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Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease
Gabrielle Lemire, Alba Sanchis‐Juan, Kathryn Russell, Samantha Baxter, Katherine R. Chao, Moriel Singer‐Berk, Emily Groopman, Isaac Wong, Eleina England, Julia K. Goodrich, Lynn Pais, Christina Austin‐Tse, Stephanie DiTroia, Emily O’Heir, Vijay Ganesh, Monica H. Wojcik, Emily Evangelista, Hana Snow, Ikeoluwa Osei‐Owusu, Jack Fu, Mugdha Singh, Yulia Mostovoy, Steve S. Huang, Kiran Garimella, Samantha L. Kirkham, Jennifer E. Neil, Diane D. Shao, Christopher A. Walsh, Emanuela Argilli, Carolyn Le, Elliott H. Sherr, Joseph G. Gleeson, Shirlee Shril, Ronen Schneider, Friedhelm Hildebrandt, Vijay G. Sankaran, Jill A. Madden, Casie A. Genetti, Alan H. Beggs, Pankaj B. Agrawal, Kinga M. Bujakowska, Emily Place, Eric A. Pierce, Sandra Donkervoort, Carsten G. Bönnemann, Lyndon Gallacher, Zornitza Stark, Tiong Yang Tan, Susan M. White, Ana Töpf, Volker Straub, Mark D. Fleming, Martin R. Pollak, Katrin Õunap, Sander Pajusalu, Kirsten A. Donald, Zandrè Bruwer, Gianina Ravenscroft, Nigel G. Laing, Daniel G. MacArthur, Heidi L. Rehm, Michael E. Talkowski, Harrison Brand, Anne O’Donnell‐Luria
Bibliographic record
VenueThe American Journal of Human Genetics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsnot available
FundersH2020 European Research CouncilEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Neurological Disorders and StrokeEstonian Research Competency CouncilLGMD2D FoundationNational Institute of Mental HealthSanofi GenzymeNational Institute of Dental and Craniofacial ResearchUC Berkeley College of ChemistryNational Health and Medical Research CouncilMuscular Dystrophy UKState Government of VictoriaKurt+Peter FoundationUniversity of California, San DiegoMurdoch Children's Research InstituteFonds de Recherche du Québec - SantéLimb Girdle Muscular Dystrophy 2IMassachusetts General HospitalChildren’s Hospital of Wisconsin Research InstituteEuropean CommissionNational Institute of Child Health and Human DevelopmentNational Institute of Diabetes and Digestive and Kidney DiseasesSanofiResearch to Prevent BlindnessNational Institutes of HealthTürkiye Bilimsel ve Teknolojik Araştırma KurumuFoundation Fighting BlindnessLimb Girdle Muscular Dystrophy 2i Research FundNational Human Genome Research InstituteUltragenyx PharmaceuticalNational Eye InstituteEesti TeadusagentuurAutism Speaks
KeywordsCopy-number variationExome sequencingExomeGeneticsGenomicsBiologyGenetic testingMendelian inheritanceMedical geneticsComputational biologyPhenotypeGenomeGene
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.286
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.005
GPT teacher head0.240
Teacher spread0.234 · 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 teacher head, not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical
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
Citations21
Published2024
Admission routes1
Has abstractno