Animals (Mar 2024)

Enhancing Genomic Prediction Accuracy for Body Conformation Traits in Korean Holstein Cattle

  • Jungjae Lee,
  • Hyosik Mun,
  • Yangmo Koo,
  • Sangchul Park,
  • Junsoo Kim,
  • Seongpil Yu,
  • Jiseob Shin,
  • Jaegu Lee,
  • Jihyun Son,
  • Chanhyuk Park,
  • Seokhyun Lee,
  • Hyungjun Song,
  • Sungjin Kim,
  • Changgwon Dang,
  • Jun Park

DOI
https://doi.org/10.3390/ani14071052
Journal volume & issue
Vol. 14, no. 7
p. 1052

Abstract

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The Holstein breed is the mainstay of dairy production in Korea. In this study, we evaluated the genomic prediction accuracy for body conformation traits in Korean Holstein cattle, using a range of π levels (0.75, 0.90, 0.99, and 0.995) in Bayesian methods (BayesB and BayesC). Focusing on 24 traits, we analyzed the impact of different π levels on prediction accuracy. We observed a general increase in accuracy at higher levels for specific traits, with variations depending on the Bayesian method applied. Notably, the highest accuracy was achieved for rear teat angle when using deregressed estimated breeding values including parent average as a response variable. We further demonstrated that incorporating parent average into deregressed estimated breeding values enhances genomic prediction accuracy, showcasing the effectiveness of the model in integrating both offspring and parental genetic information. Additionally, we identified 18 significant window regions through genome-wide association studies, which are crucial for future fine mapping and discovery of causal mutations. These findings provide valuable insights into the efficiency of genomic selection for body conformation traits in Korean Holstein cattle and highlight the potential for advancements in the prediction accuracy using larger datasets and more sophisticated genomic models.

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