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 阿尔茨海默病(AD)是导致老年人死亡的主要原因之一，目前尚无治愈方法。基因组学 研究，如定位表达数量性状基因座(EQTL)和差异基因表达，发挥着重要作用 在了解阿尔茨海默病的生物学机制和开发潜在的治疗方法方面发挥着关键作用。 在基因组学研究中，人们越来越意识到协变量(例如，定量基因表达) 可能对异质性fl反应的分布(例如，疾病表型)具有变化的影响 协变量-反应关联。这些不同的关联揭示了科学fic的发现和 包含有fifi的含义，但通常被大多数现有的分析程序忽略 响应分布的方面(例如，侧重于平均值或分位数回归的标准线性回归 在单个分位数级别)。因此，开发有效和有效的fi假设检验来检测异质性 关联性对于AD等复杂疾病的基因组学研究具有重要价值。 这一建议旨在开发几种基于分位数回归的全局Signalfi检验，它们利用所有 经过精心选择的分位数级别区域的信息，并为研究人员提供对整体 协变量对反应的影响。受我们对老龄化和老龄化的两项研究的初步数据分析的启发 痴呆症，即宗教秩序研究(ROS)和记忆与衰老计划(MAP)，我们将首先提出fi 一种全局SignifiCance检验，以彻底评估协变量对响应的所有分位数水平的影响 变量(目标1)。然后，在ROS/MAP高维基因组数据的推动下，我们将进一步开发 分别对高维响应和协变量数据进行两个全局Signifi检验(目标2)。此外， 我们将把AIMS 1-2中建议的测试应用于ROS/MAP产生的基因组数据，以确定eQTL和 差异表达基因可用于区分AD危险基因的优先顺序以确定发展潜力 治疗(目标3)。我们还将提供一个用户友好的R包来实施拟议的测试。 我们提案的创新有三个方面。(I)通过评估协变量对以下各方面反应的影响 在整个分位域中，提出的全局Signifi检验具有较强的异质性识别能力 协变量--与替代方法相比较的反应关联。(Ii)由于建议的测试既没有施加任何 严格的模型假设，不需要额外的样条线平滑或重新采样或收缩估计，它们 可以在大规模基因组数据中广泛实施。(Iii)我们在目标2建议的测试将会是一项有用的 用于检测协变量和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不同的关联和 随后在开发治疗方法的AD基因组研究中的科学fic发现。此外，我们的测试可以 可广泛应用于生物医学fi领域，为促进公共卫生提供了一条卓有成效的途径。