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Genomic selection for crossbred performance accounting for breed-specific effects.

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Genomic selection models trained on crossbred pig data improve prediction accuracy for traits like litter size and gestation length. Breed-specific effects were observed, but traditional and breed-specific models showed similar accuracy when trained on crossbred data.

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Genomic Prediction

Background:

  • Breed-specific allele effects can reduce the accuracy of traditional genomic selection models when predicting crossbred performance.
  • Understanding allele contributions from parental breeds is crucial for accurate genetic evaluations in crossbred populations.
  • This study investigates breed-specific effects in pigs for litter size and gestation length.

Purpose of the Study:

  • To estimate the contribution of alleles from parental breeds (Large White, Landrace) to the genetic variance of traits in crossbred pigs.
  • To compare the prediction accuracy of direct genomic values (DGV) from traditional genomic selection (GS) and breed-specific (BS) models.
  • To evaluate the impact of training data (purebred vs. crossbred) on the accuracy of these models.

Main Methods:

  • Utilized a dataset of 924 genotyped and phenotyped animals for each of Large White, Landrace, and two-way cross (F1) pigs.
  • Evaluated traits including litter size (LS) and gestation length (GL).
  • Compared prediction accuracies of DGV from GS and BS models trained on purebred and crossbred data.

Main Results:

  • Genetic correlation between purebred and crossbred performance exceeded 0.88 for both LS and GL.
  • Additive genetic variance was greater for alleles from Large White compared to Landrace.
  • Training on crossbred data yielded the highest prediction accuracies for both traits.

Conclusions:

  • Training genomic selection models on crossbred data enhances prediction accuracy for pig traits.
  • Evidence of breed-specific effects for LS and GL was found.
  • When trained on crossbred data, both GS and BS models demonstrated comparable prediction accuracies.