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Evolution of hybridogenetic lineages in Cataglyphis ants.

Hugo DarrasAlexandre KuhnSerge Aron
Published in: Molecular ecology (2019)
In most social Hymenoptera, a diploid egg develops into either a queen or a worker depending on environmental conditions. Hybridogenetic Cataglyphis ants display a bizarre genetic system, where queen-worker caste determination is primarily determined by genetic factors. In hybridogenetic populations, all workers are F1 hybrids of two distinct lineages, whereas new queens are nearly always pure-lineage individuals produced by clonal reproduction. The distribution and evolutionary history of these hybridogenetic populations have not yet been thoroughly analysed. Here, we studied the phylogeographic distribution of hybridogenetic populations in two closely related Spanish species: Cataglyphis humeya and Cataglyphis velox. Hybridogenesis has been previously documented in a locality of C. velox, but whether this system occurs elsewhere within the range of the two species was yet unknown. Queens and workers from 66 localities sampled across the range of the species were genotyped at 18 microsatellite markers to determine whether queens were produced by parthenogenesis and whether workers were hybrids of divergent lineages. Populations with F1 hybrid workers were identified by combining genetic, geographical and mating assortments data. In most populations of C. velox, workers were found to be hybrids of two divergent lineages. Workers were however produced via random mating in two marginal populations of C. velox, and in all populations studied of its sister species C. humeya. High-throughput sequencing data were obtained to confirm inferences based on microsatellites and to characterize relationships between populations. Our results revealed a complicated history of reticulate evolution that may account for the origin of hybridogenetic lineages in Cataglyphis.
Keyphrases
  • genetic diversity
  • genome wide
  • mental health
  • electronic health record
  • high throughput sequencing
  • single cell
  • machine learning
  • risk assessment
  • dna methylation
  • climate change
  • high resolution
  • solid state