Summary
The distribution of fitness effects of mutations is vital to our understanding of rates and patterns of adaptation. Population genetics and mutation accumulation experiments have given us insight into the distribution of fitness effects of single mutations. However, mutations often interact with each other. This is called epistasis. The role of epistatic interactions in adaptation has remained controversial. Yet, the distribution of epistatic effects is as fundamental as distribution of mutational effects themselves. Without knowing the distribution of epistatic effects, we can’t calculate the average fitness effect of a given mutation across multiple genetic backgrounds. Moreover, if epistatic interactions tend to be positive or negative on average, this will have an important effect on evolutionary dynamics. Epistatic interactions are also known to play a role in speciation, but the proportion of mutations that exhibit incompatible interactions that can lead to speciation is unknown. I will investigate the properties of epistatic interactions among mutations with two complementary approaches. First, I will estimate the probability and distribution of effects of epistatic interactions among spontaneous mutations. I will cross mutation accumulation lines that I have developed for the fungus Neurospora crassa to produce a mapping population where spontaneous mutations are segregating, and use it to estimate the distribution of epistatic effects. Second, I will estimate the proportion of substitutions that cause reproductive incompatibilities between populations from the relationship between reproductive isolation and genetic divergence. This is achieved by an evolution experiment with fission yeast, with a design that will maximize the rate of genetic divergence with minimal change in mean phenotype. The elucidation of properties of epistatic interactions will be a major breakthrough for the field of evolutionary biology.
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| Web resources: | https://cordis.europa.eu/project/id/101088581 |
| Start date: | 01-09-2023 |
| End date: | 31-08-2028 |
| Total budget - Public funding: | 1 970 533,00 Euro - 1 970 533,00 Euro |
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Original description
The distribution of fitness effects of mutations is vital to our understanding of rates and patterns of adaptation. Population genetics and mutation accumulation experiments have given us insight into the distribution of fitness effects of single mutations. However, mutations often interact with each other. This is called epistasis. The role of epistatic interactions in adaptation has remained controversial. Yet, the distribution of epistatic effects is as fundamental as distribution of mutational effects themselves. Without knowing the distribution of epistatic effects, we can’t calculate the average fitness effect of a given mutation across multiple genetic backgrounds. Moreover, if epistatic interactions tend to be positive or negative on average, this will have an important effect on evolutionary dynamics. Epistatic interactions are also known to play a role in speciation, but the proportion of mutations that exhibit incompatible interactions that can lead to speciation is unknown. I will investigate the properties of epistatic interactions among mutations with two complementary approaches. First, I will estimate the probability and distribution of effects of epistatic interactions among spontaneous mutations. I will cross mutation accumulation lines that I have developed for the fungus Neurospora crassa to produce a mapping population where spontaneous mutations are segregating, and use it to estimate the distribution of epistatic effects. Second, I will estimate the proportion of substitutions that cause reproductive incompatibilities between populations from the relationship between reproductive isolation and genetic divergence. This is achieved by an evolution experiment with fission yeast, with a design that will maximize the rate of genetic divergence with minimal change in mean phenotype. The elucidation of properties of epistatic interactions will be a major breakthrough for the field of evolutionary biology.Status
SIGNEDCall topic
ERC-2022-COGUpdate Date
31-07-2023
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