Exploration of deep-learning based classification with human SNP image graphs

Background With the advancement of NGS platform, large numbers of human variations and SNPs are discovered in human genomes. It is essential to utilize these massive nucleotide variations for the discovery of disease genes and human phenotypic traits. There are new challenges in utilizing such large numbers of nucleotide variants for polygenic disease studies. In recent years, deep-learning based machine learning approaches have achieved great successes in many areas, especially image classifications. In this preliminary study, we are exploring the deep convolutional neural network algorithm in genome-wide SNP images for the classification of human populations. Results We have processed the SNP information from more than 2,500 samples of 1000 genome project. Five major human races were used for classification categories. We first generated SNP image graphs of chromosome 22, which contained about one million SNPs. By using the residual network (ResNet 50) pipeline in CNN algorithm, we have successfully obtained classification models to classify the validation dataset. F1 scores of the trained CNN models are 95 to 99%, and validation with additional separate 150 samples indicates a 95.8% accuracy of the CNN model. Misclassification was often observed between the American and European categories, which could attribute to the ancestral origins. We further attempted to use SNP image graphs in reduced color representations or images generated by spiral shapes, which also provided good prediction accuracy. We then tried to use the SNP image graphs from chromosome 20, almost all CNN models failed to classify the human race category successfully, except the African samples. Conclusions We have developed a human race prediction model with deep convolutional neural network. It is feasible to use the SNP image graph for the classification of individual genomes.