Candidate genes for COPD in two large data sets.

P.S. Bakke, G. Zhu, A. Gulsvik, X. Kong, A.G. Agusti, P.M. Calverley, C.F. Donner, R.D. Levy, B.J. Make, P D. Pare, S.I. Rennard, J. Vestbo, E.F.M. Wouters, W. Anderson, D.A. Lomas, E.K. Silverman, S.G. Pillai

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Lack of reproducibility of findings has been a criticism of genetic association studies in complex diseases like chronic obstructive pulmonary disease (COPD). We selected 257 polymorphisms of 16 genes with reported or potential relationshipsto COPD and genotyped these variants in a case-control study which included 953 COPD cases and 956 control subjects. We explored the association of these polymorphisms to three COPD phenotypes: a COPD binary phenotype and two quantitative traits (post bronchodilator FEV1 in percent predicted and FEV1/FVC). The polymorphisms significantly associated to these phenotypes in this first study were tested in a second, family based, study that included 635 pedigrees with 1910 individuals. Significant associations to the binary COPD phenotype in both populations were seen for STAT1 (rs13010343) and NFKBIB/SIRT2 (rs2241704) (p<0.05). SNP rs17467825 and rs1155563 of the GC gene were significantly associated with FEV1 in percent predicted and FEV1/FVC, respectively in both populations (p<0.05). This study has replicated associations to COPD phenotypes in the STAT1, NFKBIB/SIRT2, and GC genes in two independent populations, the associations of the former two genes representing novel findings.
Original languageEnglish
Pages (from-to)255-263
Number of pages9
JournalEuropean Respiratory Journal
Issue number2
Publication statusPublished - Feb 2011


  • Chronic obstructive pulmonary disease
  • genetic association
  • replication
  • smoking

Cite this

Bakke, P. S., Zhu, G., Gulsvik, A., Kong, X., Agusti, A. G., Calverley, P. M., Donner, C. F., Levy, R. D., Make, B. J., Pare, P. D., Rennard, S. I., Vestbo, J., Wouters, E. F. M., Anderson, W., Lomas, D. A., Silverman, E. K., & Pillai, S. G. (2011). Candidate genes for COPD in two large data sets. European Respiratory Journal, 37(2), 255-263.