Coadapted genomes and selection on hybrids: Fisher's geometric model explains a variety of empirical patterns.
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Simon, A., Bierne, N., & Welch, J. (2018). Coadapted genomes and selection on hybrids: Fisher's geometric model explains a variety of empirical patterns.. Evol Lett, 2 (5), 472-498. https://doi.org/10.1002/evl3.66
Natural selection plays a variety of roles in hybridization, speciation, and admixture. Most research has focused on two extreme cases: crosses between closely related inbred lines, where hybrids are fitter than their parents, or crosses between effectively isolated species, where hybrids suffer severe breakdown. But many natural populations must fall into intermediate regimes, with multiple types of gene interaction, and these are more difficult to study. Here, we develop a simple fitness landscape model, and show that it naturally interpolates between previous modeling approaches, which were designed for the extreme cases, and invoke either mildly deleterious recessives, or discrete hybrid incompatibilities. Our model yields several new predictions, which we test with genomic data from Mytilus mussels, and published data from plants (Zea, Populus, and Senecio) and animals (Mus, Teleogryllus, and Drosophila). The predictions are generally supported, and the model explains a number of surprising empirical patterns. Our approach enables novel and complementary uses of genome-wide datasets, which do not depend on identifying outlier loci, or "speciation genes" with anomalous effects. Given its simplicity and flexibility, and its predictive successes with a wide range of data, the approach should be readily extendable to other outstanding questions in the study of hybridization.
Agence Nationale de la Recherché; fellowship of the French Embassy in the United Kingdom, with Churchill 546 College Cambridge; fellowship of the Labex CeMEB and the doctoral school GAIA
External DOI: https://doi.org/10.1002/evl3.66
This record's URL: https://www.repository.cam.ac.uk/handle/1810/279537
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/