Current approaches to capturing the missing heritability of complex diseases involve the application of gene-set analyses, whole genome prediction analyses, the use of biological data in the form of pathway-enrichment approaches, and genomewide modeling of gene-gene interactions (i.e., epistasis). Gene-set methods, such as the set-based method in PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/anal.shtml#set; Purcell et al., 2007) are suitable to large-scale candidate gene studies, and more recently, sets of genes identified after applying strict r2 (i.e., correlation coefficient between a pair of alleles) thresholds to GWAS data (Kendler et al., 2011). Gene-set approaches provide the benefit of large-scale permutation testing on a specified number of SNPs that enable the identification of genes that achieve gene-wise significance. Whole genome prediction models complement the gene-set approach by examining the total amount of phenotypic variance attributable to variants present on a selected platform. Although this approach has not yet been applied to AD, a recent study on height (Yang et al., 2011a) indicated that if all the SNPs on a particular genotyping platform are considered simultaneously, approximately 45% of the variation in height can be captured. Consequently,
