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A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies
Zilin Li, Xihao Li, Hufeng Zhou, Sheila M. Gaynor, Margaret Sunitha Selvaraj, Theodore Arapoglou, Corbin Quick, Yaowu Liu, Han Chen, Ryan Sun, Rounak Dey, Donna K. Arnett, Paul L. Auer, Lawrence F. Bielak, Joshua C. Bis, Thomas W. Blackwell, John Blangero, Eric Boerwinkle, Donald W. Bowden, Jennifer A. Brody, Brian E. Cade, Matthew P. Conomos, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, Paul S. de Vries, Ravindranath Duggirala, Nora Franceschini, Barry I. Freedman, Harald H.H. Göring, Xiuqing Guo, Rita R. Kalyani, Charles Kooperberg, Brian G. Kral, Leslie A. Lange, Bridget M. Lin, Ani Manichaikul, Alisa K. Manning, Lisa W. Martin, Rasika A. Mathias, James B. Meigs, Braxton D. Mitchell, May E. Montasser, Alanna C. Morrison, Take Naseri, Jeffrey R. O’Connell, Patricia A. Peyser, Bruce M. Psaty, Laura M. Raffield, Susan Redline, Alex P. Reiner, Muagututi‘a Sefuiva Reupena, Kenneth Rice, Stephen S. Rich, Jennifer A. Smith, Kent D. Taylor, Margaret A. Taub, Ramachandran S. 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Bibliographic record
VenueNature Methods · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of SaskatchewanUniversité LavalUniversity of OttawaMcGill UniversityUniversity of CalgaryProvidence Health Care
FundersNational Institute on Minority Health and Health DisparitiesNational Center for Advancing Translational SciencesNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Cancer InstituteNational Human Genome Research InstituteNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Center for Research ResourcesNational Institute of Allergy and Infectious DiseasesUniversität des SaarlandesNational Institutes of HealthRegeneron PharmaceuticalsNational Institute on AgingQuest DiagnosticsNational Institute on Drug AbuseUniversity of PennsylvaniaBoston Scientific CorporationYale UniversityEli Lilly and CompanyAstraZenecaAmgen
KeywordsComputational biologyGenomeBiologyAnnotationGeneWhole genome sequencingDNA sequencingGeneticsGenome projectGenetic associationGene AnnotationHuman genomeSingle-nucleotide polymorphismGenotype
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)0.047
metaresearch head score (Gemma)0.109
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.047
Threshold uncertainty score0.250
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 head0.059
GPT teacher head0.428
Teacher spread0.369 · 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 designTheoretical or conceptual
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
Citations109
Published2022
Admission routes1
Has abstractno