This population doesn’t exist: learning the distribution of evolutionary histories with generative adversarial networks

Numerous studies over the last decade have demonstrated the utility of machine learning methods when applied to population genetic tasks. More recent studies show the potential of deep learning methods in particular, which allow researchers to approach problems without making prior assumptions about how the data should be summarized or manipulated, instead learning their own internal representation of the data in an attempt to maximize inferential accuracy. One type of deep neural network, called Generative Adversarial Networks (GANs), can even be used to generate new data, and this approach has been used to create individual artificial human genomes free from privacy concerns. In this study, we further explore the application of GANs in population genetics by designing and training a network to learn the statistical distribution of population genetic alignments (i.e. data sets consisting of sequences from an entire population sample) under several diverse evolutionary histories—the first GAN capable of performing this task. After testing multiple different neural network architectures, we report the results of a fully differentiable Deep-Convolutional Wasserstein GAN with gradient penalty that is capable of generating artificial examples of population genetic alignments that successfully mimic key aspects of the training data, including the site frequency spectrum, differentiation between populations, and patterns of linkage disequilibrium. We demonstrate consistent training success across various evolutionary models, including models of panmictic and subdivided populations, populations at equilibrium and experiencing changes in size, and populations experiencing either no selection or positive selection of various strengths, all without the need for extensive hyperparameter tuning. Overall, our findings highlight the ability of GANs to learn and mimic population genetic data, and suggest future areas where this work can be applied in population genetics research that we discuss herein. AUTHOR SUMMARY The application of deep-learning to biological problems has expanded greatly over the last decade. One type of deep neural network, called a Generative Adversarial Network (GAN), attempts to generate artificial examples of a given type of data by learning to fool a discriminator that is simultaneously learning to discriminate between real and artificial examples. In this study, we design a GAN whose purpose is to generate artificial examples of genetic alignments from biological populations of varying evolutionary histories—essentially learning the statistical distribution of those evolutionary histories. We show that our GAN is able to mimic key aspects of the genetic alignments relevant to population genetics, and that the GAN does not require extensive tuning of the network parameters. Ultimately, this work demonstrates the ability of these networks to learn and mimic population genetic data, and highlights future areas where this work can be applied and expanded.