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Swordtail fish hybrids reveal that genome evolution is surprisingly predictable after initial hybridization.

Quinn K LangdonJeffrey S GrohStepfanie M AguillonDaniel L PowellTheresa GunnCheyenne PayneJohn J BaczenasAlex DonnyTristram O DodgeKang DuManfred SchartlOscar Ríos-CárdenasCarla Gutiérrez-RodríguezMolly MorrisMolly Schumer
Published in: PLoS biology (2024)
Over the past 2 decades, biologists have come to appreciate that hybridization, or genetic exchange between distinct lineages, is remarkably common-not just in particular lineages but in taxonomic groups across the tree of life. As a result, the genomes of many modern species harbor regions inherited from related species. This observation has raised fundamental questions about the degree to which the genomic outcomes of hybridization are repeatable and the degree to which natural selection drives such repeatability. However, a lack of appropriate systems to answer these questions has limited empirical progress in this area. Here, we leverage independently formed hybrid populations between the swordtail fish Xiphophorus birchmanni and X. cortezi to address this fundamental question. We find that local ancestry in one hybrid population is remarkably predictive of local ancestry in another, demographically independent hybrid population. Applying newly developed methods, we can attribute much of this repeatability to strong selection in the earliest generations after initial hybridization. We complement these analyses with time-series data that demonstrates that ancestry at regions under selection has remained stable over the past approximately 40 generations of evolution. Finally, we compare our results to the well-studied X. birchmanni × X. malinche hybrid populations and conclude that deeper evolutionary divergence has resulted in stronger selection and higher repeatability in patterns of local ancestry in hybrids between X. birchmanni and X. cortezi.
Keyphrases
  • genome wide
  • single molecule
  • genome wide association study
  • nucleic acid
  • genetic diversity
  • copy number
  • dna methylation
  • metabolic syndrome
  • gene expression
  • machine learning
  • single cell