Deep Learning for Population Genetic Inference

Abstract

Given genomic variation data from multiple individuals, computing the likelihood of complex
population genetic models is often infeasible. To circumvent this problem, we introduce a
novel likelihood-free inference framework by applying deep learning, a powerful modern
technique in machine learning. Deep learning makes use of multilayer neural networks to
learn a feature-based function from the input (e.g., hundreds of correlated summary statistics
of data) to the output (e.g., population genetic parameters of interest).We demonstrate
that deep learning can be effectively employed for population genetic inference and learning
informative features of data. As a concrete application, we focus on the challenging problem
of jointly inferring natural selection and demography (in the form of a population size change
history). Our method is able to separate the global nature of demography from the local
nature of selection, without sequential steps for these two factors. Studying demography
and selection jointly is motivated by Drosophila, where pervasive selection confounds
demographic analysis. We apply our method to 197 African Drosophila melanogaster
genomes from Zambia to infer both their overall demography, and regions of their genome
under selection. We find many regions of the genome that have experienced hard sweeps,
and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly,
we find that soft sweeps and balancing selection occur more frequently closer to
the centromere of each chromosome. In addition, our demographic inference suggests that
previously estimated bottlenecks for African Drosophila melanogaster are too extreme.