Integration of Omic Data to Estimate Mediation or Latent Structures

项目2：整合基因组数据以估计中介或潜在结构 摘要 经济时代已经到来，基于人口的研究正在迅速发展，以衡量多种类型的数据 探索风险因素和结果之间的潜在联系。来自互补性的数据集成 使用新的统计方法的研究途径将在每个研究领域内产生发现， 探索两者之间的领域，推动创新向前发展。总体而言，该项目侧重于开发 用于整合先验怀疑对疾病起作用的多个组学数据的统计方法 或通过调解或潜在结构化模型的特征结果。这些方法跨越了对以下研究的分析 对同一个体的多个OMIC测量，以从从多个OMIC测量的OMIC数据汇总统计 学习。在目标1中，我们将开发一个多组因果推理测试(CIT)，以促进其在大型应用中的应用 在个体上测量的多个经济体数据集，以同时对多个风险因素和 调解人。在目标2中，我们将开发一个综合模型来估计潜在的未知星团，目标是 整合多种类型的经济措施，无论是横向测量还是在多个时间点测量，以 共同估计与感兴趣的结果相关的子组。在目标3中，我们将估计联合因果关系。 使用多个SNP和多个SNP的汇总统计数据来确定中间因素或潜在结果关联 中间体。我们将利用整个计划中其他项目的方法开发 项目，并利用计算和翻译核心的专业知识和协助，我们将制定 适用于应用项目的功能强大、计算高效且用户友好的软件。总的来说，这些 方法将通过促进对潜在的更好的理解而对应用调查产生直接影响 通过识别新因素、估计联系来驱动潜在癌症病因的生物机制 在这些因素之间，并识别具有潜在不同关联的个体的亚组 机械装置。

Project 2: Integration of Omic Data to Estimate Mediation or Latent Structures Abstract The omic era is upon us and population-based studies are moving rapidly to measure multiple types of data to explore the underlying connection between risk factors and outcomes. Integration of data from complementary avenues of research using novel statistical approaches will result in discoveries within each area of research, probe the area between, and push innovation forward. Overall, this project focuses on the development of statistical approaches for the integration of multiple omics data that are suspected, a priori, to act on a disease or trait outcome via mediation or a latent structured model. The approaches span the analysis of studies with multiple omic measures on the same individuals to summary statistics from omic data measured from multiple studies. In Aim 1, we will develop a multi-omic causal inference test (CIT) to facilitate its application to large multi-omic datasets measured on individuals to simultaneously model multiple risk factors and multiple mediators. In Aim 2, we will develop an integrative model to estimate latent unknown clusters aiming to incorporate multiple types of omic measures either measured cross-sectionally or at multiple time points to jointly estimating subgroups relevant to the outcome of interest. In Aim 3, we will estimate joint causal effects of intermediate factors or latent-outcome associations using summary statistics for multiple SNPs and multiple intermediates. We will leverage methodological developments from other projects within the overall program project and, using expertise and assistance from the computational and translation cores, we will develop robust, computationally efficient, and user-friendly software for application to applied projects. Overall, these methods will have a direct impact on applied investigations by facilitating a better understanding of potential biological mechanisms driving underlying cancer etiology via identifying novel factors, estimating connections between those factors, and identifying subgroups of individuals with potentially different associated mechanisms.