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Evidence of hard-selective sweeps suggests independent adaptation to insecticides in Colorado potato beetle (Coleoptera: Chrysomelidae) populations.

Zachary P CohenYolanda H ChenRussell GrovesSean D Schoville
Published in: Evolutionary applications (2022)
Pesticide resistance provides one of the best examples of rapid evolution to environmental change. The Colorado potato beetle (CPB) has a long and noteworthy history as a super-pest due to its ability to repeatedly develop resistance to novel insecticides and rapidly expand its geographic and host plant range. Here, we investigate regional differences in demography, recombination, and selection using whole-genome resequencing data from two highly resistant CPB populations in the United States (Hancock, Wisconsin and Long Island, New York). Demographic reconstruction corroborates historical records for a single pest origin during the colonization of the Midwestern and Eastern United States in the mid- to late-19th century and suggests that the effective population size might be higher in Long Island, NY than Hancock, WI despite contemporary potato acreage of Wisconsin being far greater. Population-based recombination maps show similar background recombination rates between these populations, as well as overlapping regions of low recombination that intersect with important metabolic detoxification genes. In both populations, we find compelling evidence for hard selective sweeps linked to insecticide resistance with multiple sweeps involving genes associated with xenobiotic metabolism, stress response, and defensive chemistry. Notably, only two candidate insecticide resistance genes are shared among both populations, but both appear to be independent hard selective sweep events. This suggests that repeated, rapid, and independent evolution of genes may underlie CPB's pest status among geographically distinct populations.
Keyphrases
  • dna damage
  • dna repair
  • genetic diversity
  • genome wide
  • electronic health record
  • oxidative stress
  • loop mediated isothermal amplification
  • deep learning