Scaling the fitness effects of mutations with respect to differentially adapted Arabidopsis thaliana accessions under natural conditions
Data files
Feb 28, 2025 version files 197.50 KB
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Code_and_data.zip
196.25 KB
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README.md
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Abstract
Mutations are the ultimate source of genetic variation for natural selection to act upon. A major question in evolutionary biology is the extent to which new mutations can generate genetic variation under natural conditions to permit adaptive evolution over ecological time scales. Here we collected fitness data for chemically induced (ethylmethane sulfonate, EMS) mutant lines descended from two Arabidopsis thaliana ecotypes that show differential adaptation to the local environment of our common garden plot. Using a novel nonparametric Bayesian statistical approach, we found that both ecotypes accumulated substantial proportions of beneficial mutations. The poorly adapted ecotype exhibited higher mean mutational effects and higher variance in the fitness effects of mutations compared to the well-adapted ecotype. Furthermore, we predict that it takes less than 4000 generations for the fitness space of the two ecotypes to overlap through mutation accumulation, and that a single founder, through mutation accumulation, is able to achieve the species-wide genetic variation in less than 10,000 generations. Our results provide evidence for relatively rapid local adaptation of Arabidopsis thaliana in natural conditions through new mutations, as well as the utility of a nonparametric Bayesian method for modeling the distribution of fitness effects for field-collected data.
https://doi.org/10.5061/dryad.2rbnzs80w
Description of the data and file structure
Mutant lines were generated for Arabidopsis thaliana accessions using EMS. Lines were planted in a randomized block design in an experimental farm at the University of Maryland. Survivorship and dry weight were collected as fitness components. The data was analyzed using a novel Bayesian approach (code included).
Description: "dat_split.csv" The raw data of survivorship and dry weight for each accession in an Excel file.
Columns are: "founder" - the name for the ecotype used to generate the mutant lines, "line" - the mutant lines derived from the founder, "sub" - the sublines (A and B) derived from each founder line to account for maternal effects, "block" - the number of randomized block in the field experiment, and "weight" - the dry weight of each sample in grams.
"code_for_posterior_inference_and_plotting.ipynb" code for the statistical analysis
Code/software
Python 3 and Juypter notebook are needed to reproduce the data analysis and plots.
