cellSTAAR: incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of noncoding regions.
Journal
Nature methods
Journal Volume
23
Journal Issue
2
Start Page
338
End Page
349
ISSN
1548-7105
Date Issued
2026-02
Author(s)
Van Buren, Eric
Zhang, Yi
Li, Xihao
Selvaraj, Margaret Sunitha
Li, Zilin
Zhou, Hufeng
Palmer, Nicholette D
Arnett, Donna K
Blangero, John
Boerwinkle, Eric
Cade, Brian E
Carlson, Jenna C
Carson, April P
Chen, Yii-Der Ida
Curran, Joanne
Duggirala, Ravindranath
Fornage, Myriam
Franceschini, Nora
Graff, Misa
Gu, Charles
Guo, Xiuqing
He, Jiang
Heard-Cosa, Nancy
Hou, Lifang
Hung, Yi-Jen
Kalyani, Rita R
Kardia, Sharon L R
Kenny, Eimear
Kooperberg, Charles
Kral, Brian G
Lange, Leslie
Levy, Dan
Li, Changwei
Liu, Simin
Lloyd-Jones, Donald
Loos, Ruth J F
Manichaikul, Ani W
Martin, Lisa Warsinger
Mathias, Rasika
Minster, Ryan L
Mitchell, Braxton D
Mychaleckyj, Josyf C
Naseri, Take
North, Kari
O'Connell, Jeff
Perry, James A
Peyser, Patricia A
Psaty, Bruce M
Raffield, Laura M
Vasan, Ramachandran S
Redline, Susan
Reiner, Alex P
Rich, Stephen S
Smith, Jennifer A
Spitzer, Brian
Tang, Hua
Taylor, Kent D
Tracy, Russell
Viali, Satupa'itea
Yanek, Lisa
Zhao, Wei
Rotter, Jerome I
Peloso, Gina M
Natarajan, Pradeep
Lin, Xihong
et al.
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.
Publisher
Nature Research
Type
journal article