# Penn-DISAM: Data Integration and Statistical Analysis Methods for dGTEx Data

> **NIH NIH U01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $1,921,885

## Abstract

Abstract
 Our health and wellbeing prenatally and during the earlier years of life affect all future health and disease
risks. This time period is the most sensitive for a child’s developing brain and various other tissues of body.
Studies of developmental biology have demonstrated that gene expression patterns are not only tissue-specific
and cell type-specific, but also age regulated and controlled through coordinated action of complex tissue- and
cell-type specific networks and pathways. The NIH Developmental Genotype-Tissue Expression (dGTEx) project
aims to study gene expression patterns at a tissue-level over several early developmental windows and to char-
acterize transcriptional profiles during human development. Complementary to the human dGTEx effort, the
non-human primate (NHP) dGTEx aims to study gene expression patterns in multiple reference tissues across
developmental stages in NHP model species and compare them to human gene expression patterns. The over-
arching goal of Penn Data Integration and Statistical Analysis Methods (Penn-DISAM) project is to develop novel
statistical and computational methods specifically for effective analysis of human dGTEx and NHP dGTEx data,
including novel methods for analyzing a very large set of correlated regression functions that characterize the
age-dependent gene expression functions across different tissues and between human and primates. The dG-
TEx and NHP dGTEx data allow us to estimate the tissue-cell specific age-dependent gene expression functions
by leveraging the gene expression data measured over different ages in postnatal, early childhood, pre-pubertal
and post-pubertal developmental windows. We will particularly develop nonparametric B-spline regression to
estimate the age-dependent gene expression regression functions and summarize the data as the matrix of
functions. We will develop methods for data visualization and for statistical inference, including methods for iden-
tifying genetic variants that are associated with different gene expression distribution functions in each of the
tissues. We will also develop novel statistical warping methods to align gene expression trajectories between
human and primates, which allow us to identify genes under strong stabilizing selection, to quantify the inter-
species divergence in gene expression in each tissue and in each developmental stage, and to compare the
difference of age-dependent weighted co-expression networks between human and primates. Penn-DISAM will
work closely with the dGTEx consortium to develop, implement, test and apply these methods and the software
tools to dGTEx data. Finally, we will make all the software available via AnVIL and GitHub.

## Key facts

- **NIH application ID:** 10990746
- **Project number:** 1U01HG013841-01
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Ziyue Gao
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,921,885
- **Award type:** 1
- **Project period:** 2024-09-19 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10990746

## Citation

> US National Institutes of Health, RePORTER application 10990746, Penn-DISAM: Data Integration and Statistical Analysis Methods for dGTEx Data (1U01HG013841-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10990746. Licensed CC0.

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