Another emerging approach to replication of GWAS is the use of meta-analysis. By combining the results of different studies, statistical meta-analysis can also provide additional power for the discovery of new associations. For example, meta-analysis has recently produced the discovery of additional loci associated with BMI [99], lipid traits [100,101], and was crucial for the discovery of robust associations with diabetes [102]. Recent papers that assemble the results from different GWAS using meta-analysis often rely on imputation-based analyses because the original studies used different genotyping platforms with different SNPs [99–101]. Imputation of untyped SNPs is a convenient and often accurate procedure to synchronize genotype data of different arrays [58], and the accuracy can be very high in studies of populations that are well represented in the HapMap project. However, because imputation of data in different cohorts is based on the same reference haplotypes from the HapMap projects, there could be an intrinsic bias toward positive replications and initial results based on imputed data should be followed by actual genotyping of the missing data to confirm real effects. The efficacy of
