Bayesian Random-Effects Meta-Analysis Models in Gene Expression Studies

Random-effects meta-analysis models are commonly applied in combining effect sizes from individual gene expression studies. However, study heterogeneity is unknown and may arise from a variation of sample quality and experimental conditions. High heterogeneity of effect sizes can reduce the statistical power of the models. In addition, classical random-effects meta-analysis models are based on a normal approximation, which may be limited to small samples and its results may be biased toward the null value. A Bayesian approach was used to avoid the approximation and the biases. We applied a sample-quality weight to adjust the study heterogeneity in the Bayesian random-effects meta-analysis model with weighted between-study variance on a sample quality indicator and illustrated the application of this approach in Alzheimer’s gene expression studies