We used the PRSice package (Euesden, Lewis, & O’Reilly, 2015) in order to determine the p-value thresholds for including SNPs in the polygenic score that would optimize R2 in each of the five target samples. The p-value lower bound was 0.0, the upper bound was 0.5, and models were run at increments of 0.01. There was a narrow range of optimal thresholds (p range = 0.02 to 0.07, M = 0.036, SD = 0.021). We used this mean p-value as the inclusion threshold for computing polygenic risk scores for each target sample. We then re-merged the five target samples and regressed the Persistent Externalizing factor scores on polygenic risk scores. There was a significant linear association (β = 0.16, p < 0.01, R2 = 0.026). In other words, about 2.6% of the variance in the Persistent Externalizing factor was accounted for by polygenic risk across a relatively small number of SNPs present in the hypothesized gene set.
