Open AccessGenomic‐Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R
Author(s) -
Pérez Paulino,
Campos Gustavo,
Crossa José,
Gianola Daniel
Publication year - 2010
Publication title -
the plant genome
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 41
ISSN - 1940-3372
DOI - 10.3835/plantgenome2010.04.0005
Subject(s) - bayesian probability , lasso (programming language) , r package , computer science , genomic selection , selection (genetic algorithm) , regression , machine learning , statistical model , data mining , bayesian linear regression , linear regression , linear model , software , elastic net regularization , feature selection , artificial intelligence , bayesian inference , statistics , biology , mathematics , genotype , genetics , computational science , world wide web , single nucleotide polymorphism , gene , programming language
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 unified 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 hyper‐parameters, are also addressed.