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Bayesian linear mixed models with polygenic effects

Authors :
Jing Hua Zhao
Jian'an Luan
Peter Congdon
Zhao, Jing Hua [0000-0003-4930-3582]
Luan, Jian'an [0000-0003-3137-6337]
Apollo - University of Cambridge Repository
Source :
Journal of Statistical Software, Vol 85, Iss 1, Pp 1-27 (2018), Journal of Statistical Software; Vol 85 (2018); 1-27
Publication Year :
2019
Publisher :
Foundation for Open Access Statistic, 2019.

Abstract

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.

Details

ISSN :
15487660
Database :
OpenAIRE
Journal :
Journal of Statistical Software, Vol 85, Iss 1, Pp 1-27 (2018), Journal of Statistical Software; Vol 85 (2018); 1-27
Accession number :
edsair.doi.dedup.....aaa75afb4568a73566e9954d71a39237
Full Text :
https://doi.org/10.17863/cam.37848