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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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Published on: August 24, 2013

Genomic prediction when some animals are not genotyped.

Ole F Christensen1, Mogens S Lund

  • 1Aarhus University, Faculty of Agricultural Sciences, Dept of Genetics and Biotechnology, Blichers Allé 20, PO BOX 50, DK-8830 Tjele, Denmark. OleF.Christensen@agrsci.dk

Genetics, Selection, Evolution : GSE
|January 29, 2010
PubMed
Summary

This study introduces a new genomic prediction method for breeding values that incorporates animals without genetic data. This approach blends traditional and genomic information for more accurate predictions in livestock breeding.

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Genomic Selection

Background:

  • Genomic selection enhances genetic improvement, reduces generation time, and increases accuracy of estimated breeding values (EBVs).
  • Current genomic prediction methods often assume all animals are genotyped, which is impractical for real-world breeding programs.
  • Adapting methods for non-genotyped animals is crucial for broader application.

Purpose of the Study:

  • To extend linear mixed model methods for genomic prediction to include non-genotyped animals.
  • To develop a unified approach integrating genomic, pedigree, and phenotype data.
  • To improve the accuracy of estimated breeding values in livestock.

Main Methods:

  • An extension of a linear mixed model incorporating both genomic and polygenic genetic random effects.
  • Utilizing pedigree information to extend the genomic relationship matrix to non-genotyped animals.
  • Estimating model parameters using average information REML, yielding best linear unbiased predictions (BLUPs).

Main Results:

  • The developed model effectively blends traditional EBVs with purely genomic EBVs by integrating information from genotyped and non-genotyped animals.
  • The method provides a one-step procedure for genomic prediction, incorporating all available data types.
  • Illustrated effectiveness using a simulated dataset.

Conclusions:

  • The extended method allows seamless integration of genomic, pedigree, and phenotype data in a single step for genomic prediction.
  • This approach leads to more accurate EBVs, enhancing genetic gain in breeding programs.
  • The method has the potential to become a standard tool for genomic prediction in pig and cattle breeding.