Following the identification of all statistically significant SNPs (i.e. those that were significant at the two-tailed p < 0.05 level in the individual SNP association tests, and that met FDR < 0.10), we compared two models of sensation seeking. The baseline model regressed sensation seeking on the covariates included in the initial association tests. The second model regressed sensation seeking on covariates and those significant SNPs identified from our initial association tests. We evaluated the relative goodness-of-fit of these models to the raw data by comparing a) the total proportion of variance in sensation seeking explained, b) the model likelihoods, and c) the Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC) from each model. AIC and BIC are information theoretic measures of goodness-of-model fit, and account for model parsimony in evaluating fit. Relative to a comparison of model likelihoods, AIC and BIC are more conservative, requiring greater evidence of the predictive utility of additional predictor variables to show improved fit. Lower values of AIC and BIC indicate a relatively better fit to the data (Akaike, 1974; Schwarz, 1978).
