  The study of genetic variants can help find correlating population groups to
identify cohorts that are predisposed to common diseases and explain
differences in disease susceptibility and how patients react to drugs. Machine
learning algorithms are increasingly being applied to identify interacting GVs
to understand their complex phenotypic traits. Since the performance of a
learning algorithm not only depends on the size and nature of the data but also
on the quality of underlying representation, deep neural networks can learn
non-linear mappings that allow transforming GVs data into more clustering and
classification friendly representations than manual feature selection. In this
paper, we proposed convolutional embedded networks in which we combine two DNN
architectures called convolutional embedded clustering and convolutional
autoencoder classifier for clustering individuals and predicting geographic
ethnicity based on GVs, respectively. We employed CAE-based representation
learning on 95 million GVs from the 1000 genomes and Simons genome diversity
projects. Quantitative and qualitative analyses with a focus on accuracy and
scalability show that our approach outperforms 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.
