A Stacking Ensemble Learning Framework for Genomic Prediction

文献类型： 外文期刊

第一作者： Liang, Mang

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

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关键词： ensemble learning; stacking; genomic prediction; machine learning; prediction accuracy

期刊名称：FRONTIERS IN GENETICS （ 影响因子：3.258； 五年影响因子：4.005 ）

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年卷期： 2021 年 12 卷

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收录情况： SCI

摘要： 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.

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