DPCformer用深度学习提升作物基因组预测精度，尤其在小样本下表现更优。
DPCformer: An Interpretable Deep Learning Model for Genomic Prediction in Crops
- 融合卷积与自注意力机制，建模复杂基因型-表型关系
- 在玉米、棉花等作物上提升预测准确率最高达8.37%
- 具备可解释性，适合育种研究与小样本场景
基因组选择（GS）利用全基因组信息预测作物表型以加速育种。传统方法在复杂性状和大规模数据下预测精度不足。本文提出DPCformer，一种结合卷积神经网络与自注意力机制的深度学习模型，用于建模复杂基因型-表型关系。我们在五个作物（玉米、棉花、番茄、水稻、鹰嘴豆）的13个性状上应用该模型。采用染色体顺序排列的8维one-hot编码表示SNP数据，并使用PMF算法进行特征选择。评估显示DPCformer优于现有方法：在玉米中，抽雄天数和株高等性状准确率提升最高达2.92%；在棉花中，纤维性状准确率提升达8.37%；在小样本番茄数据上，关键性状的皮尔逊相关系数提高最多57.35%；在鹰嘴豆中，产量相关性提升16.62%。DPCformer表现出更高精度、小样本鲁棒性及增强可解释性，为精准育种和全球粮食安全提供有力工具。
原文摘要 · Abstract (English)
Genomic Selection (GS) uses whole-genome information to predict crop phenotypes and accelerate breeding. Traditional GS methods, however, struggle with prediction accuracy for complex traits and large datasets. We propose DPCformer, a deep learning model integrating convolutional neural networks with a self-attention mechanism to model complex genotype-phenotype relationships. We applied DPCformer to 13 traits across five crops (maize, cotton, tomato, rice, chickpea). Our approach uses an 8-dimensional one-hot encoding for SNP data, ordered by chromosome, and employs the PMF algorithm for feature selection. Evaluations show DPCformer outperforms existing methods. In maize datasets, accuracy for traits like days to tasseling and plant height improved by up to 2.92%. For cotton, accuracy gains for fiber traits reached 8.37%. On small-sample tomato data, the Pearson Correlation Coefficient for a key trait increased by up to 57.35%. In chickpea, the yield correlation was boosted by 16.62%. DPCformer demonstrates superior accuracy, robustness in small-sample scenarios, and enhanced interpretability, providing a powerful tool for precision breeding and addressing global food security challenges.
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