Candidate gene (CG) approaches provide a strategy for identification and characterization of major genes underlying complex phenotypes such as production traits and susceptibility to diseases, but the conclusions tend to be inconsistent across individual studies. Meta-analysis approaches can deal with these situations, e.g., by pooling effect-size estimates or combining P values from multiple studies. In this paper, we evaluated the performance of two types of statistical models, parametric and non-parametric, for meta-analysis of CG effects using simulated data. Both models estimated a "central" effect size while taking into account heterogeneity over individual studies. The empirical distribution of study-specific CG effects was multi-modal. The parametric model assumed a normal distribution for the study-specific CG effects whereas the non-parametric model relaxed this assumption by posing a more general distribution with a Dirichlet process prior (DPP). Results indicated that the meta-analysis approaches could reduce false positive or false negative rates by pooling strengths from multiple studies, as compared to individual studies. In addition, the non-parametric, DPP model captured the variation of the "data" better than its parametric counterpart.
Keywords: Bayesian models; Dirichlet process prior; Markov chain Monte Carlo; candidate genes; meta-analysis..