Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk.

Humans Lung Neoplasms / genetics Genome-Wide Association Study Genetic Predisposition to Disease Male Smoking / genetics Polymorphism, Single Nucleotide Female Risk Factors Middle Aged Genetic Loci Carcinoma, Squamous Cell / genetics Case-Control Studies White People / genetics Adenocarcinoma of Lung / genetics Aged Multifactorial Inheritance

Journal

Nature communications

ISSN: 2041-1723

Titre abrégé: Nat Commun

Pays: England

ID NLM: 101528555

Informations de publication

Date de publication:
04 Oct 2024

Historique:

received: 08 04 2024

accepted: 27 08 2024

medline: 5 10 2024

pubmed: 5 10 2024

entrez: 4 10 2024

Statut: epublish

Résumé

Lung cancer remains the leading cause of cancer mortality, despite declining smoking rates. Previous lung cancer GWAS have identified numerous loci, but separating the genetic risks of lung cancer and smoking behavioral susceptibility remains challenging. Here, we perform multi-ancestry GWAS meta-analyses of lung cancer using the Million Veteran Program cohort (approximately 95% male cases) and a previous study of European-ancestry individuals, jointly comprising 42,102 cases and 181,270 controls, followed by replication in an independent cohort of 19,404 cases and 17,378 controls. We then carry out conditional meta-analyses on cigarettes per day and identify two novel, replicated loci, including the 19p13.11 pleiotropic cancer locus in squamous cell lung carcinoma. Overall, we report twelve novel risk loci for overall lung cancer, lung adenocarcinoma, and squamous cell lung carcinoma, nine of which are externally replicated. Finally, we perform PheWAS on polygenic risk scores for lung cancer, with and without conditioning on smoking. The unconditioned lung cancer polygenic risk score is associated with smoking status in controls, illustrating a reduced predictive utility in non-smokers. Additionally, our polygenic risk score demonstrates smoking-independent pleiotropy of lung cancer risk across neoplasms and metabolic traits.

Identifiants

DOI: 10.1038/s41467-024-52129-4 PMID: 39366959

pubmed: 39366959

doi: 10.1038/s41467-024-52129-4

pii: 10.1038/s41467-024-52129-4

doi:

Types de publication

Journal Article Meta-Analysis

Langues

eng

Sous-ensembles de citation

Pagination

8629

Subventions

Organisme : Biomedical Laboratory Research and Development, VA Office of Research and Development (VA Biomedical Laboratory Research and Development)

ID : MVP000

Informations de copyright

Références

Schabath, M. B. & Cote, M. L. Cancer progress and priorities: lung cancer. Cancer Epidemiol. Biomark. Prev. 28, 1563–1579 (2019).

doi: 10.1158/1055-9965.EPI-19-0221

Leiter, A., Veluswamy, R. R. & Wisnivesky, J. P. The global burden of lung cancer: current status and future trends. Nat. Rev. Clin. Oncol. 20, 624–639 (2023).

pubmed: 37479810 doi: 10.1038/s41571-023-00798-3

Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

pubmed: 33538338 doi: 10.3322/caac.21660

Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2022. CA Cancer J. Clin. 72, 7–33 (2022).

pubmed: 35020204 doi: 10.3322/caac.21708

Bossé, Y. & Amos, C. I. A decade of GWAS results in lung cancer. Cancer Epidemiol. Biomark. Prev. 27, 363–379 (2018).

doi: 10.1158/1055-9965.EPI-16-0794

Timofeeva, M. N. et al. Influence of common genetic variation on lung cancer risk: meta-analysis of 14 900 cases and 29 485 controls. Hum. Mol. Genet. 21, 4980–4995 (2012).

pubmed: 22899653 pmcid: 3607485 doi: 10.1093/hmg/dds334

McKay, J. D. et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat. Genet. 49, 1126–1132 (2017).

pubmed: 28604730 pmcid: 5510465 doi: 10.1038/ng.3892

Byun, J. et al. Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer. Nat. Genet. 54, 1167–1177 (2022).

pubmed: 35915169 pmcid: 9373844 doi: 10.1038/s41588-022-01115-x

Wang, Y. et al. SNP rs17079281 decreases lung cancer risk through creating an YY1-binding site to suppress DCBLD1 expression. Oncogene 39, 4092–4102 (2020).

pubmed: 32231272 pmcid: 7220863 doi: 10.1038/s41388-020-1278-4

Zhang, T. et al. Genomic and evolutionary classification of lung cancer in never smokers. Nat. Genet. 53, 1348–1359 (2021).

pubmed: 34493867 pmcid: 8432745 doi: 10.1038/s41588-021-00920-0

Govindan, R. et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 150, 1121–1134 (2012).

pubmed: 22980976 pmcid: 3656590 doi: 10.1016/j.cell.2012.08.024

Wang, Z. et al. Meta-analysis of genome-wide association studies identifies multiple lung cancer susceptibility loci in never-smoking Asian women. Hum. Mol. Genet. 25, 620–629 (2016).

pubmed: 26732429 pmcid: 4731021 doi: 10.1093/hmg/ddv494

Schabath, M. B., Cress, D. & Munoz-Antonia, T. Racial and ethnic differences in the epidemiology and genomics of lung cancer. Cancer Control 23, 338–346 (2016).

pubmed: 27842323 doi: 10.1177/107327481602300405

Long, E., Patel, H., Byun, J., Amos, C. I. & Choi, J. Functional studies of lung cancer GWAS beyond association. Hum. Mol. Genet. 31, R22–R36 (2022).

pubmed: 35776125 pmcid: 9585683 doi: 10.1093/hmg/ddac140

Shi, J. et al. Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population. Nat. Commun. 14, 3043 (2023).

pubmed: 37236969 pmcid: 10220065 doi: 10.1038/s41467-023-38196-z

Dai, J. et al. Identification of risk loci and a polygenic risk score for lung cancer: a large-scale prospective cohort study in Chinese populations. Lancet Respir. Med. 7, 881–891 (2019).

pubmed: 31326317 pmcid: 7015703 doi: 10.1016/S2213-2600(19)30144-4

Nahar, R. et al. Elucidating the genomic architecture of Asian EGFR-mutant lung adenocarcinoma through multi-region exome sequencing. Nat. Commun. 9, 216 (2018).

pubmed: 29335443 pmcid: 5768770 doi: 10.1038/s41467-017-02584-z

Zanetti, K. A. et al. Genome-wide association study confirms lung cancer susceptibility loci on chromosomes 5p15 and 15q25 in an African-American population. Lung Cancer 98, 33–42 (2016).

pubmed: 27393504 doi: 10.1016/j.lungcan.2016.05.008

Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).

pubmed: 26441289 doi: 10.1016/j.jclinepi.2015.09.016

GTEx Consortium The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

doi: 10.1126/science.aaz1776

Hao, K. et al. Lung eQTLs to help reveal the molecular underpinnings of asthma. PLoS Genet. 8, e1003029 (2012).

pubmed: 23209423 pmcid: 3510026 doi: 10.1371/journal.pgen.1003029

Bossé, Y. et al. Transcriptome-wide association study reveals candidate causal genes for lung cancer. Int. J. Cancer 146, 1862–1878 (2020).

pubmed: 31696517 doi: 10.1002/ijc.32771

Koutsami, M. K. et al. Centrosome abnormalities are frequently observed in non-small-cell lung cancer and are associated with aneuploidy and cyclin E overexpression. J. Pathol. 209, 512–521 (2006).

pubmed: 16739112 doi: 10.1002/path.2005

Chan, J. Y. A clinical overview of centrosome amplification in human cancers. Int. J. Biol. Sci. 7, 1122–1144 (2011).

pubmed: 22043171 pmcid: 3204404 doi: 10.7150/ijbs.7.1122

Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. Ser. B Stat. Methodol. 82, 1273–1300 (2020).

doi: 10.1111/rssb.12388

Zou, Y., Carbonetto, P., Wang, G. & Stephens, M. Fine-mapping from summary data with the ‘sum of single effects’ model. PLoS Genet. 18, e1010299 (2022).

pubmed: 35853082 pmcid: 9337707 doi: 10.1371/journal.pgen.1010299

de Goede, O. M. et al. Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease. Cell 184, 2633–2648.e19 (2021).

pubmed: 33864768 pmcid: 8651477 doi: 10.1016/j.cell.2021.03.050

Li, Y. et al. Pan-cancer characterization of immune-related lncRNAs identifies potential oncogenic biomarkers. Nat. Commun. 11, 1000 (2020).

pubmed: 32081859 pmcid: 7035327 doi: 10.1038/s41467-020-14802-2

de Santiago, P. R. et al. Immune-related IncRNA LINC00944 responds to variations in ADAR1 levels and it is associated with breast cancer prognosis. Life Sci. 268, 118956 (2021).

pubmed: 33383047 doi: 10.1016/j.lfs.2020.118956

Chen, D. et al. Genome-wide analysis of long noncoding RNA (lncRNA) expression in colorectal cancer tissues from patients with liver metastasis. Cancer Med. 5, 1629–1639 (2016).

pubmed: 27165481 pmcid: 4867661 doi: 10.1002/cam4.738

McLaren, W. et al. The ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

pubmed: 27268795 pmcid: 4893825 doi: 10.1186/s13059-016-0974-4

Saunders, G. R. B. et al. Genetic diversity fuels gene discovery for tobacco and alcohol use. Nature 612, 720–724 (2022).

pubmed: 36477530 pmcid: 9771818 doi: 10.1038/s41586-022-05477-4

Han, B. & Eskin, E. Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am. J. Hum. Genet. 88, 586–598 (2011).

pubmed: 21565292 pmcid: 3146723 doi: 10.1016/j.ajhg.2011.04.014

Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).

pubmed: 30643251 pmcid: 6358542 doi: 10.1038/s41588-018-0307-5

Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 1–12 (2018).

Xue, A. et al. Genome-wide analyses of behavioural traits are subject to bias by misreports and longitudinal changes. Nat. Commun. 12, 20211 (2021).

pubmed: 33436567 doi: 10.1038/s41467-020-20237-6

Munafò, M. R., Tilling, K., Taylor, A. E., Evans, D. M. & Davey Smith, G. Collider scope: when selection bias can substantially influence observed associations. Int. J. Epidemiol. 47, 226–235 (2018).

pubmed: 29040562 doi: 10.1093/ije/dyx206

Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

pubmed: 25642630 pmcid: 4495769 doi: 10.1038/ng.3211

Nguyen, M.-H., Ueda, K., Nakamura, Y. & Daigo, Y. Identification of a novel oncogene, MMS22L, involved in lung and esophageal carcinogenesis. Int. J. Oncol. 41, 1285–1296 (2012).

pubmed: 22895565 doi: 10.3892/ijo.2012.1589

Amos, C. I. et al. The OncoArray Consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiol. Biomark. Prev. 26, 126–135 (2017).

doi: 10.1158/1055-9965.EPI-16-0106

Yang, W. et al. Deciphering associations between three RNA splicing-related genetic variants and lung cancer risk. NPJ Precis. Oncol. 6, 48 (2022).

pubmed: 35773316 pmcid: 9247007 doi: 10.1038/s41698-022-00281-9

Gabriel, A. A. G. et al. Genetic analysis of lung cancer and the germline impact on somatic mutation burden. J. Natl Cancer Inst. 114, 1159–1166 (2022).

pubmed: 35511172 pmcid: 9360465 doi: 10.1093/jnci/djac087

Lawrenson, K. et al. Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus. Nat. Commun. 7, 12675 (2016).

pubmed: 27601076 pmcid: 5023955 doi: 10.1038/ncomms12675

Lesseur, C. et al. Genome-wide association meta-analysis identifies pleiotropic risk loci for aerodigestive squamous cell cancers. PLoS Genet. 17, e1009254 (2021).

pubmed: 33667223 pmcid: 7968735 doi: 10.1371/journal.pgen.1009254

Fachal, L. et al. Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes. Nat. Genet. 52, 56–73 (2020).

pubmed: 31911677 pmcid: 6974400 doi: 10.1038/s41588-019-0537-1

Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).

pubmed: 29292387 pmcid: 5805593 doi: 10.1038/s41588-017-0009-4

Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).

pubmed: 29059683 pmcid: 5798588 doi: 10.1038/nature24284

Wallace, C. A more accurate method for colocalisation analysis allowing for multiple causal variants. PLoS Genet. 17, e1009440 (2021).

pubmed: 34587156 pmcid: 8504726 doi: 10.1371/journal.pgen.1009440

Gusev, A. et al. A transcriptome-wide association study of high-grade serous epithelial ovarian cancer identifies new susceptibility genes and splice variants. Nat. Genet. 51, 815–823 (2019).

pubmed: 31043753 pmcid: 6548545 doi: 10.1038/s41588-019-0395-x

Brenner, D. R. et al. Alcohol consumption and lung cancer risk: a pooled analysis from the International Lung Cancer Consortium and the SYNERGY study. Cancer Epidemiol. 58, 25–32 (2019).

pubmed: 30445228 doi: 10.1016/j.canep.2018.10.006

Larsson, S. C. et al. Smoking, alcohol consumption, and cancer: a mendelian randomisation study in UK Biobank and international genetic consortia participants. PLoS Med. 17, e1003178 (2020).

pubmed: 32701947 pmcid: 7377370 doi: 10.1371/journal.pmed.1003178

Petrelli, F. et al. Association of obesity with survival outcomes in patients with cancer: a systematic review and meta-analysis. JAMA Netw. Open 4, e213520 (2021).

pubmed: 33779745 pmcid: 8008284 doi: 10.1001/jamanetworkopen.2021.3520

Lan, T., Chen, L. & Wei, X. Inflammatory cytokines in cancer: comprehensive understanding and clinical progress in gene therapy. Cells 10, 100 (2021).

pubmed: 33429846 pmcid: 7827947 doi: 10.3390/cells10010100

Kresse, S. H. et al. LSAMP, a novel candidate tumor suppressor gene in human osteosarcomas, identified by array comparative genomic hybridization. Genes Chromosomes Cancer 48, 679–693 (2009).

pubmed: 19441093 doi: 10.1002/gcc.20675

Xie, J. et al. Copy number analysis identifies tumor suppressive lncRNAs in human osteosarcoma. Int. J. Oncol. 50, 863–872 (2017).

pubmed: 28197627 doi: 10.3892/ijo.2017.3864

Yu, G. et al. Roles of transducin-like enhancer of split (TLE) family proteins in tumorigenesis and immune regulation. Front. Cell Dev. Biol. 10, 1010639 (2022).

pubmed: 36438567 pmcid: 9692235 doi: 10.3389/fcell.2022.1010639

Sartor, I. T. S., Recamonde-Mendoza, M. & Ashton-Prolla, P. TULP3: a potential biomarker in colorectal cancer? PLoS ONE 14, e0210762 (2019).

pubmed: 30640939 pmcid: 6331117 doi: 10.1371/journal.pone.0210762

Chaudhary, P. K. & Kim, S. An insight into GPCR and G-proteins as cancer drivers. Cells 10, 3288 (2021).

pubmed: 34943797 pmcid: 8699078 doi: 10.3390/cells10123288

Murphy, C. et al. An analysis of JADE2 in non-small cell lung cancer (NSCLC). Biomedicines 11, 2576 (2023).

pubmed: 37761019 pmcid: 10526426 doi: 10.3390/biomedicines11092576

Ni, L. et al. RPAP3 interacts with Reptin to regulate UV-induced phosphorylation of H2AX and DNA damage. J. Cell. Biochem. 106, 920–928 (2009).

pubmed: 19180575 doi: 10.1002/jcb.22073

Saredi, G. et al. H4K20me0 marks post-replicative chromatin and recruits the TONSL–MMS22L DNA repair complex. Nature 534, 714–718 (2016).

pubmed: 27338793 pmcid: 4939875 doi: 10.1038/nature18312

Fang, H. et al. Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am. J. Hum. Genet. 105, 763–772 (2019).

pubmed: 31564439 pmcid: 6817526 doi: 10.1016/j.ajhg.2019.08.012

Hunter-Zinck, H. et al. Genotyping array design and data quality control in the million veteran program. Am. J. Hum. Genet. 106, 535–548 (2020).

pubmed: 32243820 pmcid: 7118558 doi: 10.1016/j.ajhg.2020.03.004

Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

pubmed: 25722852 pmcid: 4342193 doi: 10.1186/s13742-015-0047-8

Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).

pubmed: 27694958 pmcid: 5096458 doi: 10.1038/ng.3679

Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

pubmed: 27571263 pmcid: 5157836 doi: 10.1038/ng.3656

1000 Genomes Project Consortium. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

doi: 10.1038/nature15393

Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53, 1097–1103 (2021).

pubmed: 34017140 doi: 10.1038/s41588-021-00870-7

Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

pubmed: 20616382 pmcid: 2922887 doi: 10.1093/bioinformatics/btq340

McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

pubmed: 27548312 pmcid: 5388176 doi: 10.1038/ng.3643

Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

pubmed: 16632515 doi: 10.1093/biostatistics/kxj037

Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

pubmed: 22492648 pmcid: 3348564 doi: 10.1093/bioinformatics/bts163

Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).

pubmed: 26708335 doi: 10.1093/bioinformatics/btv722

Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).

pubmed: 26773131 pmcid: 4866522 doi: 10.1093/bioinformatics/btw018

Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

pubmed: 29184056 pmcid: 5705698 doi: 10.1038/s41467-017-01261-5

Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).

pubmed: 30992449 pmcid: 6467998 doi: 10.1038/s41467-019-09718-5

Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

pubmed: 21167468 pmcid: 3014363 doi: 10.1016/j.ajhg.2010.11.011

Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

pubmed: 24830394 pmcid: 4022491 doi: 10.1371/journal.pgen.1004383

Machiela, M. J. & Chanock, S. J. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555–3557 (2015).

pubmed: 26139635 pmcid: 4626747 doi: 10.1093/bioinformatics/btv402

Carroll, R. J., Bastarache, L. & Denny, J. C. R. PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment. Bioinformatics 30, 2375–2376 (2014).

pubmed: 24733291 pmcid: 4133579 doi: 10.1093/bioinformatics/btu197

Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50, 1514–1523 (2018).

pubmed: 30275531 pmcid: 6521726 doi: 10.1038/s41588-018-0222-9