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N.M. Laird 387included in the major genetic analysis statistical packages (Evangelou andIoannidis, 2013), but most software packages only implement a fixed effectsapproach. As a result, the standard meta-analyses of GWAS use the fixedeffects approach and potentially overstate the precision in the presence ofheterogeneity.34.5 ConclusionsThe DerSimonian and Laird method has weathered a great deal of criticism,and undoubtedly we need better methods for random effects analyses, especiallywhen the endpoints of interest are proportions and when the numberof studies being combined is small and or the sample sizes within each studyare small. Most meta-analyses involving clinical trials acknowledge the importanceof assessing variation in study effects, and new methods for quantifyingthis variation are widely used (Higgins and Thompson, 2002). In addition,meta-regression methods for identifying factors influencing heterogeneity areavailable (Berkey et al., 1995; Thompson and Higgins, 2002); these can be usedto form subsets of studies which are more homogeneous. There is an extensiveliterature emphasizing the necessity and desirability of assessing heterogeneity,and many of these reinforce the role of study design in connection withheterogeneity. The use of meta-analysis in genetic epidemiology to find diseasegenes is still relatively new, but the benefits are widely recognized (Ioannidiset al., 2007). Better methods for implementing random effects methods withasmallnumberofstudieswillbeespeciallyusefulhere.ReferencesBeecher, H.K. (1955). The powerful placebo. Journal of the American MedicalAssociation, 159:1602–1606.Berkey, C.S., Hoaglin, D.C., Mosteller, F., and Colditz, G.A. (1995). Arandom-effects regression model for meta-analysis. Statistics in Medicine,14:395–411.Berlin, J.A., Laird, N.M., Sacks, H.S., and Chalmers, T.C. (1989). A comparisonof statistical methods for combining event rates from clinical trials.Statistics in Medicine, 8:141–151.Cochran, W.G. (1954). The combination of estimates from different experiments.Biometrics, 10:101–129.