Geonomics: Forward-Time, Spatially Explicit, and Arbitrarily Complex Landscape Genomic Simulations

Abstract

Understanding the drivers of spatial patterns of genomic diversity has emerged as a major goal of evolutionary genetics. The flexibility of forward-time simulation makes it especially valuable for these efforts, allowing for the simulation of arbitrarily complex scenarios in a way that mimics how real populations evolve. Here, we present Geonomics, a Python package for performing complex, spatially explicit, landscape genomic simulations with full spatial pedigrees that dramatically reduces user workload yet remains customizable and extensible because it is embedded within a popular, general-purpose language. We show that Geonomics results are consistent with expectations for a variety of validation tests based on classic models in population genetics and then demonstrate its utility and flexibility with a trio of more complex simulation scenarios that feature polygenic selection, selection on multiple traits, simulation on complex landscapes, and non-stationary environmental change. We then discuss runtime, which is primarily sensitive to landscape raster size, memory usage, which is primarily sensitive to maximum population size, and other caveats related to the model’s methods for approximating recombination and movement. Taken together, our tests and demonstrations show that Geonomics provides an efficient and robust platform for population genomic simulations that capture complex spatial and evolutionary dynamics.

Efficient ancestry and mutation simulation with msprime 1.0

“…, Thornton 2014 ; Kelleher et al 2016 ; Becheler et al 2019 ; Haller and Messer 2019 ). The primary interface for is through a thoroughly documented Python API, which has encouraged the development of an ecosystem of downstream tools ( Terhorst et al 2017 ; Chan et al 2018 ; Spence and Song 2019 ; Adrion et al 2020a , 2020b ; Kamm et al 2020 ; McKenzie and Eaton 2020 ; Montinaro et al 2020 ; Rivera-Colón et al 2021 ; Terasaki Hart et al 2021 ). As well as providing a stable and efficient platform for building downstream applications, ’s Python API makes it much easier to build reproducible simulation pipelines, as the entire workflow can be encapsulated in a single script, and package and version dependencies explicitly stated using the or package managers.…”

“…The availability of as a liberally licensed (MIT) open source toolkit has enabled several other projects ( e.g. , Haller and Messer 2019 ; Kelleher et al 2019 ; Terasaki Hart et al 2021 ; Wohns et al 2021 ) to take advantage of the same efficient data structures used in , and we hope that many more will follow. While a full discussion of tree sequences and the capabilities of is beyond the scope of this article, we summarize some aspects that are important for simulation.…”

Deep learning and satellite imagery predict genetic diversity and differentiation

“…This would provide not only a tool for more comprehensible validations of DL approaches for population genetics applications but also for shedding new light on mechanistic details of how a species perceives its habitat. Some additions to recent software developments could contribute to complete this toolbox in the near future (Rebaudo et al., 2013; Terasaki Hart et al., 2021).…”

The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock

“…Analyses of simulated data using, e.g., CDPOP [230] is usually advised to demonstrate the effectiveness of the method before moving to the analysis of empirical data (see, e.g., [211,213,231]). GEONOMICS, a Python package, performs forward-time, individual-based, continuous-space population genomic simulations on complex landscapes [232]. GEONOMICS includes several analytical steps using models of a landscape with one or more environmental layers (geotiff files as input), each of which can undergo environmental changes, as well as species having genomes with realistic architecture and associated phenotypes.…”