Cargando…
A nested mixture model for genomic prediction using whole-genome SNP genotypes
Genomic prediction exploits single nucleotide polymorphisms (SNPs) across the whole genome for predicting genetic merit of selection candidates. In most models for genomic prediction, e.g. BayesA, B, C, R and GBLUP, independence of SNP effects is assumed. However, SNP effects are expected to be loca...
Autores principales: | Zeng, Jian, Garrick, Dorian, Dekkers, Jack, Fernando, Rohan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862491/ https://www.ncbi.nlm.nih.gov/pubmed/29561877 http://dx.doi.org/10.1371/journal.pone.0194683 |
Ejemplares similares
-
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) -
Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle
por: Lee, Joonho, et al.
Publicado: (2017) -
Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data
por: Deng, Tianyu, et al.
Publicado: (2022) -
Fast parallelized sampling of Bayesian regression models for whole-genome prediction
por: Zhao, Tianjing, et al.
Publicado: (2020) -
Accuracy of prediction of simulated polygenic phenotypes and their underlying quantitative trait loci genotypes using real or imputed whole-genome markers in cattle
por: Hassani, Saeed, et al.
Publicado: (2015)