J Genomics 2014; 2:1-19. doi:10.7150/jgen.5054 This volume

Research Paper

Meta-Analysis of Candidate Gene Effects Using Bayesian Parametric and Non-Parametric Approaches

Xiao-Lin Wu1,2✉, Daniel Gianola1,2,3, Guilherme J. M. Rosa2,3, Kent A. Weigel1

1. Department of Dairy Science, University of Wisconsin, Madison, WI 53706, USA;
2. Department of Animal Sciences, University of Wisconsin, Madison, WI 53706, USA;
3. Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53706, USA.

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Wu XL, Gianola D, Rosa GJM, Weigel KA. Meta-Analysis of Candidate Gene Effects Using Bayesian Parametric and Non-Parametric Approaches. J Genomics 2014; 2:1-19. doi:10.7150/jgen.5054. Available from https://www.jgenomics.com/v02p0001.htm

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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, candidate genes, Dirichlet process prior, Markov chain Monte Carlo, meta-analysis.