# Statistical Methods for Characterizing Molecular Mechanisms of Human Tissue Development and Disease

> **NIH NIH U01** · NEW YORK GENOME CENTER · 2024 · $1,799,133

## Abstract

Project Summary
The unique biospecimens and data from the Developmental GTEx (dGTEx) project create an exciting
opportunity and need for novel methods development. In this project, we propose to develop a set of statistical
methods and analytical approaches that are designed to extract insights into human and non-human primate
development from the multi-modal and multi-tissue data from dGTEx post-mortem donors. Analysis of the
dGTEx data requires statistical models that can take advantage of the rich structure and diversity of the data
across ages and modalities, while addressing some of the inherent challenges. Models explicitly informed by
age can capture developmental trajectories of gene regulation, genetic effects, and tissue structure. The range
of data modalities also creates an opportunity for novel methods to capture additional effects and improved
resolution at the cell-type, isoform, and structural levels. However, these data are also inherently complex,
representing a mixture of cell types along with biological and technical noise. The ambitious study design of
dGTEx also comes with challenges of donor recruitment that limit sample size. The methods proposed here
are designed to leverage the benefits of temporal multi-modal data, while addressing data complexity and
limited sample size. In our first aim, we will analyze transcriptome variation across the human lifespan, with
improved transcript annotation and new methods to characterize how gene expression, alternative splicing and
cell type composition change during development and how they contribute in driving phenotypic change.
Secondly, we will use multi-modal data from GTEx to capture changes in gene regulatory networks during
development. From the dGTEx histology images and spatial transcriptomics data we will model developmental
trajectories of tissue structures, and describe their molecular characteristics as well as role in disease. In our
third aim we will map and characterize genetic regulatory variation in dGTEx and apply predictive models for
improved predictions of regulatory variants in pediatric tissues. In addition to empowering biological discovery,
this work has the potential to uncover disease risk factors and mechanisms that originate or manifest during
early life.

## Key facts

- **NIH application ID:** 10990778
- **Project number:** 1U01HG013843-01
- **Recipient organization:** NEW YORK GENOME CENTER
- **Principal Investigator:** Alexis Battle
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,799,133
- **Award type:** 1
- **Project period:** 2024-09-24 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10990778

## Citation

> US National Institutes of Health, RePORTER application 10990778, Statistical Methods for Characterizing Molecular Mechanisms of Human Tissue Development and Disease (1U01HG013843-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10990778. Licensed CC0.

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