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Whole-genome sequencing reveals high complexity of copy number variation at insecticide resistance loci in malaria mosquitoes.

Eric R LucasAlistair MilesNicholas J HardingChris S ClarksonMara K N LawniczakDominic P KwiatkowskiDavid WeetmanMartin J Donnellynull null
Published in: Genome research (2019)
Polymorphisms in genetic copy number can influence gene expression, coding sequence, and zygosity, making them powerful actors in the evolutionary process. Copy number variants (CNVs) are however understudied, being more difficult to detect than single-nucleotide polymorphisms. We take advantage of the intense selective pressures on the major malaria vector Anopheles gambiae, caused by the widespread use of insecticides for malaria control, to investigate the role of CNVs in the evolution of insecticide resistance. Using the whole-genome sequencing data from 1142 samples in the An. gambiae 1000 genomes project, we identified 250 gene-containing CNVs, encompassing a total of 267 genes of which 28 were in gene families linked to metabolic insecticide resistance, representing significant enrichment of these families. The five major gene clusters for metabolic resistance all contained CNVs, with 44 different CNVs being found across these clusters and multiple CNVs frequently covering the same genes. These 44 CNVs are widespread (45% of individuals carry at least one of them) and have been spreading through positive selection, indicated by their high local frequencies and extended haplotype homozygosity. Our results demonstrate the importance of CNVs in the response to selection, highlighting the urgent need to identify the contribution of each CNV to insecticide resistance and to track their spread as the use of insecticides in malaria endemic countries intensifies and as the operational deployment of next-generation bed nets targeting metabolic resistance gathers pace. Our detailed descriptions of CNVs found across the species range provide the tools to do so.
Keyphrases
  • copy number
  • genome wide
  • mitochondrial dna
  • aedes aegypti
  • dna methylation
  • gene expression
  • plasmodium falciparum
  • zika virus
  • dengue virus
  • transcription factor
  • artificial intelligence
  • data analysis