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Long-Term Impact of Genomic Selection on Genetic Gain Using Different SNP Density

Author

Listed:
  • Xu Zheng

    (Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China)

  • Tianliu Zhang

    (Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
    Henan International Joint Laboratory of Nutrition Regulation and Ecological Raising of Domestic Animal, College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China)

  • Tianzhen Wang

    (Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China)

  • Qunhao Niu

    (Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China)

  • Jiayuan Wu

    (Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China)

  • Zezhao Wang

    (Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China)

  • Huijiang Gao

    (Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China)

  • Junya Li

    (Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China)

  • Lingyang Xu

    (Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China)

Abstract

Genomic selection (GS) has been widely used in livestock breeding. However, the long-term impact of GS on genetic gain, as well as inbreeding levels, has not been fully explored in beef cattle. In this study, we carried out simulation analysis using different approaches involving two types of SNP density (54 K and 100 K) and three levels of heritability traits (h 2 = 0.1, 0.3, and 0.5) to explore the long-term effects of selection strategies on genetic gain and average kinship coefficients. Our results showed that GS can improve the genetic gain across generations, and the GBLUP strategy showed slightly better performance than the BayesA model. Higher trait heritability can generate higher genetic gain in all scenarios. Moreover, simulation results using GBLUP and BayesA strategies showed higher average kinship coefficients compared with other strategies. Our study suggested that it is important to design GS strategies by considering the SNP density and trait heritability to achieve long-term and sustainable genetic gain and to effectively control inbreeding levels.

Suggested Citation

  • Xu Zheng & Tianliu Zhang & Tianzhen Wang & Qunhao Niu & Jiayuan Wu & Zezhao Wang & Huijiang Gao & Junya Li & Lingyang Xu, 2022. "Long-Term Impact of Genomic Selection on Genetic Gain Using Different SNP Density," Agriculture, MDPI, vol. 12(9), pages 1-12, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1463-:d:914430
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    References listed on IDEAS

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    1. Heather M. Burrow & Raphael Mrode & Ally Okeyo Mwai & Mike P. Coffey & Ben J. Hayes, 2021. "Challenges and Opportunities in Applying Genomic Selection to Ruminants Owned by Smallholder Farmers," Agriculture, MDPI, vol. 11(11), pages 1-15, November.
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