TY - JOUR AU - Zhao, Jing Hua AU - Luan, Jian'an AU - Congdon, Peter PY - 2018/06/13 Y2 - 2024/03/29 TI - Bayesian Linear Mixed Models with Polygenic Effects JF - Journal of Statistical Software JA - J. Stat. Soft. VL - 85 IS - 6 SE - Articles DO - 10.18637/jss.v085.i06 UR - https://www.jstatsoft.org/index.php/jss/article/view/v085i06 SP - 1 - 27 AB - We considered Bayesian estimation of polygenic effects, in particular heritability in relation to a class of linear mixed models implemented in R (R Core Team 2018). 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 (Plummer 2017) and Stan (Carpenter et al. 2017) as well as several R packages. In doing so, we have not only provided facilities in R linking standalone programs such as GCTA (Yang, Lee, Goddard, and Visscher 2011) 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. ER -