For continuously distributed outcomes, GENEVA WGs, like other large-scale collaborative meta-analyses [Lindgren et al., 2009; Thorgeirsson et al., 2010], have applied the same transformation to all datasets for a single outcome. These transformations are discussed in a phenotype-specific manner for each meta-analysis and vary across phenotypes. Logical and extreme outliers are deleted or recoded before transformations are applied to avoid non-normal error distributions. The removal or recoding of outliers, application of a common transformation and a strategy for covariate selection (see below) ensures comparability and interpretation of the estimates from the contributing studies.
