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Efficient ReML inference in variance component mixed models using a Min-Max algorithm.

Fabien Laporte1, Alain Charcosset1, Tristan Mary-Huard1,2

  • 1Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, Gif-sur-Yvette, France.

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This summary is machine-generated.

A new Min-Max (MM) algorithm improves Restricted Maximum Likelihood (ReML) estimation for variance component mixed models. This computational method is efficient and versatile for large datasets in statistical genetics and plant breeding.

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Area of Science:

  • Statistical Genetics
  • Quantitative Genetics
  • Computational Biology

Background:

  • Variance component mixed models are essential in many fields, with Restricted Maximum Likelihood (ReML) being the standard for parameter estimation.
  • Increasing dataset sizes and model complexity necessitate computational improvements in ReML estimation algorithms.

Purpose of the Study:

  • To introduce a novel Min-Max (MM) algorithm for ReML inference.
  • To enhance the MM algorithm with speed-up procedures for improved computational performance.
  • To compare the MM algorithm against existing state-of-the-art methods in statistical genetics.

Main Methods:

  • Development and implementation of the Min-Max (MM) algorithm for ReML estimation.
  • Integration of speed-up techniques into the MM algorithm.
  • Comparative analysis of computational performance using diverse plant breeding experimental datasets.

Main Results:

  • The MM algorithm demonstrates competitive performance, ranking among the top two methods in most evaluated settings.
  • The MM algorithm exhibits greater versatility compared to several existing algorithms.
  • The MM algorithm is identified as a promising alternative to the traditional AI-ReML algorithm.

Conclusions:

  • The developed MM algorithm offers significant computational advantages for ReML estimation in variance component mixed models.
  • The MM algorithm, available in the MM4LMM R-package, is a valuable tool for researchers dealing with large-scale genetic data.
  • This work contributes to advancing computational efficiency in statistical genetics and related fields.