Genomic prediction method based on channel spatial attention mechanism

Single Nucleotide Polymorphism (SNP) is a common form of genetic variation, and genomic prediction is an emerging technique that utilizes SNP information to predict phenotypes in animals and plants. It is gradually being applied in animal and plant breeding as well as human disease risk assessment. Traditional statistical learning methods can only focus on linear interactions between the genome and phenotypes. Machine learning methods and deep learning methods have become popular due to their ability to recognize non-linear interactions between SNPs. However, existing deep learning methods often only learn short-distance interactions between SNPs and overlook long-distance interactions. Therefore, we propose a genomic prediction method called DCNNCSA (DualCNN Channel Spatial Attention) based on a channel spatial attention mechanism. DCNNCSA first uses a dual-branch convolutional neural network to extract features in the channel and spatial dimensions. Then, it utilizes a channel spatial attention mechanism to identify long-distance interactions between SNPs, thereby improving the accuracy of genomic prediction. Experimental evaluations are conducted on datasets including wheat 2000 and wheat 599 datasets. The results demonstrate that DCNNCSA outperforms rrBLUP, LASSO, XGBoost, Random Forest, DeepGS, and DLGWAS methods in terms of prediction accuracy.