This analysis was implemented in Plink2 (https://www.cog-genomics.org/plink2/general_usage) (Chang et al., 2015) using the set option. A set based analysis follows a series of steps: (1) Single SNP analysis is run for all SNPs within the sets. (2) Within each set, a specified number of SNP(s) that are below a specified significance threshold are selected based on a set LD criterion. (3) The set is then permuted using the phenotype status to obtain an empirical p-value adjusted to address multiple comparisons within each set (Purcell et al., 2007). We set the SNP selection significance threshold at 0.05, LD criterion at r2 > 0.8, used a top SNP approach selecting only the top hit SNP within each set and permuted the data set 10,000 times. Since we had 105 sets in the analysis, we applied false discovery rate (FDR, Benjamini-Hochberg procedure with an alpha threshold of 0.05) correction. We tested two models for association analyses with symptom variables, the first model adjusting for base covariates and a second model adjusting for neurocognition in addition to base covariates. We tested cognitive variables adjusting for just the base covariates.
