Genomic prediction accounting for dominance and epistatic genetic effects on litter size traits in Large White pigs

文献类型： 外文期刊

第一作者： Chen, Jianmei

作者： Chen, Jianmei;Dou, Tengfei;Wu, Ziyi;Bai, Liyao;Han, Xuelei;Qiao, Ruimin;Wang, Kejun;Yang, Feng;Li, Xin-Jian;Li, Xiu-Ling;Chen, Jianmei;Dou, Tengfei;Zhang, Yongqian;Yang, Songbai;Li, Xiu-Ling;Xu, Man;Wang, Xianwei;Zhang, Yongqian;Yang, Songbai;Xu, Shiqian;Li, Xin-Jian

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关键词： genetic parameter; genomic selection; nonadditive effect; prediction accuracy; reproductive trait

期刊名称：JOURNAL OF ANIMAL SCIENCE （ 影响因子：2.9； 五年影响因子：3.3 ）

ISSN： 0021-8812

年卷期： 2025 年 103 卷

页码：

收录情况： SCI

摘要： Litter size traits of sows are crucial for the economic benefits of the pig industry. Three phenotypic traits of 1,206 Large White pigs, the total number born (TNB), number born alive (NBA), and number of healthy piglets (NHP), were recorded. We evaluated a series of genomic best linear unbiased prediction models that sequentially added additive effects (model A), dominance effects (model A + D), and epistatic effects (model A + D + AA, model A + D + AA + AD, and model A + D + AA + AD + DD) using chip data and imputed whole-genome sequencing (WGS) data to estimate genetic parameters and predictive accuracy. The reproductive traits of sows showed low heritability in this study, with narrow heritability of the 3 traits ranging from 0.030 to 0.064, and broad heritability ranging from 0.125 to 0.145. The inclusion of nonadditive effects in the model improved the accuracy of genomic selection. In the chip data, compared with that of the A model, the A + D + AA + AD + DD model showed the greatest increase in accuracy for TNB, NBA, and NHP, with improvements of 1.78%, 1.67%, and 1.74%, respectively. Additionally, the accuracy of the imputed WGS data was greater compared to the chip data. For the TNB, NBA, and NHP traits, the predictive accuracy of the imputed WGS data improved by 3.26%, 7.72%, and 3.00%, respectively, compared with that of the chip data. In summary, these results suggest that nonadditive effects in genomic selection could improve prediction accuracy and should be considered in pig genomic evaluation procedures. The study objectives were to investigate dominance and epistatic effects improves the prediction accuracy of litter size traits in Large White pigs at both low-density chip data and high-density imputed whole-genome sequencing data. Improving the reproductive performance of sows is of the utmost importance to the pig industry. However, traditional breeding methods often fail to achieve significant genetic progress. Therefore, this study aimed to assess the effectiveness of genomic selection models that account for dominance and epistatic effects in improving predictive accuracy. Our findings showed that integrating additive and nonadditive effects into these models can improve genomic prediction accuracy. These results suggest that nonadditive effects play a critical role in pig breeding.

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