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Multitrait Bayesian shrinkage and variable selection models with the BGLR-R package.

Paulino Pérez-Rodríguez1,2, Gustavo de Los Campos2,3,4

  • 1Colegio de Postgraduados, Montecillo, Estado de México 56230, Mexico.

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|August 4, 2022
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Summary
This summary is machine-generated.

The BGLR-R package now supports multitrait Bayesian regressions for genomic studies. This enhanced software offers flexible prior specifications and covariance structures for improved genetic analysis.

Keywords:
BayesianGenPredGenomic PredictionGibbs samplingShared Data Resourcegenomic regressionshigh-dimensional regressionmultitrait modelsmultivariate modelspedigree regressions

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • The BGLR-R package is a widely used tool for single-trait Bayesian regression in genomic studies.
  • Recent advancements have extended its capabilities to handle multitrait models.

Purpose of the Study:

  • To introduce and describe the new multitrait functionality within the BGLR-R package.
  • To provide an overview of the implemented models, methods, and prior specifications.
  • To benchmark the performance of the multitrait function.

Main Methods:

  • Implementation of Bayesian multitrait regression models.
  • Inclusion of arbitrary numbers of random-effects terms.
  • Support for diffuse, Gaussian, and Gaussian-spike-slab multivariate priors.
  • Flexible (co)variance parameter specifications (unstructured, diagonal, factor analytic, recursive).
  • Gibbs sampling using R and C programming languages.

Main Results:

  • The BGLR-R package now accommodates complex multitrait genomic analyses.
  • The software offers a wider range of prior and covariance options compared to existing packages.
  • Performance benchmarks demonstrate the efficiency of the implemented Gibbs sampler.

Conclusions:

  • The updated BGLR-R package provides a powerful and flexible tool for advanced genomic data analysis.
  • The multitrait functionality enhances the ability to model complex genetic architectures.
  • The package is suitable for researchers conducting sophisticated genetic studies.