Cargando…
Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits
BACKGROUND: Accurate genomic prediction requires a large reference population, which is problematic for traits that are expensive to measure. Traits related to milk protein composition are not routinely recorded due to costly procedures and are considered to be controlled by a few quantitative trait...
Autores principales: | Gebreyesus, Grum, Lund, Mogens S., Buitenhuis, Bart, Bovenhuis, Henk, Poulsen, Nina A., Janss, Luc G. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5718071/ https://www.ncbi.nlm.nih.gov/pubmed/29207947 http://dx.doi.org/10.1186/s12711-017-0364-8 |
Ejemplares similares
-
Reliability of genomic prediction for milk fatty acid composition by using a multi-population reference and incorporating GWAS results
por: Gebreyesus, Grum, et al.
Publicado: (2019) -
Estimation of genetic parameters and detection of chromosomal regions affecting the major milk proteins and their post translational modifications in Danish Holstein and Danish Jersey cattle
por: Buitenhuis, Bart, et al.
Publicado: (2016) -
Genomic Prediction Using Multi-trait Weighted GBLUP Accounting for Heterogeneous Variances and Covariances Across the Genome
por: Karaman, Emre, et al.
Publicado: (2018) -
Genetic parameters for milk fatty acids in Danish Holstein cattle based on SNP markers using a Bayesian approach
por: Krag, Kristian, et al.
Publicado: (2013) -
Efficiency of population structures for mapping of Mendelian and imprinted quantitative trait loci in outbred pigs using variance component methods
por: Heuven, Henri CM, et al.
Publicado: (2005)