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Genome-enabled prediction using the BLR (Bayesian Linear Regression) R-package.

Gustavo de Los Campos1, Paulino Pérez, Ana I Vazquez

  • 1Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA.

Methods in Molecular Biology (Clifton, N.J.)
|June 13, 2013
PubMed
Summary
This summary is machine-generated.

The Bayesian linear regression (BLR) R package offers various Bayesian regression models for continuous traits, including Bayesian LASSO. It is widely used for genomic evaluations in animal and plant breeding.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • The Bayesian linear regression (BLR) R package provides a suite of Bayesian regression models for analyzing continuous traits.
  • It was initially developed for the Bayesian LASSO (BL) method and later extended to include fixed effects and pedigree-based regressions.
  • The package has undergone significant development, including optimization in C for speed and comprehensive documentation.

Purpose of the Study:

  • To review the statistical models implemented within the BLR R package.
  • To provide practical examples illustrating the application of the BLR package for genomic analyses.
  • To highlight the package's utility in animal and plant breeding programs.

Main Methods:

  • Implementation of Bayesian linear regression models, including Bayesian LASSO.
  • Extension of models to incorporate fixed effects and pedigree information.
  • Algorithmic optimization using the C programming language for enhanced computational performance.

Main Results:

  • The BLR package offers a robust framework for various Bayesian regression analyses.
  • The package has been successfully applied in numerous publications since its 2010 launch.
  • BLR is routinely employed for genomic evaluations in animal and plant breeding.

Conclusions:

  • The BLR R package is a valuable tool for researchers and practitioners in statistical genetics and breeding.
  • Its comprehensive models and efficient implementation facilitate advanced genomic analyses.
  • The package's continued development and application underscore its importance in the field.