VMGP: A unified variational auto-encoder based multi-task model for multi-phenotype, multi-environment, and cross-population genomic selection in plants

文献类型： 外文期刊

第一作者： Zhao, Xiangyu

作者： Zhao, Xiangyu;Li, Jinlong;Zhang, Dongfeng;Zhang, Qiusi;Liu, Zhongqiang;Wang, Kaiyi;Sun, Fuzhen;Tan, Changwei;Ma, Hongxiang;Zhao, Xiangyu;Wang, Kaiyi;Zhao, Xiangyu;Zhang, Dongfeng;Liu, Zhongqiang;Wang, Kaiyi

作者机构：

关键词： Genomic selection; Variational auto-encoder; Multi-task; Deep learning; Genomic prediction

期刊名称：ARTIFICIAL INTELLIGENCE IN AGRICULTURE （ 影响因子：12.4； 五年影响因子：12.7 ）

ISSN： 2097-2113

年卷期： 2025 年 15 卷 4 期

页码：

收录情况： SCI

摘要： Plant breeding stands as a cornerstone for agricultural productivity and the safeguarding of food security. The advent of Genomic Selection heralds a new epoch in breeding, characterized by its capacity to harness wholegenome variation for genomic prediction. This approach transcends the need for prior knowledge of genes associated with specific traits. Nonetheless, the vast dimensionality of genomic data juxtaposed with the relatively limited number of phenotypic samples often leads to the "curse of dimensionality", where traditional statistical, machine learning, and deep learning methods are prone to overfitting and suboptimal predictive performance. To surmount this challenge, we introduce a unified Variational auto-encoder based Multi-task Genomic Prediction model (VMGP) that integrates self-supervised genomic compression and reconstruction with multiple prediction tasks. This approach provides a robust solution, offering a formidable predictive framework that has been rigorously validated across public datasets for wheat, rice, and maize. Our model demonstrates exceptional capabilities in multi-phenotype and multi-environment genomic prediction, successfully navigating the complexities of cross-population genomic selection and underscoring its unique strengths and utility. Furthermore, by integrating VMGP with model interpretability, we can effectively triage relevant single nucleotide polymorphisms, thereby enhancing prediction performance and proposing potential cost-effective genotyping solutions. The VMGP framework, with its simplicity, stable predictive prowess, and open-source code, is exceptionally wellsuited for broad dissemination within plant breeding programs. It is particularly advantageous for breeders who prioritize phenotype prediction yet may not possess extensive knowledge in deep learning or proficiency in parameter tuning. (c) 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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