Equation 1 models the alcohol misuse composite using a Poisson distribution (with a logarithmic link) to account for positive skew. The model includes an intercept (β0), a main effect of the GABRA2 TT genotype (β1), a main effect of intervention condition (β2), and an interaction between GABRA2 genotype and intervention condition (β3, the G x I interaction). These coefficients were modeled as continuous functions of age, denoted by the (age) modifier, which allowed us to examine variation in gene, intervention, and G x I effects from the youngest (11.2 years) to the oldest age (20.0 years) included across grades 7–12. Incident rate ratio (IRR) effect sizes and predicted alcohol misuse means were estimated by exponentiating slope and intercept coefficients (IRR=eβ) respectively. Modeling was executed in three steps. First, we estimated the full model as specified in Equation 1. We evaluated the significance of the age-varying G x I interaction effect (β3) by exponentiating the Poisson coefficient to yield an IRR effect size and comparing this estimate to a value of 1 (corresponding to a Poisson β=0). The G x I
