机器学习在畜禽基因组选择中的应用进展

doi:10.11843/j.issn.0366-6964.2024.07.001

摘要/Abstract

摘要：

基因组选择的广泛应用大大加快了畜禽的遗传进展。随着畜禽芯片的商业化和测序成本的不断降低，可获得的畜禽基因组信息越来越丰富。基因型标记数量远远超过具有表型数据的样本个数，基因组信息之间的关系更加复杂等问题也随之出现，使得最佳线性无偏预测(best linear unbiased prediction，BLUP)和Bayes等传统评估模型的使用受到极大限制。机器学习算法不依赖于预定的方程模型，可以更好地处理非线性关系，为以上问题提供了解决方案，因此逐步被应用于基因组选择中。本文综述了基因组选择的发展，阐述了几种常用于基因组选择中的机器学习算法的原理，并对机器学习在畜禽基因组选择中的应用现状和实现方式进行了总结，最后对机器学习在畜禽育种中面临的问题进行了探讨并对其发展进行了展望。

关键词: 基因组选择, 畜禽, 机器学习, 算法, 模型

Abstract:

The extensive application of genomic selection has significantly accelerated genetic advancements in livestock and poultry. With the commercialization of livestock and poultry chips and the continuous reduction of sequencing costs, the available genomic information for livestock and poultry has become increasingly abundant. Many challenges have arisen in genomic selection, such as the number of genotypic markers far exceeds the number of samples with phenotype data, and the relationships between genomic information have become more complex. These problems greatly restrict the use of traditional evaluation models such as best linear unbiased prediction (BLUP) and Bayes. Machine learning algorithms, which do not rely on predetermined equation models, have demonstrated superior capability in handling nonlinear relationships. Machine learning methods can offer solutions to the aforementioned challenges, thus they are gradually being applied in genomic selection. This paper reviewed the developmental of genomic selection, elucidated the principles of several commonly used machine learning algorithms. Furthermore, the current status and implementation methods of machine learning in livestock and poultry genomic selection were summerized. Finally, the challenges faced by machine learning in livestock and poultry breeding, as well as its development prospects were discussed.

Key words: genomic selection, livestock and poultry, machine learning, algorithm, model

中图分类号:

S813.1

王进部, 李佳, 任德明, 王立贤, 王立刚. 机器学习在畜禽基因组选择中的应用进展[J]. 畜牧兽医学报, 2024, 55(7): 2775-2785.

Jinbu WANG, Jia LI, Deming REN, Lixian WANG, Ligang WANG. Progress in the Application of Machine Learning in Livestock and Poultry Genomic Selection[J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(7): 2775-2785.

图/表 6

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核函数Kernel functions	表达式Expression
线性核Linear kernel	K(x·y)=(x·y)
高斯核Gaussian kernel	$K(x, y)=\exp \left(-\frac{\\|x-y\\|^2}{2 \sigma^2}\right)$
多项式核Polynomial kernel	K(x·y)=[(x·y)+c]^d
Sigmoid核Sigmoid kernel	K(x, y)=tanh[v(x, y)+c]

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