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Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies
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Bibliographic record
VenueNature Genetics · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of SaskatchewanUniversité de MontréalUniversité LavalUniversity of OttawaMcGill UniversityUniversity of Calgary
FundersNational Institute on Minority Health and Health DisparitiesNational Center for Research ResourcesNational Institute of Environmental Health SciencesNational Human Genome Research InstituteNational Institute of Nursing ResearchNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Center for Advancing Translational SciencesMedical Research CouncilNational Institute of Neurological Disorders and StrokeEvans Medical FoundationSchool of Medicine, Boston UniversityAmerican Heart AssociationBroad InstituteJohns Hopkins UniversityChildren's Hospital of PhiladelphiaWake Forest UniversityNational Cancer InstituteAmerican Diabetes AssociationEmory UniversityUniversity of PennsylvaniaNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsBiobankBiologyMeta-analysisWhole genome sequencingComputational biologyExome sequencingExomeGenome-wide association studyGeneticsBioinformaticsGenomePhenotypeSingle-nucleotide polymorphismGenotypeGeneMedicine
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)0.011
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.057
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 head0.071
GPT teacher head0.357
Teacher spread0.285 · 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.
Study designMeta-analysis
DomainMethods
GenreReview
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
Citations62
Published2022
Admission routes1
Has abstractno