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We extended this framework to investigate gene\u2013smoking interactions in lung cancer susceptibility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To identify trans-omics gene\u2013smoking interactions affecting lung cancer risk and to assess how biomarkers modify effect of smoking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted the first trans-omics gene\u2013smoking interaction study of lung cancer by integrating consortium-scale individual genotype data (27\u200a737 cases vs 449\u200a910 noncases) from the International Lung Cancer OncoArray Consortium (ILCCO-OncoArray), Transdisciplinary Research Into Cancer of the Lung (TRICL), Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO), and the UK Biobank (UKB) with alliance-based summary-level molecular quantitative trait loci (xQTL) data, involving DNA methylation, gene expression, protein, and metabolite. Based on the identified biomarkers, we developed a molecular modifying score (MMS) to delineate gene\u2013smoking interaction patterns and stratify smokers at high risk of lung cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Measurements and Main Results<\/jats:title>\n                    <jats:p>Eight biomarkers showing significant interactions with smoking were identified through a 2-phase analytic strategy, comprising CpG sites in the nicotinic acetylcholine receptor region and gene RP11-326C3.14. The MMS, constructed by integrating these biomarkers with their effect estimates derived from meta-analysis of all available datasets, effectively stratified lung cancer risk among smokers. Trans-omics integrative analysis revealed functional relationships across molecular layers, particularly implicating the NELFE gene in smoking-related carcinogenesis pathways.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The trans-omics association study (xWAS) framework enables systematic discovery of trans-omics gene\u2013environment interactions. The MMS effectively delineates the patterns of the interaction effects and facilitates risk stratification. Additionally, we launched a free online platform, LungCancer-xWAS-GxE (http:\/\/bigdata.njmu.edu.cn\/LungCancer-xWAS-GxE\/).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ajrccm\/aamaf097","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T12:21:49Z","timestamp":1767615709000},"page":"1115-1128","source":"Crossref","is-referenced-by-count":4,"title":["A trans-omics gene\u2013smoking interaction study of lung cancer based on consortium data"],"prefix":"10.1093","volume":"212","author":[{"given":"Ning","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University , Nanjing, Jiangsu,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowen","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University 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