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Fundamental patterns of structural evolution revealed by chromosome-length genomes of cactophilic Drosophila.

Kyle M BenowitzCarson W AllanColine C JaworskiMichael J SandersonFernando DiazXingsen ChenLuciano M Matzkin
Published in: Genome biology and evolution (2024)
A thorough understanding of adaptation and speciation requires model organisms with both a history of ecological and phenotypic study as well as a complete set of genomic resources. In particular, high-quality genome assemblies of ecological model organisms are needed to assess the evolution of genome structure and its role in adaptation and speciation. Here, we generate new genomes of cactophilic Drosophila, a crucial model clade for understanding speciation and ecological adaptation in xeric environments. We generated chromosome-level genome assemblies and complete annotations for seven populations across D. mojavensis, D. arizonae, and D. navojoa. We use this data first to establish the most robust phylogeny for this clade to date, and to assess patterns of molecular evolution across the phylogeny, showing concordance with a priori hypotheses regarding adaptive genes in this system. We then show that structural evolution occurs at constant rate across the phylogeny, varies by chromosome, and is correlated with molecular evolution. These results advance the understanding of the D. mojavensis clade by demonstrating core evolutionary genetic patterns and integrating those patterns to generate new gene-level hypotheses regarding adaptation. Our data are presented in a new public database (cactusflybase.arizona.edu), providing one of the most in-depth resources for the analysis of inter- and intraspecific evolutionary genomic data. Furthermore, we anticipate that the patterns of structural evolution identified here will serve as a baseline for future comparative studies to identify the factors that influence the evolution of genome structure across taxa.
Keyphrases
  • genome wide
  • copy number
  • climate change
  • electronic health record
  • healthcare
  • mental health
  • human health
  • emergency department
  • machine learning
  • gram negative
  • multidrug resistant
  • current status