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Shared Selection and Genetic Architecture Drive Strikingly Repeatable Evolution in Long-Term Experimental Hybrid
Gregory L Owens1, Celine Caseys2, Nora Mitchell3,4
1Department of Biology, University of Victoria, Victoria, BC, Canada.
Molecular Biology and Evolution
|January 21, 2025
Summary
Evolutionary biology can be predictive. Hybrid sunflowers evolved in parallel, showing strong selection on introgressed alleles, influenced by recombination rates and genomic architecture.
Area of Science:
- Evolutionary Biology
- Genetics
- Ecology
Background:
- Understanding evolutionary repeatability is key to predicting evolutionary trajectories.
- Hybridization is a significant source of genetic variation and evolutionary innovation.
- Genomic architecture and recombination rates can influence selection pressures.
Purpose of the Study:
- To investigate the factors controlling evolutionary repeatability in hybrid populations.
- To determine the extent of parallel evolution in experimentally evolved sunflowers.
- To assess the roles of natural selection, genomic architecture, and recombination in shaping evolutionary outcomes.
Main Methods:
- Experimentally evolved four replicate hybrid sunflower populations at natural sites for up to 14 years.
- Tracked introgressed ancestry across the genome to identify regions under selection.
- Analyzed the influence of recombination rate on selection against introgressed ancestry.
Main Results:
- Observed strong negative selection against introgressed ancestry on most chromosomes, with positive selection on one.
- Found that high recombination rates reduced selection against introgressed ancestry.
- Demonstrated highly parallel evolution across replicates, with shared selection driving 88% of introgressed allele frequency changes.
Conclusions:
- Evolutionary processes, particularly through hybridization, can be highly repeatable.
- Natural selection, genomic architecture, and recombination are central to evolutionary repeatability.
- This study confirms the predictive potential of evolutionary biology.
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