cellSTAAR: incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of noncoding regions

Authors

Eric Van Buren
Yi Zhang
Xihao Li
Margaret Sunitha Selvaraj
Zilin Li
Nicholette D. Palmer
Donna K. Arnett
John Blangero, The University of Texas Rio Grande ValleyFollow
Eric Boerwinkle
Joanne E. Curran, The University of Texas Rio Grande ValleyFollow
Ravi Duggirala, The University of Texas Rio Grande ValleyFollow

Document Type

Article

Publication Date

2025

Abstract

Understanding how rare genetic variants influence complex traits remains a major challenge, particularly when these variants lie in noncoding regions of the genome. The effects of variants within candidate cis-regulatory elements (cCREs) often depend on the cell type, making interpretation difficult. Here we introduce cellSTAAR, which integrates whole-genome sequencing data with single-cell assay for transposase-accessible chromatin using sequencing data to capture variability in chromatin accessibility across cell types via the construction of cell-type-specific functional annotations and regulatory elements. To reflect the uncertainty in cCRE–gene linking, cellSTAAR uses a comprehensive strategy to link cCREs to their target genes. We applied cellSTAAR to data from the Trans-Omics for Precision Medicine consortium (n ≈ 60,000) and replicated our findings using the UK Biobank (n ≈ 190,000). Across four lipid traits, cellSTAAR improved the detection of biologically meaningful associations and enhanced biological interpretability. These results demonstrate the potential of cell-type-aware approaches to boost discovery in rare variant whole-genome sequencing association studies.

Comments

https://rdcu.be/e6C7J

Recommended Citation

Van Buren, E., Zhang, Y., Li, X., Selvaraj, M. S., Li, Z., Zhou, H., Palmer, N. D., Arnett, D. K., Blangero, J., Boerwinkle, E., Cade, B. E., Carlson, J. C., Carson, A. P., Chen, Y.-D. I., Curran, J., Duggirala, R., Fornage, M., Franceschini, N., Graff, M., … Lin, X. (2026). cellSTAAR: Incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of noncoding regions. Nature Methods, 23(2), 338–349. https://doi.org/10.1038/s41592-025-02919-5  

Publication Title

Nature Methods

DOI

10.1038/s41592-025-02919-5

Academic Level

faculty

Mentor/PI Department

Office of Human Genetics