Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition

BACKGROUND: Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genoty...

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Detalles Bibliográficos
Autores principales: Meuwissen, Theo H. E., Indahl, Ulf G., Ødegård, Jørgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5745964/
https://www.ncbi.nlm.nih.gov/pubmed/29281962
http://dx.doi.org/10.1186/s12711-017-0369-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5745964/
https://www.ncbi.nlm.nih.gov/pubmed/29281962
http://dx.doi.org/10.1186/s12711-017-0369-3

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