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Using pooled data for genomic prediction in a bivariate framework with missing data.

Johnna L Baller1, Stephen D Kachman2, Larry A Kuehn3

  • 1Department of Animal Science, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.

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|June 14, 2022
PubMed
Summary
This summary is machine-generated.

Pooling beef cattle samples for genetic evaluations improves accuracy, especially with optimized pool sizes and minimized phenotypic variation. This method efficiently incorporates commercial data, even with missing records or genotyping gaps.

Keywords:
DNA poolingbeef cattlebivariate modelsgenomic prediction

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • Economically efficient genetic evaluations are crucial for livestock improvement.
  • Utilizing commercial animal data in genetic evaluations presents challenges due to cost and data availability.
  • Pooling samples offers a potential solution for cost-effective genotype data acquisition.

Purpose of the Study:

  • To test a multivariate framework for genetic evaluations using pooled genotype data in a simulated beef cattle population.
  • To assess the impact of pool size and construction method (random vs. minimizing phenotypic variation) on genetic evaluation accuracy.
  • To investigate the effects of missing records and genotyping gaps on the accuracy of estimated breeding values (EBVs).

Main Methods:

  • Simulated a 15-generation beef cattle population (32,000 animals) with two heritable traits.
  • Induced missing records via sequential culling and random imputation.
  • Explored genotyping gaps and constructed pools (1, 20, 50, 100 animals) randomly or by minimizing phenotypic variation.
  • Estimated EBVs using a bivariate single-step genomic best linear unbiased prediction (GBLUP) model.

Main Results:

  • Pools of 20 animals, optimized by minimizing phenotypic variation, achieved accuracies comparable to individual progeny data.
  • Genotyping gaps significantly reduced EBV accuracies for parents in the affected generation.
  • Pooling, regardless of size or data issues, generally improved accuracy compared to having no data from the latest generation.

Conclusions:

  • Sample pooling is a viable strategy for incorporating commercial animal data into multivariate genetic evaluations.
  • The method is robust to varying genetic correlations, missing phenotypes, and genotyping gaps.
  • Optimizing pool construction by minimizing phenotypic variation enhances accuracy, making it a practical approach for beef cattle genetic improvement.