# Integrative Analysis Methods for the dGTEx Initiative

> **NIH NIH U01** · UNIVERSITY OF CHICAGO · 2024 · $611,365

## Abstract

ABSTRACT
This research project aims to develop methods and tools and conduct collaborative research for the integrative
analysis of data generated by the Developmental Genotype-Tissue Expression (dGTEx) initiative, non-human primate
(NHP) dGTEx project, existing GTEx project, and other studies. In Aim 1, we will develop methods for mapping
expression quantitative trait loci (eQTLs) across developmental stages in multiple tissue- and cell-types.
Based on our prior work, we will employ novel multi-view learning (machine learning) methods into the proposed
general QTL framework for detecting various types of QTLs. Our framework estimates the latent probabilities of
QTL binary status (presence or absence), extracts common and specific low-rank patterns from multiple groups
and tissues/cell-types, and incorporates the patterns in estimating the posterior probability of non-zero effect and
posterior mean/standard deviation for each input statistic. These outputs can be used for further flexible
inference in detecting various types of eQTLs. The proposed QTL framework is adaptive to a variety of integrative
analyses of dGTEx, NHP, GTEx and other datasets. In Aim 2, we will develop a series of multi-age-group
Mendelian randomization (MR) models to identify risk genes and assess their causal effects in multiple
tissues/cell types and age groups. We will extend the models to multi-trait analysis jointly assessing the causal
effects in child and adult populations, to multivariable MR analysis accounting for other molecular traits, and to
multi-cell MR analysis for detecting sparse cell-level causal effects. In Aim 3, we will engage in the dGTEx
data analysis. We will work with the Steering Committee to guarantee the scientific rigor and efficiency
of dGTEx analysis, and to ensure the timely dissemination of initial findings to the broader research
community. The project will develop scalable and efficient software. The insights gained through the analysis of dGTEx
data will enhance the translational potential of genomic findings in medicine and healthcare, reshaping our approach
to understanding and treating diseases rooted in developmental gene regulation.

## Key facts

- **NIH application ID:** 10990871
- **Project number:** 1U01MH139345-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Lin Chen
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $611,365
- **Award type:** 1
- **Project period:** 2024-09-02 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10990871

## Citation

> US National Institutes of Health, RePORTER application 10990871, Integrative Analysis Methods for the dGTEx Initiative (1U01MH139345-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10990871. Licensed CC0.

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