MétaCan
Menu
Back to cohort
Record W4400024441 · doi:10.17615/mt16-xj68

Combined Associations of a Polygenic Risk Score and Classical Risk Factors with Breast Cancer Risk

2024· article· en· W4400024441 on OpenAlexfundno aff
Nasim Mavaddat, Minouk J. Schoemaker, Hiltrud Brauch, Thomas Brüning, David J. Hunter, Montserrat García‐Closas, Jose E. Castelao, Eunjung Lee, Mikael Eriksson, Xiaohong R. Yang, N. Chatterjee, Kamila Czene, Paul D.P. Pharoah, Andrew Wilcox, Stig E. Bojesen, A. Jung, Cari M. Kitahara, Daniele Campa, Robert J. MacInnis, Pabitra Pal Choudhury, Q. Wang, I.L. Andrulis, Walter C. Willett, Marike Gabrielson, Guillermo Pita, Ben Schöttker, Esther M. John, Trevor Ahearn, Michael E. Jones, Martha S. Linet, Christine L. Clarke, Ross L. Prentice, Kathryn J. Ruddy, Alicia Beeghly‐Fadiel, P.A. Fasching, Yon‐Dschun Ko, Federico Canzian, S.L. Neuhausen, Volker Arndt, Lin Fritschi, John J. Spinelli, Manuela Gago-Domínguez, Mia M. Gaudet, R. Kaaks, Per Hall, Rodney J. Scott, Jane Heyworth, Anna González‐Neira, Elke M. van Veen, J. Benitez, Manjeet K. Bolla, S. M. Gapstur, Paul L. Auer, Joe Dennis, Diether Lambrechts, Melissa C. Southey, Jennifer Stone, Angela Cox, Heiko Becher, Arif B. Ekici, Stacey J. Winham, Zumuruda Abu-Ful, U. Hamann, Xiao‐Ou Shu, Jolanta Lissowska, B. Holleczek, kConFab AOCS Investigators, Robert N. Hoover, Anthony Howell, Laura E. Beane Freeman, Allison W. Kurian, L. Le Marchand, Alicja Wolk, Simon S. Cross, C. Saunders, Alison M. Dunning, William G. Newman, X. Wang, Renske Keeman, Peter Kraft, Qiuyin Cai, Ana Llaneza, Caroline Weltens, Ángel Carracedo, Niclas Håkansson, ABCTB Investigators, John L. Hopper, Jiayu Dai, Ann Smeets, Georgia Chenevix‐Trench, Pascal Guénel, Sara Lindström, Hedy S. Rennert, Argyrios Ziogas, Marı́a Elena Martı́nez, Melissa A. Troester, Clarice R. Weinberg, Aaron D. Norman, D. Gareth Evans, Andrew F. Olshan, Eric C. Polley, D.F. Easton, H. Anton-Culver, Catriona McLean, Brad Carter, Jonine D. Figueroa, G. Rennert, H.S. Earp, Nick Orr, Lothar Haeberle, Sigrid Hatse, Roger L. Milne, Sabine Behrens, Jack A. Taylor, Celine M. Vachon, Chi Gao, Kyriaki Michailidou, Janet E. Olson, Graham G. Giles, F. Lejbkowicz, Håkan Olsson, Fredrick R. Schumacher, Rulla M. Tamimi, C.M. Perou, C.A. Haiman, L. Bernstein, Hermann Brenner, Dale P. Sandler, S.J. Chanock, Anthony J. Swerdlow, F.J. Couch, Christopher G. Scott, Marina Beckmann, A. Heather Eliassen, Stella Koutros, Katie M. O’Brien, Jenny Chang‐Claude, Kristan J. Aronson, Wei Zheng, Taylor Maurer, Marjanka K. Schmidt

Bibliographic record

VenueEdinburgh Research Explorer (University of Edinburgh) · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsnot available
FundersMedical Research and Materiel CommandNational Heart, Lung, and Blood InstituteUniversitätsklinikum Hamburg-EppendorfProgramme Grants for Applied ResearchInstituto de Salud Carlos IIICancer Council TasmaniaNational Health and Medical Research CouncilMedical Research CouncilInstitut National Du CancerCenters for Disease Control and PreventionHellenic Health FoundationRheinische Friedrich-Wilhelms-Universität BonnInstitut Gustave-RoussyDeutsche KrebshilfeSwedish Cancer FoundationAgence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du TravailVetenskapsrådetBundesministerium für Bildung und ForschungMinisterio de Economía y CompetitividadInstitut National de la Santé et de la Recherche MédicaleCancer AustraliaAgence Nationale de la RechercheBreast Cancer Research FoundationMcGill UniversityDeutsche Gesetzliche UnfallversicherungGentofte HospitalDeutsche ForschungsgemeinschaftCancer Council South AustraliaFonds Wetenschappelijk OnderzoekCancerfondenNational Cancer InstituteCancer Institute NSWNational Breast Cancer FoundationUniversity of CambridgeCancer Research UKDivision of Cancer Prevention, National Cancer InstituteNational Institute for Health and Care ResearchAssociazione Italiana per la Ricerca sul CancroLon V. Smith FoundationU.S. ArmyU.S. Department of Health and Human ServicesMayo ClinicNational Institutes of HealthDavid F. and Margaret T. Grohne Family FoundationLigue Contre le CancerDeutsches KrebsforschungszentrumAgency for Science, Technology and ResearchDeutschen Konsortium für Translationale KrebsforschungNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchFondation de FranceSundhed og Sygdom, Det Frie ForskningsrådCancer Council VictoriaCalifornia Department of Public HealthEuropean CommissionWorld Cancer Research FundStavros Niarchos FoundationCancer Council NSWSusan G. Komen for the Cure
KeywordsPolygenic risk scoreBreast cancerMedicineOncologyDemographyCancerInternal medicineBiologyGeneticsSingle-nucleotide polymorphismGeneGenotypeSociology

Abstract

fetched live from OpenAlex

We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.

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 machine prediction

Teacher imitation

Not 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)0.006
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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.291
Teacher spread0.253 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEdinburgh Research Explorer (University of Edinburgh)Same topicNutrition, Genetics, and DiseaseFrench-language works237,207