A Genome-wide Association Study of Emphysema and Airway Quantitative Imaging Phenotypes

NETT Genetics, ECLIPSE, and COPDGene Investigators, Michael H Cho, Peter J Castaldi, Craig P Hersh, Brian D Hobbs, R Graham Barr, Ruth Tal-Singer, Per Bakke, Amund Gulsvik, Raúl San José Estépar, Edwin J R Van Beek, Harvey O Coxson, David A Lynch, George R Washko, Nan M Laird, James D Crapo, Terri H Beaty, Edwin K Silverman

Research output: Contribution to journalArticlepeer-review

Abstract

Rationale: Chronic obstructive pulmonary disease (COPD) is defined by the presence of airflow limitation on spirometry, yet COPD subjects can have marked differences in CT imaging. These differences may be driven by genetic factors. We hypothesized that a genome-wide association study of quantitative imaging would identify loci not previously identified in analyses of COPD or spirometry. In addition, we sought to determine whether previously described genome-wide significant COPD and spirometric loci were associated with emphysema or airway phenotypes. Objective: To identify genetic determinants of quantitative imaging phenotypes. Methods: We performed a genome-wide association study on two quantitative emphysema and two quantitative airway imaging phenotypes in the COPDGene (non-Hispanic white and African-American), ECLIPSE, NETT, and GenKOLS studies; and on % gas trapping in COPDGene. We also examined specific loci reported as genome-wide significant for spirometric phenotypes related to airflow limitation or COPD. Results: The total sample size across all cohorts was 12,031, of which 9,338 were from COPDGene. We identified five loci associated with emphysema-related phenotypes, one with airway-related phenotypes, and two with gas trapping. These loci included previously reported associations, including the HHIP, 15q25, and AGER loci, as well as novel associations near SERPINA10 and DLC1. All previously reported COPD and a significant number of spirometric GWAS loci were at least nominally (P < 0.05) associated with either emphysema or airway phenotypes. Conclusions: Genome-wide analysis may identify novel risk factors for quantitative imaging characteristics in COPD, and also identify imaging features associated with previously identified lung function loci. 

Original languageEnglish
Pages (from-to)559-569
JournalAmerican Journal of Respiratory and Critical Care Medicine
Volume192
Issue number5
Early online date1 Jun 2015
DOIs
Publication statusPublished - 2015

Keywords

  • emphysema
  • airway
  • genetics
  • COPD

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