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使用SNP上下文和基于DNABERT-2的全基因组特征嵌入的作物表型预测

Huan Li^1,2, Yunpeng Cui^3,4, Tan Sun^1,2

¹Institute of Agricultural Information, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.

|January 13, 2026

概括

使用DNABERT-2的新基因组预测模型捕获了SNP上下文,提高了作物育种的准确性. 对于低到中等可遗传性特征,SNP上下文模式优异,而全基因组模式则提高了高度可遗传性特征的准确性.

科学领域:

基因组学就是基因组学.
植物育种植物育种
机器学习机器学习

背景情况:

传统的基因组预测模型忽视了SNP序列背景对作物表型的影响.
精英作物育种需要精确的基因组预测,以加速遗传收益.

研究的目的:

开发用于基因组预测的新型特征嵌入方法,其中包括SNP序列上下文.
与传统方法相比,评估这些方法的性能.

主要方法:

利用DNABERT-2,一个跨物种基因组基础模型,用于SNP-context和全基因组特征嵌入.
应用于高维嵌入的维度减小技术 (PCA,PLS).
测试了大米和玉米数据集的测试方法,分析了不同特征维度和上下文长度的预测准确性.

主要成果:

具有SNP上下文嵌入的机器学习模型显示出比传统SNP特征更高的准确性和更低的MAE.
最佳的上下文长度 (1000-3000 bp) 改善了低至中等遗传性特征的预测.
全基因组嵌入增强了高度遗传性特征的预测准确性,优于现有的深度学习模型.

结论:

基于DNABERT-2的特征嵌入有效地捕捉SNP上下文,克服传统模型的局限性.
对于低到中等可遗传性特征,SNP上下文模式是最佳的;对于高度可遗传性特征,全基因组模式是优越的.
为植物育种者提供灵活的框架,以根据特征复杂性选择预测方法,加速作物改进.

关键词:

在DNABert-2中使用.缩小尺寸的缩小方式基因组预测基因组预测部分最小正方形.主要组件分析的主要组件分析.嵌入SNP-上下文功能嵌入.整基因组的特征嵌入方式.

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