基于机器学习的猪生长性状基因组预测

doi:10.16288/j.yczz.23-120

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Hereditas(Beijing) ›› 2023, Vol. 45 ›› Issue (10): 922-932.doi: 10.16288/j.yczz.23-120

• Research Article • Previous Articles Next Articles

Genomic prediction of pig growth traits based on machine learning

Dong Chen^1,^2,³(), Shujie Wang^1,^2,³, Zhenjian Zhao^1,^2,³, Xiang Ji^1,^2,³, Qi Shen^1,^2,³, Yang Yu^1,^2,³, Shengdi Cui^1,^2,³, Junge Wang^1,^2,³, Ziyang Chen^1,^2,³, Jinyong Wang⁴, Zongyi Guo⁴, Pingxian Wu⁴, Guoqing Tang^1,^2,³()

1. Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
2. Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
3. State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
4. National Center of Technology Innovation for Pigs, Chongqing 402460, China

Received:2023-04-26 Revised:2023-08-14 Online:2023-10-20 Published:2023-08-16
Contact: Guoqing Tang E-mail:1123278154@qq.com;tyq003@163.com
Supported by:
Strategic Priority Research Program of the National Center of Technology Innovation for Pigs(NCTIP-XD/B01);Sichuan Science and Technology Program(2020YFN0024);Sichuan Science and Technology Program(2021ZDZX0008);Sichuan Science and Technology Program(2021YFYZ0030);Sichuan Innovation Team of Pig(sccxtd-2022-08)

Abstract

Abstract:

This study aimed to assess and compare the performance of different machine learning models in predicting selected pig growth traits and genomic estimated breeding values (GEBV) using automated machine learning, with the goal of optimizing whole-genome evaluation methods in pig breeding. The research employed genomic information, pedigree matrices, fixed effects, and phenotype data from 9968 pigs across multiple companies to derive four optimal machine learning models: deep learning (DL), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB). Through 10-fold cross-validation, predictions were made for GEBV and phenotypes of pigs reaching weight milestones (100 kg and 115 kg) with adjustments for backfat and days to weight. The findings indicated that machine learning models exhibited higher accuracy in predicting GEBV compared to phenotypic traits. Notably, GBM demonstrated superior GEBV prediction accuracy, with values of 0.683, 0.710, 0.866, and 0.871 for B100, B115, D100, and D115, respectively, slightly outperforming other methods. In phenotype prediction, GBM emerged as the best-performing model for pigs with B100, B115, D100, and D115 traits, achieving prediction accuracies of 0.547, followed by DL at 0.547, and then XGB with accuracies of 0.672 and 0.670. In terms of model training time, RF required the most time, while GBM and DL fell in between, and XGB demonstrated the shortest training time. In summary, machine learning models obtained through automated techniques exhibited higher GEBV prediction accuracy compared to phenotypic traits. GBM emerged as the overall top performer in terms of prediction accuracy and training time efficiency, while XGB demonstrated the ability to train accurate prediction models within a short timeframe. RF, on the other hand, had longer training times and insufficient accuracy, rendering it unsuitable for predicting pig growth traits and GEBV.

Key words: genomic estimated breeding values, growth traits, automated machine learning, performance comparison

Dong Chen, Shujie Wang, Zhenjian Zhao, Xiang Ji, Qi Shen, Yang Yu, Shengdi Cui, Junge Wang, Ziyang Chen, Jinyong Wang, Zongyi Guo, Pingxian Wu, Guoqing Tang. Genomic prediction of pig growth traits based on machine learning[J]. Hereditas(Beijing), 2023, 45(10): 922-932.

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References 30

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公司	芯片、系谱及固定效应数据	校正背膘表型数据	校正日龄表型数据
巨星农牧	2008	1554	1554
新希望	2093	2002	1997
明兴	1355	1342	1342
铁骑力士	2083	859	859
天兆	1575	1261	508
德康	854	853	853
合计	9968	7871	7113

参数名	GEBV估计参数	表型预测参数
ntrees	61	35
max_depth	7	8
min_rows	10	100
distribution	gaussian	gaussian
sample_rate	0.8	0.6
col_sample_rate	0.8	1
col_sample_rate_per_tree	0.8	1
histogram_type	UniformAdaptive	UniformAdaptive
categorical_encoding	Enum	Enum

参数名	GEBV估计参数	表型预测参数
ntrees	11	15
max_depth	20	20
sample_rate	0.632	0.632
histogram_type	UniformAdaptive	UniformAdaptive
categorical_encoding	Enum	Enum
distribution	gaussian	gaussian

参数名	GEBV估计参数	表型预测参数
distribution	gaussian	gaussian
categorical_encoding	OneHotInternal	OneHotInternal
ntrees	111	71
max_depth	5	10
learn_rate	0.3	5
col_sample_rate	0.8	0.8
colsample_bylevel	0.8	0.8
tree_method	exact	exact
booster	gbtree	dart
reg_lambda	0.001	100
reg_alpha	0.1	1
dmatrix_type	sparse	sparse
backend	cpu	cpu

参数	GEBV估计参数	表型预测参数
activation	RectifierWithDropout	RectifierWithDropout
hidden	[100,100,100]	[100, 100, 100]
epochs	222	315
epsilon	1.00E-09	1.00E-08
rate	0.005	0.005
input_dropout_ratio	0.2	0.05
hidden_dropout_ratios	[0.2, 0.2, 0.2]	[0.2, 0.2, 0.2]
distribution	gaussian	gaussian
tweedie_power	1.5	1.5
categorical_encoding	OneHotInternal	OneHotInternal

Genomic prediction of pig growth traits based on machine learning

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