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Validation of an approximate REML algorithm for parameter estimation in a multitrait, multiple across-country

J Tarrés1, Z Liu, V Ducrocq

  • 1Vereinigte Informationssysteme Tierhaltung w.v., Heideweg 1, 27283 Verden, Germany. joaquim.tarres@dga.jouy.inra.fr

Journal of Dairy Science
|September 21, 2007
PubMed
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A new approximate REML algorithm accurately estimates genetic (co)variance components for multitrait, multiple across-country evaluation (MT-MACE) models. This method, using effective daughter contribution (EDC), provides precise and unbiased estimates, improving international genetic evaluations.

Area of Science:

  • Animal Breeding and Genetics
  • Quantitative Genetics
  • Statistical Genetics

Background:

  • International genetic evaluations require accurate estimation of genetic (co)variance components within and across countries.
  • Existing methods for large datasets can be computationally intensive, necessitating efficient algorithms.
  • Multitrait, multiple across-country evaluation (MT-MACE) models offer improved accuracy by aligning international and national evaluations.

Purpose of the Study:

  • To develop and validate an approximate restricted maximum likelihood (REML) algorithm for estimating genetic (co)variance components in MT-MACE models.
  • To assess the accuracy and precision of the developed algorithm using simulated data.
  • To compare the performance of the approximate REML algorithm with existing average information REML (AI-REML) methods.

Related Experiment Videos

Main Methods:

  • Development of an approximate REML algorithm based on the expectation maximization REML (EM-REML) algorithm.
  • Utilized multiple-trait effective daughter contribution (EDC) to approximate diagonal elements of the inverse coefficient matrix for computational efficiency.
  • Validated the algorithm through two simulation studies: single-trait and multiple-trait models, comparing results with AI-REML.

Main Results:

  • The approximate EM-REML algorithm provided unbiased and highly precise estimates of within- and across-country genetic correlations in both single- and multiple-trait simulations.
  • Estimates were comparable to those obtained using AI-REML software, demonstrating the algorithm's accuracy.
  • The MT-MACE model, with the approximate EM-REML, effectively corrected for biases in national genetic trend estimations when including a time effect.

Conclusions:

  • The developed approximate EM-REML algorithm is suitable for parameter estimation in MT-MACE models across various scenarios.
  • The method enhances the accuracy and precision of international genetic evaluations, particularly for large datasets.
  • The MT-MACE approach, coupled with the approximate EM-REML, offers a robust framework for global genetic improvement programs.