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Optimization and application of genome prediction model in rapeseed: flowering time, yield components, and oil content as examples

文献类型： 外文期刊

作者： Yu, Wenkai 1 ; Wang, Xinao 1 ; Wang, Hui 1 ; Wang, Wenxiang 1 ; Cheng, Hongtao 1 ; Mei, Desheng 1 ; Jiang, Lixi 3 ; Hu, Qiong 1 ; Liu, Jia 1 ;

作者机构： 1.Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Oil Crops Res Inst, Key Lab Biol & Genet Improvement Oil Crops, Xudong 2nd Rd 2, Wuhan 430062, Peoples R China

2.Chinese Acad Agr Sci, Shenzhen Grad Sch, 7 Pengfei Rd, Shenzhen 518100, Peoples R China

3.Zhejiang Univ, Inst Crop Sci, 866 Yuhangtang Rd, Hangzhou 310058, Peoples R China

期刊名称：HORTICULTURE RESEARCH （ 影响因子：8.5； 五年影响因子：9.1 ）

ISSN： 2662-6810

年卷期： 2025 年 12 卷 8 期

页码：

收录情况： SCI

摘要： Rapeseed is the second largest oilseed crop in the world with short domestication and breeding history. This study developed a batch of genomic prediction models for flowering time (FT), oil content, and yield components in rapeseed. Using worldwide 404 breeding lines, the optimal prediction model for FT and five quality and yield traits was established by comparison with efficient traditional models and machine learning (ML) models. The results indicate that quantitative trait loci (QTLs) and significant variations identified by genome-wide association study (GWAS) can significantly improve the prediction accuracy of complex traits, achieving over 90% accuracy in predicting FT and thousand grain weight. The Genomic Best Linear Unbiased Prediction (GBLUP) and Bayes-Lasso models provided the most accurate prediction overall, while ML models such as GBDT (Gradient-Boosting Decision Trees) exhibited strong predictive performance. Our study provides genome selection solution for the high prediction accuracy and selection of complex traits in rapeseed breeding. The use of a diverse panel of 404 worldwide lines ensures that the findings are broadly applicable across different rapeseed breeding programs.

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