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）是当前无法治愈的老年人的主要死亡原因之一。 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的生物学机制和发展潜在的Terapeutics治疗方面的关键作用。 在基因组学研究中，人们对协变量（例如定量基因表达）的意识达到了异常。 可能对反应分布（例如疾病表型）的影响有所不同 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 因此，在一个分位数中）。 关联对复杂疾病（例如AD）的基因组学研究具有很大价值。 该提案旨在开发严重的基于回归的全球意义测试，该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。 痴呆症，即宗教秩序研究（ROS）以及记忆和老化项目（地图），我们将首先提出 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（AIM 1）。 两个全球意见测试对高维响应和协变量的数据（AIM 2）。 我们将在AIMS 1-2中将支撑测试应用于ROS/MAP生成的基因组学数据，以识别EQTL和 可以使用的差异表达基因 治疗（AIM 3）。 我们的提议的创新是三倍。 整个分位数域，支撑的全球重要性测试具有优越的塔塔 与替代方法相比，协变量 - 响应关联。 严格的模型保障，也需要其他花纹平滑或重新采样或收缩估算，它们 可以在大规模基因组学数据中广泛实施。 用于检测协变量与多个响应之间的异质关联的工具。 时间项目的成功结合与促进检测到的异质关联和你 随后的AD基因组学研究中的科学发现，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。