# Going from Genetic Associations to Identification of Causative Genes

> **NIH NIH P01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $583,766

## Abstract

ABSTRACT
 Many studies, including analyses in this overall Program of research, have conducted genome-wide
association studies (GWAS) to identify genes and loci associated with complex disease. While a number of
genetic association signals have been uncovered, the challenge of identifying causative genes at these genetic
loci remains. As such, the goal of this Project is to move from GWAS association to identification of causal
effector genes relevant to obstructive sleep apnea (OSA) and excessive sleepiness, two key traits studied in this
Program. To fill this critical gap in evidence this Project combines state-of-the-art approaches in cell-based and
animal models. Analyses will begin by interrogating genetic loci from recent GWAS on OSA and sleepiness, and
be extended to evaluate loci from ongoing analyses, including those in Projects 01 and 03. In Aim 1, we will
conduct in silico physical `variant to gene mapping' based on our established Assay for Transposase Accessible
Chromatin sequencing (ATAC-seq) plus genome-wide promotor-focused Capture C data on relevant cell types
- osteoblasts and adipocytes (for anatomy; relevant to Project 01) and neurons and primary astrocytes (for
sleepiness; relevant to Project 03). These 3D Genomics analyses will identify the most likely causal genes and
variants by determining which candidates at implicated loci directly interact with regions of open chromatin. After
identifying likely causal genes and variants, follow-up analyses in animal models will be performed to understand
if candidate effector genes act by affecting OSA-related anatomy in mice (Aim 2) and sleep behavior reflective
of sleepiness and disturbed sleep in Drosophila and zebrafish (Aim 3). Specifically, Aim 2 will utilize the novel
multivariate genotype-phenotype mapping pipeline we developed to identify causal genes affecting OSA risk
through effects on craniofacial shape and/or tongue fat. In applying this method, this Aim leverages both existing
and newly generated data in a large sample of Diversity Outbred mice with genetic data and anatomical traits
quantified via imaging. This cutting-edge approach facilitates determination of the effects of multiple genes on
multivariate phenotypes using high-dimensional data to compare directions, not just magnitudes, of associations.
For sleepiness, Aim 3 will utilize Drosophila RNAi lines to study the impact of knocking-down candidate genes
on sleep-related phenotypes (including sleep amounts, sleep fragmentation, and sleep drive). Then, genes
shown to affect sleep in Drosophila will be validated in a vertebrate model by utilizing CRISPR-Cas9 methods to
knock-out these same genes in zebrafish and studying the effects on similar sleep phenotypes. Taken together,
results in this Project and the other projects in this Program will provide essential knowledge about effector genes
for OSA and for sleepiness in OSA in humans. This knowledge is crucial for understanding the biological and
clinical impact...

## Key facts

- **NIH application ID:** 10880339
- **Project number:** 5P01HL160471-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Allan I Pack
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $583,766
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10880339

## Citation

> US National Institutes of Health, RePORTER application 10880339, Going from Genetic Associations to Identification of Causative Genes (5P01HL160471-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10880339. Licensed CC0.

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