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

Authors

• Eric Van Buren, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Cambridge, MA, USA.
• Yi Zhang, Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA.
• Xihao Li, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
• Margaret Sunitha Selvaraj, Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
• Zilin Li, School of Mathematics and Statistics, Northeast Normal University, Changchun, China.
• Hufeng Zhou, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Cambridge, MA, USA.
• Nicholette D. Palmer, Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
• Donna K. Arnett, Provost Office, University of South Carolina, Columbia, SC, USA.
• John Blangero, Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA.
• Eric Boerwinkle, Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
• Brian E. Cade, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
• Jenna C. Carlson, Department of Human Genetics and Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.
• April P. Carson, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA.
• Yii-Der Ida Chen, The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
• Joanne Curran, Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA.
• Ravindranath Duggirala, Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA.
• Myriam Fornage, Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
• Nora Franceschini, Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
• Misa Graff, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
• Charles Gu, Division of Biology & Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA.
• Xiuqing Guo, The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
• Jiang He, Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
• Nancy Heard-Cosa, Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
• Lifang Hou, Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.
• Yi-Jen Hung, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
• Rita R. Kalyani, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Sharon L. Kardia, Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
• Eimear Kenny, Institute for Genomic Health, Icahn School of Medicine, New York, NY, USA.
• Charles Kooperberg, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
• Brian G. Kral, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Leslie Lange, Department of Biomedical Informatics, University of Colorado, School of Medicine, Aurora, CO, USA.
• Dan Levy, Framingham Heart Study, Framingham, MA, USA.

Document Type

Journal Article

Publication Date

2-1-2026

Journal

Nature methods

Volume

23

Issue

2

DOI

10.1038/s41592-025-02919-5

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.

APA Citation

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.; Kenny, Eimear; Kooperberg, Charles; Kral, Brian G.; Lange, Leslie; and Levy, Dan, "cellSTAAR: incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of noncoding regions" (2026). GW Authored Works. Paper 8786.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/8786

Department

Medicine