  Local ancestry inference (LAI) allows identification of the ancestry of all
chromosomal segments in admixed individuals, and it is a critical step in the
analysis of human genomes with applications from pharmacogenomics and precision
medicine to genome-wide association studies. In recent years, many LAI
techniques have been developed in both industry and academic research. However,
these methods require large training data sets of human genomic sequences from
the ancestries of interest. Such reference data sets are usually limited,
proprietary, protected by privacy restrictions, or otherwise not accessible to
the public. Techniques to generate training samples that resemble real haploid
sequences from ancestries of interest can be useful tools in such scenarios,
since a generalized model can often be shared, but the unique human sample
sequences cannot. In this work we present a class-conditional VAE-GAN to
generate new human genomic sequences that can be used to train local ancestry
inference (LAI) algorithms. We evaluate the quality of our generated data by
comparing the performance of a state-of-the-art LAI method when trained with
generated versus real data.
