Application of spherical variational autoencoders to human population genetic structure

This work introduces a novel framework to address population structure in human genetic studies. Starting from a simple yet widely used PCA-based approach, we propose a spherical variational autoencoder capable of enhancing and combining principal components to produce more informative results. We apply this framework to relevant datasets and show that the obtained embeddings can effectively capture population structure, even with a limited model architecture.