Evaluating
the performance of genomic selection on purebred population by incorporating
crossbred data in pigs

doi:10.1016/j.jia.2023.09.004

Journal of Integrative Agriculture

2024, Vol. 23

Issue (02): 639-648 DOI: 10.1016/j.jia.2023.09.004

Animal Science · Veterinary Medicine

Advanced Online Publication | Current Issue | Archive | Adv Search

Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs

Jun Zhou^1*, Qing Lin^1*, Xueyan Feng¹, Duanyang Ren¹, Jinyan Teng¹, Xibo Wu², Dan Wu², Xiaoke Zhang¹, Xiaolong Yuan¹, Zanmou Chen¹, Jiaqi Li¹, Zhe Zhang¹, Hao Zhang^1#

¹ College of Animal Science, South China Agricultural University/State Key Laboratory of Swine and Poultry Breeding Industry/National Engineering Research Center for Breeding Swine Industry, Guangzhou 510642, China

² Guangxi State Farms Yongxin Animal Husbandry Group Co., Ltd., Nanning 530022, China

Abstract
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摘要【背景】基因组选择（Genomic selection, GS）技术在家畜育种中得到了广泛应用，极大地加快了复杂性状的遗传进展。群体大小是影响预测准确性的重要因素之一，但是构建一个足够规模的纯种参考群是十分困难的，特别是在胴体性状上或某些地方猪群体中。杂交群体作为纯种群体的后代，保留了亲代一半的遗传物质，与直接组合两个不相关的纯种群体来扩大参考群体规模相比，将相关杂交种纳入参考群体进行基因组预测可能更有意义。【目的】不过，引入杂交群体信息时，杂交个体的选择、参考群的构建、模型方法的选择等方面的研究尚未明确，因此该方向的研究可以为今后猪基因组选择的应用提供一种新思路。【方法】本研究基于两个基础纯种群体(P_A和P_B)的真实基因型数据，使用SBVB软件模拟纯种后代(P_AS和P_BS)和杂交后代(C_AB)，使用对BLUP（Best linear unbiased prediction）、GBLUP（Genomic best linear unbiased prediction）、ssGBLUP（Single-step genomic best linear unbiased prediction）方法对杂交群体信息加入到纯种群体基因组选择的应用效果进行研究。【结果】研究结果表明：（1）使用群体内期望亲缘关系最大化（Maximizing the expected genetic relationship, REL）方法进行关键个体的选择，基因组预测准确性要略微优于选择离纯种群体亲缘关系最远（Relationship farthest from purebred population, FP）和选择离纯种群体亲缘关系最近（relationship closest from purebred population, CP）的方法；（2）在加入杂交群体后，基因组预测的准确性有明显提升，在P_A中，C_AB加入参考群预测准确性接近于加入P_AS；（3）遗传评估模型中加入杂交群体后，提升可靠性与亲缘关系提升倍数表现出显著相关，其秩相关为 0.60~0.70；（4）对于亲缘关系偏低（Cor(P_i,P_AorB)<10）的个体，可靠性提升显著低于其他个体。【结论】基于上述结果，可以得到以下结论：加入杂交群体数据可以有效提高纯种群体基因组预测准确性和个体估计育种值可靠性，纯种群体与杂交群体的亲缘关系是提高纯种个体基因组预测可靠性的关键因素。

Abstract Genomic selection (GS) has been widely used in livestock, which greatly accelerated the genetic progress of complex traits. The population size was one of the significant factors affecting the prediction accuracy, while it was limited by the purebred population. Compared to directly combining two uncorrelated purebred populations to extend the reference population size, it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction. In this study, we simulated purebred offspring (P_AS and P_BS) and crossbred offspring (C_AB) base on real genotype data of two base purebred populations (P_A and P_B), to evaluate the performance of genomic selection on purebred while incorporating crossbred information. The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship (REL) was better than the other methods (individuals closet or farthest to the purebred population, CP/FP) in term of the prediction accuracy. Furthermore, the prediction accuracy of reference populations combining P_A and C_AB was significantly better only based on P_A, which was similar to combine P_A and P_AS. Moreover, the rank correlation between the multiple of the increased relationship (MIR) and reliability improvement was 0.60–0.70. But for individuals with low correlation (Cor(P_i, P_{A or B}), the reliability improvement was significantly lower than other individuals. Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only. The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.

Keywords: pigs crossbred population genomic selection reference population construction relationship

Received: 02 November 2022 Accepted: 29 June 2023

Fund: This work was supported by the earmarked fund for China Agriculture Research System (CARS-35) and the National Natural Science Foundation of China (32022078).

About author: Jun Zhou, E-mail: zj13416297035@163.com; Qing Lin, E-mail: qing_lin1996@126.com; #Correspondence Hao Zhang, Tel/Fax: +86-20-85283495; E-mail: zhanghao@scau.edu.cn * These authors contributed equally to this study.

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Jun Zhou, Qing Lin, Xueyan Feng, Duanyang Ren, Jinyan Teng, Xibo Wu, Dan Wu, Xiaoke Zhang, Xiaolong Yuan, Zanmou Chen, Jiaqi Li, Zhe Zhang, Hao Zhang. 2024.

Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs . Journal of Integrative Agriculture, 23(02): 639-648.

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