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Randomized Controlled Trial
. 2011 Oct;66(10):903-9.
doi: 10.1136/thx.2010.146118. Epub 2010 Dec 16.

Impact of non-linear smoking effects on the identification of gene-by-smoking interactions in COPD genetics studies

P J Castaldi  1 , D L Demeo, C P Hersh, D A Lomas, I C Soerheim, A Gulsvik, P Bakke, S Rennard, P Pare, J Vestbo; AATGM Investigators; ICGN Investigators; E K Silverman
Collaborators, Affiliations
Randomized Controlled Trial

Impact of non-linear smoking effects on the identification of gene-by-smoking interactions in COPD genetics studies

P J Castaldi et al. Thorax. 2011 Oct.

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).

Conclusion: 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.

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Conflict of interest statement

COMPETING INTERESTS:

PDP served on the Advisory Board for Talecris Biotherapeutics and received grant support from GSK, Merck, $100,001 or more, the NIH $50,001–$100,000, CIHR (Canada), and AllerGenNCE $100,001 or more.

Figures

Figure 1
Figure 1
Figures 1-D. FEV1, % predicted by pack-years scatterplot in 4 large cohorts (Panel A, Alpha-1 Antitrypsin Genetic Modifiers Study; Panel B, Boston Early-Onset COPD Study; Panel C, Norway Case-Control Study; Panel D, International COPD Genetics Network) with smoothing curve. Flattening of curve occurs at FEV1 levels between 30 and 50% of predicted.
Figure 1
Figure 1
Figures 1-D. FEV1, % predicted by pack-years scatterplot in 4 large cohorts (Panel A, Alpha-1 Antitrypsin Genetic Modifiers Study; Panel B, Boston Early-Onset COPD Study; Panel C, Norway Case-Control Study; Panel D, International COPD Genetics Network) with smoothing curve. Flattening of curve occurs at FEV1 levels between 30 and 50% of predicted.
Figure 1
Figure 1
Figures 1-D. FEV1, % predicted by pack-years scatterplot in 4 large cohorts (Panel A, Alpha-1 Antitrypsin Genetic Modifiers Study; Panel B, Boston Early-Onset COPD Study; Panel C, Norway Case-Control Study; Panel D, International COPD Genetics Network) with smoothing curve. Flattening of curve occurs at FEV1 levels between 30 and 50% of predicted.
Figure 1
Figure 1
Figures 1-D. FEV1, % predicted by pack-years scatterplot in 4 large cohorts (Panel A, Alpha-1 Antitrypsin Genetic Modifiers Study; Panel B, Boston Early-Onset COPD Study; Panel C, Norway Case-Control Study; Panel D, International COPD Genetics Network) with smoothing curve. Flattening of curve occurs at FEV1 levels between 30 and 50% of predicted.
Figure 2
Figure 2
Observed power to detect gene-by-smoking interactions for the pack-years versus piecewise linear approach to model smoking. Simulation study based on data from the Genetic Modifiers of Alpha-1 Antitrypsin Disease Study. Simulation parameters are as follows: minor allele frequency = 25%, genetic main effect = −1 unit from observed FEV1 percent predicted per copy of minor allele, gene-by-smoking effect varies as shown. For this power analysis, the threshold for detecting an effect was set at alpha <0.05 for the null hypothesis that the gene-by-smoking effect is equal to zero. For gene-by-smoking effects the piecewise linear model is more powerful. At low values of the gene-by-smoking interaction, the total pack-years approach appears more powerful due to upwardly biased estimates of the gene-by-smoking interaction (values shown in Table 2).
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Comment in

  • A spline for the time.
    Schwartz J. Schwartz J. Thorax. 2011 Oct;66(10):841-2. doi: 10.1136/thx.2010.154195. Epub 2011 May 26. Thorax. 2011. PMID: 21617170 No abstract available.

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