bioRxivEvolutionary Biology
Intra-Species Differences in Population Size shape Life History and Genome Evolution
Abstract
The evolutionary forces shaping life history trait divergence within species are largely unknown. Killifish (oviparous Cyprinodontiformes) evolved an annual life cycle as an exceptional adaptation to life in arid savannah environments characterized by seasonal water availability. The turquoise killifish (Nothobranchius furzeri) is the shortest-lived vertebrate known to science and displays differences in lifespan among wild populations, representing an ideal natural experiment in the evolution and diversification of life history. Here, by combining genome sequencing and population genetics, we investigate the evolutionary forces shaping lifespan among turquoise killifish populations. We generate an improved reference assembly for the turquoise killifish genome, trace the evolutionary origin of the sex chromosome, and identify genes under strong positive and purifying selection, as well as those evolving neutrally. We find that the shortest-lived turquoise killifish populations, which dwell in fragmented and isolated habitats at the outer margin of the geographical range of the species, are characterized by small effective population size and accumulate throughout the genome several small to large-effect deleterious mutations due to genetic drift. The genes most affected by drift in the shortest-lived turquoise killifish populations are involved in the WNT signalling pathway, neurodegenerative disorders, cancer and the mTOR pathway. As the populations under stronger genetic drift are the shortest-lived ones, we propose that limited population size due to habitat fragmentation and repeated population bottlenecks, by causing the genome-wide accumulation of deleterious mutations, cumulatively contribute to the short adult lifespan in turquoise killifish populations.
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: statistical model
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