We used now published data from 210,316 European-ancestry adults (from the GIANT consortium) pertaining to marginal effects meta-analyses for BMI that had been performed separately by strata of smoking (45,968 smokers vs. 164,355 non-smokers) . The genetic marginal effect estimates, calculated separately within each of the two strata, were compared using a heterogeneity test  to infer the presence or absence of SNP × smoking interaction effects. The same analyses were performed using physical activity as a binary stratifying variable in up to 180,287 European-ancestry adults (42,065 physically active vs. 138,222 physically inactive) . We calculated Spearman correlations between the P-values derived from the marginal effects meta-analysis and the Pint from the interaction effects meta-analysis (i.e., the between-strata heterogeneity test for SNP × smoking and SNP × physical activity interactions from the GIANT consortium); these tests were undertaken for all SNPs and those SNPs that were nominally significant (Pm<0.05) in the marginal effects analysis. We then performed enrichment analyses to test if the numbers of nominally significant (Pint<0.05) GWAS-derived SNPs from both SNP × physical activity and SNP × smoking analyses were greater than expected by chance under the binomial distribution. We further calculated the OR of having Pint<0.05 given Pv<0.05 versus Pv≥0.05 both SNP × physical activity and SNP × smoking interaction analyses in a pruned set of TwinGene SNPs produced using the—indep-pairwise 50 5 0.8 command in PLINK .