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Bayesian linear mixed models with polygenic effects

Accepted version
Peer-reviewed

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Type

Article

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Authors

Zhao, JH 
Congdon, P 

Abstract

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.

Description

Keywords

49 Mathematical Sciences, 4905 Statistics

Journal Title

Journal of Statistical Software

Conference Name

Journal ISSN

1548-7660
1548-7660

Volume Title

85

Publisher

Foundation for Open Access Statistic
Sponsorship
Medical Research Council (MC_UU_12015/1)