Cargando…
Fast parallelized sampling of Bayesian regression models for whole-genome prediction
BACKGROUND: Bayesian regression models are widely used in genomic prediction, where the effects of all markers are estimated simultaneously by combining the information from the phenotypic data with priors for the marker effects and other parameters such as variance components or membership probabil...
Autores principales: | Zhao, Tianjing, Fernando, Rohan, Garrick, Dorian, Cheng, Hao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7087391/ https://www.ncbi.nlm.nih.gov/pubmed/32293243 http://dx.doi.org/10.1186/s12711-020-00533-x |
Ejemplares similares
-
A fast and efficient Gibbs sampler for BayesB in whole-genome analyses
por: Cheng, Hao, et al.
Publicado: (2015) -
A certain invariance property of BLUE in a whole‐genome regression context
por: Gianola, Daniel, et al.
Publicado: (2019) -
Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals
por: Fernando, Rohan L., et al.
Publicado: (2016) -
Interpretable artificial neural networks incorporating Bayesian alphabet models for genome-wide prediction and association studies
por: Zhao, Tianjing, et al.
Publicado: (2021) -
A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses
por: Fernando, Rohan L, et al.
Publicado: (2014)