基因组选择和选配技术在猪育种中的应用

doi:10.11843/j.issn.0366-6964.2024.07.003

Abstract

Abstract:

Selection and mating are the core contents of pig breeding work, and scientific and rational selection and mating are important means to accelerate genetic improvement. With the rapid development of biological breeding technology, genomic selection (GS) and genomic mating (GM) techniques have played significant roles in the genetic improvement of pigs. GS is a method that uses molecular markers covering the entire genome to evaluate and predict the genetic potential of breeding target individuals, which achieves early selection by constructing models to predict individual genomic breeding values. GM, on the other hand, is about optimizing mating combinations to produce superior offspring for the rapid transmission of excellent traits. This article reviews the development, application progress and future prospects of GS and GM techniques in pig breeding, aiming to provide a reference in pig breeding in China.

Key words: genomic prediction, genomic mating, molecular breeding, pig

CLC Number:

S828.2

Tengfei DOU, Jiahao WU, Ziyi WU, Liyao BAI, Xinjian LI, Xuelei HAN, Ruimin QIAO, Kejun WANG, Feng YANG, Yining WANG, Xiuling LI. Application Progress of Genomic Selection and Mating Allocation Techniques in Pig Breeding[J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(7): 2795-2808.

Figures/Tables 3

Table 1

Table 2

Application of genomic selection in pig"

分类 Category	作者 Author	方法 Method	应用 Application	文献 Reference
直接法 Direct algorithm	Lopez等	ssGBLUP、BLUP	在24 828头纯种杜洛克群体中，ssGBLUP估计日增重、背膘厚度、眼肌面积、瘦肉百分比四个性状的GEBV准确度分别为0.30、0.33、0.38、0.40	[45]
	Song等	ssGBLUP、GBLUP、BLUP	在大白猪群体中胸宽性状研究中，双性状ssGBLUP模型较传统BLUP模型预测准确性最大增益为2.6%，ssGBLUP较GBLUP和BLUP相比，预测准确性均有所提高	[46]
	周隽等	ssGBLUP、GBLUP、BLUP	在杜洛克群体中，100 kg眼肌面积，100 kg背膘厚和100 kg日龄性状中ssGBLUP较BLUP预测准确性分别提高14.7~51.1%，较GBLUP预测准确性提升13.4%~45.7%	[47]
	Gao等	ssGBLUP	在长白猪采食量数据中，ssGBLUP预测准确性提高3.2%	[48]
	张锡飞等	ssGBLUP	在杜洛克、长白、大白猪生长性状中，ssGBLUP预测准确性平均提升2%~7%，繁殖性状中预测准确性平均提升1%~13%	[49]
间接法 Indirect algorithm	Kjetså等	BayesC、BayesGC	对长白猪6种母性性状比较了GBLUP，BayesC和BayesGC方法的预测准确性，结果表明3周内仔猪死亡率性状BayesGC较GBLUP相比准确性提高9.8%	[50]
	Lee等	BayesB	对2 432头大白猪的体高、体长和总乳头数性状用BayesB方法预测，预测准确性分别为0.52、0.60和0.51	[51]
	Do等	BayesA、BayesB、BayesLASSO	在杜洛克猪群体中，对日采食量，剩余采食量性状的研究显示, 预测准确性范围分别是0.508~0.531，0.506到0.532，其中BayesA的预测准确性较好	[52]
	彭潇等	BayesA、BayesB、BayesC、BayesLASSO、BayesR	对大白猪100 kg日龄、100 kg背膘厚和母猪乳头数3种性状进行了基因组选择分析，预测准确性都在0.35以下，指出对于低等遗传力性状可以选用BayesA方法	[53]
机器学习 Machine learning algorithm	Budhlakoti等	RKHS	在母猪繁殖性状中RKHS方法较其它方法相比，模型更适合数据，预测准确性更高	[54]
	Alves等	SVR、BRANN、RF	在猪繁殖性状中，机器学习方法较GBLUP，BLASSO方法预测准确性提高了6.3%和4.8%左右	[55]
	Wang等	SVR、KRR、RF、Adaboost.RT	在大白猪繁殖性状中，BayesHE方法较GBLUP方法预测准确性提高了3.8%~20.8%	[56]
	An等	KcRR、SVR	在生猪测试数据集中，KcRR较GBLUP预测准确性平均提高4.82%	[57]
	Wang等	DNNGP	在模拟数据中，DNNGP较GBLUP预测准确性提高61.5~164.2%	[58]
	Yin等	KAML	在测试数据中，KAML计算效率，准确性更高	[59]
	Piles等	SVM、Gradient Boosting	在猪剩余采食量性状中，SVR较GBLUP相比，预测模型稳定性和准确性更好	[60]

Table 2

Table 3

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方法 Method	所有标记的效应的假设分布 Assumed distribution of effect	所有标记的效应方差分布公式 Formula of effect variance distribution
rrBLUP	g_i~N(0, σ_{g_i}²)	constant
BayesA	g_i~N(0, σ_{g_i}²)	σ_{g_i}²~X^-2(v, s)
BayesB	$ \left\{\begin{array}{lr}g_i=0 & \pi \\g_i \sim N\left(0, \sigma_{g_i}^2\right) & 1-\pi\end{array}\right.$	$ \begin{cases}\sigma_{g_i}^2=0 & \pi \\ \sigma_{g_i}^2 \sim X^{-2}(v, s) & 1-\pi\end{cases}$
BayesC	$ \left\{\begin{array}{lr}g_i=0 & \pi \\g_i \sim N\left(0, \sigma_{g_i}^2\right) & 1-\pi \\\pi \sim U(0, 1)\end{array}\right.$	$ \left\{\begin{array}{lr}\sigma_{g_i}^2=0 & \pi \\\sigma_{g_i}^2 \sim X^{-2}(v, s) & 1-\pi \\\pi \sim U(0, 1)\end{array}\right.$
BayesCπ	$ \left\{\begin{array}{lr}g_i=0 & \pi \\g_i \sim N\left(0, \sigma_{g_i}^2\right) & 1-\pi \\\pi \sim U(0, 1)\end{array}\right.$	$ \left\{\begin{array}{lr}\sigma_{g_i}^2=0 & \pi \\ \sigma_{g_i}^2 \sim X^{-2}(v, s) & 1-\pi \\ \pi \sim U(0, 1)\end{array}\right.$
BayesDπ	$ \left\{\begin{array}{lr}g_i=0 & \pi \\g_i \sim N\left(0, \sigma_{g_i}^2\right) & 1-\pi \\\pi \sim U(0, 1) &\end{array}\right.$	$ \left\{\begin{array}{lr}\sigma_{\beta_i}^2=0 & \pi \\\sigma_{g_i}^2 \sim X^{-2}(v, \operatorname{Gamma}(1, 1)) & 1-\pi \\\pi \sim U(0, 1) &\end{array}\right.$
BayesLASSO	g_i~N(0, σ_{g_i}²)	$ \sigma_{g_i}^2 \sim {Expon}\left(\frac{\lambda^2}{2}\right)$
BayesR	$ \begin{cases}g \sim N\left(0, \sigma_{g_i}^2\right) & \pi_1 \\ g_i \sim N\left(0, 0.01 \sigma_{g_i}^2\right) & \pi_2 \\ g_i \sim N\left(0, 0.001 \sigma_{g_i}^2\right) & \pi_3 \\ g_i \sim N\left(0, 0.000\;1 \sigma_{g_i}^2\right) & \pi_4\end{cases}$	$ \sigma_{g_i}^2 \sim X^{-2}(v, s)$

作者Author	应用Application	文献Reference
Zhao等	对中国地方猪研究结果表明，OCS和GOCS方法表现出更多的遗传增益，还保持较高水平的遗传多样性	[75]
Tang等	在杜长大商品猪群体中进行选配，基因组选配较随机交配后代料重比下降0.12、30~120 kg日龄降低4.64 d，眼肌面积增加2.65 cm²	[76]
Zhao等	在纯种大白猪群中，以GROH为基础的交配方案ΔG比同质交配高0.9%~2.6%，近交增量率ΔF比同质交配低13%~83.3%	[77]
Pryce等	利用基因型信息及ROH矩阵选配较随机交配相比，基因组选配对后代遗传进展影响最小，育种效果更高效	[78]
Liu等	利用基因组信息选配与随机交配方案相比，近交增量降低28%~44%，遗传进展提高14%	[79]
张鹏飞等	比较3种基因组选配方案与同质选配的差异，基因组选配近交率低22.2%~94.1%，遗传方差高10.8%~32.2%	[80]
He等	对宁乡猪采用基因组GOCS选配时，近交率大多维持在每代5%以下，而不考虑选配时，近交率则增加到每代10.5%至15.3%之间	[81]

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Application Progress of Genomic Selection and Mating Allocation Techniques in Pig Breeding

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