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Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R

Author: Perez, P.
Author: De los Campos, G.
Author: Crossa, J.
Author: Gianola, D.
Year: 2010
ISSN: 0011-183X
URI: http://hdl.handle.net/10883/1820
Abstract: The availability of dense molecular markers has made possible the use of genomic selection in plant and animal breeding. However, models for genomic selection pose several computational and statistical challenges and require specialized computer programs, not always available to the end user and not implemented in standard statistical software yet. The R-package BLR (Bayesian Linear Regression) implements several statistical procedures (e.g., Bayesian Ridge Regression, Bayesian LASSO) in a unifi ed framework that allows including marker genotypes and pedigree data jointly. This article describes the classes of models implemented in the BLR package and illustrates their use through examples. Some challenges faced when applying genomic-enabled selection, such as model choice, evaluation of predictive ability through cross-validation, and choice of hyperparameters, are also addressed.
Format: PDF
Language: English
Publisher: Crop Science Society of America
Type: Article
Region: Global
Pages: 106-116
Issue: 2
Volume: 3
DOI: 10.3835/plantgenome2010.04.0005
Agrovoc: GENETICS
Agrovoc: GENETIC MARKERS
Agrovoc: STATISTICAL METHODS
Agrovoc: BAYESIAN THEORY
Agrovoc: COMPUTER APPLICATIONS
Journal: The Plant Genome


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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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