# Integrative Approaches for Identifying Causal Gene-Cell Type Pairs of Complex Disease

> **NIH NIH R35** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $423,069

## Abstract

Project Summary
Identifying causal genes and cell types underlying disease etiologies are essential for designing targeted
diagnostic and treatment strategies. Genome-wide association study (GWAS), DNA-sequencing, and RNA-
sequencing studies have identified potentially causal genes in multiple human diseases. While these methods
provide disease-associated “gene lists”, they suffer from major shortcomings given the lack of cell-type
information. First, each tissue is composed of multiple cell types with diverse contributions to disease
phenotypes, and thus studies using bulk-tissue data alone result in the ambiguity of the causal cell populations.
Secondly, causal gene signals from rare cell types may be masked in bulk tissues. Finally, understanding
which genes are perturbed in which cell types is required for designing downstream functional studies. To
identify the gene-cell pairs driving human disease, systematic approaches to integrate patient-cohort data with
cell-type-specific data are urgently needed. My research program aims to identify causal genes and cell types
driving human diseases using multi-omics approaches. Our central hypothesis is that dysregulated genes
mapped to specific cell types drive disease etiologies. Previously, we developed algorithms that integrate
large-scale data of common and rare genomic variants, epigenomes, transcriptomes, and proteomes to identify
causal genes in tissue affecting specific cell types, providing strong biological and technical foundations for the
project. Further, the proposed approaches are empowered by rapidly-expanding cell-specific epigenomic and
transcriptomic data using sorted cell populations or single-cell profiling. In the next 5-year period, we will
specifically develop algorithms that integrate genomic findings from patient cohorts with cell-specific
transcriptomic data, addressing two major questions: (1) What are the gene-cell type pairs contributing to
disease etiologies? (2) How are expressions of disease-associated genes regulated at a single-cell
level? The proposed project will strongly impact the field by discovering gene-cell pairs associated with a wide
range of diseases for downstream investigation. The development will afford new methods to integrate purified
and single-cell transcriptome data to expand on findings from large-scale patient genomic cohorts. In the long
term, the successfully identified gene-cell pairs can be translated into diagnostic markers or treatment targets
of human disease.

## Key facts

- **NIH application ID:** 10029020
- **Project number:** 1R35GM138113-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Kuan-lin Huang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $423,069
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10029020

## Citation

> US National Institutes of Health, RePORTER application 10029020, Integrative Approaches for Identifying Causal Gene-Cell Type Pairs of Complex Disease (1R35GM138113-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10029020. Licensed CC0.

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