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

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，目前没有治愈方法。Genomics 研究，如定位表达数量性状基因座（eQTL）和差异基因表达，发挥了重要作用。 在了解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） 可能对响应的分布具有变化的影响（例如，疾病表型）反映了一种异质性 协变量-反应关联。这些异质的关联揭示了科学发现的洞察力， 这意味着重大的影响，但往往被大多数现有的分析程序所忽视，这些程序被局限于一个狭窄的 响应分布的方面（例如，标准线性回归，侧重于均值或分位数回归 在单个分位数水平上）。因此，开发有效的假设检验来检测异质性 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球显著性检验， 在一个精心挑选的分位数水平区域的信息，并为研究人员提供整体的评价， 协变量对响应的影响。受我们对两项关于衰老和 痴呆症，即宗教秩序研究（ROS）和记忆和衰老项目（MAP），我们将首先提出 全面显著性检验，以全面评估协变量对所有分位数水平响应的影响 变量（目标1）。在ROS/MAP中AD的高维基因组学数据的激励下，我们将进一步发展 分别针对高维响应和协变量数据的两个全局显著性检验（目标2）。此外，委员会认为， 我们将把目标1-2中提出的检验应用于ROS/MAP产生的基因组学数据，以鉴定eQTL， 差异表达的基因，可用于优先考虑AD的风险基因， 治疗（目标3）。我们还将提供一个用户友好的R包来实现拟议的测试。 我们的提案有三个方面的创新。(i)通过评估协变量对不同年龄组响应的影响， 在整个分位数域中，建议的全局显著性检验具有上级能力来识别异质 与替代方法相比，协变量-响应关联。(ii)由于拟议的测试既不施加任何 严格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， 可以广泛应用于大规模基因组学数据。(iii)我们在目标2中提出的测试将作为一个有用的 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异质关联， 随后在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 应用于广泛的生物医学领域，为促进公共卫生提供了富有成效的途径。

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.