基于芯片数据的长白猪繁殖性状基因组选择研究

doi:10.11843/j.issn.0366-6964.2025.01.020

Abstract

Abstract:

The study aimed to compare the prediction accuracy and computational efficiency of different genomic prediction models, explore the application value and prospects of support vector machine (SVM) regression and RandomForest regression in genomic prediction. In this study, the 50K liquid chip was used to predict the reproductive traits of 1 001 Landrace pigs by GBLUP, BayesB, BayesLASSO, support vector machine regression and RandomForest regression. It was found that support vector machine regression radial basis function kernel had the highest predictive accuracy of reproductive traits such as total litter size, live litter size and litter weight. The predictive accuracy of live litter size and litter weight reached the maximum value when the parameter C value was 1, and the predictive accuracy of total litter size reached the maximum value when the parameter C value was 2. When using RandomForest regression to evaluate parameters such as ntree, mtry and nodesize in reproductive traits such as total litter size, number of live litter and litter weight, it was found that the predictive accuracy showed a certain randomness with the change of parameters. The RandomForest regression model showed the highest accuracy in total litter size, live litter size and litter weight, followed by support vector machine regression. The predictive accuracy of GBLUP, BayesB, BayesLasso models was poor and consistent. In the correlation of cross-validation of different models, it can also be found that there is a strong correlation between the results of different models ranging from 0.806 to 0.995. Non-parametric machine learning models such as support vector machine regression and RandomForest regression have certain advantages in pig reproductive trait genome selection, but the running time limits the use of this machine learning algorithm to some extent. With the optimization of the algorithm, non-parametric machine learning models such as support vector machine regression and RandomForest regression will have a good application prospect.

Key words: Landrace pig, reproductive traits, gene chip, genomic selection, machine learning

CLC Number:

S828.3

YANG Wenpan, LIU Xiangjie, LUO Dongxiang, CHEN Menghui, XIE Ying, FANG Yuexin, LIN Tingyan, LI Aimin, LI Wenjing, DENG Zheng, DING Nengshui. Research on Genomic Selection of Reproductive Traits in Landrace Pigs Based on Chip Data[J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(1): 213-221.

Figures/Tables 5

Table 1

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References 40

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性状 Trait	加性方差 σ_a²	永久环境效应方差 σ_ep²	残差效应方差 σ_e²	遗传力 h²	重复力 R_ep²
总产仔数TNB	1.181±0.231	0.791±0.189	9.568±0.203	0.102±0.019	0.171±0.015
产活仔数NBA	0.948±0.200	0.615±0.174	9.777±0.207	0.084±0.017	0.138±0.015
窝重LWT	1.633±0.390	1.765±0.369	19.751±0.419	0.071±0.016	0.147±0.014

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Research on Genomic Selection of Reproductive Traits in Landrace Pigs Based on Chip Data

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