GPC: An expressive and tractable deep generative model for genetic variation data

GPC: An expressive and tractable deep generative model for genetic variation data | bioRxiv Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results GPC: An expressive and tractable deep generative model for genetic variation data View ORCID Profile Prateek Anand , View ORCID Profile Anji Liu , Meihua Dang , View ORCID Profile Boyang Fu , View ORCID Profile Xinzhu Wei , View ORCID Profile Guy Van den Broeck , View ORCID Profile Sriram Sankararaman doi: https://doi.org/10.1101/2023.05.16.541036 Prateek Anand 1 Department of Computer Science, UCLA , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Prateek Anand For correspondence: prateek{at}cs.ucla.edu Anji Liu 2 Department of Computer Science, National University of Singapore , Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anji Liu Meihua Dang 3 Department of Computer Science, Stanford University , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Boyang Fu 4 Department of Biomedical Informatics , Harvard Medical School, Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Boyang Fu Xinzhu Wei 5 Department of Computational Biology, Cornell University , Ithaca, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Xinzhu Wei Guy Van den Broeck 1 Department of Computer Science, UCLA , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Guy Van den Broeck Sriram Sankararaman 1 Department of Computer Science, UCLA , Los Angeles, CA, USA 6 Department of Human Genetics , UCLA, Los Angeles, CA, USA 7 Department of Computational Medicine, UCLA , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sriram Sankararaman Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Generative models play an important role in population genetics, being used to generate artificial genomes (AGs) that help benchmark methods, test evolutionary hypotheses, and construct reference panels for imputation while working around data-sharing restrictions. Existing generative models of genetic variation, however, struggle to faithfully express dependencies in the data while retaining tractability and preserving privacy. We introduce Genetic Probabilistic Circuits (GPC), a deep generative model for genetic variation data based on hidden Chow-Liu trees represented as probabilistic circuits. GPC generalizes traditional hidden Markov models by allowing arbitrary tree structures over latent variables, enabling it to capture long-range dependencies among SNPs. GPC is tractable, supporting exact computation of marginal and conditional probabilities that enables both AG generation and direct genotype imputation (avoiding the need for simulating AGs). We show that GPC generates AGs that accurately reproduce population structure and linkage disequilibrium patterns across a range of length scales. Compared to other deep generative approaches, GPC consistently improves imputation accuracy, with particularly strong gains for low-frequency variants and in populations that are not well-represented in public reference panels. Finally, we show that GPC better preserves privacy of the training data, thereby providing a practical framework for AG generation in settings with limited data access. Competing Interest Statement The authors have declared no competing interest. Footnotes ↵ * Joint supervision Imputation Figures 5, 6, S5, and S6 now properly extend to 0.5 MAF; Author affiliations updated https://github.com/sriramlab/GPC Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license . Back to top Previous Next Posted April 19, 2026. 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Share GPC: An expressive and tractable deep generative model for genetic variation data Prateek Anand , Anji Liu , Meihua Dang , Boyang Fu , Xinzhu Wei , Guy Van den Broeck , Sriram Sankararaman bioRxiv 2023.05.16.541036; doi: https://doi.org/10.1101/2023.05.16.541036 Share This Article: Copy Citation Tools GPC: An expressive and tractable deep generative model for genetic variation data Prateek Anand , Anji Liu , Meihua Dang , Boyang Fu , Xinzhu Wei , Guy Van den Broeck , Sriram Sankararaman bioRxiv 2023.05.16.541036; doi: https://doi.org/10.1101/2023.05.16.541036 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetics Subject Areas All Articles Animal Behavior and Cognition (8164) Biochemistry (19027) Bioengineering (15171) Bioinformatics (45807) Biophysics (22958) Cancer Biology (20248) Cell Biology (27495) Clinical Trials (138) Developmental Biology (14233) Ecology (21529) Epidemiology (2067) Evolutionary Biology (25940) Genetics (16436) Genomics (23979) Immunology (19175) Microbiology (43486) Molecular Biology (18481) Neuroscience (95839) Paleontology (717) Pathology (3064) Pharmacology and Toxicology (5253) Physiology (8339) Plant Biology (16378) Scientific Communication and Education (2123) Synthetic Biology (4675) Systems Biology (10450) Zoology (2436)

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