For all 3369 SNPs, we used logistic regression to predict case-status using the main effect model (Equation 2) and rAOS interaction model (Equation 3), and tallied the number of nominally significant loci using three traditional thresholds (p<0.05, p<0.01 and p<0.001) – results of these counts are summarized in Table 1. This count is intended as a simple metric of the relative utility of each model, and does not reflect adjustment for multiple testing or linkage disequilibrium. In interaction models, the p-values reflect joint effects of SNP and SNP x rAOS interactions (2 degree of freedom test), whereas for the main-effect models, tallies reflect 1-degree of freedom tests for the effect of SNP only. For each threshold, substantially more loci met the p-value criterion under the interaction model than under the main-effect model, with 40–60% more “hits” in each case (e.g., 448 SNPs with p<0.05 in the interaction model, vs. 268 in the main-effect model). The increased number of low p-values may stem in part from overfitting, resulting from the additional degree of freedom to the model. However, the addition of
