Role of epistatic interactions in evolution (EPISTAT)
Main funder
Funder's project number: 101088581
Funds granted by main funder (€)
- 1 970 533,00
Funding program
Project timetable
Project start date: 01/09/2023
Project end date: 31/08/2028
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, called epistasis, and interactions are common. Nevertheless, 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. In particular, if epistatic interactions tend to be positive or negative on average, this will have an important effect on evolutionary dynamics. Moreover, epistatic interactions are known to play a role in speciation, but the proportion of mutations that exhibits 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 leverage tetrad analysis, and mutation accumulation lines that I have developed for the fungus \emph{Neurospora crassa}. I will 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 by using experimental evolution with fission yeast. The experimental design will maximize the rate of genetic divergence with minimal change in mean phenotype. Results of this project will address fundamental questions that will progress the field forward
