One of the most rapidly growing applications of meta-analysis is in genetic epidemiology [6]–[8]. Meta-analysis is becoming standard practice for publications of genome-wide association studies that search for common genetic variants regulating complex traits and disease risk. A torrent of such studies have started appearing in the most prestigious journals with major prospects for the delineation of the genetic risk factors underlying the most common diseases and traits [9]. The results of the genome-wide associations are typically combined with the results of additional replication studies on the most promising variants; occasionally results from other genome-wide investigations are also included in meta-analytic calculations [10]–[13]. However, these early applications of meta-analyses on such datasets have not accommodated between-study heterogeneity in the data synthesis. In the presence of between-study heterogeneity in the genetic effects, there may be important implications for the interpretation of the results.
