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Mapping autism risk loci using genetic linkage and chromosomal rearrangements
Péter Szatmári, Ping G. Tepper, Ann Thompson, Jennifer Skaug, Andrew D. Paterson, Lars Feuk, Qian Cheng, Christian R. Marshall, Stephen W. Scherer, Lonnie Zwaigenbaum, Jessica Brian, Lili Senman, Wendy Roberts, John B. Vincent, Susan E. Bryson, Marshall B. Jones, Veronica J. Vieland, La Vonne Mangin, Christopher W. Bartlett, Alberto M. Segre, Rhinda Goedken, Michael L. Cuccaro, Margaret A. Pericak‐Vance, John R. Gilbert, Harry H. Wright, Ruth K. Abramson, Catalina Betancur, Marion Leboyer, Thomas Bourgeron, Eric Hollander, Jeremy M. Silverman, Kenneth L. Davis, Joseph D. Buxbaum, Linda Lotspeich, Joachim Hallmayer, James S. Sutcliffe, Jonathan L. Haines, Susan E. Folstein, Joseph Piven, Val C. Sheffield, Kacie J. Meyer, Thomas H. Wassink, Daniel H. Geschwind, Maja Bućan, W. Ted Brown, Rita M. Cantor, Stan F. Nelson, John N. Constantino, T. Conrad Gilliam, Martha R. Herbert, Janet Miller, Clara Lajonchere, David H. Ledbetter, Christa Lese‐Martin, Carol A Samango-Sprouse, Sarah Spence, Fred R. Volkmar, Matthew W. State, Rudolph E. Tanzi, Hilary Coon, William M. McMahon, Jeff Munson, Géraldine Dawson, Annette Estes, Lambertus Klei, Nancy J. Minshew, Bernie Devlin, Pamela Flodman, Moyra Smith, M. Anne Spence, Chang-En Yu, Gerard D. Schellenberg, Elena Korvatska, Patricia M. Rodier, Chris Stodgell, Ellen M. Wijsman, Kerstin Wittemeyer, Bernadette Rogé, Carine Mantoulan, Bärbel Felder, Sabine M. Klauck, Annemarie Poustka, Claudia Schuster, Gabi Schmötzer, Sven Bölte, Fritz Poustka, Sabine Feineis-Matthews, Evelyn Herbrecht, Κaterina Papanikolaou, John Tsiantis, Claudio Toma, Elena Maestrini, Elena Bacchelli, Francesca Blasi, Simona Carone, Hermán van Engeland, Wouter Staal, Chantal Kemner, Marjolein Langemeijer, Frederieke Koop, Maretha Jonge, Channa T. Hijmans, Gillian Baird, Patrick Bolton, Michael Rutter, Emma Weisblatt, Catherine Aldred, Julie-Anne Wilkinson, Jonathan Green, Andrew Pickles, Helen McConachie, T. P. Berney, Ann Le Couteur, Jeremy Parr, Aislinn Hutchinson, Anthony Bailey, K Francis, Simon Wallace, Gemma Honeyman, Gabrielle Barnby, Anthony P. Monaco, Nuala Sykes, Janine A. Lamb, Kazuhiro Kobayashi, Inês Sousa, Bennett Leventhal, Stephen J. Guter, Edwin H. Cook
Bibliographic record
VenueNature Genetics · 2007
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsMcGill UniversityCentre for Addiction and Mental HealthMontreal Children's HospitalIzaak Walton Killam Health CentreHospital for Sick ChildrenMcMaster University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Research ResourcesNational Institute of Child Health and Human DevelopmentNational Institute of Neurological Disorders and StrokeCanadian Institutes of Health ResearchUniversity of California, San FranciscoAssistance publique-Hôpitaux de ParisFondation pour la Recherche MédicaleNational Institute of Mental HealthHospital for Sick ChildrenCentre National de la Recherche ScientifiqueSundhed og Sygdom, Det Frie ForskningsrådFondation de FranceDeutsche ForschungsgemeinschaftOntario Genomics InstituteOrangeInstitut National de la Santé et de la Recherche MédicaleUniversity of South CarolinaNational Institutes of HealthOntario GenomicsGenome CanadaMedical Research CouncilHoward Hughes Medical InstituteAutism SpeaksUniversity of MiamiWellcome TrustVanderbilt University
KeywordsBiologyGeneticsCopy-number variationAutismGenetic architectureLinkage (software)Heritability of autismGenetic linkageSNPChromosomeGeneCandidate geneSynaptogenesisGenetic variationSingle-nucleotide polymorphismQuantitative trait locusGenomePhenotypeGenotypeNeuroscience
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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 head0.026
GPT teacher head0.311
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.
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
Citations1,416
Published2007
Admission routes2
Has abstractno