Recently, Ballard et al. [42] compared seven multi-marker association tests, including single marker analysis using the best-scoring SNP, and found principal component regression [43] is the most powerful among them. In addition, several recent studies also proposed using a subset of SNPs with the lowest P-values. The selection of the SNP subset can be based on a fixed truncation point [44–46] or data adaptive thresholds [12]. It has been shown that the SNP selection process can improve power over other approaches that either include all SNPs or use only the most significant SNPs [12,37].
