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：将OMIC数据集成到估计调解或潜在结构 抽象的 OMIC时代即将到来，基于人群的研究正在迅速进行，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。互补的数据集成 使用新颖统计方法的研究途径将导致每个研究领域的发现， 探测两者之间的区域，并将创新向前推动。总体而言，该项目着重于 统计方法是整合多个疑似先验的OMIC数据，以对疾病作用 或通过中介或潜在结构化模型的特征结果。这种方法涵盖了研究的分析 对同一个体的多种OMIC措施，从多个测量的OMIC数据汇总统计数据 研究。在AIM 1中，我们将开发一个多摩变因果推理测试（CIT），以促进其应用于大型 对个体测量的多摩变数据集，以同时对多个风险因素进行建模 调解人。在AIM 2中，我们将开发一个综合模型，以估算旨在的潜在未知簇，以 在横截面或多个时间点上合并多种类型的OMIC测量 共同估计与关注结果相关的亚组。在AIM 3中，我们将估计关节因果效应 使用多个SNP和多个的摘要统计数据的中间因素或潜在结果关联 中间人。我们将利用整个计划中其他项目的方法论发展 项目以及使用计算和翻译核心的专业知识和帮助，我们将开发 适用于应用项目应用程序的稳健，计算高效和用户友好的软件。总体而言，这些 方法将通过促进对潜力的更好理解来直接影响应用调查 通过识别新因素，估计连接的生物学机制，驱动癌症病因的潜在病因 在这些因素之间，并确定具有潜在不同相关的个体的亚组 机制。