Pathway analysis offers a complementary perspective to interpret GWAS, incorporating repositories of expert knowledge, represented in biological pathway databases and gene ontologies. This approach evaluates whether the signals detected by a GWAS are overrepresented for families of biologically related genes. By shifting from the evaluation of individual SNPs to pathways of genes –under the commonly accepted hypothesis that causal variants are not randomly distributed across the genome, but instead lie in functionally related genes– we can prioritize the variants that do not reach genome-wide significance level [15]. This concept was extensively employed in the identification of expression profiles of microarrays, and since then has been adapted to mine GWAS datasets [15]–[17]. Employing different statistical methods and implementations, pathway analysis has been applied to a variety of neurological and psychiatric diseases [17]; implicating axon guidance for Parkinson disease [18], neuronal cell adhesion and membrane scaffolding for schizophrenia and Bipolar disorder [19], and immune system and cholesterol metabolism for Alzheimer’s disease [20].
