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Record W2808886822 · doi:10.1093/jnci/djy099

Novel Common Genetic Susceptibility Loci for Colorectal Cancer

2018· article· en· W2808886822 on OpenAlexafffund
Stephanie L. Schmit, Christopher K. Edlund, Fredrick R. Schumacher, Jian Gong, Tabitha A. Harrison, Jeroen R. Huyghe, Chenxu Qu, Marilena Melas, David Van Den Berg, Hansong Wang, Stephanie Tring, Sarah J. Plummer, Demetrius Albanes, M. Henar Alonso, Christopher I. Amos, Kristen Anton, Aaron K. Aragaki, Volker Arndt, Elizabeth L. Barry, Sonja I. Berndt, Stéphane Bezieau, Stephanie A. Bien, Amanda M. Bloomer, Juergen Boehm, Marie‐Christine Boutron‐Ruault, Hermann Brenner, Stefanie Brezina, Daniel D. Buchanan, Katja Butterbach, Bette J. Caan, Peter T. Campbell, Christopher S. Carlson, Jose E. Castelao, Andrew T. Chan, Jenny Chang‐Claude, Stephen J. Chanock, Iona Cheng, Ya‐Wen Cheng, Lee Soo Chin, James M. Church, Timothy R. Church, Gerhard A. Coetzee, Michelle Cotterchio, Marcia Cruz Correa, Keith R. Curtis, David Duggan, Douglas F. Easton, Dallas R. English, Edith J. M. Feskens, Rocky Fischer, Liesel M. FitzGerald, Barbara K. Fortini, Lars G. Fritsche, Charles S. Fuchs, Manuela Gago-Domínguez, Manish Gala, Steven Gallinger, W. James Gauderman, Graham G. Giles, Edward L. Giovannucci, Stephanie M. Gogarten, Clicerio González‐Villalpando, Elena M. Gonzalez-Villalpando, William M. Grady, Joel K. Greenson, Andrea Gsur, Marc J. Gunter, Christopher A. Haiman, Jochen Hampe, Sophia Harlid, John F. Harju, Richard B. Hayes, Philipp Hofer, Michael Hoffmeister, John L. Hopper, Shu-Chen Huang, José María Huerta, Thomas J. Hudson, David J. Hunter, Gregory Idos, Motoki Iwasaki, Rebecca D. Jackson, Eric J. Jacobs, Sun Ha Jee, Mark A. Jenkins, Wei-Hua Jia, Shuo Jiao, Amit D. Joshi, Laurence N. Kolonel, Suminori Kono, Charles Kooperberg, Vittorio Krogh, Tilman Küehn, Sébastien Küry, Andrea Z. LaCroix, Cecelia Laurie, Flavio Lejbkowicz, Mathieu Lemire, Heinz‐Josef Lenz, David Levine, Christopher I. Li, Li Li, Wolfgang Lieb, Yi Lin, Noralane M. Lindor, Yun-Ru Liu, Fotios Loupakis, Yingchang Lu, Frank Luh, Jing Ma, Christoph Mancao, Frank J. Manion, Sanford D. Markowitz, Vicente Martín, Koichi Matsuda, Keitaro Matsuo, Kevin McDonnell, Roger L. Milne, Antonio J. Molina, Bhramar Mukherjee, Neil Murphy, Polly A. Newcomb, Kenneth Offit, Hanane Omichessan, Domenico Palli, Jesús Paredes Cotoré, Julyann Pérez‐Mayoral, Paul D.P. Pharoah, John D. Potter, Conghui Qu, Leon Raskin, Gad Rennert, Hedy S. Rennert, Bridget M. Riggs, Clemens Schafmayer, Robert E. Schoen, Thomas A. Sellers, Daniela Seminara, Gianluca Severi, Wei Shi, David Shibata, Xiao‐Ou Shu, Erin M. Siegel, Martha L. Slattery, Melissa C. Southey, Zsofia K. Stadler, Mariana C. Stern, Sebastian Stintzing, Darin Taverna, Stephen N. Thibodeau, Duncan C. Thomas, Antonia Trichopoulou, Shoichiro Tsugane, Cornelia M. Ulrich, Fränzel J.B. van Duijnhoven, Bethany van Guelpan, Joseph Vijai, Jarmo Virtamo, Stephanie J. Weinstein, Emily White, Aung Ko Win, Alicja Wolk, Michael O. Woods, Anna H. Wu, Kana Wu, Yong-Bing Xiang, Yun Yen, Brent W. Zanke, Yi-Xin Zeng, Ben Zhang, Niha Zubair, Sun‐Seog Kweon, Jane C. Figueiredo, Wei Zheng, Loı̈c Le Marchand, Annika Lindblom, Vı́ctor Moreno, Ulrike Peters, Graham Casey, Li Hsu, David V. Conti, Stephen B. Gruber

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsOttawa HospitalUniversity of OttawaInstitute of AgingMemorial University of NewfoundlandOntario Institute for Cancer ResearchMount Sinai HospitalCancer Care Ontario
FundersCommon FundNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeNational Cancer InstituteNIH Office of the DirectorNational Institute on Drug AbuseNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteOntario Ministry of Research and InnovationInstituto de Salud Carlos IIIUniversity of MiamiJapan Society for the Promotion of ScienceNational Human Genome Research InstituteCalifornia Department of Public HealthSchool of Medicine, Vanderbilt UniversityCancer Council VictoriaCanadian Institutes of Health ResearchNational Institute of General Medical SciencesChonnam National University Hwasun HospitalSeventh Framework ProgrammeWereld Kanker Onderzoek FondsConseil Régional des Pays de la LoireStanford UniversityVetenskapsrådetStockholms Läns LandstingOntario Ministry of Research, Innovation and ScienceServicio Gallego de SaludNorris Cotton Cancer CenterMinistry of Education, Culture, Sports, Science and TechnologyMinisterio de Economía y CompetitividadChonnam National UniversityZonMwCanadian Cancer Society Research InstituteDeutsche ForschungsgemeinschaftOntario Institute for Cancer ResearchNational Institutes of HealthMike and Josie Harper Cancer Research InstituteVanderbilt UniversityState of Connecticut Department of Public HealthUniversity of ChicagoWorld Cancer Research Fund InternationalMemorial Sloan-Kettering Cancer CenterJunta de Castilla y LeónWorld Health OrganizationUniversity of North CarolinaDivision of Cancer Prevention, National Cancer InstituteHarvard UniversityAmerican Cancer SocietyUniversité de GenèveFederación Española de Enfermedades RarasWageningen University and ResearchCancer Research UKTranscanEuropean CommissionGroupement des Entreprises Françaises dans la lutte contre le CancerRoswell Park Cancer InstituteFred Hutchinson Cancer Research CenterUniversity of PennsylvaniaNational Institute of Diabetes and Digestive and Kidney DiseasesWorld Cancer Research FundBroad InstituteU.S. Department of Health and Human Services
KeywordsColorectal cancerGeneticsGenetic predispositionBiologyCancerMedicineGene

Abstract

fetched live from OpenAlex

BACKGROUND: Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10-8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. METHODS: We conducted a GWAS in European descent CRC cases and control subjects using a discovery-replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10-8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. RESULTS: The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10-8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. CONCLUSIONS: This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screening.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.358
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, 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

Citations190
Published2018
Admission routes2
Has abstractyes

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