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.
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.
Authors: Dawn L Demeo; Robert A Sandhaus; Alan F Barker; Mark L Brantly; Edward Eden; N Gerard McElvaney; Stephen Rennard; Esteban Burchard; James M Stocks; James K Stoller; Charlie Strange; Gerard M Turino; Edward J Campbell; Edwin K Silverman Journal: Thorax Date: 2007-03-27 Impact factor: 9.139
Authors: E K Silverman; H A Chapman; J M Drazen; S T Weiss; B Rosner; E J Campbell; W J O'DONNELL; J J Reilly; L Ginns; S Mentzer; J Wain; F E Speizer Journal: Am J Respir Crit Care Med Date: 1998-06 Impact factor: 21.405
Authors: Sean P David; Ange Wang; Kristopher Kapphahn; Haley Hedlin; Manisha Desai; Michael Henderson; Lingyao Yang; Kyle M Walsh; Ann G Schwartz; John K Wiencke; Margaret R Spitz; Angela S Wenzlaff; Margaret R Wrensch; Charles B Eaton; Helena Furberg; W Mark Brown; Benjamin A Goldstein; Themistocles Assimes; Hua Tang; Charles L Kooperberg; Charles P Quesenberry; Hilary Tindle; Manali I Patel; Christopher I Amos; Andrew W Bergen; Gary E Swan; Marcia L Stefanick Journal: EBioMedicine Date: 2016-01-11 Impact factor: 8.143
Authors: Amanda P Henry; Kelly Probert; Ceri E Stewart; Dhruma Thakker; Sangita Bhaker; Sheyda Azimi; Ian P Hall; Ian Sayers Journal: Respir Res Date: 2019-08-01
Authors: Woori Kim; Dmitry Prokopenko; Phuwanat Sakornsakolpat; Brian D Hobbs; Sharon M Lutz; John E Hokanson; Louise V Wain; Carl A Melbourne; Nick Shrine; Martin D Tobin; Edwin K Silverman; Michael H Cho; Terri H Beaty Journal: Am J Epidemiol Date: 2021-05-04 Impact factor: 4.897