In breeding, single nucleotide polymorphisms (SNPs) in the genome are often used to predict quantitative phenotypes to assist breeding, thereby improving breeding efficiency. The traditional statistical analysis method is limited by many factors including missing data, and its performance sometimes can not meet the requirements. In this paper, we proposed a multi-scale feature convolutional neural network model (MSF-CNN) to predict plant traits. The model extracted SNP features at three different scales through convolution and analyzed the significance of SNP sites through the weight of the SNPs input into the model. The test results showed that MSF-CNN model performed with higher accuracy than the known methods and other deep learning models in phenotype prediction on the datasets with missing genotypic data. This paper also studied the contribution of genotype to traits through saliency map, and discovered several significant SNP loci. These results showed that, compared with other known methods available at present, the deep learning model proposed in this paper can obtain more accurate prediction results of quantitative phenotypes, and can also effectively and efficiently identify SNPs associated with genome-wide association research.
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