# Integrative modelling of single-cell data to elucidate the genetic architecture of complex disease

> **NIH NIH R01** · DANA-FARBER CANCER INST · 2024 · $507,248

## Abstract

PROJECT SUMMARY/ABSTRACT
Leveraging Genome Wide Association Studies (GWAS) to understand disease has proven challenging, as the
underlying biological mechanisms are often poorly captured by bulk tissues. Recent advances in single-cell
sequencing have led to a torrent of data across multiple modalities, contexts, and individuals, which provide an
unprecedented opportunity to understand disease biology at high resolution. We hypothesize that the fine-scale
cellular contexts captured by single-cell data will be effective at explaining disease heritability and fine-mapping
disease mechanisms. However, current approaches to integrate single-cell data with GWAS largely rely on off-
the-shelf approaches developed for bulk sequencing, which obscure the rich phenotypic diversity present in
individual cells within and across canonical cell types. The sparse and highly variable nature of single-cell data
has additionally posed challenges for robustly identifying single-cell quantitative trait loci (QTL). Single-cell data
continues to increase in size and complexity, emphasizing the need for scalable integrative modeling. Here, we
propose a 5 year research plan to develop novel approaches for integrating single-cell data with GWAS by
modeling complex cellular phenotypes not captured by existing bulk approaches. Our proposal will identify novel
disease-relevant cell states; leverage multiple single-cell modalities to fine-map disease variants and their target
genes; and discover novel single-cell QTLs associated with disease. Our specific aims are: Aim 1: Leveraging
single-cell epigenetic data to identify heritable components of disease; Aim 2: Leveraging single-cell data to fine-
map disease variants and their mechanisms; Aim 3: Defining the regulatory effects of disease variants using
population-scale scRNA-seq. While our proposed approaches are broadly applicable to common diseases, we
will benchmark them on immune-related traits and neuropsychiatric traits which we have studied extensively with
bulk datasets in published work and where we have now aggregated a large collection of relevant single-cell
datasets. Our collaboration has multiple strengths: our focus on functional data integration across multiple single-
cell modalities; our broad statistical and computational expertise; and our extensive, data-driven publication
record on common disease.

## Key facts

- **NIH application ID:** 10879333
- **Project number:** 1R01HG013083-01A1
- **Recipient organization:** DANA-FARBER CANCER INST
- **Principal Investigator:** ALEXANDER GUSEV
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $507,248
- **Award type:** 1
- **Project period:** 2024-08-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10879333

## Citation

> US National Institutes of Health, RePORTER application 10879333, Integrative modelling of single-cell data to elucidate the genetic architecture of complex disease (1R01HG013083-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10879333. Licensed CC0.

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