Scientia Agricultura Sinica ›› 2023, Vol. 56 ›› Issue (18): 3682-3692.doi: 10.3864/j.issn.0578-1752.2023.18.015

• ANIMAL SCIENCE·VETERINARY SCIENCE • Previous Articles     Next Articles

Research Progress on Machine Learning for Genomic Selection in Animals

LI MianYan(), WANG LiXian(), ZHAO FuPing()   

  1. Key Laboratory of Animal Genetics Breeding and Reproduction (Poultry), Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193
  • Received:2022-09-17 Accepted:2023-06-28 Online:2023-09-16 Published:2023-09-21
  • Contact: ZHAO FuPing


Genomic selection is defined as using the molecular marker information that covered the whole genome to estimate individual’s breeding values. Using genome information can avoid many problems caused by pedigree errors so as to improve selection accuracy and shorten breeding generation intervals. According to different statistical models, methods of estimated genomic breeding value (GEBV) can be divided into based on BLUP (best linear unbiased prediction) theory, based on Bayesian theory and others. At present, GBLUP and its improved method ssGBLUP have been widely employed. Accuracy is the most used evaluation metric for genomic selection models, which is to evaluate the similarity between the true value and the estimated value. The factors that affect the accuracy can be reflected from the model, which can be divided into controllable factors and uncontrollable factors. Traditional genomic selection methods have promoted the rapid development of animal breeding, but these methods are currently facing many challenges such as multi-population, multi-omics, and computing. What’s more, they cannot capture the nonlinear relationship between high-dimensional genomic data. As a branch of artificial intelligence, machine learning is very close to biological mastery of natural language processing. Machine learning extracts features from data and automatically summarizes the rules and use to make predictions for new data. For genomic information, machine learning does not require distribution assumptions, and all marker information can be considered in the model. Compared with traditional genomic selection methods, machine learning can more easily capture complex relationships between genotypes, phenotypes, and the environment. Therefore, machine learning has certain advantages in animal genomic selection. According to the amount and type of supervision received during training, machine learning can be classified into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The main difference is whether the input data is labeled. The machine learning methods currently applied in animal genomic selection are all supervised learning. Supervised learning can handle both classification and regression problems, requiring the algorithm to be provided with labeled data and the desired output. In recent years, the application of machine learning in animal genomic selection has been increasing, especially in dairy and beef cattle. In this review, machine learning algorithms are divided into three categories: single algorithm, ensemble algorithm and deep learning, and their research progress in animal genomic selection were summarized. The most used single algorithms are KRR and SVR, both of which use kernel tricks to learn nonlinear functions and map data to higher-dimensional kernel spaces in the original space. Currently commonly used kernel functions are linear kernel, cosine kernel, Gaussian kernel, and polynomial kernel. Deep learning, also known as a deep neural network, consists of multiple layers of connected neurons. An ensemble learning algorithm refers to fusing different learners together to obtain a stronger supervised model. In the past decade, the related literature on machine learning and deep learning has shown exponential growth. And its application in genomic selection is also gradually increasing. Although machine learning has obvious advantages in some aspects, it still faces many challenges in estimating the genetic breeding value of complex traits in animals. The interpretability of some models is low, which is not conducive to the adjustment of data, parameters, and features. Data heterogeneity, sparsity, and outliers can also cause data noise for machine learning. There are also problems such as overfitting, large marks and small samples, and parameter adjustment. Therefore, each step needs to be handled carefully while training the model. This paper introduced the traditional methods of genomic selection and the problems they face, the concept and classification of machine learning. We discussed the research progress and current challenges of machine learning in animal genomic selection. A Case and some application suggestions were given to provide a certain reference for the application of machine learning in animal genomic selection.

Key words: machine learning, deep learning, genomic selection, animal breeding

Table 1

Applications of machine learning in animal genomic selection"

Traditional methods
Machine learning algorithms
2008 猪 Pig 贝叶斯回归、BLUP 核回归、RKHS回归 [27]
2011 合成数据集 Synthetic dataset BayesLASSO SVR [28]
2012 奶牛 Dairy cattle BayesLASSO RKHS回归、RBFNN [29]
2013 模拟数据集 Simulated dataset GBLUP、BayesR、BayesLASSO RKHS回归、RBFNN、BRNN [30]
2014 猪 Pig LRC KRRC、KNN、KLRC [31]
2016 奶牛 Dairy cattle GBLUP RF [32]
2016 奶牛、马、玉米 Dairy cattle、horse、corn GBLUP RF、SVM、Boosting [33]
2018 奶牛、猪、松树 Dairy cattle、pig、pine GBLUP、BayesLASSO ABNN [34]
2020 内洛尔牛 Nellore cattle GBLUP、BayesB MLP、CNN、RF、Gradient Boosting [35]
2020 肉牛 Beef cattle GBLUP、BSLMM、BayesR KAML [36]
2021 肉牛、奶牛、松树 Beef cattle、cattle、pine GBLUP、BayesB SVR、KCRR [24]
2021 猪 Pig GBLUP、BayesLASSO SVR、BRNN、RF [37]
2021 猪 Pig GBLUP SVR、KRR、RF、Adaboost.RT [26]
2021 合成数据集 Synthetic dataset GBLUP、BayesB SELF [38]
2022 奶牛 Dairy cattle GBLUP、ssGBLUP、BayesHE SVR、KRR、RF、Adaboost.R2 [39]

Fig. 1

Number of relevant publications about machine learning per year in 2011-2022"

Table 2

The comparison of different methods of 3 traits"

Evaluation indicators
corr 0.780 (0.009) 0.769 (0.008) 0.776 (0.010) 0.778 (0.013)
mse 0.392 (0.016) 0.409 (0.017) 0.397 (0.016) 0.395 (0.016)
corr 0.860 (0.006) 0.813 (0.008) 0.813 (0.008) 0.809 (0.012)
mse 0.262 (0.010) 0.340 (0.011) 0.343 (0.014) 0.351 (0.015)
corr 0.729 (0.012) 0.726 (0.012) 0.742 (0.013) 0.740 (0.010)
mse 0.469 (0.019) 0.474 (0.019) 0.450 (0.018) 0.453 (0.023)
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