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=12 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 tool for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) is currently unsatisfactory. To improve the genomic predictions, we constructed a stacking ensemble learning framework (SELF), integrating three machine learning methods, to predict genomic estimated breeding values (GEBVs). The present study evaluated the prediction ability of SELF by analyzing three real datasets, with different genetic architecture; comparing the prediction accuracy of SELF, base learners, genomic best linear unbiased prediction (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 was, on average, 7.70% higher than GBLUP in three datasets. Except for the milk fat percentage (MFP) traits, of the German Holstein dairy cattle dataset, SELF was more robust than BayesB in all remaining traits. Therefore, we believed that SEFL has the potential to be promoted to estimate GEBVs in other animals and plants.