Association tests were initially run on each individual SNP (coded as 0, 1, or 2, representing the number of minor alleles), performing a linear regression in R (an open-source statistical program; R Development Core Team, 2009) of the sensation seeking score on that SNP and all covariates (i.e. PC1, PC2, age, and sex). We implemented two additional methods to ensure that any significant results were greater than chance. First, in addition to p-values, we calculated the false discovery rate (FDR) for each regression-weight p-value reaching significance. FDR controls the proportion of false-positive results expected from all those tests declared significant and is calculated as: (1)Pi≤(i/m)*α Where i is the rank order of the test (ranked in terms of ascending p-values), m is the total number of independent tests, α is the p-value cut-off for significance, and Pi is the p-value for test i (Benjamini & Hochberg, 1995). We set our maximum FDR at 0.10, interpreted as no more than 10% of the SNPs declared significant based on p < 0.05 would be false positives. For our purposes, we set the value of m to 8, the number of genes included in our analyses.
