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.

项目概要/摘要 阿尔茨海默病（AD）是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，例如绘制表达数量性状位点 (eQTL) 和差异基因表达，发挥着重要作用 在理解 AD 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） 可能对反映异质性的反应分布（例如疾病表型）产生变化的影响 协变量-反应关联。这些异质的关联提供了对科学发现和 带来重大影响，但往往被大多数现有的仅限于狭窄范围的分析程序所忽视。 响应分布的方面（例如，关注均值或分位数回归的标准线性回归 在单个分位数水平）。因此，开发有效且高效的假设检验来检测异质性 这种关联对于 AD 等复杂疾病的基因组学研究具有重要价值。 该提案旨在开发几种基于分位数回归的全局显着性检验，该检验利用了所有 精心选择的分位数水平区域的信息，并为研究人员提供总体评估 协变量对响应的影响。受到我们对衰老和衰老这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）和记忆与衰老项目（MAP），我们首先会提出 全局显着性测试，用于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中AD高维基因组数据的推动下，我们将进一步开发 分别针对高维响应和协变量数据进行两个全局显着性检验（目标 2）。而且， 我们将把目标 1-2 中提出的测试应用于 ROS/MAP 生成的基因组数据，以识别 eQTL 和 差异表达基因可用于优先考虑 AD 风险基因，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 包来实施建议的测试。 我们的提案的创新之处在于三个方面。 (i) 通过评估协变量对响应的影响 在整个分位数域中，所提出的全局显着性检验具有识别异质性的优越能力 与替代方法相比的协变量-反应关联。 (ii) 由于拟议的测试并未强加任何 严格的模型假设也不需要额外的样条平滑或重新采样或收缩估计，它们 可以广泛应用于大规模基因组数据。 (iii) 我们在目标 2 中提出的测试将作为一个有用的测试 用于检测协变量和多重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 AD 基因组学研究中的科学发现用于开发治疗方法。此外，我们的测试可以 广泛应用于生物医学领域，为促进公共卫生提供了富有成效的途径。