As there may be a large number of causal risk variants that have small effects (Manolio et al., 2009), we also tested sets of variants for association with AD. These risk variants may be too small to detect individually because of low statistical power, but may be detectable when their effects are aggregated (Wang et al., 2011). Recently, methods have been developed that test the association of a set of variants with a phenotype of interest, including, but not limited to, risk profile scoring and gene-set testing. One disadvantage of these methods is that the sets are often formed based on nominal significance with AD, which can make the results difficult to interpret as there is no link to how these sets affect gene function or expression. Instead, our sets were formed based on biological function (e.g. likely to affect regulatory function or protein coding). By grouping variants in this manner, our results are more interpretable and allowed generation of functional hypotheses about how the variant set may influence AD.
