Genetic variance in contrasting environments
Data files
Oct 04, 2023 version files 715.21 KB
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greenhouse_brassica_2019_data.csv
98.98 KB
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greenhouse_data_explore.RData
17.18 KB
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heatarrays_data_explore.RData
81.05 KB
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MCMC_2021_cleaned.csv
304.40 KB
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multienviro_animal_pedigree.csv
115.44 KB
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multienviro_data_explore.RData
89.16 KB
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README_metadata.txt
7.76 KB
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README_Rdata.txt
1.24 KB
Abstract
The evolutionary response of a trait to directional selection depends upon the level of additive genetic variance. It has been long argued that sustained selection will tend to deplete additive genetic variance as favoured alleles approach fixation. Non-additive genetic variance, due to interactions among alleles within and between loci, does not immediately contribute to an evolutionary response, although shifts in the allele frequencies within and between interacting loci may convert interaction variance into additive variance. Here we consider the possibility that an environmental shift may alter allelic interactions in ways that convert nonadditive into additive genetic variance. Specifically, we performed experiments that used a Bayesian implementation of the animal model to estimate the additive, dominance, and maternal components of variance for a pedigreed population of Brassica rapa. One experiment was performed in a field that mimicked agricultural conditions from which the base population was drawn, while the other was performed in the benign conditions of a greenhouse. Although the additive genetic variance was elevated in the greenhouse condition, no consistent pattens emerged that would indicate a conversion of dominance variance. The unusually low genetic variance and broad confidence intervals for the variance estimates obtained through this analysis preclude definitive interpretations. Thus, we promote further investigation to determine if between-environment changes in additive genetic variance can be traced to conversion of nonadditive variance.
Usage notes are detailed in the README files (separate files for data and R scripts).