← Back to cohort
A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer
Lang Wu, Wei Shi, Jirong Long, Xingyi Guo, Kyriaki Michailidou, Jonathan Beesley, Manjeet K. Bolla, Xiao‐Ou Shu, Yingchang Lu, Qiuyin Cai, Fares Al‐Ejeh, Esdy Rozali, Qin Wang, Joe Dennis, Bingshan Li, Chenjie Zeng, Helian Feng, Alexander Gusev, Richard Barfield, Irene L. Andrulis, Hoda Anton‐Culver, Volker Arndt, Kristan J. Aronson, Paul L. Auer, Myrto Barrdahl, Caroline Baynes, Matthias W. Beckmann, Javier Benı́tez, Marina Bermisheva, Carl Blomqvist, Natalia Bogdanova, Stig E. Bojesen, Hiltrud Brauch, Hermann Brenner, Louise A. Brinton, Per Broberg, Sara Y. Brucker, Barbara Burwinkel, Trinidad Caldés, Federico Canzian, Brian D. Carter, Jose E. Castelao, Jenny Chang‐Claude, Ting‐Yuan David Cheng, Hans Christiansen, Christine L. Clarke, Margriet Collée, Sten Cornelissen, Fergus J. Couch, David G. Cox, Angela Cox, Simon S. Cross, Julie M. Cunningham, Kamila Czene, Mary B. Daly, Peter Devilee, Kimberly F. Doheny, Thilo Dörk, Isabel dos‐Santos‐Silva, Martine Dumont, Miriam Dwek, Ursula Eilber, A. Heather Eliassen, Christoph Engel, Mikael Eriksson, Laura Fachal, Peter A. Fasching, Jonine D. Figueroa, Dieter Flesch‐Janys, Olivia Fletcher, Henrik Flyger, Lin Fritschi, Marike Gabrielson, Manuela Gago-Domínguez, Susan M. Gapstur, Montserrat García‐Closas, Mia M. Gaudet, Maya Ghoussaini, Graham G. Giles, Mark S. Goldberg, Anna González‐Neira, Pascal Guénel, Christopher A. Haiman, Niclas Håkansson, Per Hall, Emily Hallberg, Ute Hamann, Patricia Harrington, Alexander Hein, Belynda Hicks, Peter Hillemanns, Antoinette Hollestelle, Robert N. Hoover, John L. Hopper, Guanmengqian Huang, Keith Humphreys, David J. Hunter, Anna Jakubowska, Wolfgang Janni, Esther M. John, Nichola Johnson, Kristine Jones, Michael E. Jones, Audrey Jung, Rudolf Kaaks, Michael J. Kerin, Э. К. Хуснутдинова, Veli‐Matti Kosma, Vessela N. Kristensen, Diether Lambrechts, Loı̈c Le Marchand, Jingmei Li, Sara Lindström, Jolanta Lissowska, Wing‐Yee Lo, Sibylle Loibl, Jan Lubiński, Craig Luccarini, Michael P. Lux, Robert J. MacInnis, Tom Maishman, Ivana Maleva Kostovska, JoAnn E. Manson, Sara Margolin, Dimitrios Mavroudis, Hanne Meijers‐Heijboer, Usha Menon, Jeffery Meyer, Anna Marie Mulligan, Susan L. Neuhausen, Heli Nevanlinna, Patrick Neven, Sune F. Nielsen, Børge G. Nordestgaard, Olufunmilayo I. Olopade, Janet E. Olson, Håkan Olsson, Paolo Peterlongo, Julian Peto, Dijana Plaseska‐Karanfilska, Ross L. Prentice, Nadège Presneau, Katri Pylkäs, Brigitte Rack, Paolo Radice, Nazneen Rahman, Gad Rennert, Hedy S. Rennert, Valerie Rhenius, Atocha Romero, Jane Romm, Anja Rudolph, Emmanouil Saloustros, Dale P. Sandler, Elinor J. Sawyer, Marjanka K. Schmidt, Rita K. Schmutzler, Andreas Schneeweiß, Rodney J. Scott, Christopher G. Scott, Sheila Seal, Mitul Shah, Martha J. Shrubsole, Ann Smeets, Melissa C. Southey, John J. Spinelli, Jennifer Stone, Harald Surowy, Anthony J. Swerdlow, Rulla M. Tamimi, William Tapper, Jack A. Taylor, Mary Beth Terry, Daniel C. Tessier, Abigail Thomas, Kathrin Thöne, Rob A.�E.�M. Tollenaar, Diana Torres, Thérèse Truong, Michael Untch, Celine M. Vachon, David Van Den Berg, Daniel Vincent, Quinten Waisfisz, Clarice R. Weinberg, Camilla Wendt, Alice S. Whittemore, Hans Wildiers, Walter C. Willett, Robert Winqvist, Alicja Wolk, Lucy Xia, Xiaohong R. Yang, Argyrios Ziogas, Elad Ziv, Alison M. Dunning, Paul D.P. Pharoah, Jacques Simard, Roger L. Milne, Stacey L. Edwards, Peter Kraft, Douglas F. Easton, Georgia Chenevix‐Trench, Wei Zheng
Bibliographic record
VenueNature Genetics · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsMcGill University and Génome Québec Innovation CentreUniversity of British ColumbiaMcGill UniversityUniversité LavalQueen's UniversityBC Cancer AgencyRoyal Victoria HospitalCentre hospitalier universitaire de QuébecMount Sinai HospitalLunenfeld-Tanenbaum Research InstituteUniversity Health NetworkUniversity of Toronto
FundersNational Cancer InstituteCanadian Institutes of Health ResearchMedical Research CouncilMinistero dello Sviluppo EconomicoNational Institutes of HealthVanderbilt University Medical CenterCancer Research UKGovernment of CanadaFondation du cancer du sein du QuébecVanderbilt UniversityMinistère du Développement Économique, de l’Innovation et de l’ExportationNational Institute for Health and Care ResearchGenome CanadaEuropean Commission
KeywordsBiologyTranscriptomeBreast cancerCandidate geneGeneticsGeneComputational biologyCancerBioinformaticsGene expression
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)0.000
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.011
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 head0.007
GPT teacher head0.295
Teacher spread0.288 · 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
Citations246
Published2018
Admission routes2
Has abstractno