Over the last years, GWA studies have made major contributions to the efforts of gene mapping (2, 42) yielding numerous novel genetic associations, many of which have been successfully replicated in subsequent studies (77). However, early studies utilized small sample sizes and were, thus, underpowered to detect the small effect sizes expected for common traits under the common disease-common variant model (83); these variants would require large sample sizes, especially when their frequency is low (76). Consequently, some of the early GWA findings were later disputed in larger studies (18, 36, 68). Although not very common with GWA studies in general, this lack of replication was very common for the results of candidate-gene studies. Results of many such studies had shown surprisingly low reproducibility rates in subsequent larger studies and meta-analyses thereof (38, 39, 63, 96), revealing a large amount of false-positive discoveries (13, 41). Hence, meta-analysis of available GWA data from different studies was soon recognized as the appropriate method in order to achieve adequate sample sizes and optimal power for the discovery of genetic associations with modest effect sizes (19, 87).
