then tested for association with any phenotypic trait or disease. The GWAS approach is theoretically capable of identifying all of the common genetic variants, represented by SNPs, associated with a disease or trait. This is known as the common-disease, common-variant hypothesis which presumes that relatively common genetic variants with low penetrance are the primary genetic factors within a population that underlie common diseases (Chakravarti, 1999; Lander, 1996; Pritchard and Cox, 2002; Reich and Lander, 2001). In addition, initial GWAS assumed that common genetic variants (SNPs) contributing to disease susceptibility would have moderate to large effects and thus would require relatively small sample sizes to detect significant associations. Two of the first GWAS conducted on age-related macular degeneration and exfoliation glaucoma supported this assumption (Haines et al., 2005; Thorleifsson et al., 2007). However, subsequent GWAS indicated that very large sample sizes are required to achieve the statistical power necessary to detect the small effects of risk loci for the majority of complex traits and disease, especially for psychiatric disorders. In addition, very stringent statistical corrections are required to control for multiple testing leading to an increase in false-positives due to >1 million SNPs typically interrogated in a single GWAS. A Bonferroni-corrected
