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Caudill, V.

Publications and source records attributed to Caudill, V..

2 recordsLinked to original sources

Genetic architecture, spatial heterogeneity, and the coevolutionary arms race between newts and snakes

AbstractCoevolution between two species can lead to exaggerated phenotypes that vary in a cor-related manner across space. However, the conditions under which we expect such spatially varying coevolutionary patterns in polygenic traits are not well-understood. We investigate the coevolutionary dynamics between two species undergoing reciprocal adaptation across space and time, using simulations inspired by the Taricha newt - Thamnophis garter snake system. One striking observation from this system is that newts in some areas carry much more tetrodotoxin than in other areas, and garter snakes that live near more toxic newts tend to be more resistant to this toxin, a correlation seen across several broad geographic areas. Furthermore, snakes seem to be "winning" the coevolutionary arms race, i.e., having a high level of resistance compared to local newt toxicity, despite substantial variation in both toxicity and resistance across the range. We explore how possible genetic architectures of the toxin and resistance traits would affect the coevolutionary dynamics by manipulating both mutation rate and effect size of mutations across many simulations. We find that coevolutionary dynamics alone were not sufficient in our sim-ulations to produce the striking mosaic of levels of toxicity and resistance observed in nature, but simulations with ecological heterogeneity (in trait costliness or interaction rate) did produce such patterns. We also find that in simulations, newts tend to "win" across most combinations of genetic architectures, although the species with higher mutational genetic variance tends to have an advantage.

evolutionary biology↗

Expanding the stdpopsim species catalog, and lessons learned forrealistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

bioinformatics↗