Innovative statistical approaches are being pioneered to make biological sense out of GWAS data. Rather than analyzing each individual SNP, gene-set analysis (GSA) methods have been proposed that evaluate global evidence of association with a set of related genes enabling the identification of cellular or molecular pathways or biological pathways that are implicated in alcoholism, for example by using all 200 pathways listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [77]. Another approach that has been proposed is to use stratified False Discovery Rate methods to uncover new loci likely to replicate in independent samples. One recent study has demonstrated enrichment of polygenic effects, particularly for SNPs tagging regulatory and coding genic elements [78]. For example, a study in 33,332 patients and 27,888 controls used a combination of polygenic risk score analyses and pathway analyses to support a role for calcium channel signaling genes across five psychiatric disorders [79].
