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Technical note: an R package for fitting generalized linear mixed models in animal breeding.

A I Vazquez1, D M Bates, G J M Rosa

  • 1Department of Dairy Science, University of Wisconsin, Madison 53706, USA. anainesvs@gmail.com

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|October 13, 2009
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Summary
This summary is machine-generated.

The pedigreemm R package enables mixed models with correlated random effects for genetic analysis of continuous and discrete traits. It extends existing mixed model capabilities for quantitative genetics research.

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Area of Science:

  • Quantitative genetics
  • Statistical genetics
  • Computational biology

Background:

  • Mixed models are crucial for analyzing continuous and discrete traits in quantitative genetics.
  • Standard mixed model routines often lack the ability to incorporate correlated random effects inherent in genetic data (e.g., sire relationships).
  • This limitation necessitates specialized tools for accurate genetic analysis.

Purpose of the Study:

  • To introduce the pedigreemm R package, an extension of lme4.
  • To enable fitting of mixed models with correlated random effects for Gaussian, binary, and count data in genetic analyses.
  • To provide practical examples of pedigreemm's application in genetic studies.

Main Methods:

  • Utilizes a post-multiplication approach with the Cholesky factor of the (co)variance matrix to induce correlations between random factor levels (e.g., sire relationships).
  • Implements approximations to maximum likelihood and REML estimation methods.
  • Extends the capabilities of the lme4 package for specialized genetic models.

Main Results:

  • pedigreemm successfully fits mixed models incorporating correlated random effects for various response types (Gaussian, binary, count).
  • The package provides a flexible framework for complex genetic models previously unavailable in standard software.
  • Demonstrates the practical utility of pedigreemm through illustrative examples.

Conclusions:

  • The pedigreemm R package offers a powerful solution for mixed model analysis in quantitative genetics.
  • It facilitates the accurate modeling of genetic relationships, improving the analysis of complex traits.
  • Researchers can leverage pedigreemm for advanced genetic analyses using R.