Before revision:  Our experimental results, with a focus on accuracy and scalability , show the effectiveness and superiority compared to the state-of-the-art  .
After revision:  Our experimental results, with a focus on accuracy and scalability , show the effectiveness and superiority compared to the state-of-the-art approaches such as VariantSpark and ADMIXTURE. In particular, CEC can cluster targeted population groups in 22 hours with an adjusted rand index of 0.915, the normalized mutual information of 0.92, and the clustering accuracy of 89\%. Contrarily, the CAE classifier can predict the geographic ethnicity of unknown samples with an F1 and Mathews correlation coefficient(MCC) score of 0.9004 and 0.8245, respectively. To provide interpretations of the predictions, we identify significant biomarkers using gradient boosted trees(GBT) and SHAP. Overall, our approach is transparent and faster than the baseline methods, and scalable for 5\% to 100\% of the full human genome .