基因组选择在肉牛中的研究进展

doi:10.11843/j.issn.0366-6964.2024.09.002

Abstract

Abstract:

The development of genomic selection has left a profound mark on beef cattle breeding. In China, the multitude of beef cattle breeds with small population sizes has led to a slow pace in genetic breeding efforts. Most studies indicate that purebred beef cattle populations with large reference groups gain little benefit from multi-breed evaluations, while breeds with smaller reference groups can benefit from such evaluations without adversely affecting the assessment of purebred performance. In the field of beef cattle genetic breeding research, the use of genomic information provides an opportunity to enhance the genetic improvement of beef cattle, thereby determining the value of multi-breed reference groups such as hybrid breeds for beef cattle genetic breeding. Multi-breed genomic assessment, as a beneficial tool for increasing the genetic gain of populations, can evaluate the genomes of beef cattle populations using multi-breed models. This review focuses on the perspective of genomic selection in beef cattle, highlighting the use of genomic selection across multi-breed beef cattle breeds, discussing the mechanisms by which genomic selection is applied in beef cattle, with the aim of providing new insights for research into genomic selection in multi-breed beef cattle and exploring the potential applications of genomic selection.

Key words: beef cattle, Multi-species genome selection, Citespace software, visual analysis

CLC Number:

S813.2

Hongxia JIA, Zaixia LIU, Le ZHOU, Yanchun BAO, Chenxi HUO, Pengpeng ZUO, Mingjuan GU, Risu NA, Wenguang ZHANG. Research Progress of Genomic Selection in Beef Cattle[J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(9): 3757-3768.

Figures/Tables 4

Table 1

Application of genome selection in beef cattle"

分类 Category	作者 Author	方法 Method	应用 Application	文献 Reference
直接法 Direct algorithm	Lourenco等	ssGBLUP、BLUP	在安格斯牛的初生重、断奶重、断奶后增重、产犊难易四个性状中，ssGBLUP预测较BLUP预测有所提高	[23]
	Mehrban等	ssGBLUP、BLUP	评估单性状和多性状韩牛胴体性状和生长性状中，ssGBLUP模型的准确性(0.52)皆高于基于谱系的BLUP(0.34)	[24]
	Londoñ-Gil等	ssGBLUP、BLUP	在谱系缺失下内洛尔牛生长和奶牛生产力相关性状的预测准确性中，在已知谱系比例较低的情况下，ssGBLUP较BLUP的基因组估计育种价值准确性更高	[25]
	Onogi等	ssGBLUP、BLUP	对日本黑牛7种脂肪酸和大理石花纹、胴体质量、眼肌面积性状的预测准确性中，ssGBLUP较BLUP实际预测准确性更高	[26]
	Brzáková等	ssGBLUP、BLUP	在预测夏洛莱牛首次产犊年龄、产犊间隔和生产寿命性状时，有基因分的型动物使用ssGBLUP方法可以将GEBV的准确性平均提高19%	[27]
	Naserkheil等	ssGBLUP、PBLUP	在肉牛10种原始切割性状的预测准确性中，ssGBLUP(0.52~0.83)在10个性状上的表现都优于PBLUP(0.45~0.75)	[28]
	Valerio-Hernández等	ssGBLUP、GBLUP、PBLUP	在预测布劳恩牛的出生重、断奶重和一岁体重形状中，种群规模小的情况下ssGBLUP的表现不如PBLUP和GBLUP	[29]
	Bonifazi等	ssSNPBLUP、PBLUP	在比较肉牛断奶重性状的预测准确性中，ssSNPBLUP较PBLUP直接计算的EBV准确性平均提高了13.7%，母畜EBV的平均准确性提高了25.8%	[30]
	Alvarenga等	ssGBLUP、WssGBLUP	在预测不同训练种群情景的杂交肉牛GEBV的性能，基于单一性状的ssGBLUP适用于具有相似遗传结构的杂交种群的基因组评估	[31]
	Ribeiro等	ssGBLUP、WssGBLUP	在预测肉牛的剩余采食量性状时，使用WssGBLUP(0.29)比ssGBLUP(0.10)的预测更准确	[32]
	Mancin等	ssGBLUP、WssGBLUP、PBLUP	在估计伦德纳牛的平均日增重、平均肉质评分和平均屠宰率性状中，ssGBLUP较PBLUP计算的准确性平均提高30%, WssGBLUP可以观察到主要的偏差和离散	[33]
	Comin等	ssGBLUP、WssGBLUP、PBLUP	在海福特牛传染性角膜结膜炎发病率性状的预测准确中，使用WssGBLUP较ssGBLUP方法进一步提高预测准确性	[22]
	Naserkheil等	WssGBLUP	在韩牛一岁重和胴体性状的预测准确性中，使用WssGBLUP提高QTL检测统计	[34]
间接法 Indirect algorithm	Ma等	BayesR、GBLUP	在评估中国三种肉牛之间不同标记密度和遗传相关性的预测准确性中, BayesR将品种内预测准确性从7.1%提高到14.3%	[35]
	Zhu等	BayesA、BayesB、BayesCπ、BayesR、GBLUP	在中国西门塔尔牛的生长、胴体和肉质等20个性状的预测准确性中，BayesR的平均预测准确性较GBLUP增加了1.7%	[36]
	Zhu等	BayesA、BayesB、BayesCπ、BayesFA、BayesFB、BayesFCπ	对1 136头西门塔尔肉牛的胴体重量、活重和里脊肉重量三个性状的交叉验证分析表明，BayesFCπ的预测准确性较BayesCπ好	[37]
	Somavilla等	BayesA、BayesCπ、BGBLUP	对718头内洛尔肉牛平均日增重性状预测时BayesCπ模型导致的预测偏差较小，准确度范围为0.18至0.27	[38]
	Mehrban等	BayesC、Bayes LASSO、GBLUP	对1 183头韩牛的胴体性状的直接基因组育种值准确性评估中，BayesC比BayesLASSO和GBLUP的准确性高7%	[39]
	Li等	BayesB、BayesBH、BayesBH+ Bayes B、GBLUP、GHBLUP	在评估基于单倍型等位基因的基因组预测中，BayesBH+BayesB较BayesB模型的平均计算时间可以减少29.3%	[40]
机器学习 Machine learning algorithm	Liang等	TPE、KRR、SVR、GBLUP	对于中国西门塔尔肉牛和罗布洛利松种群的生长性状，KRR-TPE的预测	[41]
			准确性分别比GBLUP平均提高了8.73%和6.08%
	Alves等	SVR、BRANN、RF、GBLUP	将SVR与GBLUP和BLASSO进行比较，阴囊周长增加了8.3%，早期	[42]
		、Bayes LASSO	妊娠增加了4.5%，持久性增加了4.8%
	Li等	GRM、XGBoost、RF	来自RF和GBM的SNP亚群的性能明显优于基因组中均匀分布的亚群(0.18~0.29)	[43]
	Brito等	ANN、BayesRR、Bayes Lasso、BayesA、BayesB、BayesCπ、	ANN在内洛尔牛肉嫩度的基因组预测中达到了最高准确率(0.33)	[44]
	Nishio等	SVR、GK、RF、XGBoost、GBLUP、WGBLUP	XGBoost、SVR和GK在首次产犊年龄、产犊难易和妊娠期性状的预测准确性较BLUP分别提高了28.4%、19.0%和36.4%	[45]
	Liang等	SVR、KRR、RF、Adaboost.RT、GBLUP	4种机器学习方法相对于GBLUP的平均改进率分别为12.8%、14.9%、5.4%和14.4%	[46]
	Srivastava等	RF、XGBoost、SVM、GBLUP	XGBoost对胴体重量和大理石花纹性状的预测相关性较好	[47]
	Santana等	SVM、Adaboost、NB、DT、DNN、NN、MLP	SVM在内洛尔牛生殖性状基因组预测中具有更好的预测能力和计算时间效率	[48]
	Liang等	SVR、KRR、EN、SELF、GBLUP、BayesB	SELF在中国西门塔尔牛、德国荷斯坦牛、火炬松的9个性状中的平均预测准确性较GBLUP提高了7.70%	[49]
	An等	SVR、BayesB、GBLUP、K_CRR	KCRR在模拟GS群体、中国西门塔尔牛、德国荷斯坦牛、火炬松、猪4个品种的三个性状上预测准确性更好	[50]
	Mota等	SVR、MLNN、STGBLUP、BayesA、BayesB、BayesC、BRR、Bayes LASSO	在1 156头内洛尔牛中测量饲料效率性状的预测准确性，使用MLNN、SVR和MTGBLUP的准确性提高了8.9%、14.6%和13.7%	[51]

Table 1

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Fig. 2

Fig. 3

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Research Progress of Genomic Selection in Beef Cattle

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