title: Impact of non-linear smoking effects on the identification of gene-by-smoking interactions in COPD genetics studies.
abstract: BACKGROUND
  The identification of gene-by-environment interactions is important for understanding the genetic basis of chronic obstructive pulmonary disease (COPD). Many COPD genetic association analyses assume a linear relationship between pack-years of smoking exposure and forced expiratory volume in 1 s (FEV(1)); however, this assumption has not been evaluated empirically in cohorts with a wide spectrum of COPD severity.
  
  
  METHODS
  The relationship between FEV(1) and pack-years of smoking exposure was examined in four large cohorts assembled for the purpose of identifying genetic associations with COPD. Using data from the Alpha-1 Antitrypsin Genetic Modifiers Study, the accuracy and power of two different approaches to model smoking were compared by performing a simulation study of a genetic variant with a range of gene-by-smoking interaction effects.
  
  
  RESULTS
  Non-linear relationships between smoking and FEV(1) were identified in the four cohorts. It was found that, in most situations where the relationship between pack-years and FEV(1) is non-linear, a piecewise linear approach to model smoking and gene-by-smoking interactions is preferable to the commonly used total pack-years approach. The piecewise linear approach was applied to a genetic association analysis of the PI*Z allele in the Norway Case-Control cohort and a potential PI*Z-by-smoking interaction was identified (p=0.03 for FEV(1) analysis, p=0.01 for COPD susceptibility analysis).
  
  
  CONCLUSIONS
  In study samples of subjects with a wide range of COPD severity, a non-linear relationship between pack-years of smoking and FEV(1) is likely. In this setting, approaches that account for this non-linearity can be more powerful and less biased than the more common approach of using total pack-years to model the smoking effect.
authors.affiliation: Institute for Clinical Researchand Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USA. pcastaldi@tuftsmedicalcenter.org
authors.forename: P J
authors.initials: PJ
authors.lastname: Castaldi
authors.forename: D L
authors.initials: DL
authors.lastname: Demeo
authors.forename: C P
authors.initials: CP
authors.lastname: Hersh
authors.forename: D A
authors.initials: DA
authors.lastname: Lomas
authors.forename: I C
authors.initials: IC
authors.lastname: Soerheim
authors.forename: A
authors.initials: A
authors.lastname: Gulsvik
authors.forename: P
authors.initials: P
authors.lastname: Bakke
authors.forename: S
authors.initials: S
authors.lastname: Rennard
authors.forename: P
authors.initials: P
authors.lastname: Pare
authors.forename: J
authors.initials: J
authors.lastname: Vestbo
authors.forename: E K
authors.initials: EK
authors.lastname: Silverman
mesh_terms: D004247:DNA / Q000235:genetics*; D005260:Female; D005500:Follow-Up Studies; D005541:Forced Expiratory Volume; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D009664:Norway / Q000453:epidemiology; D020641:Polymorphism, Single Nucleotide; D029424:Pulmonary Disease, Chronic Obstructive / Q000453:epidemiology / Q000235:genetics* / Q000503:physiopathology; D012129:Respiratory Function Tests; D012189:Retrospective Studies; D012307:Risk Factors; D012907:Smoking / Q000009:adverse effects / Q000235:genetics* / Q000378:metabolism; D000515:alpha 1-Antitrypsin / Q000235:genetics* / Q000378:metabolism
chemical_list: D000515:alpha 1-Antitrypsin; D004247:DNA