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Genome-wide association meta-analyses combining multiple risk phenotypes provide insights into the genetic architecture of cutaneous melanoma susceptibility
Maria Teresa Landi, D. Timothy Bishop, Stuart MacGregor, Mitchell J. Machiela, Alexander Stratigos, Paola Ghiorzo, Myriam Brossard, Donato Calista, Jiyeon Choi, Maria Concetta Fargnoli, Tongwu Zhang, Monica Rodolfo, Adam J. Trower, Chiara Menin, J. Julian Martinez, Andreas Hadjisavvas, Lei Song, Irene Stefanaki, Richard A. Scolyer, Rose Yang, Alisa M. Goldstein, Míriam Potrony, Katerina Kypreou, Lorenza Pastorino, Paola Queirolo, Cristina Pellegrini, Laura Cattaneo, Matthew Zawistowski, Pol Giménez‐Xavier, A. L. Rodriguez, Lisa Elefanti, Siranoush Manoukian, Licia Rivoltini, Blair H. Smith, Maria A. Loizidou, Laura Del Regno, Daniela Massi, Mario Mandalà, Kiarash Khosrotehrani, Lars A. Akslen, Christopher I. Amos, Per Arne Andresen, Marie‐Françoise Avril, Esther Azizi, H. Peter Soyer, Véronique Bataille, Bruna Dalmasso, Lisa Bowdler, Kathryn P. Burdon, Wei V. Chen, Veryan Codd, Jamie E. Craig, Tadeusz Dębniak, Mario Falchi, Shenying Fang, Eitan Friedman, Sarah Simi, Pilar Galán, Zaida García‐Casado, Elizabeth M. Gillanders, Scott D. Gordon, Adèle C. Green, Nelleke A. Gruis, Johan Hansson, Mark Harland, Jessica Harris, Per Helsing, Anjali K. Henders, Marko Hočevar, Veronica Höiom, David J. Hunter, Christian Ingvar, Rajiv Kumar, Julie Lang, G.M. Lathrop, Jeffrey E. Lee, Xin Li, Jan Lubiński, Rona M. MacKie, M. Malt, Josep Malvehy, Kerrie McAloney, Hamida Mohamdi, Anders Molven, Eric K. Moses, Rachel Ε. Neale, Srdjan Novaković, Dale R. Nyholt, Håkan Olsson, Nick Orr, Lars G. Fritsche, Joan Anton Puig‐Butille, Abrar A. Qureshi, Graham Radford‐Smith, Juliette A. Randerson‐Moor, Celia Requena, Casey Rowe, Marianna Sanna, Dirk Schadendorf, Hans‐Joachim Schulze, Lisa A. Simms, B. Mark Smithers, Fengju Song, Anthony J. Swerdlow, Nienke van der Stoep, Nicole A. Kukutsch, Alessia Visconti, Leanne Wallace, Sarah V. Ward, Lawrie Wheeler, Richard A. Sturm, Amy Hutchinson, Kristine Jones, Michael Malasky, Aurélie Vogt, Weiyin Zhou, Karen A. Pooley, David E. Elder, Jiali Han, Belynda Hicks, Nicholas K. Hayward, Peter A. Kanetsky, Chad M. Brummett, Grant W. Montgomery, Catherine M. Olsen, Caroline Hayward, Alison M. Dunning, Nicholas G. Martin, Εvangelos Εvangelou, Graham J. Mann, Georgina V. Long, Paul D.P. Pharoah, Douglas F. Easton, Jennifer H. Barrett, Anne Ε. Cust, Gonçalo R. Abecasis, David L. Duffy, David C. Whiteman, Helen Gogas, Arcangela De Nicolo, Margaret A. Tucker, Julia Newton‐Bishop, Ketty Peris, Stephen J. Chanock, Florence Démenais, Kevin M. Brown, Susana Puig, Eduardo Nagore, Jianxin Shi, Mark M. Iles, Matthew H. Law
Bibliographic record
VenueNature Genetics · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsMcGill University and Génome Québec Innovation Centre
FundersNational Cancer InstituteMedical Research CouncilNational Institutes of HealthCancer Research UKWellcome Trust
KeywordsGenome-wide association studyGenetic architectureBiologyMelanomaGeneticsGenetic associationSingle-nucleotide polymorphismMeta-analysisGenetic predispositionPhenotypeGenotypeGeneMedicinePathology
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)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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 head0.033
GPT teacher head0.318
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 teacher head, not a consensus.
Study designNot applicable
Domainnot available
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
Citations239
Published2020
Admission routes1
Has abstractno