Advanced

Genome-wide association study of INDELs identified four novel susceptibility loci associated with lung cancer risk

Dai, Juncheng ; Huang, Mingtao ; Amos, Christopher I. ; Hung, Rayjean J. ; Tardon, Adonina ; Andrew, Angeline ; Chen, Chu ; Christiani, David C. ; Albanes, Demetrius and Rennert, Gadi , et al. (2019) In International Journal of Cancer
Abstract

Genome-wide association studies (GWAS) have identified 45 susceptibility loci associated with lung cancer. Only less than SNPs, small insertions and deletions (INDELs) are the second most abundant genetic polymorphisms in the human genome. INDELs are highly associated with multiple human diseases, including lung cancer. However, limited studies with large-scale samples have been available to systematically evaluate the effects of INDELs on lung cancer risk. Here, we performed a large-scale meta-analysis to evaluate INDELs and their risk for lung cancer in 23,202 cases and 19,048 controls. Functional annotations were performed to further explore the potential function of lung cancer risk INDELs. Conditional analysis was used to clarify... (More)

Genome-wide association studies (GWAS) have identified 45 susceptibility loci associated with lung cancer. Only less than SNPs, small insertions and deletions (INDELs) are the second most abundant genetic polymorphisms in the human genome. INDELs are highly associated with multiple human diseases, including lung cancer. However, limited studies with large-scale samples have been available to systematically evaluate the effects of INDELs on lung cancer risk. Here, we performed a large-scale meta-analysis to evaluate INDELs and their risk for lung cancer in 23,202 cases and 19,048 controls. Functional annotations were performed to further explore the potential function of lung cancer risk INDELs. Conditional analysis was used to clarify the relationship between INDELs and SNPs. Four new risk loci were identified in genome-wide INDEL analysis (1p13.2: rs5777156, Insertion, OR = 0.92, p = 9.10 × 10−8; 4q28.2: rs58404727, Deletion, OR = 1.19, p = 5.25 × 10−7; 12p13.31: rs71450133, Deletion, OR = 1.09, p = 8.83 × 10−7; and 14q22.3: rs34057993, Deletion, OR = 0.90, p = 7.64 × 10−8). The eQTL analysis and functional annotation suggested that INDELs might affect lung cancer susceptibility by regulating the expression of target genes. After conducting conditional analysis on potential causal SNPs, the INDELs in the new loci were still nominally significant. Our findings indicate that INDELs could be potentially functional genetic variants for lung cancer risk. Further functional experiments are needed to better understand INDEL mechanisms in carcinogenesis.

(Less)
Please use this url to cite or link to this publication:
author
, et al. (More)
(Less)
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
genome-wide association studies, INDELs, lung cancer
in
International Journal of Cancer
publisher
John Wiley & Sons
external identifiers
  • scopus:85074828493
  • pmid:31577861
ISSN
0020-7136
DOI
10.1002/ijc.32698
language
English
LU publication?
yes
id
d6ac0de9-9a6d-4ee7-a247-6ffd7e3c829a
date added to LUP
2019-12-04 12:06:04
date last changed
2019-12-05 02:22:05
@article{d6ac0de9-9a6d-4ee7-a247-6ffd7e3c829a,
  abstract     = {<p>Genome-wide association studies (GWAS) have identified 45 susceptibility loci associated with lung cancer. Only less than SNPs, small insertions and deletions (INDELs) are the second most abundant genetic polymorphisms in the human genome. INDELs are highly associated with multiple human diseases, including lung cancer. However, limited studies with large-scale samples have been available to systematically evaluate the effects of INDELs on lung cancer risk. Here, we performed a large-scale meta-analysis to evaluate INDELs and their risk for lung cancer in 23,202 cases and 19,048 controls. Functional annotations were performed to further explore the potential function of lung cancer risk INDELs. Conditional analysis was used to clarify the relationship between INDELs and SNPs. Four new risk loci were identified in genome-wide INDEL analysis (1p13.2: rs5777156, Insertion, OR = 0.92, p = 9.10 × 10<sup>−8</sup>; 4q28.2: rs58404727, Deletion, OR = 1.19, p = 5.25 × 10<sup>−7</sup>; 12p13.31: rs71450133, Deletion, OR = 1.09, p = 8.83 × 10<sup>−7</sup>; and 14q22.3: rs34057993, Deletion, OR = 0.90, p = 7.64 × 10<sup>−8</sup>). The eQTL analysis and functional annotation suggested that INDELs might affect lung cancer susceptibility by regulating the expression of target genes. After conducting conditional analysis on potential causal SNPs, the INDELs in the new loci were still nominally significant. Our findings indicate that INDELs could be potentially functional genetic variants for lung cancer risk. Further functional experiments are needed to better understand INDEL mechanisms in carcinogenesis.</p>},
  author       = {Dai, Juncheng and Huang, Mingtao and Amos, Christopher I. and Hung, Rayjean J. and Tardon, Adonina and Andrew, Angeline and Chen, Chu and Christiani, David C. and Albanes, Demetrius and Rennert, Gadi and Fan, Jingyi and Goodman, Gary and Liu, Geoffrey and Field, John K. and Grankvist, Kjell and Kiemeney, Lambertus A. and Le Marchand, Loic and Schabath, Matthew B. and Johansson, Mattias and Aldrich, Melinda C. and Johansson, Mikael and Caporaso, Neil and Lazarus, Philip and Lam, Stephan and Bojesen, Stig E. and Arnold, Susanne and Landi, Maria Teresa and Risch, Angela and Wichmann, H. Erich and Bickeboller, Heike and Brennan, Paul and Shete, Sanjay and Melander, Olle and Brunnstrom, Hans and Zienolddiny, Shan and Woll, Penella and Stevens, Victoria and Hu, Zhibin and Shen, Hongbing},
  issn         = {0020-7136},
  language     = {eng},
  publisher    = {John Wiley & Sons},
  series       = {International Journal of Cancer},
  title        = {Genome-wide association study of INDELs identified four novel susceptibility loci associated with lung cancer risk},
  url          = {http://dx.doi.org/10.1002/ijc.32698},
  doi          = {10.1002/ijc.32698},
  year         = {2019},
}