In:
Molecular Ecology Resources, Wiley, Vol. 21, No. 8 ( 2021-11), p. 2689-2705
Kurzfassung:
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 are 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 fail 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.
Materialart:
Online-Ressource
ISSN:
1755-098X
,
1755-0998
DOI:
10.1111/1755-0998.13386
Sprache:
Englisch
Verlag:
Wiley
Publikationsdatum:
2021
ZDB Id:
2406833-0
SSG:
12