Histogram of the posterior probabilities of having a positive (negative) SNP effect by Bayesian Threshold LASSO model (BTL) in the total population.

<p>The dot point line indicates the cut-off point of 80% above which SNPs were considered.</p

Venn diagrams showing the overlapping between the SNPs selected by Bayesian Threshold model (BTL) and AUC-Random Forest (AUC-RF).

<p>(A) Number of SNPs detected by each method in the total population. (B) Number of SNPs detected by each method in the non-smoker subset. (C) Number of common SNPs detected by BTL in the total population and non-smoker subset, with posterior probabilities of at least 80% and 75% of having an effect different from 0. (D) Number of SNPs detected by AUC-RF in both the total population and the non-smoker subset.</p

Risk estimates from Bayesian Threshold LASSO model (BTL), considering a posterior probability of 75%, and from logistic regression analyses among non-smokers.

a<p>Posterior mean of the OR, calculated from the BTL analyses. Similar values for the median were obtained for each SNP.</p>b<p>It corresponds to , where .</p>c<p>OR obtained from the adjusted logistic regression.</p>d<p><i>p</i>-value of the trend obtained from the adjusted logistic regression.</p><p>SNPs also selected by AUC-RF are bold-faced.</p

Relative variable importance for the top 12 polymorphisms selected by AUC-RF in the total population.

a<p>Calculated by dividing the raw variable importance measurement by that with the highest MDG, that of smoking status.</p