PT  - JOURNAL ARTICLE
AU  - Zhanpeng Wang
AU  - Jiaping Wang
AU  - Michael Kourakos
AU  - Nhung Hoang
AU  - Hyong Hark Lee
AU  - Iain Mathieson
AU  - Sara Mathieson
TI  - Automatic inference of demographic parameters using Generative Adversarial Networks
AID  - 10.1101/2020.08.05.237834
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2020.08.05.237834
4099  - http://biorxiv.org/content/early/2021/02/09/2020.08.05.237834.short
4100  - http://biorxiv.org/content/early/2021/02/09/2020.08.05.237834.full
AB  - Population genetics relies heavily on simulated data for validation, inference, and intuition. In particular, since the evolutionary “ground truth” for real data is always limited, simulated data is crucial for training supervised machine learning methods. Simulation software can accurately model evolutionary processes, but requires many hand-selected input parameters. As a result, simulated data often fails to mirror the properties of real genetic data, which limits the scope of methods that rely on it. Here, we develop a novel approach to estimating parameters in population genetic models that automatically adapts to data from any population. Our method, pg-gan, is based on a generative adversarial network that gradually learns to generate realistic synthetic data. We demonstrate that our method is able to recover input parameters in a simulated isolation-with-migration model. We then apply our method to human data from the 1000 Genomes Project, and show that we can accurately recapitulate the features of real data.Competing Interest StatementThe authors have declared no competing interest.