Nominally significant gene-environment interactions by outcome and exposure.

<p>Genes are sorted in ascending order of interaction p-value within outcome-exposure strata.</p>a<p>significant after Bonferroni-correction for testing 152 genes (α = .00033).</p>b<p>marginally significant after Bonferroni-correction for testing 152 genes (α = .00033).</p

Distribution of interaction p-values across genes mapping to pathways with weak interaction signals.

<p>P-values of interaction on the gene-level are given on a minus log₁₀ scale (y-axis), i.e. higher bars represent smaller interaction p-values. (A) Genes of the mitochondrial dysfunction pathway interacting with PM10 and packyears exposure between surveys on FEV₁/FVC decline. (B) Genes of the methane metabolism pathway interacting with PM10 and packyears exposure between surveys on FEF_25–75 decline. (C) Genes of the apoptosis signaling pathway interacting with PM10 and packyears exposure between surveys on FEV₁ decline.</p

Mapping of candidate oxidative-stress genes to molecular pathways of interest.

<p>Mapping of candidate oxidative-stress genes to molecular pathways of interest.</p

Distribution of main characteristics by smoking status at follow-up and PM₁₀ exposure during follow-up (N = 650¹).

1<p>restricted to sample with complete data on all three lung function parameters. Regarding FEV₁, sample size with complete covariate data would be n = 669.</p>2<p>means for age and lung function values, medians for pack years exposures.</p

Effect estimates of the strongest interacting SNP from each nominally significant gene on FEV₁/FVC decline (n = 650).

<p>SNP-estimates are based on an additive model. Beta-estimates represent percentages of decline in FEV₁/FVC over 11 years per effect allele and/or for an exposure contrast of one interquartile range (IQR). All estimates are taken from the same interaction model. Positive values mean an attenuation, and negative ones an acceleration of FEV₁/FVC decline. Rows are sorted according to ascending interaction p-values.</p>*<p>significant after Bonferroni correction for testing 12679 SNPs (α = 3.9×10E-6).</p><p>gen: genotyped SNP; imp: imputed SNP; All1: allele 1 (effect allele); All2: allele 2 (baseline allele); FreqAll1: frequency of allele 1.</p

Evaluation of Land Use Regression Models for NO₂ and Particulate Matter in 20 European Study Areas: The ESCAPE Project

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO₂₎ and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO₂. LUR models have been developed for NO₂, PM_2.5 absorbance, and copper (Cu) in PM₁₀ based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and “hold-out evaluation (HEV)” using the correlation of predicted NO₂ or PM concentrations with measured NO₂ concentrations at the 20 additional NO₂ sites in each area. For NO₂, PM_2.5 absorbance and PM₁₀ Cu, the median LOOCV <i>R</i>²s were 0.83, 0.81, and 0.76 whereas the median HEV <i>R</i>² were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV <i>R</i>² and HEV <i>R</i>² for PM_2.5 absorbance and PM₁₀ Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV <i>R</i>²s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites