We then compared hypothesis-driven candidate genes for schizophrenia with ISC GWAS results to assess whether the hypothesis-driven candidate gene list had over-representation of smaller ISC p-values than expected by chance. These analyses were conducted using ALIGATOR (Holmans et al., 2009) and InRich (Lee et al., 2011). These programs use different algorithms to assess whether GWAS findings are over-represented for small p-values with reference to a pre-defined set of genes (i.e., a pathway). ALIGATOR uses permutation to account for variable numbers of SNPs per gene, different patterns of linkage disequilibrium between SNPs (within the same gene), and varying gene sizes. We considered SZGene hypothesis-driven candidate genes as a “pathway” and used ALIGATOR to estimate the probability that this list contained an over-representation of smaller ISC GWAS p-values. The ISC GWAS results were input to ALIGATOR which assigned these SNPs to UCSC hg18 RefSeq genes (Pruitt et al., 2005). We determined the significance threshold (generally 0.002–0.004) that designated the top 5% of all genes as “significant” (Holmans et al., 2009). The key statistical comparison is akin to a 2×2 table of whether
