Deep Learning for Population Genetic Inference

With the advent of large-scale whole-genome variation data, population geneticists are currently interested in considering increasingly more complex models. However, statistical inference in this setting is a challenging task, as computing the likelihood of a complex population genetic model is a difficult problem both theoretically and computationally. In this paper, we introduce a novel likelihood-free inference framework for population genomics by applying deep learning, which is an active area of machine learning research. To our knowledge, deep learning has not been employed in population genomics before. A recent survey article [1] provides an accessible introduction to deep learning, and we provide a high-level description below.