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cellSTAAR: incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of noncoding regions
Eric Van Buren, Margaret Sunitha Selvaraj, Zilin Li, Hufeng Zhou, Nicholette D. Palmer, Donna K. Arnett, John Blangero, Eric Boerwinkle, B. Cade, Jenna C. Carlson, April P. Carson, Y -D I Chen, Ravindranath Duggirala, M. FORNAGE, Nora Franceschini, Charles C Gu, Xiuqing Guo, Jiang He, Nancy Heard-Cosa, Lifang Hou, Y. U.Chen Hung, Rita R. Kalyani, Slr Kardia, Eimear Kenny, Charles Kooperberg, Brian G. Kral, Leslie Lange, Changwei Li, Simin Liu, Donald Lloyd-Jones, Ruth J. F. Loos, A. Manichaikul, Rasika A. Mathias, Ryan L. Minster, Braxton D. Mitchell, Josyf C. Mychaleckyj, Take Naseri, K. E. North, James A. Perry, P M Peyser, Bruce M. Psaty, Y. Li, Vasan Rs, S. Redline, Brian Spitzer, Hua Tang, Kent D. Taylor, Russell P. Tracy, Satupa’itea Viali, Lisa Yanek, Wei Zhao, Namiko Abe, Abecasis Gb, Francois Aguet, Christine M. Albert, Laura Almasy, Alvaro Alonso, S. J. Ament, Peter Anderson, Pramod Anugu, Kristin Ardlie, Dan Arking, Allison Ashley-Koch, Stella Aslibekyan, Paul Auer, D. A. 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Bibliographic record
VenueNature Methods · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of OttawaMcGill UniversityUniversity of CalgaryUniversité LavalUniversity of SaskatchewanUniversité de MontréalRoyal Ottawa Mental Health CentreProvidence Health Care
FundersNational Institute on Minority Health and Health DisparitiesNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer Institute
KeywordsChromatinBiobankDNA sequencingPrecision medicineGenetic associationAssociation (psychology)ChIA-PETBiological dataGenetic variants
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.019
Threshold uncertainty score0.101
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.066
GPT teacher head0.358
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations0
Published2025
Admission routes1
Has abstractno