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Zhang, Ruyang; Shen, Sipeng; Wei, Yongyue; Zhu, Ying; Li, Yi; Chen, Jiajin; Guan, Jinxing; Pan, Zoucheng; Wang, Yuzhuo; Zhu, Meng; Xie, Junxing; Xiao, Xiangjun; Zhu, Dakai; Li, Yafang; Albanes, Demetrios; Landi, Maria Teresa; Caporaso, Neil E.; Lam, Stephen; Tardon, Adonina; Chen, Chu; Bojesen, Stig E.; Johansson, Mattias; Risch, Angela; Bickeboeller, Heike; Wichmann, H-Erich; Rennert, Gadi; Arnold, Susanne; Brennan, Paul; McKay, James D.; Field, John K.; Shete, Sanjay S.; Le Marchand, Loic; Liu, Geoffrey; Andrew, Angeline S.; Kiemeney, Lambertus A.; Zienolddiny-Narui, Shan; Behndig, Annelie; Johansson, Mikael; Cox, Angela; Lazarus, Philip; Schabath, Matthew B.; Aldrich, Melinda C.; Dai, Juncheng; Ma, Hongxia; Zhao, Yang; Hu, Zhibin; Hung, Rayjean J.; Amos, Christopher I.; Shen, Hongbing; Chen, Feng and Christiani, David C. (2022): A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians. In: Journal of Thoracic Oncology, Vol. 17, No. 8: pp. 974-990

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Introduction: Although genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G x 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 x 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 x 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), ORinteraction = 1.17, p = 6.57 x 10(-13);rs3135369(BTNL2) and rs2858859(HLA-DQA1,) ORinteraction = 1.17, p = 2.43 x 10(-13);rs2858859(HLA-DQA1) and rs9275572(HLA-DQA2), ORinteraction = 1.15, p = 2.84 x 10(-13);rs2853668(TERT) and rs62329694(CLPTM1L), ORinteraction = 0.73, p = 2.70 x 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), ORinteraction = 1.13, p = 0.008;rs3135369(BTNL2) and rs2858859(HLA-DQA1), ORinteraction = 1.11, p = 5.23 x 10-4;rs3135369(BTNL2) and rs9271300(HLA-DQA1), ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G x G information score was remarkable in lung cancer risk stratification. Conclusions: Important G x G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.

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