# Novel Bayesian statistical tools for integrating multi-omics data to help elucidate the genomic etiology of complex phenotypes

> **NIH NIH R35** · EMORY UNIVERSITY · 2020 · $397,045

## Abstract

Project Summary/Abstract
Genome-wide association studies (GWAS) have successfully mapped many thousands of loci for complex
phenotypes, yet the manner by which such loci influence these phenotypes has proven elusive as the majority
of associations have unclear biological significance. Recent work has shown that GWAS associations are
enriched in transcription regulatory and enhancer regions. To leverage this information for studying complex
phenotypes, current studies map molecular quantitative trait loci (QTL) with respect to multi-omics (i.e.,
epigenetic, transcriptomic, proteomic, and metabonomic) data and then incorporate molecular QTL in GWAS for
functional association studies. However, the impact of this approach is limited because existing methods usually
only analyze cis-acting molecular QTL and fail to consider the complicating effects that linkage disequilibrium
(LD) has on the mapping uncertainty of molecular QTL (disentangling true causal variation from nearby
correlated null variations). These limitations reduce the yield of functional association studies for considering
incomplete information about molecular QTL. This proposal will develop novel Bayesian statistical methods for
improved integrative multi-omics studies with real applications for validation. Our proposed methods have
potential to elucidate the genomic etiology of many complex phenotypes, by increasing the precision of mapping
molecular QTL and identification of risk genes. These novel Bayesian methods are built upon our recent work
and will account for prior knowledge for the parameters of interest through flexible prior distribution assumptions
and account for LD by jointly modeling genome-wide variants. (i) First, we will extend our recently proposed
Bayesian GWAS method to enable mapping both cis- and trans-acting (genome-wide) molecular QTL. We will
model different genetic architectures for cis- and trans-acting variants by assuming respective prior distributions.
Our previously derived scalable Bayesian inference algorithm will also be adapted for this new model. (ii) Next,
we will develop novel Bayesian methods for functional association studies, which will take the mapping
uncertainty of molecular QTL into account through flexible prior assumptions for variant effect sizes. (iii) Finally,
to make the most use of public summary-level multi-omics data of large sample sizes, we will derive new
Bayesian inference algorithms using only summary-level data while obtaining equivalent results as using
individual-level data for our proposed Bayesian methods. (iv) We will validate the proposed methods by applying
them to multi-omics and GWAS data from well-characterized older adults and relevant public summary-level
data to study Alzheimer's disease (AD) dementia and other complex phenotypes. My lab has access to the well-
characterized AD dementia related phenotypic, multi-omics, and GWAS data from older adults participating in
the Religious Orders Study (ROS) and Memory ...

## Key facts

- **NIH application ID:** 10028615
- **Project number:** 1R35GM138313-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Jingjing Yang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $397,045
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10028615

## Citation

> US National Institutes of Health, RePORTER application 10028615, Novel Bayesian statistical tools for integrating multi-omics data to help elucidate the genomic etiology of complex phenotypes (1R35GM138313-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10028615. Licensed CC0.

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