7. Cut-and-Paste Bodies: The Shock of Genetic Simulation

ABSTRACT The change in gene frequency for two X-linked mutants, y and w, in a number of experiments was compared to that predicted from a genetic simulation program which utilized estimated differences in relative mating ability, fecundity, and viability. The simulation gave excellent predictions of gene frequency change even when experiments were started with different initial gene frequencies in the males and females or when the two loci were segregating simultaneously. The rate of elimination was slower when there were unequal initial gene frequencies than when males and females had equal initial gene frequencies. Simulation demonstrated that this was a general phenomenon when there is strong selection but that the opposite is true for weak selection. In two other experiments, the mating advantage of wild-type males was balanced by a fecundity advantage in mutant females. In all four replicates of both experiments, the mutant was maintained for several generations at the high initial frequency but then decreased quickly and was eliminated. Results obtained restarting one of these experiments with flies from a generation after the decline in gene frequency indicated that a linked gene and not frequency-dependent selection was responsible for the unpredictable gene-frequency change in the mutant. Using a least squares technique, it was found that a recessive fecundity locus 15 map units from the w locus gave the best fit for bothexperiments.

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A community-maintained standard library of population genetic models

10.1101/2019.12.20.885129 ◽

2019 ◽

Cited By ~ 7

Author(s):

Jeffrey R. Adrion ◽

Christopher B. Cole ◽

Noah Dukler ◽

Jared G. Galloway ◽

Ariella L. Gladstein ◽

...

Keyword(s):

Population Genetic ◽

Genomic Data ◽

Simulation Models ◽

Easy Access ◽

Major Barrier ◽

Simulation Engine ◽

Complex Models ◽

Demographic Inference ◽

Genetic Simulation ◽

Inference Methods

AbstractThe explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

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SLiM 3: Forward genetic simulations beyond the Wright–Fisher model

10.1101/418657 ◽

2018 ◽

Cited By ~ 58

Author(s):

Benjamin C. Haller ◽

Philipp W. Messer

Keyword(s):

Individual Variation ◽

Evolutionary Biology ◽

Continuous Space ◽

New Horizons ◽

Genetic Modeling ◽

Fisher Model ◽

Genetic Simulation ◽

Flexible Foundation ◽

Conceptual Overview ◽

Opening Up

AbstractWith the desire to model population genetic processes under increasingly realistic scenarios, forward genetic simulations have become a critical part of the toolbox of modern evolutionary biology. The SLiM forward genetic simulation framework is one of the most powerful and widely used tools in this area. However, its foundation in the Wright–Fisher model has been found to pose an obstacle to implementing many types of models; it is difficult to adapt the Wright–Fisher model, with its many assumptions, to modeling ecologically realistic scenarios such as explicit space, overlapping generations, individual variation in reproduction, density-dependent population regulation, individual variation in dispersal or migration, local extinction and recolonization, mating between subpopulations, age structure, fitness-based survival and hard selection, emergent sex ratios, and so forth. In response to this need, we here introduce SLiM 3, which contains two key advancements aimed at abolishing these limitations. First, the new non-Wright–Fisher or “nonWF” model type provides a much more flexible foundation that allows the easy implementation of all of the above scenarios and many more. Second, SLiM 3 adds support for continuous space, including spatial interactions and spatial maps of environmental variables. We provide a conceptual overview of these new features, and present several example models to illustrate their use. These two key features allow SLiM 3 models to go beyond the Wright–Fisher model, opening up new horizons for forward genetic modeling.

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