that, for the majority of complex traits, much larger samples are needed to achieve the statistical power necessary to detect the small effects of risk loci. It is now recognized that, for the most part, the risk loci identified could explain only a small portion of the variance in the traits (i.e., the “missing heritability problem”) (Manolio et al., 2009). Thus, the focus has been broadened from use of a standard, Bonferroni-corrected genome-wide significance threshold (generally 5 × 10−8 for GWAS) to the use of other indicators of association (Yang et al., 2011). Additionally, results from early GWAS led to the development of large meta- and mega-analysis-based collaborative efforts, which have identified replicable risk loci for a variety of complex traits. Following an overview of the 12 AD GWAS that have been published using a classical approach, we will consider these approaches in detail in relation to the lessons learned in the study of AD genetics. Figure 1 describes the seminal events in AD GWAS discovery.
