Risk analysis was performed using conditional logistic regression by estimating odds ratios (ORs) and their 95% confidence intervals (CI). Risk effects of smoking measured as cotinine and CPD on lung cancer risk were analyzed for ten categories of increasing cotinine levels (76-200 nmol/L, 201-400 nmol/L, 401-600 nmol/L etc) with subjects with cotinine levels below 75nmol/L as a reference category equivalent to never smokers(24), and also for deciles of CPD defined by control individuals using never smokers as a reference category. ORs for SNPs were calculated using the per rare allele log-additive model as overall significance test (P). We subsequently adjusted for various smoking variables, including cotinine levels, average CPD, and duration of smoking. We conducted exploratory analysis using two models: i) adjusted by quartiles of smoking variables defined by the distribution among corresponding controls and ii) adjusted by continuous smoking variables. The results for both models are presented. Further, unconditional logistic regression models were used to allow stratification by smoking status (current, former and never smokers), adjusting for matching variables (gender, date of birth, date of blood collection and country).
