# Integrative multivariate association and genomic analyses

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2020 · $371,573

## Abstract

ABSTRACT
Over the last decade, scientists have identified many thousands of disease/trait susceptibility loci, with more to
be discovered. However, the biological mechanisms by which these variants affect gene function and
downstream biological processes remain unclear. A promising path forward is to study the effects of genetic
variation on cellular/molecular phenotypes, such as the transcriptome, proteome, and epigenome (i.e., “omics”
phenotypes). Additionally, the analysis of the joint associations of a genetic variant to complex trait(s) and
omics-phenotypes has the potential to elucidate mechanisms underlying known associations or to reveal novel
relationships between genetic variants and complex traits. Our first aim is to develop methods to integrate QTL
association summary statistics from multiple studies/tissue-/cell-types with overlapping or independent
samples to identify the omics QTLs and multi-omics QTLs with coordinated effects (and potentially different
effect sizes) on multiple omics phenotypes in different conditions. Moreover, most existing omics QTL analyses
focus on cis-associations, because the study of trans-associations is underpowered after considering multiple
testing adjustment. In our second aim, we will propose novel methods to detect a particular yet quite prevalent
type of trans-association – the type mediated by a cis-gene transcript. Different than the trans-associations
with extreme effects that are often tissue-specific, the trans-associations mediated by cis-gene expression
often present effects shared among functionally related tissue types. As such, our proposed mediation
methods will borrow information across tissue types to improve power. An ultimate goal is how to further utilize
(cis- and trans-) QTLs in disease/trait-mapping and further understand their disease/trait relevance. In the third
aim, by harnessing gene-specific patterns of how eQTL effects are shared across different tissue types, we will
develop improved methods over existing methods for transcriptome-wide association studies. We will propose
models predicting gene expression levels in multiple tissue types and further associate genotype-predicted
expression levels in disease-relevant tissue types with complex diseases/traits using existing GWAS data. In
the three aims, we will analyze breast cancer, schizophrenia, and height, respectively, as three focused traits
in each aim by integrating data from Genotype-Tissue Expression Project (GTEx), Clinical Proteomic Tumor
Analysis Consortium (CPTAC), UK Biobank and summary statistics from large-scale genome-wide association
studies consortia. The proposed methods can be applied to other related diseases and traits. Our work will
identify new gene candidates associated with complex traits, as well as provide new hypotheses, tools, and
data resources that will accelerate future research efforts to understand the susceptibility mechanisms of
human diseases.

## Key facts

- **NIH application ID:** 10003357
- **Project number:** 5R01GM108711-07
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Lin Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $371,573
- **Award type:** 5
- **Project period:** 2014-02-15 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10003357

## Citation

> US National Institutes of Health, RePORTER application 10003357, Integrative multivariate association and genomic analyses (5R01GM108711-07). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10003357. Licensed CC0.

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