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Genomic prediction with NetGP based on gene network and multi-omics data in plants

文献类型： 外文期刊

作者： Zhao, Longyang 1 ; Tang, Ping 1 ; Luo, Jinjing 2 ; Liu, Jianxiang 1 ; Peng, Xin 2 ; Shen, Mengyuan 2 ; Wang, Chengrui 2 ; Zhao, Junliang 2 ; Zhou, Degui 2 ; Fan, Zhilan 7 ; Chen, Yibo 2 ; Wang, Runfeng 6 ; Tang, Xiaoyan 8 ; Xu, Zhi 1 ; Liu, Qi 2 ;

作者机构： 1.Guilin Univ Elect Technol, Guilin, Peoples R China

2.Guangdong Acad Agr Sci, Rice Res Inst, Guangzhou, Peoples R China

3.Minist Agr & Rural Affairs, Coconstruct Minist & Prov, Key Lab Genet & Breeding High Qual Rice Southern C, Guangzhou, Peoples R China

4.Guangdong Key Lab New Technol Rice Breeding, Guangzhou, Peoples R China

5.Guangdong Rice Engn Lab, Guangzhou, Peoples R China

6.Guangdong Acad Agr Sci, Crops Res Inst, Guangdong Prov Key Lab Crop Genet Improvement, Guangzhou, Peoples R China

7.Beijing Normal Univ, Hong Kong Baptist Univ United Int Coll, Zhuhai, Peoples R China

8.South China Normal Univ, Sch Life Sci, Guangdong Prov Key Lab Biotechnol Plant Dev, Guangzhou, Guangdong, Peoples R China

关键词： genomic selection; feature selection; deep learning; multi-omics predictions; gene network

期刊名称：PLANT BIOTECHNOLOGY JOURNAL （ 影响因子：10.5； 五年影响因子：12.4 ）

ISSN： 1467-7644

年卷期： 2025 年 23 卷 4 期

页码：

收录情况： SCI

摘要： Genomic selection (GS) is a new breeding strategy. Generally, traditional methods are used for predicting traits based on the whole genome. However, the prediction accuracy of these models remains limited because they cannot fully reflect the intricate nonlinear interactions between genotypes and traits. Here, a novel single nucleotide polymorphism (SNP) feature extraction technique based on the Pearson-Collinearity Selection (PCS) is firstly presented and improves prediction accuracy across several known models. Furthermore, gene network prediction model (NetGP) is a novel deep learning approach designed for phenotypic prediction. It utilizes transcriptomic dataset (Trans), genomic dataset (SNP) and multi-omics dataset (Trans + SNP). The NetGP model demonstrated better performance compared to other models in genomic predictions, transcriptomic predictions and multi-omics predictions. NetGP multi-omics model performed better than independent genomic or transcriptomic prediction models. Prediction performance evaluations using several other plants' data showed good generalizability for NetGP. Taken together, our study not only offers a novel and effective tool for plant genomic selection but also points to new avenues for future plant breeding research.

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