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Bayesian linear mixed models with polygenic effects
- 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.
- Subjects :
- Statistics and Probability
family-based design
relationship matrix
Theoretical computer science
Relation (database)
Computer science
Bayesian probability
Population
Bayesian linear mixed models
heritability
Generalized linear mixed model
Matrix (mathematics)
Software system
education
lcsh:Statistics
lcsh:HA1-4737
genomewide association study
Bayes estimator
education.field_of_study
Class (computer programming)
polygenic effects
4905 Statistics
49 Mathematical Sciences
Statistics, Probability and Uncertainty
Software
Subjects
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