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Unknown-parent groups in single-step genomic evaluation.

I Misztal1, Z G Vitezica, A Legarra

  • 1Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA.

Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie
|July 17, 2013
PubMed
Summary
This summary is machine-generated.

Bias in genomic predictions can arise when unknown parent groups (UPG) are used in single-step genomic best linear unbiased prediction (ssGBLUP). Several methods are presented to mitigate this bias, ensuring more accurate genetic evaluations.

Keywords:
Biasgenomic evaluationgenomic relationshipsunknown-parent groups

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Area of Science:

  • Quantitative Genetics
  • Animal Breeding
  • Genomic Evaluation

Background:

  • Single-step genomic best linear unbiased prediction (ssGBLUP) integrates pedigree and genomic data for genetic evaluations.
  • Unknown parent groups (UPG) are incorporated into BLUP models to handle missing pedigrees or complex population structures.
  • UPG in ssGBLUP models are influenced by genomic relationships, which can lead to bias if not properly accounted for.

Purpose of the Study:

  • To identify and present methods for eliminating or reducing bias in ssGBLUP when using unknown parent groups (UPG).
  • To address the impact of genomic relationship contributions on UPG solutions and subsequent genetic predictions.

Main Methods:

  • Modification of mixed model equations to incorporate genomic contributions to UPG.
  • Implementation of UPG as separate effects within the model.
  • Approximation strategies involving compatible construction of genomic and pedigree relationships.
  • Inclusion of an additional polygenic effect that accounts for UPG.

Main Results:

  • Ignoring genomic contributions to UPG in ssGBLUP can result in biased genetic predictions.
  • Various approaches exist to manage these contributions, each with implications for computational cost and complexity.
  • Refinements in UPG definitions and relationship matrix construction are crucial for minimizing bias.

Conclusions:

  • Accurate handling of unknown parent groups is essential for reliable genomic evaluations in ssGBLUP.
  • The choice of method depends on balancing accuracy, computational efficiency, and data processing requirements.
  • Further research into optimal UPG definitions and relationship matrix construction is warranted.