Pathway analysis represents an alternative analytical approach to interrogating GWAS data. A number of formal pathway-based analytical methods have been described (Hong et al., 2009). Essentially, these methods attempt to establish if SNPs mapping to genes in a pathway show more evidence of association with a disorder than other SNPs in the GWAS study, or SNPs mapping to other pathways. Pathway refers to groups of genes that are similar in some way – e.g., highly expressed in a tissue like prefrontal cortex, crucial to a biological process like neuronal differentiation, etc. The approach can be applied to test for involvement of specific pathways, to perform a hypothesis-free test of many different pathways, or to investigate whether pre-identified risk genes may be involved in the same molecular pathway or process. Investigating at the level of molecular pathways rather than individual risk variants may offer several potential advantages by being robust to the effects of genetic heterogeneity or in reducing the total multiple testing burden in analysis. However, this approach is dependent on the quality of annotation of the pathways being investigated
