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Selection of core animals in the Algorithm for Proven and Young using a simulation model.

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Summary

Choosing core animals for genomic evaluations using the Algorithm for Proven and Young (APY) is crucial. Random or across-generation core definitions improve accuracy when parentage is unknown, ensuring reliable genetic predictions.

Keywords:
APYgenetic evaluationgenomic selectionimputationsingle-step genomic BLUP

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Area of Science:

  • Animal Breeding and Genetics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • The Algorithm for Proven and Young (APY) facilitates single-step genomic Best Linear Unbiased Prediction (ssGBLUP) in large populations.
  • APY separates genotyped animals into core and non-core subsets for computational efficiency in genomic relationship matrix inversion.

Purpose of the Study:

  • To investigate the impact of different core subset definitions on the accuracy and bias of genomic evaluations.
  • To provide guidance on selecting core animals within the APY framework for large-scale genetic assessments.

Main Methods:

  • Simulations involving 95,010 animals across five generations with 50,000 genotypes and 25,500 SNPs.
  • Mimicking genotyping errors and missing pedigree data.
  • Comparing core animal definitions based on individual generations, equal generational representation, and random selection.

Main Results:

  • Core definitions showed similar accuracies and biases for sufficiently large core sizes, even with imperfect genotypes.
  • Random and across-generation core definitions significantly improved accuracy and reduced bias when parentage was unknown (p ≤ 0.05).

Conclusions:

  • The choice of core definition has minimal impact on ssGBLUP accuracy with adequate core size and good genotype quality.
  • Random or across-generation core selection strategies are recommended when dealing with unknown parentage in genomic evaluations.