Integration of Omic Data to Estimate Mediation or Latent Structures

整合组学数据来估计中介或潜在结构

• 批准号: 10411240
• 负责人: David V Conti
• 金额: $ 25.68万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10411240
• 关键词: Accounting; Area; Automobile Driving; Biological; Biological Markers; Biology; Cancer Etiology; Colorectal Cancer; Complex; Computer software; Data; Data Set; Development; Disease; Disease Outcome; Etiology; Exposure to; FAIR principles; Gene Expression; Genes; Genetic; Genomics; Goals; Heterogeneity; Individual; Investigation; Joints; Malignant Neoplasms; Malignant neoplasm of prostate; Mathematics; Measurement; Measures; Mediating; Mediation; Mediator of activation protein; Methodology; Methods; Modeling; Molecular; Multiomic Data; Outcome; Pathway interactions; Phenotype; Population; Population Study; Process; Proteomics; Research; Risk; Risk Factors; Specific qualifier value; Statistical Methods; Structure; Subgroup; Techniques; Technology; Testing; The Cancer Genome Atlas; Time; Tissues; Translations; cancer genomics; data integration; data reduction; feature selection; genome wide association study; innovation; instrument; interest; metabolomics; microbiome; multidimensional data; multiple data types; multiple omics; novel; phenomics; pleiotropism; programs; statistics; trait; transcriptomics; user friendly software

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.

项目 2：整合组学数据来估计中介或潜在结构 抽象的 组学时代即将到来，基于人群的研究正在迅速发展，以测量多种类型的数据 探索风险因素和结果之间的潜在联系。整合互补数据 使用新颖的统计方法的研究途径将在每个研究领域产生新的发现， 探索两者之间的领域，推动创新向前发展。总体而言，该项目的重点是开发 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 或通过中介或潜在结构化模型得到的特质结果。这些方法涵盖了研究分析 对同一个人进行多项组学测量，以汇总从多个组测量的组学数据的统计数据 研究。在目标 1 中，我们将开发多组学因果推理测试（CIT），以促进其在大规模应用中的应用。 对个体进行测量的多组学数据集，可同时对多个风险因素和多个风险因素进行建模 调解员。在目标 2 中，我们将开发一个综合模型来估计潜在的未知簇，旨在 结合多种类型的组学测量，无论是横截面测量还是在多个时间点测量 联合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效应 使用多个 SNP 和多个 SNP 的汇总统计来分析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发展 项目，并利用计算和翻译核心的专业知识和帮助，我们将开发 强大、计算高效且用户友好的软件，适用于应用项目。总体而言，这些 方法将通过促进更好地了解潜在的研究对应用研究产生直接影响 通过识别新因素、估计关联来驱动潜在癌症病因学的生物机制 在这些因素之间进行分析，并确定具有潜在不同相关性的个体亚组 机制。