Creating Artificial Human Genomes Using Generative Models
Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent from population genetics. Yet a known limitation of this field is the reduced access to many genetic databases due to concerns about violations of individual privacy, although they would provide a rich resource for data mining and integration towards advancing genetic studies. Here we demonstrate that we can train deep generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) to learn the high dimensional distributions of real genomic datasets and create artificial genomes (AGs). Additionally, we ensure none to little privacy loss while generating high quality AGs. To illustrate the promising outcomes of our method, we show that augmenting reference panels with AGs improves imputation quality for low frequency alleles. In summary, AGs have the potential to become valuable assets in genetic studies by providing high quality anonymous substitutes for private databases.
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Burak Yelmen (add twitter)
Aurelien Decelle (add twitter)
Linda Ongaro (edit)
Davide Marnetto (add twitter)
Corentin Tallec (edit)
Francesco Montinaro (edit)
Cyril Furtlehner (add twitter)
Luca Pagani (edit)
Flora Jay (add twitter)
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Bioinformatics

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