Geonomics: Forward-Time, Spatially Explicit, and Arbitrarily Complex Landscape Genomic Simulations.
Geonomics: Forward-Time, Spatially Explicit, and Arbitrarily Complex Landscape Genomic Simulations.
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DOI：
10.1093/molbev/msab175
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发表时间：
2021-09-27
影响因子：
10.7
通讯作者：
Wang IJ
中科院分区：
文献类型：
--
作者：
Terasaki Hart DE;Bishop AP;Wang IJ
了解基因组多样性空间格局的驱动因素已成为进化遗传学的主要目标。前向时间模拟的灵活性使其对这些工作特别有价值，允许以模仿真实种群进化的方式模拟任意复杂的场景。在这里，我们介绍了Geonomics，这是一个Python包，用于执行复杂的、空间显式的、具有完整空间谱系的景观基因组模拟，它极大地减少了用户的工作量，但仍然保持可定制和可扩展，因为它嵌入在一种流行的通用语言中。我们展示了基于群体遗传学经典模型的各种验证测试的结果与预期一致，然后通过三种更复杂的模拟场景（包括多基因选择、多性状选择、复杂景观模拟和非平稳环境变化）展示了其实用性和灵活性。然后我们讨论运行时，它主要对景观栅格大小敏感，内存使用，它主要对最大人口大小和重组率敏感，以及与模型近似重组和移动方法相关的其他注意事项。总之，我们的测试和演示表明，基因组学为捕获复杂的空间和进化动态的种群基因组模拟提供了一个有效和强大的平台。
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 nonstationary 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 recombination rate, 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.
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影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
4.3
作者：
Kelleher J;Etheridge AM;McVean G
通讯作者：
McVean G
影响因子：
6.6
作者：
Landguth, Erin L.;Bearlin, Andrew;Dunham, Jason
通讯作者：
Dunham, Jason
影响因子：
56.9
作者：
Bay, Rachael A.;Harrigan, Ryan J.;Ruegg, Kristen
通讯作者：
Ruegg, Kristen
影响因子：
3.3
作者：
Kim, Y;Nielsen, R
通讯作者：
Nielsen, R