2019/11/27 by Daniel Mas Montserrat, Carlos Bustamante, Carlos D. Bustamante +4 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #FOS: Biological sciences #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Genomics (q-bio.GN) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #q-bio.GN #stat.ML
paper · pdf · doi:10.48550/arxiv.1911.13220
arxiv created 2019/11/27 · openalex publication_date 2019/11/27 · arxiv updated 2019/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.