对AIM 1中的模型和发现验证 （ALSPAC）数据集和另一个独立的数据集。这项研究的长期目标是为 开发个性化早期识别方法和针对RD风险的儿童的早期干预。