GEFormer: A genotype-environment interaction-based genomic prediction method that integrates the gating multilayer perceptron and linear attention mechanisms.

IF 17.1 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Molecular Plant Pub Date : 2025-03-03 Epub Date: 2025-01-28 DOI:10.1016/j.molp.2025.01.020
Zhou Yao, Mengting Yao, Chuang Wang, Ke Li, Junhao Guo, Yingjie Xiao, Jianbing Yan, Jianxiao Liu
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引用次数: 0

Abstract

The integration of genotypic and environmental data can enhance genomic prediction accuracy for crop field traits. Existing genomic prediction methods fail to consider environmental factors and the real growth environments of crops, resulting in low genomic prediction accuracy. In this work, we developed GEFormer, a genotype-environment interaction genomic prediction method that integrates gating multilayer perceptron (gMLP) and linear attention mechanisms. First, GEFormer uses gMLP to extract local and global features among SNPs. Then, Omni-dimensional Dynamic Convolution is used to extract the dynamic and comprehensive features of multiple environmental factors within each day, taking into consideration the real growth pattern of crops. A linear attention mechanism is used to capture the temporal features of environmental changes. Finally, GEFormer uses a gating mechanism to effectively fuse the genomic and environmental features. We examined the accuracy of GEFormer for predicting important agronomic traits of maize, rice, and wheat under three experimental scenarios: untested genotypes in tested environments, tested genotypes in untested environments, and untested genotypes in untested environments. The results showed that GEFormer outperforms six cutting-edge statistical learning methods and four machine learning methods, especially with great advantages under the scenario of untested genotypes in untested environments. In addition, we used GEFormer for three real-world breeding applications: phenotype prediction in unknown environments, hybrid phenotype prediction using an inbred population, and cross-population phenotype prediction. The results showed that GEFormer had better prediction performance in actual breeding scenarios and could be used to assist in crop breeding.

整合门控机制MLP和线性注意机制的玉米基因型-环境互作基因组预测方法GEFormer
基因型和环境数据的整合可以提高作物田间性状的预测精度。现有的基因组预测方法没有考虑环境因素,没有考虑作物的真实生长环境,导致基因组预测精度较低。本文提出了一种基于门控机制MLP和线性注意机制的玉米基因型-环境互作基因组预测方法——GEFormer。首先,利用门控多层感知器(gMLP)提取snp之间的局部和全局特征;然后,在考虑作物真实生长模式的情况下,利用全维动态卷积提取每天内多个环境因子的动态综合特征。线性注意机制用于捕捉环境变化的时间特征。最后,利用门控机制将基因组特征与环境特征有效融合。在测试环境下的未测试基因型、未测试环境下的测试基因型、未测试环境下的未测试基因型三种实验情景下,验证了GEFormer预测玉米、水稻和小麦重要农艺性状的准确性。实验结果表明,GEFormer优于6种前沿统计学习方法和4种机器学习方法。此外,它在未经测试的环境中未经测试的基因型的实验场景中显示出巨大的优势。此外,我们将GEFormer应用于三个实际育种应用:未知环境下的表型预测、使用近交系群体的杂交表型预测和跨群体表型预测。结果表明,GEFormer在实际育种场景中具有较好的预测效果,可用于辅助作物育种。
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来源期刊
Molecular Plant
Molecular Plant 植物科学-生化与分子生物学
CiteScore
37.60
自引率
2.20%
发文量
1784
审稿时长
1 months
期刊介绍: Molecular Plant is dedicated to serving the plant science community by publishing novel and exciting findings with high significance in plant biology. The journal focuses broadly on cellular biology, physiology, biochemistry, molecular biology, genetics, development, plant-microbe interaction, genomics, bioinformatics, and molecular evolution. Molecular Plant publishes original research articles, reviews, Correspondence, and Spotlights on the most important developments in plant biology.
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