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Technical note: Genetic groups in single-step single nucleotide polymorphism best linear unbiased predictor.

Jeremie Vandenplas1, Herwin Eding2, Mario P L Calus1

  • 1Animal Breeding and Genomics, Wageningen UR, PO 338, 6700 AH, Wageningen, the Netherlands.

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Summary

Including genetic groups in the pedigree for single-step SNP BLUP (ssSNPBLUP) reduces computational costs. This method simplifies dairy cattle genetic evaluations compared to fitting genetic groups as covariates.

Keywords:
genomic evaluationphantom parentssingle-stepunknown parents groups

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Biotechnology

Background:

  • Genetic groups (unknown or phantom parents) address selection in dairy cattle not explained by known relationships.
  • The single-step genomic best linear unbiased prediction (ssGBLUP) framework extended genetic group theory.
  • Genetic groups can be incorporated into ssGBLUP via regression effects or by integrating them into the pedigree.

Purpose of the Study:

  • To adapt the Quaas and Pollak transformation for single-step SNP BLUP (ssSNPBLUP).
  • To enable the inclusion of genetic groups directly within the pedigree matrix in ssSNPBLUP.
  • To evaluate the impact of this inclusion on computational efficiency.

Main Methods:

  • Applied the Quaas and Pollak transformation to the ssSNPBLUP system of equations.
  • Integrated genetic groups into the pedigree matrix.
  • Compared computational performance with traditional covariate-based fitting.

Main Results:

  • The Quaas and Pollak transformation successfully allowed genetic groups to be included in the pedigree for ssSNPBLUP.
  • This approach resulted in a reduced memory burden.
  • Computational costs were significantly lower compared to fitting genetic groups as covariates.

Conclusions:

  • Incorporating genetic groups into the pedigree within ssSNPBLUP is computationally advantageous.
  • This method offers a more efficient alternative for dairy cattle genetic evaluations.
  • The study demonstrates a practical improvement for genomic evaluation methodologies.