Other comparisons of chips have been carried out but have either focussed exclusively on estimating coverage [8], have been limited in scope of which chips have been evaluated [9] or have used analytical calculations that do not properly take into account the complex LD structure of the human genome [10],[11] or failed to assess the impact of imputation correctly [11]. A recent paper [12] has used chip data to assess the performance of the chips but the small sample size (N = 359) means that these results cannot be used to assess power of new study designs of more realistic sizes. In addition, the simulations of quantitative phenotypes used the Signal to Noise Ratio (SNR) to measure effect size of the causal SNP which is non-standard and difficult to interpret. For binary traits, simulations assumed a disease prevalence of 25%, a relative risk of 3 and a sample size of only 75 cases and 75 controls. These parameter settings are not realistic for genome-wide association studies or useful when designing new studies.
