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

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.Author summary: Human genetic data are essential for studying disease, but they are also deeply personal, and privacy rules often prevent researchers from sharing them. One way around this is to create artificial genomes: synthetic data that reproduce the statistical patterns of a real population without belonging to any real person. To be useful, artificial genomes have to capture how variants across the genome relate to one another, and they must not reveal information about the people whose data was used to build them. Existing methods struggle to do both at once. We describe a model that learns which positions in the genome most strongly influence each other and connects them directly, instead of assuming that only neighboring positions matter. The model is built so that its probabilities can be calculated exactly, which lets it fill in a person’s missing genetic information directly rather than by first simulating artificial data. We find that this improves the accuracy of filling in missing variants, especially rare ones and those in populations that are poorly represented in public reference collections, while revealing less about the individuals used for training.