# Integration of Omic Data to Estimate Mediation or Latent Structures

> **NIH NIH P01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2022 · $256,757

## Abstract

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.

## Key facts

- **NIH application ID:** 10411240
- **Project number:** 2P01CA196569-07A1
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** David V Conti
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $256,757
- **Award type:** 2
- **Project period:** 2016-07-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10411240

## Citation

> US National Institutes of Health, RePORTER application 10411240, Integration of Omic Data to Estimate Mediation or Latent Structures (2P01CA196569-07A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10411240. Licensed CC0.

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