Currently, most consortia in human genome epidemiology perform meta-analysis: individual cohorts are analyzed separately, and then summary statistics are combined (typically with a fixed-effects model, such as that implemented in METAL (97)). The alternative, in which individual-level genotypes are combined into a single dataset before analysis, is sometimes called “mega-analysis.” In theory, mega-analysis may avoid whatever information loss (and hence a small power loss) is inherent in sharing summary statistics rather than pooling individual level data. Although the power loss from this loss of information is minimal for studies of common variants (62), it may be more sizeable for rare/uncommon variants. There are also other potential advantages of mega-analysis. For example, in meta-analyses, any analyses, whether simple associations or more complex ones (such as interactions or conditional analyses) need to be coordinated across each of the analysts of the participating teams. In addition, meta-analysis would not be able to detect cryptic relatedness between individuals from different studies, while this is feasible in mega-analysis. Mega-analysis may be particularly valuable for studies of rare variants, where pooling individuals may both increase the
