AUTHOR=Liang Mang , Chang Tianpeng , An Bingxing , Duan Xinghai , Du Lili , Wang Xiaoqiao , Miao Jian , Xu Lingyang , Gao Xue , Zhang Lupei , Li Junya , Gao Huijiang 

TITLE=A Stacking Ensemble Learning Framework for Genomic Prediction

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.600040

DOI=10.3389/fgene.2021.600040

ISSN=1664-8021

ABSTRACT=Machine learning (ML) is perhaps the most useful for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) was unsatisfactory in existing research. To improve the genomic predictions, we constructed a stacking ensemble learning framework (SELF) integrated three machine learning methods to predict genomic estimated breeding values (GEBVs). We evaluated the prediction ability of SELF by three real datasets with different genetic architecture and compared the prediction accuracy of SELF, base learners, GBLUP and BayesB. For each trait, SELF performed better than base learners, which included support vector regression (SVR), kernel ridge regression (KRR) and elastic net (ENET). The prediction accuracy of SELF had an average 7.70% improvement compared with GBLUP in three datasets. Except for the milk fat percentage (MFP) traits of the German Holstein dairy cattle dataset, SELF more robust than BayesB in the remaining traits. Therefore, we believed SEFL had the potential to be promoted to estimate GEBVs in other animals and plants.