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Multi-ancestry genome-wide association study of kidney cancer identifies 63 susceptibility regions
Mark P. Purdue, Diptavo Dutta, Mitchell J. Machiela, Bryan R. Gorman, Timothy Winter, Dayne Okuhara, Sara Cleland, Aida Ferreiro-Iglesias, Paul Scheet, Aoxing Liu, Chao Wu, Samuel O. Antwi, James Larkin, Stênio C Zequi, Maxine Sun, Keiko Hikino, Ali Hajiran, Keith A. Lawson, Flavio Mavignier Cárcano, Odile Blanchet, Brian Shuch, Kenneth G. Nepple, G. Margue, Debasish Sundi, W. Ryan Diver, Maria Aparecida Azevedo Koike Folgueira, Adrie van Bokhoven, Florencia Neffa, Kevin M Brown, Jonathan N. Hofmann, Jongeun Rhee, Meredith Yeager, Nathan R Cole, Belynda D Hicks, Michelle R Manning, Amy Hutchinson, N. Rothman, Wen‐Yi Huang, W. Marston Linehan, Adriana Lori, Matthieu Ferragu, Merzouka Zidane-Marinnes, Sérgio Serrano, Wesley J Magnabosco, Ana Paula Vilas, Ricardo Decia, Florencia Carusso, Laura S Graham, Kyra Anderson, Mehmet Asım Bilen, Cletus A. Arciero, Isabelle Pellegrin, Solène Ricard, Ghislaine Scélo, Rosamonde E. Banks, Naveen S Vasudev, Naeem Soomro, Grant D. Stewart, Adebanji Adeyoju, Stephen Bromage, David Hrouda, Norma Gibbons, Poulam M. Patel, Mark Sullivan, Andrew Protheroe, Francesca I Nugent, Michelle J Fournier, Xiaoyu Zhang, Lisa J Martin, Maria Komisarenko, Timothy Eisen, Sonia Cunningham, Denise C. Connolly, Robert G. Uzzo, Давид Заридзе, Anush Mukeria, Ivana Holcátová, Anna Horňáková, Lenka Foretová, Vladimir Janout, Dana Mates, Viorel Jinga, Ștefan Rașcu, Mirjana Mijušković, Slaviša Savić, Saša Milosavljević, Valérie Gaborieau, Behnoush Abedi‐Ardekani, James McKay, Mattias Johansson, Larry Phouthavongsy, L. Anne Hayman, Jason Li, Ilinca Lungu, Stephania M Bezerra, Aline G. de Souza, Cláudia Tarcila Gomes Sares, Rodolfo B Reis, Fábio Pescarmona Gallucci, Mauricio D Cordeiro, Mark Pomerantz, Gwo‐Shu M. Lee, Matthew L. Freedman, Anhyo Jeong, Samantha Greenberg, Alejandro Sanchez, R. Houston Thompson, Vidit Sharma, David D Thiel, Colleen T. Ball, Diego Abreu, Elaine T. Lam, William Carlos Nahas, Viraj A. Master, Alpa V. Patel, Jean‐Christophe Bernhard, Neal D. Freedman, Pierre Bigot, Rui Manuel Reis, Leandro M. Colli, Antonio Finelli, Brandon J. Manley, Chikashi Terao, Toni K. Choueiri, Dirce M Carraro, Richard S. Houlston, Jeanette E. Eckel‐Passow, Philip H Abbosh, Andrea Ganna, Paul Brennan, Jian Gu, Stephen J. Chanock
Bibliographic record
VenueNature Genetics · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsOntario Institute for Cancer ResearchPrincess Margaret Cancer CentreUniversity Health Network
FundersMedical Research CouncilNational Institutes of HealthWorld Health Organization
KeywordsBiologyKidney cancerGenome-wide association studyOdds ratioLocus (genetics)Expression quantitative trait lociQuantitative trait locusGeneticsSingle-nucleotide polymorphismGenetic associationCancerGenetic architectureKidney diseaseGeneGenotypeInternal medicineMedicineEndocrinology
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)0.002
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.514
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 head0.044
GPT teacher head0.380
Teacher spread0.336 · 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
Citations48
Published2024
Admission routes1
Has abstractno