Global significance test based on quantile regression with applications to genomic studies of Alzheimer’s disease

基于分位数回归的全局显着性检验及其在阿尔茨海默病基因组研究中的应用

• 批准号: 10303743
• 负责人: Qi Zheng
• 金额: $ 25.71万
• 依托单位: UNIVERSITY OF LOUISVILLE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10303743
• 关键词: Affect; Age-Years; Aging; Alzheimer' s Disease; Awareness; Biological; Biomedical Research; Brain Diseases; Cause of Death; Cohort Studies; Complex; Confounding Factors (Epidemiology); Data; Data Analyses; Dementia; Development; Dimensions; Disease; Disease regression; Elderly; Etiology; Evaluation; Foundations; Gene Expression; Gene Expression Profiling; Genes; Genetic Predisposition to Disease; Genomics; Goals; Heritability; Joints; Light; Linear Regressions; Memory; Methods; Modeling; Neurodegenerative Disorders; Pattern; Phenotype; Play; Probability; Procedures; Public Health; Quantitative Trait Loci; Research Personnel; Role; Sampling; Single Nucleotide Polymorphism; Standardization; Testing; Therapeutic; Tissue-Specific Gene Expression; United States; base; biological research; differential expression; disease phenotype; flexibility; follow-up; genome-wide; genomic data; genomic tools; high dimensionality; improved; innovation; insight; multidimensional data; neglect; novel; religious order study; research and development; response; risk variant; statistics; theories; tool; user-friendly

Project Summary/Abstract Alzheimer's disease (AD) is one of the leading causes of death for the elderly with no current cure. Genomics studies, such as mapping expression quantitative trait loci (eQTL) and differential gene expressions, play a critical role in understanding the biological mechanisms of AD and developing potential therapeutic treatments. In genomics studies, there has been growing awareness that the covariates (e.g., quantitative gene expression) may have changing effects on the distribution of responses (e.g., disease phenotypes) reflecting a heterogeneous covariates-response association. Those heterogeneous associations shed insight on scientific discoveries and entail significant implications but are often neglected by most existing analysis procedures confined to a narrow aspect of the response distribution (e.g., standard linear regression focusing on the mean or quantile regression at a single quantile level). Thus, the development of valid and efficient hypothesis tests to detect heterogeneous associations is of great value to genomics studies of complex diseases such as AD. This proposal aims to develop several quantile regression-based global significance tests, which utilize all information across a well-chosen region of quantile levels and provide researchers with evaluations of the overall impacts of covariates on the response. Inspired by our preliminary data analysis on the two studies of aging and dementia, namely Religious Orders Study (ROS) and Memory and Aging Project (MAP), we will first propose a global significance test to thoroughly evaluate covariates' impact across all quantile levels of the response variable (Aim 1). Then motivated by high-dimensional genomics data of AD in ROS/MAP, we will further develop two global significance tests for high-dimensional responses and covariates data, respectively (Aim 2). Moreover, we will apply the proposed tests in Aims 1-2 to the genomics data generated by ROS/MAP to identify eQTL and differentially expressed genes that can be used to prioritize risk genes of AD for identifying developing potential treatments (Aim 3). We will also provide a user-friendly R package to implement the proposed tests. The innovation of our proposal is three-fold. (i) By evaluating the impacts of covariates on responses across the entire quantile domain, the proposed global significance tests have a superior power to identify heterogeneous covariates-response associations compared to alternative methods. (ii) As the proposed tests neither impose any stringent model assumption nor require additional splines smoothing or re-sampling or shrinkage estimation, they can be broadly implemented in large-scale genomics data. (iii) Our proposed test in Aim 2 will serve as a useful tool for detecting heterogeneous associations between covariates and multiple responses. The successful completion of this project will facilitate detecting heterogeneous associations and the subsequent scientific discoveries in AD genomics studies for developing treatments. Moreover, our tests can be applied to a broad scope of biomedical fields, resulting in a fruitful avenue for promoting public health.

项目摘要/摘要 阿尔茨海默氏病（AD）是老年人目前无法治愈的老年人死亡原因之一。基因组学 研究，例如映射表达定量性状基因座（EQTL）和差异基因表达式 在理解AD的生物学机制和开发潜在的治疗治疗方面的关键作用。 在基因组学研究中，人们越来越认识到协变量（例如定量基因表达） 可能对反应反应的反应分布（例如疾病表型）产生变化 协变量 - 响应协会。那些异质关联对科学发现和 需要显着意义，但通常被大多数现有的分析程序忽略了 响应分布的方面（例如，关注平均或分位数回归的标准线性回归 在一个分位数上）。这，开发有效和有效的假设检验，以检测异质 关联对复杂疾病（例如AD）的基因组学研究具有很大价值。 该建议旨在开发几种基于分位数回归的全球意义测试，这些测试利用了所有测试 精心挑选的分位数区域的信息，并为研究人员提供整体评估 协变量对响应的影响。受到我们对衰老和衰老研究的初步数据分析的启发 痴呆症，即宗教秩序研究（ROS）以及记忆和老化项目（地图），我们将第一个提案 一项全球意义测试，以彻底评估协变量在所有分位数响应水平上的影响 变量（目标1）。然后是由ROS/MAP中AD的高维基因组数据进行的，我们将进一步发展 两项全球意见测试分别针对高维响应和协变量数据（AIM 2）。而且， 我们将在AIMS 1-2中应用所提出的测试，以识别ROS/MAP生成的基因组数据，以识别EQTL和 差异表达的基因可用于确定AD的风险基因优先级以识别发展潜力 治疗（目标3）。我们还将提供一个用户友好的R软件包来实施建议的测试。 我们的提议的创新是三倍。 （i）评估协变量对跨越响应的影响 整个分位数域，拟议的全球意义测试具有较高的能力来识别异质 与替代方法相比，协变量 - 响应关联。 （ii）作为拟议的测试施加任何 严格的模型假设，也不需要其他花纹平滑或重新采样或收缩估计，它们 可以在大规模基因组学数据中广泛实施。 （iii）我们在AIM 2中提出的测试将是有用的 用于检测协变量与多个响应之间的异质关联的工具。 该项目的成功完成将有助于检测异质关联和 随后的AD基因组学研究中的科学发现，用于开发治疗。而且，我们的测试可以 应用于广泛的生物医学领域，从而为促进公共卫生提供了富有成果的途径。