This repository contains the scripts and data required to replicate the analyses in Thia et al.’s, “Evolution of an acetylcholinesterase gene complex and its contribution toward organophosphate insensitivity in an invasive mite pest”, submitted to Pest Management Science.

In this work, Thia et al. use a combination of experimental selection and pool-seq genomic analyses to understand the genetic mechanisms underpinning organophosphate insensitivity in the redlegged earth mite, Halotydeus destructor. There is a special emphasis on disentangling the roles of copy number variation and target-site mutations in the acetylcholinesterase genes, ace, and radiated ace-like genes.

There are three major analyses: (1) an F_ST genome scan to identify outlier loci between alive (insensitive) and dead (sensitive) mites; (2) an analysis of ace copy number variation between alive and dead mites; and (3) an analysis of candidate target-site mutations in the ace gene.

Pools of H. destructor were sequenced using a pool-seq approach. Whole genome libraries were sequences for pools of alive and dead mites, mapped to the reference genome, and used to derive pooled allele frequency estimates.

F_ST genome scans were performed using a combination of the OutFlank method (Whitlock & Lotterhos 2015 Am. Nat.: modified for pool-seq data) and the poolfstat R package (Hivert et al. 2018 Genetics). Significant outlier F_ST values were used to identify genomic regions that were divergent between alive and dead mites, which may represent genomic regions responding to organophosphate selection.

Copy number variation was estimated as the read depth ratios between H. destructor ace genes and a set of single copy BUSCO genes. An analysis of copy number variation was used to test whether alive mites had greater ace copy number than dead mites.

Target-site mutation analyses were used to assess whether alive mites had higher frequencies of candidate alleles known to confer resistance in other arthropods. 

There are two ZIP files associated with this repository: Dryad_Data.zip and Dryad_Scripts.zip.

Unzip these files and place all the contents in the same directory. An R project file, Hdes_OP_insensitivity_2023.Rproj, in the Dryad_Scripts.zip file can be used to run all the analyses presented in this research.

The README.md file provides a summary of this repository, and the PROJECT_DETAILS.pdf contains descriptions of the implementation, the data, and the scripts.