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Genomic predictions for crossbred dairy cows by combining solutions from purebred evaluation based on breed origin of alleles. | LitMetric

AI Article Synopsis

  • Genomic predictions have been effective for purebred dairy cattle but are not commonly available for crossbred dairy cows, prompting the need for reliable genomic estimated breeding values (GEBV) for them.
  • A study evaluated 5,467 Danish crossbred dairy cows to determine if combining estimated marker effects from purebred evaluations could yield accurate GEBV, focusing on the breed origin of alleles.
  • The research found that using a breed of origin model resulted in better predictive ability for traits like protein yield compared to a simpler breed proportion model and concluded that integrating marker effects with breed origin considerations leads to reliable genomic predictions for crossbred dairy cows.

Article Abstract

Genomic predictions have been applied for dairy cattle for more than a decade with great success, but genomic estimated breeding values (GEBV) are not widely available for crossbred dairy cows. The large reference populations already in place for genomic evaluations of many pure breeds makes it interesting to use the accurate solutions, in particular the estimated marker effects, from these evaluations for calculation of GEBV for crossbred heifers and cows. Effects of marker alleles in crossbred animals can depend on breed origin of the alleles (BOA). Therefore, our aim was to investigate if reliable GEBV for crossbred dairy cows can be obtained by combining estimated marker effects from purebred evaluations based on BOA. We used data on 5,467 Danish crossbred dairy cows with contributions from Holstein, Jersey, and Red Dairy Cattle breeds. We assessed BOA assignment on their genotypes and found that we could assign 99.3% of the alleles to a definite breed of origin. We compared GEBV for 2 traits, protein yield and interval between first and last insemination of cows, with 2 models that both combine estimated marker effects from the genomic evaluations of the pure breeds: a breed of origin model that accounts for BOA and a breed proportion model that only accounts for genomic breed proportions in the crossbred animals. We accounted for the difference in level between the purebred evaluations by including intercepts in the models based on phenotypic averages. The predictive ability for protein yield was significantly higher from the breed of origin model, 0.45 compared with 0.43 from the breed proportion model. Furthermore, for the breed proportion model, the GEBVs had level bias, which made comparison across groups with different breed composition skewed. We therefore concluded that reliable genomic predictions for crossbred dairy cows can be obtained by combining estimated marker effects from the genomic evaluations of purebreds using a model that accounts for BOA.

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Source
http://dx.doi.org/10.3168/jds.2021-21644DOI Listing

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