Application of ensemble learning to genomic selection in chinese simmental beef cattle

文献类型： 外文期刊

第一作者： Liang, Mang

作者： Liang, Mang;Miao, Jian;Wang, Xiaoqiao;Chang, Tianpeng;An, Bingxing;Duan, Xinghai;Xu, Lingyang;Gao, Xue;Zhang, Lupei;Li, Junya;Gao, Huijiang

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关键词： ensemble learning; genomic selection; machine learning

期刊名称：JOURNAL OF ANIMAL BREEDING AND GENETICS （ 影响因子：2.38； 五年影响因子：2.54 ）

ISSN： 0931-2668

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收录情况： SCI

摘要： Genomic selection (GS) using the whole-genome molecular makers to predict genomic estimated breeding values (GEBVs) is revolutionizing the livestock and plant breeding. Seeking out novel strategies with higher prediction accuracy for GS has been the ultimate goal of breeders. With the rapid development of artificial intelligence, machine learning algorithms were applied to estimate the GEBVs increasingly. Although some machine learning methods have better performance in phenotype prediction, there is still considerable room for improvement. In this study, we applied an ensemble-learning algorithm, Adaboost.RT, which integrated support vector regression (SVR), kernel ridge regression (KRR) and random forest (RF), to predict genomic breeding values of three economic traits (carcass weight, live weight, and eye muscle area) in Chinese Simmental beef cattle. Predictive accuracy measured as the Pearson correlation between the corrected phenotypes and predicted GEBVs. Moreover, we compared the reliability of SVR, KRR, RF, Adaboost.RT and GBLUP methods. The result showed that machine learning methods outperformed GBLUP, and the average improvement of four machine learning methods over the GBLUP was 12.8%, 14.9%, 5.4% and 14.4%, respectively. Among the four machine learning methods, the reliability of Adaboost.RT was comparable to KRR with higher stability. We therefore believe that the Adaboost.RT algorithm is a reliable and efficient method for GS.

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