Three major factors have made GWAS popular and feasible in a relatively short time and are critically reviewed in [8]. They are the common disease, common variant model (CD-CV) developed in the mid 1990s [9], the catalog of common variants created by the international HapMap project [10], and the rapid development of microtechnology for massive parallel genotyping [11,12]. The CD-CV model hypothesized that the genetic profile of common diseases is determined by genetic variants that are common in the population (frequency > 0.05) and have, individually, a small effect on the disease. This conjecture was based on both theoretical arguments and examples of heterogeneity of disease associated alleles including, for example, APOE-ε4 [13]. The CD-CV model made a strong case for the viability of GWAS because if the model was correct, the genetic basis of common diseases could be discovered by searching for common variants with different allele frequencies between cases and controls. To make this approach operational, the genetic community needed access to possibly all common genetic variants [14], and to technology for massive parallel measurements of these variants [15].
