Application of ensemble learning to genomic selection in chinese simmental beef cattle. (22nd October 2020)

• Record Type: Journal Article
• Authors: Liang, Mang; Miao, Jian; Wang, Xiaoqiao; Chang, Tianpeng; An, Bingxing; Duan, Xinghai; Xu, Lingyang; Gao, Xue; Zhang, Lupei; Li, Junya; Gao, Huijiang
• Is Part Of: Journal of animal breeding and genetics. Volume 138:Number 3(2021)
• Page Start: 291
• Page End: 299
• Publication Date: 2020-10-22
• Subjects: ensemble learning -- genomic selection -- machine learning; Livestock -- Breeding -- Periodicals; Livestock -- Genetics -- Periodicals; 636.0820
• DOI: 10.1111/jbg.12514
• Languages: English
• ISSNs: 0931-2668

Abstract

Abstract: 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.