  Local-ancestry inference (LAI), also referred to as ancestry deconvolution,
provides high-resolution ancestry estimation along the human genome. In both
research and industry, LAI is emerging as a critical step in DNA sequence
analysis with applications extending from polygenic risk scores (used to
predict traits in embryos and disease risk in adults) to genome-wide
association studies, and from pharmacogenomics to inference of human population
history. While many LAI methods have been developed, advances in computing
hardware (GPUs) combined with machine learning techniques, such as neural
networks, are enabling the development of new methods that are fast, robust and
easily shared and stored. In this paper we develop the first neural network
based LAI method, named LAI-Net, providing competitive accuracy with
state-of-the-art methods and robustness to missing or noisy data, while having
a small number of layers.
