Growth mixture modeling42,43 was used to identify homogeneous subgroups of individuals manifesting distinct patterns of change in their externalizing behavior from early adolescence through young adulthood (ie, from 12 to 22 years of age). Conventional growth curve modeling assumes a mean pattern of change in behavior within the population, with individual differences expressed in terms of normal variability around specified growth parameters (ie, intercept and slope coefficients that define the level and shape of the change44). Growth mixture modeling is a widely used extension of this procedure that allows for the possibility of 2 or more discrete subgroups of individuals within a population, each having unique mean trajectories.45 Individuals are classified into groups by probability of class membership conditioned on their response pattern across the 9 repeated measurements of self-reported externalizing behavior. To determine the influence of genotype on trajectory class membership within the resulting GMM, probability of class membership was regressed on genotype. For these analyses, each of the SNPs were coded 0, 1, or 2, reflecting an additive genotypic model. This coding is in reference to the number
