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Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis
Athena Hadjixenofontos, Ashley Beecham, Jacob L. McCauley, Clara P. Manrique, Margaret A Pericak‐Vance, Ioanna Konidari, Philip L. De Jager, Michelle Lee, Irene Y. Frohlich, Nikolaos A. Patsopoulos, Céline Bellenguez, Colin Freeman, Alexander Dilthey, Dionysia K. Xifara, Gavin Band, Peter Donnelly, Chris C. A. Spencer, Loukas Moutsianas, Amy Strange, Matti Pirinen, Gil McVean, Mary F. Davis, Nathalie Schnetz‐Boutaud, Jonathan L. Haines, Alastair Compston, Barnaby Fiddes, Stephen Sawcer, Anu Kemppinen, Maria Ban, Amie Baker, Chris Cotsapas, Cristin McCabe, David A. Hafler, Carl A. Anderson, Jeffrey C. Barrett, Sarah Hunt, Sarah Edkins, Panos Deloukas, Tejas Shah, Hannah Blackburn, Cordelia Langford, Robert Andrews, David J. Booth, Steve Vucic, Graeme J. Stewart, Leentje Cosemans, An Goris, Bénédicte Dubois, Annette Oturai, Per Soelberg Sørensen, Helle Bach Søndergaard, Finn Sellebjerg, Janna Saarela, Virpi Leppä, Isabelle Cournu‐Rebeix, Bertrand Fontaine, Vincent Damotte, Angela Jochim, Muni Hoshi, Achim Berthele, Thomas Korn, Rebecca Selter, Viola Biberacher, Verena Grummel, Helena Kronsbein, Dorothea Buck, Claes Martin, Volker Siffrin, Frauke Zipp, Felix Luessi, Christiane Graetz, Eva Zindler, Lucia Corrado, Sandra D’Alfonso, Melissa Sorosina, Paola Brambilla, Giuseppe Liberatore, Vittorio Martinelli, Mariaemma Rodegher, Bruce Taylor, Elisabeth Gulowsen Celius, Inger‐Lise Mero, Hanne F. Harbo, Benedicte A. Lie, Magdalena Lindén, Helga Westerlind, Jan Hillert, Jenny Link, Maja Jagodic, Ingrid Kockum, Tomas Olsson, Izaura Lima Bomfim, Lou Brundin, Fredrik Piehl, Emilie Sundqvist, Stacy J. Caillier, Jorge R. Oksenberg, Bruce Cree, Sergio E. Baranzini, Pierre‐Antoine Gourraud, Stephen L. Hauser, Julia Mescheriakova, Rogier Hintzen, Lisa F. Barcellos, Hong Quach, Catherine Schaefer, Ling Shen, Cristina Agliardi, Lars Alfredsson, Mehdi Alizadeh, Luisa Bernardinelli, Thomas M. C. Binder, Simon Broadley, Bruno Brochet, William Camu, Wassila Carpentier, Irène Coman, Daniele Cusi, Gilles Defer, Silvia Delgado, Alessia Di Sapio, Martin Duddy, Irina Elovaara, Nikos Evangelou, Judith Field, James S. Wiley, André Franke, Daniela Galimberti, Christian Gieger, Andrew Graham, Håkon Håkonarson, Christopher Halfpenny, Gillian Hall, Per Hall, Anders Hamsten, James Harley, Timothy Harrower, Clive Hawkins, Garrett Hellenthal, Charles Hillier, John Zajicek, Jeremy Hobart, Ilijas Jelčić, B. Kendall, Allan G. Kermode, Trevor J. Kilpatrick, Keijo Koivisto
Bibliographic record
VenueNature Genetics · 2013
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill University and Génome Québec Innovation CentreMcGill University
FundersNational Center for Research ResourcesNational Cancer InstituteNational Institute of Allergy and Infectious DiseasesMedical Research CouncilNational Institutes of HealthNational Institute of Environmental Health SciencesInstitut National de la Santé et de la Recherche MédicaleKarolinska InstitutetHjärt-LungfondenVetenskapsrådetCrohn's and Colitis UKWellcome TrustInfrastructures en Biologie Santé et AgronomieMultiple Sclerosis SocietyKnut och Alice Wallenbergs StiftelseStockholms Läns LandstingNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Institute for Health and Care Research
KeywordsMultiple sclerosisGenome-wide association studyBiologyGenotypingGeneticsMajor histocompatibility complexGenetic association1000 Genomes ProjectSingle-nucleotide polymorphismComputational biologyGenotypeImmune systemGeneImmunology
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 head0.044
GPT teacher head0.318
Teacher spread0.274 · 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
Citations1,442
Published2013
Admission routes1
Has abstractno