The power of genome-wide association studies (GWAS) to discover common genetic variants associated with complex traits has been empirically demonstrated [1-6]. The single-SNP analysis tests genetic association on individual SNPs and identifies only the most significant SNPs because of the stringent statistical criteria necessary for minimizing false positive hits. The identified SNPs, however, represent only a small fraction of the genetic variants contributing to complex traits; the majority of the variations remain hidden within the statistical ''noise'' [7,8]. Genetic variants with small individual effect sizes but jointly significant genetic effects would be missed by single-SNP analysis. As a result, identified genetic variants only explain a small fraction of heritability for most studied traits[9].
