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Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes
James McKay, Younghun Han, Xuchen Zong, Robert Carreras‐Torres, David C. Christiani, Neil E. Caporaso, Mattias Johansson, Xiangjun Xiao, Yafang Li, Jinyoung Byun, Alison M. Dunning, Karen A. Pooley, David C. Qian, Xuemei Ji, Geoffrey Liu, Maria Timofeeva, Stig E. Bojesen, Xifeng Wu, Loı̈c Le Marchand, Demetrius Albanes, Heike Bickeböller, Melinda C. Aldrich, William S. Bush, Adonina Tardón, Gad Rennert, M. Dawn Teare, John K. Field, Lambertus A. Kiemeney, Philip Lazarus, Aage Haugen, Stephen Lam, Matthew B. Schabath, Angeline S. Andrew, Hongbing Shen, Yun‐Chul Hong, Jian‐Min Yuan, Pier Alberto Bertazzi, Angela Cecilia Pesatori, Yuanqing Ye, Nancy Diao, Li Su, Ruyang Zhang, Yonathan Brhane, Natasha B. Leighl, Jakob Sidenius Johansen, Anders Mellemgaard, Walid Saliba, Christopher A. Haiman, Lynne R. Wilkens, Ana Fernández‐Somoano, Guillermo Fernández‐Tardón, Erik H.F.M. van der Heijden, Jin Hee Kim, Juncheng Dai, Zhibin Hu, Michael P.A. Davies, Michael W. Marcus, Hans Brunnström, Jonas Manjer, Olle Melander, David C. Muller, Kim Overvad, Antonia Trichopoulou, Rosario Tumino, Jennifer A. Doherty, Matt P Barnett, Chu Chen, Gary E. Goodman, Angela Cox, Fiona Taylor, Penella J. Woll, Irene Brüske, H‐Erich Wichmann, Judith Manz, Thomas R Muley, Angela Risch, Albert Rosenberger, Kjell Grankvist, Mikael Johansson, Frances A. Shepherd, Ming‐Sound Tsao, Susanne M. Arnold, Eric B. Haura, Ciprian Bolca, Ivana Holcátová, Vladimír Janout, Milica Kontić, Jolanta Lissowska, Anush Mukeria, Simona Ognjanovic, Tadeusz Orłowski, Ghislaine Scélo, Beata Świątkowska, Давид Заридзе, Per Bakke, Vidar Skaug, Shanbeh Zienolddiny, Eric J. Duell, Lesley M. Butler, Woon‐Puay Koh, Yu‐Tang Gao, Richard S. Houlston, Victoria L. Stevens, Philippe Joubert, Maxime Lamontagne, David C. Nickle, Ma’en Obeidat, Wim Timens, Bin Zhu, Lei Song, Linda Kachuri, María Soler Artigas, Martin D. Tobin, Louise V. Wain, Þórunn Rafnar, Thorgeir E. Thorgeirsson, Gunnar W. Reginsson, Kāri Stefánsson, Dana B. Hancock, Laura J. Bierut, Margaret R. Spitz, Nathan Gaddis, Sharon M. Lutz, Fangyi Gu, Eric O. Johnson, Ahsan Kamal, Claudio W. Pikielny, Dakai Zhu, Sara Lindströem, Xia Jiang, Rachel F. Tyndale, Georgia Chenevix‐Trench, Jonathan Beesley, Yohan Bossé, Stephen J. Chanock, Paul Brennan, Maria Teresa Landi, Christopher I. Amos
Bibliographic record
VenueNature Genetics · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversité LavalSt. Paul's HospitalCentre for Addiction and Mental HealthInstitut universitaire de cardiologie et de pneumologie de QuébecUniversity of British ColumbiaPublic Health OntarioUniversity of TorontoUniversity Health NetworkSinai Health SystemLunenfeld-Tanenbaum Research InstitutePrincess Margaret Cancer CentreBC Cancer Agency
FundersNational Cancer InstituteMedical Research CouncilUniversité LavalFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchNovo Nordisk FondenCancer Research UKWellcome TrustCancer Care OntarioInstitut universitaire de cardiologie et de pneumologie de Québec, Université LavalNational Institute on Drug AbuseDartmouth CollegeWorld Health Organization
KeywordsBiologyLung cancerGeneticsLung cancer susceptibilityGenome-wide association studyGenetic associationComputational biologySingle-nucleotide polymorphismGenotypeGenePathology
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)0.002
metaresearch head score (Gemma)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.013
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 head0.029
GPT teacher head0.385
Teacher spread0.356 · 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
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
Citations794
Published2017
Admission routes2
Has abstractno