课题基金 / 基金详情

Integration of Omic Data to Estimate Mediation or Latent Structures

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
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
未结题
起止时间:
2016-07-01 至 2027-08-31

项目摘要

项目成果

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中文摘要
翻译
项目2:整合组学数据以估计中介或潜在结构
英文摘要
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.
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国内基金
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