Classical GWAS approaches have identified loci associated with AD risk, but these explain only a small proportion of the risk for AD and related phenotypes (Agrawal et al., 2012; Bühler et al., 2015). These studies suggest that, as with other complex traits, a large number of causal risk variants contribute to the development of AD and that variants individually have comparatively small effects on risk (Manolio et al., 2009). Because of this and the stringent correction for the large number of SNPs tested in GWAS, large samples are necessary to detect effects reliably. Alternatives to single SNP analysis of GWAS data have examined individually non-significant loci en masse, which are largely disregarded in traditional GWAS. Here, we discuss two categories of post-GWAS approaches: 1) studies that focus on so-called polygenic methods, heritability analyses and genetic risk prediction, and 2) studies that focus on identifying sets of genes and/or pathways influencing risk. Table 2 provides an overview of these studies.
