Bayesian linear mixed models with polygenic effects
Journal of Statistical Software
Foundation for Open Access Statistics
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Zhao, J. H., Luan, J., & Congdon, P. (2018). Bayesian linear mixed models with polygenic effects. Journal of Statistical Software, 85 https://doi.org/10.18637/jss.v085.i06
We considered Bayesian estimation of polygenic effects, in particular heritability in relation to a class of linear mixed models implemented in R. Our approach is applicable to both family-based and population-based studies in human genetics with which a genetic relationship matrix can be derived either from family structure or genome-wide data. Using a simulated and a real data, we demonstrate our implementation of the models in the generic statistical software systems JAGS and Stan as well as several R packages. In doing so, we have not only provided facilities in R linking standalone programs such as GCTA and other packages in R but also addressed some technical issues in the analysis. Our experience with a host of general and special software systems will facilitate investigation into more complex models for both human and nonhuman genetics.
External DOI: https://doi.org/10.18637/jss.v085.i06
This record's URL: https://www.repository.cam.ac.uk/handle/1810/290630