基因组选择在猪杂交育种中的应用

doi:10.16288/j.yczz.19-253

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Hereditas(Beijing) ›› 2020, Vol. 42 ›› Issue (2): 145-152.doi: 10.16288/j.yczz.19-253

• Review • Previous Articles Next Articles

The application of genomic selection in pig cross breeding

Anqi Yang^1,^2,³(), Bin Chen^1,³(), Maoliang Ran^1,³(), Guangmin Yang², Cheng Zeng²

^1. College of Animal Science ＆ Technology, Hunan Agricultural University, Changsha 410128, China
^2. Hunan Micolta Bioresource Inc, Changsha 410331, China
^3. Hunan Provincial Key Laboratory for Genetic Improvement of Domestic Animal, Changsha 410128, China

Received:2019-08-28 Revised:2020-02-08 Online:2020-02-17 Published:2020-02-19
Supported by:
the National Key Research and Development(2017YFD0501504);the National Key Research and Development(2016YFD0501308);Special Fund for the Industrial Technology System Construction of Modern Agriculture(CARS-36)

Abstract

Abstract:

Genomic selection is a form of marker-assisted selection in which genetic markers covering the entire genome are used so that all quantitative trait loci are in linkage disequilibrium with at least one marker. Genomic selection improves the efficiency and accuracy of breeding and it is widely used in purebred breeding across many animal species. However, some studies indicate that the accuracy of genome selection in cross breeding needs to be improved,especially in cross population. As one of the most extensive breeding methods employed in the swine industry, cross breeding has significant, potential research and economic value to further improve its performance by combining with genomic selection. In this review, we summarize the application of genomic selection in pigs, and elucidate the genomic selection deficiencies in breeding hybrid pigs. This review will also provide valuable insights for the future application and improvement of genomic selection in pig cross breeding.

Key words: genomic selection, swine, cross breeding, statistical genetics

Anqi Yang, Bin Chen, Maoliang Ran, Guangmin Yang, Cheng Zeng. The application of genomic selection in pig cross breeding[J]. Hereditas(Beijing), 2020, 42(2): 145-152.

Figures/Tables 2

Table 1

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主要研究性状	主要结论	文献来源
总产仔数(h²=0.16); 流产率(h²=0.16)	两种性状GEBV和EBV估计值的平均准确性分别为0.82、0.83;当不断有高胎次母猪作为研究对象时基因组选择准确性分别降低至0.33~0.65范围内。	34
窝总产仔数	利用一步法计算的母猪基因组育种值准确性为0.28~0.49,传统BLUP的育种值准确性为0.22;公猪无显著差异。	35
窝总产仔数	GEBV值计算准确性比养殖公司宣称的计算结果准确性高20%。	36
总产仔数(不同群体h²分别为0.21,0.14,0.19)	表型值的实际值与预测值的平均相关性总是高于传统方法,杂交群体高0.26,纯繁群体高0.15~0.22。	37
繁殖阶段总产仔数, 窝总产仔数,死亡率	对比一步法、GBLUP、传统BLUP法等预测结果发现：一步法、GBLUP法预测准确性平均为0.171和0.209,BLUP法准确性平均为0.091。	38
总产仔数	与传统方法比,一步法使结果准确性提升19%且认为基因组选择的准确性高于传统的系谱指数和育种值选种,且低遗传力性状提高幅度最大。	39

主要研究性状	主要结论	文献来源
生长速度、背膘厚度、肉品质、饲料利用率、肌内脂肪含量	GBLUP提升遗传进展,减少近交系数。基因组选择比传统选择方法对性状的遗传进展提升高27%~33%。候选群体中只有亲缘关系和表型值明确的条件下,基因组选择对遗传改良速度的增加效果不明显。	40
平均日增重、背膘厚	利用基因组选择和RRBLUP法对性状的遗传进展有很大促进作用。利用高密度芯片可以降低连锁不平衡对性状的影响。	41
采食量、平均日增重、背膘厚、肌肉厚度、肌内脂肪含量	使用Bayes-A法,无论高密度芯片和低密度芯片对基因进行分析都不能使GEBV值获得较高准确性。	42
日采食量、平均日增重、背膘厚	日采食量、平均日增重、背膘厚GEBV准确性分别为0.508~0.531、0.506~0.532、0.308~0.362。	43
宰后45 min猪体温度和眼肌pH值	相比于RRBLUP、Bayes-A和Bayes-B等模型,异方差统计模型可增强GEBV的准确性,但准确性提高幅度较小。	44

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