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. 2018 Aug 13;9(1):3221.
doi: 10.1038/s41467-018-05074-y.

Identification of Susceptibility Pathways for the Role of Chromosome 15q25.1 in Modifying Lung Cancer Risk

Xuemei Ji  1 Yohan Bossé  2   3 Maria Teresa Landi  4 Jiang Gui  1 Xiangjun Xiao  1 David Qian  1 Philippe Joubert  3 Maxime Lamontagne  3 Yafang Li  1 Ivan Gorlov  1 Mariella de Biasi  5   6 Younghun Han  1 Olga Gorlova  1 Rayjean J Hung  7 Xifeng Wu  8 James McKay  9 Xuchen Zong  7 Robert Carreras-Torres  9 David C Christiani  10   11 Neil Caporaso  4 Mattias Johansson  9 Geoffrey Liu  7 Stig E Bojesen  12   13   14 Loic Le Marchand  15 Demetrios Albanes  4 Heike Bickeböller  16 Melinda C Aldrich  17 William S Bush  17   18 Adonina Tardon  19   20 Gad Rennert  21   22 Chu Chen  23 M Dawn Teare  24 John K Field  25 Lambertus A Kiemeney  26 Philip Lazarus  27 Aage Haugen  28 Stephen Lam  29 Matthew B Schabath  30 Angeline S Andrew  31 Hongbing Shen  32 Yun-Chul Hong  33 Jian-Min Yuan  34 Pier A Bertazzi  35   36 Angela C Pesatori  35   36 Yuanqing Ye  8 Nancy Diao  10 Li Su  10 Ruyang Zhang  10   32 Yonathan Brhane  7 Natasha Leighl  37 Jakob S Johansen  38 Anders Mellemgaard  38 Walid Saliba  21   22 Christopher Haiman  39 Lynne Wilkens  15 Ana Fernandez-Somoano  19   20 Guillermo Fernandez-Tardon  19   20 Erik H F M van der Heijden  26 Jin Hee Kim  40 Juncheng Dai  32 Zhibin Hu  32 Michael P A Davies  25 Michael W Marcus  25 Hans Brunnström  41 Jonas Manjer  42 Olle Melander  42 David C Muller  43 Kim Overvad  42 Antonia Trichopoulou  44 Rosario Tumino  45 Jennifer Doherty  31   46 Gary E Goodman  46   47 Angela Cox  48 Fiona Taylor  48 Penella Woll  48 Irene Brüske  49 Judith Manz  49 Thomas Muley  50   51 Angela Risch  52 Albert Rosenberger  16 Kjell Grankvist  53 Mikael Johansson  54 Frances Shepherd  55 Ming-Sound Tsao  55 Susanne M Arnold  56 Eric B Haura  57 Ciprian Bolca  58 Ivana Holcatova  59 Vladimir Janout  59 Milica Kontic  60 Jolanta Lissowska  61 Anush Mukeria  62 Simona Ognjanovic  63 Tadeusz M Orlowski  64 Ghislaine Scelo  9 Beata Swiatkowska  65 David Zaridze  62 Per Bakke  66 Vidar Skaug  28 Shanbeh Zienolddiny  28 Eric J Duell  67 Lesley M Butler  34 Woon-Puay Koh  68   69 Yu-Tang Gao  70 Richard Houlston  71 John McLaughlin  72 Victoria Stevens  73 David C Nickle  74 Ma'en Obeidat  75 Wim Timens  76 Bin Zhu  4 Lei Song  4 María Soler Artigas  77   78 Martin D Tobin  77   78 Louise V Wain  77   78 Fangyi Gu  4 Jinyoung Byun  1 Ahsan Kamal  1 Dakai Zhu  1 Rachel F Tyndale  79   80   81 Wei-Qi Wei  82 Stephen Chanock  4 Paul Brennan  9 Christopher I Amos  83   84
Free PMC article

Identification of Susceptibility Pathways for the Role of Chromosome 15q25.1 in Modifying Lung Cancer Risk

Xuemei Ji et al. Nat Commun. .
Free PMC article


Genome-wide association studies (GWAS) identified the chromosome 15q25.1 locus as a leading susceptibility region for lung cancer. However, the pathogenic pathways, through which susceptibility SNPs within chromosome 15q25.1 affects lung cancer risk, have not been explored. We analyzed three cohorts with GWAS data consisting 42,901 individuals and lung expression quantitative trait loci (eQTL) data on 409 individuals to identify and validate the underlying pathways and to investigate the combined effect of genes from the identified susceptibility pathways. The KEGG neuroactive ligand receptor interaction pathway, two Reactome pathways, and 22 Gene Ontology terms were identified and replicated to be significantly associated with lung cancer risk, with P values less than 0.05 and FDR less than 0.1. Functional annotation of eQTL analysis results showed that the neuroactive ligand receptor interaction pathway and gated channel activity were involved in lung cancer risk. These pathways provide important insights for the etiology of lung cancer.

Conflict of interest statement

The authors declare no competing interests.


Fig. 1
Fig. 1
Schematic overview of the study design. (1) In the discovery phase, a total of 310,276 SNPs were the same in both the 1st and 2nd discovery cohorts and were applied for association analyses and meta-analyses. (2) SNPs within the 15q25.1 locus, which were associated with lung cancer risk with logistic regression P values of less than 5 × 10−8 in the 1st discovery cohort and in the meta-analysis of the discovery cohorts, were selected as index SNPs. (3) The epistasis test between SNPs in the whole genome and the index SNPs within chromosome 15q25.1 locus were conducted for both discovery cohorts and a meta-analysis was performed to combine the epistasis results. (4) The SNPs, which interacted with the index SNPs with an epistasis P value of less than 0.05 in the 1st discovery cohort and in the meta-analysis of both discovery cohorts, and less than 0.10 in the 2nd discovery cohort, were selected as the candidate SNPs. (5) The index SNPs and the candidate SNPs with the logistic regression P values in the meta-analysis of discovery cohorts were applied for GWAS pathway analysis. (6) In the replication phase, the index SNPs and the candidate SNPs with the logistic regression P values in an independent cohort were applied for GWAS pathway analysis to validate the susceptibility pathway enriched in step 5. (7) The most significant genes in the whole genome regulated by SNPs in chromosome 15q25.1, which were selected with the eQTL study, were employed for pathway analysis. (8) The individual and combined effects of genes in the pathways on lung cancer risk were calculated. (9) A similar process to select index SNPs and candidate SNPs and to carry out GWAS pathway analyses in the subgroups of smokers and non-smokers were conducted
Fig. 2
Fig. 2
Gene network of the susceptibility pathways/GO terms. Each node is a gene, which was the selected gene in our GWAS pathway analysis and is shown in Supplementary Data 4. The connecting lines are drawn if the two genes have a relationship such as co-expression, shared protein domain, physical interactions, co-localization, or pathway. The thickness of the lines represents the degree of similarity between two genes. Gene network is produced using GeneMANIA. a The network of KEGG neuroactive ligand receptor interaction pathway consists of 23 genes and only TRPV1 in the associated gene list identified by our GWAS enrichment was shown no direct relationship with other associated genes. b The network of gated channel activity GO term consists all of 22 genes identified by our GWAS enrichment. c The network of ion channel activity GO term consists all of 24 genes identified by our GWAS enrichment. d The network of cation channel activity GO term consists all of 22 genes identified by our GWAS enrichment. e The network of substrate-specific channel activity GO term consists all of 24 genes identified by our GWAS enrichment. f The network of cation transmembrane transporter activity GO term consists 29 and only SLC4A4 in the associated gene list genes identified by our GWAS enrichment was shown no direct relationship with other associated genes
Fig. 3
Fig. 3
Association among susceptibility pathways and GO terms. a Each node is a subcomponent. Connecting lines are drawn if the overlapping coefficient between the two nodes is greater than 0.8. This picture was drawn by Cytoscape with EnrichmentMap plugin, using the standard gene set file. b The relationship between the susceptibility GO terms. The relationship between the GO terms was based on the knowledge from the European Bioinformatics Institute. All gene sets which were enriched in our GWAS pathway analyses were colored. Of the GO terms, 10 terms belonged to a transporter activity term. Both substrate-specific transporter activity term and transmembrane transporter activity term are part of the transporter activity term and have a child term of substrate-specific transmembrane transporter activity. Both terms of substrate-specific channel activity and ion transmembrane transporter activity are part of the substrate-specific transmembrane transporter activity term, and have a child term of ion channel activity which had a child term of cation channel activity. In addition, cation transmembrane transporter activity was part of ion transmembrane transporter activity, and had a child term of metal ion transmembrane transporter activity. Gated channel activity term was a child term of transmembrane transporter activity and shared several child terms with ion channel activity term

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