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Record W4225130908 · doi:10.1016/j.jtho.2022.04.011

A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians

2022· article· en· W4225130908 on OpenAlexaff
Ruyang Zhang, Sipeng Shen, Yongyue Wei, Ying Zhu, Yi Li, Jiajin Chen, Jinxing Guan, Zoucheng Pan, Yuzhuo Wang, Meng Zhu, Junxing Xie, Xiangjun Xiao, Dakai Zhu, Yafang Li, Demetrius Albanes, Maria Teresa Landi, Neil E. Caporaso, Stephen Lam, Adonina Tardón, Chu Chen, Stig E. Bojesen, Mattias Johansson, Angela Risch, Heike Bickeböller, H‐Erich Wichmann, Gad Rennert, Susanne M. Arnold, Paul Brennan, James McKay, John K. Field, Sanjay Shete, Loı̈c Le Marchand, Geoffrey Liu, Angeline S. Andrew, Lambertus A. Kiemeney, Shan Zienolddiny-Narui, Annelie Behndig, Mikael Johansson, Angela Cox, Philip Lazarus, Matthew B. Schabath, Melinda C. Aldrich, Juncheng Dai, Hongxia Ma, Yang Zhao, Zhibin Hu, Christopher I. Amos, Hongbing Shen, Feng Chen, David C. Christiani

Bibliographic record

VenueJournal of Thoracic Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsSinai Health SystemLunenfeld-Tanenbaum Research InstituteBC Cancer AgencyPrincess Margaret Cancer CentreUniversity of British ColumbiaUniversity of Toronto
FundersNational Cancer InstituteNational Key Research and Development Program of ChinaJiangsu Planned Projects for Postdoctoral Research FundsNational Institute of Environmental Health SciencesGovernment of Jiangsu ProvinceQinglan Project of Jiangsu Province of ChinaNational Institutes of HealthNanjing Medical UniversityNational Natural Science Foundation of ChinaPriority Academic Program Development of Jiangsu Higher Education InstitutionsNatural Science Foundation of Jiangsu ProvinceWorld Health OrganizationNational Institute on AgingCentre International de Recherche sur le CancerMedical Research CouncilChina Postdoctoral Science Foundation
KeywordsMedicineGeneEthnic groupLung cancerGeneticsGenomeScale (ratio)OncologyBiologyAnthropology

Abstract

fetched live from OpenAlex

Introduction Although genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC). Methods Leveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers. Results With the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828 C6orf10 and rs204999 PRRT1 , OR interaction = 1.17, p = 6.57 × 10 −13 ; rs3135369 BTNL2 and rs2858859 HLA-DQA1 , OR interaction = 1.17, p = 2.43 × 10 −13 ; rs2858859 HLA-DQA1 and rs9275572 HLA-DQA2 , OR interaction = 1.15, p = 2.84 × 10 −13 ; rs2853668 TERT and rs62329694 CLPTM1L , OR interaction = 0.73, p = 2.70 × 10 −13 ). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828 C6orf10 and rs204999 PRRT1 , OR interaction = 1.13, p = 0.008; rs3135369 BTNL2 and rs2858859 HLA-DQA1 , OR interaction = 1.11, p = 5.23 × 10 −4 ; rs3135369 BTNL2 and rs9271300 HLA-DQA1 , OR interaction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G × G information score was remarkable in lung cancer risk stratification. Conclusions Important G × G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.023
GPT teacher head0.387
Teacher spread0.364 · 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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Citations41
Published2022
Admission routes1
Has abstractyes

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