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Table 2 Real data application – ER stress x air pollution interaction in the SALIA study

From: Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression

  p-value PM2.5 p-value PM2.5 absorbance p-value PM10 p-value NO2
Best SNP1 (raw p-value2) 0.016 0.040 0.064 0.012
Best SNP1 (Bonferroni-corrected p-value) 0.130 0.316 0.516 0.095
Weighted GRS3 (p-value GRSxE term) 0.014 0.063 0.102 0.078
Unweighted GRS (p-value GRSxE term) 0.038 0.062 0.249 0.122
  1. Interaction between air pollution exposure and eight SNPs of the endoplasmatic reticulum (ER) stress pathway on the levels of leukotriene (LT) B4 measured in induced sputum (low vs. high, cut point at 3rd quartile) in 402 women from the SALIA study (p-values are given for the GxE interaction). Air pollution exposures: PM2.5: fine inhalable particles, with diameters that are generally 2.5 μm and smaller; PM2.5 absorbance: filter absorbance of PM2.5 (soot); PM10: inhalable particles, with diameters that are generally 10 μm and smaller; NO2: Nitrogen dioxide
  2. All models were adjusted for age, BMI (kg/m2), smoking history, passive smoking, level of education and indoor air pollution (heating with fossil fuels and exposure to indoor mold)
  3. 1: The “best SNP” (additive model) had the lowest p-value for the marginal genetic effect as well as for the GxE interaction term (rs2254958, compare Hüls et al. [12])
  4. 2: P-values derived from individual SNP by exposure interaction estimates, not corrected for the number of SNPs tested
  5. 3: weights were estimated by applying a lasso regression on the combined marginal genetic effects of all eight SNPs on the binary health outcome (low vs. high levels of leukotriene (LT) B4)