Genotype-Phenotype Associations in Reading Disorders

项目概要/摘要 阅读障碍和其他基于语言的阅读障碍（RD）占接受 在美国，特殊教育服务影响了5%到17%的学龄儿童， 比女孩更危险。患有RD的儿童面临学业失败和未来就业不足的高风险。的 目前RD的诊断依赖于行为症状。这意味着，只有在 孩子开始学习阅读。到目前为止，已经错过了一个潜在的干预时机。 由于RD具有较高的遗传力，因此利用遗传标记可以早期识别和预防RD 率现有关于RD的遗传学研究局限于基因-RD关联的研究， 一次一个基因的评估。这种方法效率不高，因为成千上万的 基因需要被评估;它也不是有效的，因为它有很高的风险错过检测重要基因 这是因为对太多基因进行了过于严格的多重测试校正。同时，现有的一次一个基因 该方法仅检查每个基因与RD的边缘关联，而不考虑联合效应 多个基因及其相互作用与RD的关系。对于像RD这样的复杂表型，多基因- 相互作用机制更合理，并得到最近研究的支持。尽管如此， 同时发现基因并描述它们的相互作用。这部分是 因为这个领域还没有能够利用现代机器学习的发展， 有效和高效的遗传大数据建模和分析方法。现有的另一个局限性 他们一直专注于单一的行为缺陷，没有考虑人口统计学 差本项目的短期目标是确定与RD相关的基因组， 基因集丰富的功能生物学途径，表征基因-RD协会在整个 多种阅读能力的行为缺陷和人口统计学差异，并验证 使用现有的基于人口的数据集的结果。这些目标将通过以下组合来实现： 先进的机器学习算法和路径分析，适用于现有的基于人群的研发 数据源有两个具体目标：目标1侧重于识别重要基因及其表达。 使用稀疏机器学习模型和路径分析与研发相关的交互作用;目标2侧重于 使用雅芳父母和儿童纵向研究验证目标1中的模型和发现 （ALSPAC）数据集和另一个独立数据集。这项研究的长期目标是促进 发展个性化的早期识别方法和早期干预儿童风险的RD。

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.