Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition
Abstract 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 th...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | deu |
| Published: |
BMC
2017-12-01
|
| Series: | Genetics Selection Evolution |
| Online Access: | http://link.springer.com/article/10.1186/s12711-017-0369-3 |