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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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| Publicado no: | Genet Sel Evol |
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| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BioMed Central
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5745964/ https://ncbi.nlm.nih.gov/pubmed/29281962 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12711-017-0369-3 |
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