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Record W2293438364 · doi:10.1093/hmg/ddv494

Meta-analysis of genome-wide association studies identifies multiple lung cancer susceptibility loci in never-smoking Asian women

2016· review· en· W2293438364 on OpenAlexaff
Zhaoming Wang, Wei Jie Seow, Kouya Shiraishi, Chao A. Hsiung, Keitaro Matsuo, Jie Liu, Kexin Chen, Taiki Yamji, Yang Yang, I‐Shou Chang, Chen Wu, Yun‐Chul Hong, Laurie Burdett, Charles C. Chung, Shengchao A. Li, Meredith Yeager, Amy Hutchinson, Wei Hu, Neil E. Caporaso, Maria Teresa Landi, Nilanjan Chatterjee, Minsun Song, Joseph F. Fraumeni, Takashi Kohno, Jun Yokota, Hideo Kunitoh, Kyota Ashikawa, Yukihide Momozawa, Yataro Daigo, Tetsuya Mitsudomi, Yasushi Yatabe, Toyoaki Hida, Zhibin Hu, Juncheng Dai, Hongxia Ma, Guangfu Jin, Bao Song, Zhehai Wang, Sensen Cheng, Zhihua Yin, Xuelian Li, Yangwu Ren, Peng Guan, Jiang Chang, Wen Tan, Chien‐Jen Chen, Gee‐Chen Chang, Ying-Huang Tsai, Wu‐Chou Su, Kuan‐Yu Chen, Ming-Shyan Huang, Yuh-Min Chen, Hong Zheng, Haixin Li, Ping Cui, Huan Guo, Ping Xu, Li Liu, Motoki Iwasaki, Taichi Shimazu, Shoichiro Tsugane, Junjie Zhu, Gening Jiang, Fei Ke, Jae Yong Park, Yeul Hong Kim, Jae Sook Sung, Kyong Hwa Park, Young Tae Kim, Yoo Jin Jung, Chang Hyun Kang, In Kyu Park, Hee Nam Kim, Hyo-Sung Jeon, Jin Eun Choi, Yi Young Choi, Jin Hee Kim, In‐Jae Oh, Young‐Chul Kim, Sook Whan Sung, Jun Suk Kim, Ho-Il Yoon, Sun‐Seog Kweon, Min‐Ho Shin, Adeline Seow, Ying Chen, Wei-Yen Lim, Jianjun Liu, Maria Pik Wong, Victor Lee, Bryan A. Bassig, Margaret A. Tucker, Sonja I. Berndt, Wong‐Ho Chow, Bu-Tian Ji, Junwen Wang, Jun Xu, Alan Sihoe, Jcm Ho, John K. Chan, Jiu-Cun Wang, Daru Lu, Xueying Zhao, Zhenhong Zhao, Junjie Wu, Hongyan Chen, Jin Li, Fusheng Wei, Guoping Wu, She-Juan An, Xu‐Chao Zhang, Jian Su, Yi‐Long Wu, Yu-Tang Gao, Yong-Bing Xiang, Xingzhou He, Jihua Li, Wei Zheng, Xiao‐Ou Shu, Qiuyin Cai, Robert J. Klein, William Pao, Charles Lawrence, H. Dean Hosgood, Chin‐Fu Hsiao, Li-Hsin Chien, Ying-Hsiang Chen, Chung–Hsing Chen, Wen‐Chang Wang, Chih‐Yi Chen, Chih-Liang Wang, Chong‐Jen Yu, Huiling Chen, Yu-Chun Su, Fang-Yu Tsai, Yi-Song Chen, Yao-Jen Li, Tsung‐Ying Yang, Chien‐Chung Lin, Pan‐Chyr Yang, Tangchun Wu, Dongxin Lin, Baosen Zhou, Jinming Yu, Hongbing Shen, Michiaki Kubo, Stephen J. Chanock, Nathaniel Rothman, Qing Lan

Bibliographic record

VenueHuman Molecular Genetics · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsInstitute of Population and Public Health
FundersNational Cancer InstituteNational Institutes of HealthNational Cancer CenterNational Natural Science Foundation of China
KeywordsBiologyLung cancerGenome-wide association studyGeneticsGenomeGenetic associationCancerMeta-analysisBioinformaticsComputational biologyGeneSingle-nucleotide polymorphismOncologyInternal medicineGenotype

Abstract

fetched live from OpenAlex

Genome-wide association studies (GWAS) of lung cancer in Asian never-smoking women have previously identified six susceptibility loci associated with lung cancer risk. To further discover new susceptibility loci, we imputed data from four GWAS of Asian non-smoking female lung cancer (6877 cases and 6277 controls) using the 1000 Genomes Project (Phase 1 Release 3) data as the reference and genotyped additional samples (5878 cases and 7046 controls) for possible replication. In our meta-analysis, three new loci achieved genome-wide significance, marked by single nucleotide polymorphism (SNP) rs7741164 at 6p21.1 (per-allele odds ratio (OR) = 1.17; P = 5.8 × 10(-13)), rs72658409 at 9p21.3 (per-allele OR = 0.77; P = 1.41 × 10(-10)) and rs11610143 at 12q13.13 (per-allele OR = 0.89; P = 4.96 × 10(-9)). These findings identified new genetic susceptibility alleles for lung cancer in never-smoking women in Asia and merit follow-up to understand their biological underpinnings.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.495
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.080
GPT teacher head0.390
Teacher spread0.310 · 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.

Study designMeta-analysis
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

Citations58
Published2016
Admission routes1
Has abstractyes

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