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

Xu Zheng, Tianliu Zhang, Tianzhen Wang, Qunhao Niu, Jiayuan Wu, Zezhao Wang, Huijiang Gao, Junya Li and Lingyang Xu ()
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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
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

Agriculture, 2022, vol. 12, issue 9, 1-12

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.

Keywords: genomic selection; genetic gain; average kinship coefficient; simulation; beef cattle (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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