# Mapping Multiple Complex and Omics Trait-Associations Using Summary Statistics

> **NIH NIH F31** · UNIVERSITY OF CHICAGO · 2020 · $36,658

## Abstract

Project Summary
For many disease-associated genetic variants, the functional mechanism through which the variant affects
disease susceptibility is unknown. Because genetic variants also affect molecular phenotypes such as the
transcriptome, methylome and proteome, studying “omics” outcomes may lead to an improved understanding
of disease processes. In particular, joint analysis of multi-omics data may enhance our knowledge of how
genetic effects on these outcomes are coordinated in a multi-level molecular system to contribute to disease
susceptibility. Since genetic effects on molecular phenotypes may further depend on tissue, cell type, or other
conditions, the scientific community would benefit from continued development of methods to integrate multi-
omics data across conditions or contexts. However, the large scale of the data coupled with unknown
correlation structures across features or conditions makes such analyses challenging. In this project, we
propose efficient methods to integrate summary statistics from multiple studies of genetic effects on complex
and omics phenotypes. To improve upon existing multi-omics integrative approaches that take summary
statistics as input, we expand joint analyses to more than three data types or conditions, and allow the sets of
statistics to come from overlapping samples. Preliminary results presented in the application demonstrate that
the proposed methods are computationally feasible and produce results that are consistent with current
biological knowledge. Proposed applications of the methods have the potential to identify novel associations or
provide new evidence for known associations between omics features and cancer risk. The success of this
work will provide flexible methods and computational tools that can be applied to other diseases and settings.

## Key facts

- **NIH application ID:** 9911704
- **Project number:** 1F31CA239557-01A1
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Kevin James Gleason
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $36,658
- **Award type:** 1
- **Project period:** 2020-01-06 → 2020-08-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9911704

## Citation

> US National Institutes of Health, RePORTER application 9911704, Mapping Multiple Complex and Omics Trait-Associations Using Summary Statistics (1F31CA239557-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9911704. Licensed CC0.

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