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Record W2074801231 · doi:10.1371/journal.pgen.1003284

Evidence of Gene–Environment Interactions between Common Breast Cancer Susceptibility Loci and Established Environmental Risk Factors

2013· article· en· W2074801231 on OpenAlexafffund
Stefan Nickels, Thérèse Truong, Rebecca Hein, Kristen N. Stevens, Katharina Buck, Sabine Behrens, Ursula Eilber, Martina E. Schmidt, Lothar Häberle, Alina Vrieling, Mia M. Gaudet, Jonine D. Figueroa, Nils Schoof, Amanda B. Spurdle, Anja Rudolph, Peter A. Fasching, John L. Hopper, Enes Makalic, Daniel F. Schmidt, Melissa C. Southey, Matthias W. Beckmann, Arif B. Ekici, Olivia Fletcher, Lorna J. Gibson, Isabel dos‐Santos‐Silva, Julian Peto, Manjeet K. Humphreys, Jean Wang, Emilie Cordina‐Duverger, F. Ménégaux, Børge G. Nordestgaard, Stig E. Bojesen, Charlotte Lanng, Hoda Anton‐Culver, Argyrios Ziogas, Leslie Bernstein, Christina A. Clarke, Hermann Brenner, Heiko Müller, Volker Arndt, Christa Stegmaier, Hiltrud Brauch, Thomas Brüning, Volker Harth, The GENICA Network, Arto Mannermaa, Vesa Kataja, Veli-Matti Kosma, Jaana M. Hartikainen, AOCS Management Group, Diether Lambrechts, Dominiek Smeets, Patrick Neven, Robert Paridaens, Dieter Flesch-Janys, Nadia Obi, Shan Wang-Gohrke, Fergus J. Couch, Janet E. Olson, Celine M. Vachon, Graham G. Giles, Gianluca Severi, Laura Baglietto, Kenneth Offit, Esther M. John, Alexander Miron, Irene L. Andrulis, Julia A. Knight, Gord Glendon, Anna Marie Mulligan, Stephen J. Chanock, Jolanta Lissowska, Jianjun Liu, Angela Cox, Helen Cramp, Dan Connley, Sabapathy P. Balasubramanian, Alison M. Dunning, Mitul Shah, Amy Trentham-Dietz, Polly A. Newcomb, Linda Titus, Kathleen M. Egan, Elizabeth K. Cahoon, Preetha Rajaraman, Alice J. Sigurdson, Michele M. Doody, Pascal Guénel, Paul D. P. Pharoah, Marjanka K. Schmidt, Per Hall, Doug F. Easton, Montserrat García‐Closas, Roger L. Milne, Jenny Chang‐Claude

Bibliographic record

VenuePLoS Genetics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity Health NetworkLunenfeld-Tanenbaum Research InstitutePublic Health OntarioUniversity of TorontoMount Sinai Hospital
FundersNational Institutes of HealthRheinische Friedrich-Wilhelms-Universität BonnMedical Research CouncilUniversität des SaarlandesInstitut National Du CancerHerlev HospitalFondation de FranceLigue Contre le CancerLon V. Smith FoundationNational Institute for Health and Care ResearchNational Health and Medical Research CouncilGeorgetown UniversityCancer Research UKAgence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du TravailHuntsman Cancer InstituteCancer Care OntarioDeutsche Gesetzliche UnfallversicherungDeutsches KrebsforschungszentrumEuropean CommissionCHIST-ERAAgence Nationale de la RechercheNational Cancer InstituteSundhed og Sygdom, Det Frie Forskningsråd
KeywordsBiologyBreast cancerGeneticsGene–environment interactionGeneBioinformaticsCancerGenotype

Abstract

fetched live from OpenAlex

Various common genetic susceptibility loci have been identified for breast cancer; however, it is unclear how they combine with lifestyle/environmental risk factors to influence risk. We undertook an international collaborative study to assess gene-environment interaction for risk of breast cancer. Data from 24 studies of the Breast Cancer Association Consortium were pooled. Using up to 34,793 invasive breast cancers and 41,099 controls, we examined whether the relative risks associated with 23 single nucleotide polymorphisms were modified by 10 established environmental risk factors (age at menarche, parity, breastfeeding, body mass index, height, oral contraceptive use, menopausal hormone therapy use, alcohol consumption, cigarette smoking, physical activity) in women of European ancestry. We used logistic regression models stratified by study and adjusted for age and performed likelihood ratio tests to assess gene-environment interactions. All statistical tests were two-sided. We replicated previously reported potential interactions between LSP1-rs3817198 and parity (Pinteraction = 2.4 × 10(-6)) and between CASP8-rs17468277 and alcohol consumption (Pinteraction = 3.1 × 10(-4)). Overall, the per-allele odds ratio (95% confidence interval) for LSP1-rs3817198 was 1.08 (1.01-1.16) in nulliparous women and ranged from 1.03 (0.96-1.10) in parous women with one birth to 1.26 (1.16-1.37) in women with at least four births. For CASP8-rs17468277, the per-allele OR was 0.91 (0.85-0.98) in those with an alcohol intake of <20 g/day and 1.45 (1.14-1.85) in those who drank ≥ 20 g/day. Additionally, interaction was found between 1p11.2-rs11249433 and ever being parous (Pinteraction = 5.3 × 10(-5)), with a per-allele OR of 1.14 (1.11-1.17) in parous women and 0.98 (0.92-1.05) in nulliparous women. These data provide first strong evidence that the risk of breast cancer associated with some common genetic variants may vary with environmental risk factors.

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)0.006
metaresearch head score (Gemma)0.010
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 score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.265
Teacher spread0.242 · 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".

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Citations167
Published2013
Admission routes2
Has abstractyes

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