In general, when between-study heterogeneity is demonstrated or cannot be excluded, random effects models have been accepted as the default across different applications of meta-analysis and this should be accepted also for GWA investigations [1], [2], [5]. Fixed effects may sometimes result into misleading inferences. In the presence of heterogeneity, the main assumption of fixed effects is violated and their application is inappropriate. However, a caveat for random effects is that they tend to diminish the difference in the relative weighting of small vs. larger studies. This is a drawback in situations where small studies may suffer more from errors or biases than larger studies. Disproportionate weighting of the biased small studies would then lead to erroneous results. This situation may typically arise when the data to be synthesized have been collected retrospectively from published information and publication bias is operating in the field [27]. Small studies may have been published preferentially when they show significant results while the evidence from larger studies may be available regardless of the results. Thus the total available evidence from larger studies may be more unbiased, even if single larger studies may not necessarily be more unbiased than single smaller studies.
