# Using the continuum of genetic causality to investigate trans regulatory mechanisms.

> **NIH NIH K99** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $119,611

## Abstract

ABSTRACT
Genome-wide association studies have uncovered thousands of disease-associated variants that localize to
noncoding genomic regions and influence disease risk through altered cis (local) and trans (distal) gene
regulation. Though cis regulatory variation has been extensively characterized across many cell and tissue types,
trans regulatory effects are more challenging to ascertain. Trans regulation is partly mediated by transcription
factors (TFs), a class of proteins that have widespread influence on gene regulatory networks. Regulatory
elements containing TF binding sites are enriched for complex disease heritability. Interestingly, many early
onset, highly penetrant monogenic diseases are caused by coding variation in the same TFs that are implicated
in complex diseases. Together, this suggests that a shared set of regulatory networks underlies a subset of
common, complex diseases and rare, monogenic diseases. The goal of this project is to uncover regulatory
networks controlled by monogenic disease-associated TFs and decipher their involvement in processes that
drive complex disease phenotypes. In Aim 1 (K99), I will perform a low-throughput arrayed CRISPR screen
targeting 13 monogenic diabetes TFs, use detailed genomic profiles (RNA-seq for gene expression, ATAC-seq
for chromatin accessibility, and CUT&Tag for histone modifications) with integrative machine learning
approaches to construct regulatory networks, and incorporate human genetics data to infer how genetic variation
propagates through layers of regulatory information. The focus of Aim 2 (K99) is to develop a novel method to
jointly characterize the effects of coding variants in transcription factors and noncoding variants in their cognate
motifs to construct a comprehensive regulatory interface map. After transitioning to the R00 independent phase,
I will apply the approaches developed in Aims 1 and 2 to examine the influence of cell type and stimulation on
variant effects and network structure in Aim 3. Collectively, these aims will reveal the fundamental gene
regulatory networks underlying phenotypes shared by common polygenic and rare monogenic diseases and
provide insight into mechanisms by which disease-associated genetic variation exerts its influence. The specific
methods and frameworks developed will be broadly applicable to other comparable complex and monogenic
diseases beyond those explored here. To achieve these research objectives, my mentors Dr. Stephen Parker
(human genetics and genomics) and Jacob Kitzman (genomic technologies) and I have outlined a
comprehensive training plan. To aid with this plan, we have assembled a world-class mentorship committee with
diverse expertise in large-scale functional genomics screens (Drs. Melina Claussnitzer and Anna Gloyn),
machine learning (Dr. Anshul Kundaje), and human genetics (Drs. Michael Boehnke and Karen Mohlke). I will
also receive training in state-of-the-art human organoid culture systems from collaborator Dr. J...

## Key facts

- **NIH application ID:** 10948548
- **Project number:** 1K99HG013676-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Adelaide E Tovar
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $119,611
- **Award type:** 1
- **Project period:** 2024-08-29 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10948548

## Citation

> US National Institutes of Health, RePORTER application 10948548, Using the continuum of genetic causality to investigate trans regulatory mechanisms. (1K99HG013676-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10948548. Licensed CC0.

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