猪基因组选择“两步走”策略的计算机模拟评估

【背景】基因组选择育种自2001年被MEUWISSEN等提出以来,已广泛应用在奶牛、猪等重要家畜的育种中,并显著加快了其重要经济性状的遗传改良速度。2017年,在全国畜牧总站的组织协调下,在全国生猪遗传改良计划框架内,猪全基因组选择育种平台项目正式启动。【目的】尽管基因组选择在种猪选育中取得了良好的效果,基因分型技术的不断升级也带来了成本的持续下降,但对于我国多数核心育种场依然面临着基因芯片分型个体数量不足、基因组选择实施流程不完善等问题,限制了该技术的大规模推广应用。结合我国生猪育种的实际情况,研究提出了一种“终测选择-早期选择”的“两步走”基因组选择策略。“终测选择”指在终测结束后利用一步法基因组BLUP对后备猪进行遗传评估,当群体中芯片分型个体数量达到一定规模后进行“早期选择”。【方法】以杜洛克、长白和大白三个种猪品种真实的50K基因芯片数据作为基础群体对不同品种分别进行大群模拟,共模拟4个世代,前3个世代作为基础群体,第4个世代作为测试群体,每个个体模拟两个性状（中等遗传力性状和低遗传力性状）,利用猪基因组选择育种平台基于HIBLUP软件计算不同品种、不同性状的育种值,比较一步法基因组BLUP和常规BLUP两种方法的预测准确性。根据测试群个体有无终测成绩对其基因组育种值影响大小来评估早期选择效果。【结果】分析表明在3个品种内中等遗传力性状的终测选择效果和早期选择效果均好于低遗传力性状。一步法基因组BLUP的选择准确性均优于常规BLUP的选择准确性,并且随着测试群中芯片分型个体数量的增加、群体规模的扩大,预测准确性越来越高。一步法基因组BLUP的早期选择效果好于常规BLUP,当群体中芯片数量达到2 000张时就可以开展早期选择,阉割排名后30%的个体,可以保证前1%的优秀个体不会被错误淘汰,并且随着芯片数量的增加、早期选择的效果会越来越好。【结论】基因组选择“两步走”的策略符合我国国情、容易在生猪育种中推广实施。当芯片数量较少时,可以开展“终测选择”,一定程度上提高选择的准确性,提高育种效率;当芯片数量较多时,可以开展“早期选择”,对排名靠后的猪只个体进行早期阉割,增加优秀个体的测定量,增大选择强度、加快遗传进展。“两步走”策略符合我国生猪产业基因组选择育种的实际需求,该策略的实施将有利于推动我国猪基因组选择的应用、加快种猪改良进程。

To Evaluate the “Two-Step” Genomic Selection Strategy in Pig by Simulation

【Background】 Since genomic selection (GS) was proposed by MEUWISSEN et al. in 2001, it has been widely used in the breeding of dairy cows, pigs, and other livestock, and has significantly improved the speed of genetic gain of various economic traits. In 2017, with the organization and coordination of the National Grazing Headquarter Station and within the framework of the National Swine Improvement Program, the genomic selection platform for pig breeding was officially launched. Although genomic selection has made positive achievements in pig breeding, and the developing of advanced genotyping technology reduced the costs dramatically, some issues were still existed, including the insufficient number of genotyped individuals in majority of core breeding farms and the inappropriate implementation processes has restricted its wide application in practice.【Objective】In combination with the actual situation of domestic pig breeding, the “two-step” strategy for genomic selection was proposed in this study, that is, the off-test evaluation and the early-stage prediction. Off-test evaluation referred to the genetic evaluation of replacement pigs by SSGBLUP after off-test, and early-stage prediction was carried out when the number of chips reached a certain scale. 【Method】 In this study, the 50 K chip datasets of three breeds consisting of Duroc, Landrace, and Yorkshire were used as the base group to simulate the large-scale population of different breeds, respectively. The four generations were simulated: the first three generations were treated as the base population, and the fourth generation as the test population, two traits with medium and low heritability was simulated for each individual. The estimated breeding values of SSGBLUP and traditional BLUP model for different traits were calculated by the pig genomic selection platform based on the HIBLUP software. The predictive performance of early-stage was evaluated according to whether the individual’s testing records have influence their genomic estimated breeding values (GEBV) in test population. 【Result】The results showed that the predictive performance of off-test evaluation and early-stage for traits with medium heritability were better than those with low heritability. The selection accuracy of SSGBLUP was better than traditional BLUP. Moreover, with the increase of the number of chips and the expansion of the population size, the prediction accuracy was higher. The early-stage predictive performance of SSGBLUP was better than that of traditional BLUP, the early-stage prediction could be carried out when the number of genotyped pigs reached about 2 000, and castrating the last 30% individuals according to GEBV could ensure that the top 1% excellent individuals would not be mistakenly eliminated. And the prediction accuracy performance was increasing with the increased number of genotyped pigs. 【Conclusion】 The “two-step” strategy pretty was conformed to the state of domestic breeding program, and was easy to implement and promote the pig breeding in China. When the number of genotyped pigs was small, off-test evaluation could be carried out to improve the accuracy of selection, as well as efficiency, to a certain extent; when the number of genotyped pigs was large, early-stage prediction could be performed by castrating the pigs on the lower rank of GEBV, which could increase the amount of testing for more excellent pigs, and could also strength the selection intensity and accelerate the genetic gain. The “two-step” strategy was in line with the actual requirements of genomic selection in pig industry. The implementation of this strategy could further promote the application of genomic selection and speed up the genetic gain in pig breeding.