Optimal power is very important in finding new disease genes. Increased power may be achieved by combining datasets. Meta-analysis is a set of methods that allows the quantitative combination of data from multiple studies. These methods also allow the quantitative evaluation of the consistency or inconsistency/heterogeneity of the results across multiple datasets. Meta-analysis methods have been applied for several decades in a large variety of scientific fields and there are already several textbooks and handbooks thereof [7,8], some of which also cover genetic epidemiology [8]. However, the combination of large-scale data from GWA studies offers a new challenge for quantitative synthesis. In this review, we will focus on the peculiarities and specific issues that arise in setting up, gathering and processing information, and analysing data in meta-analyses of GWA studies (Figure 1).
