Biology subjects

Thia, J. A.

Publications and source records attributed to Thia, J. A..

5 recordsLinked to original sources

bioRxiv2022

Evolution of an acetylcholinesterase gene complex and its contribution to organophosphate insensitivity in an invasive mite pest

BACKGROUNDInvasive Australian populations of redlegged earth mite, Halotydeus destructor (Tucker), are evolving increasing organophosphate resistance. In addition to the canonical ace gene, the target gene of organophosphates, the H. destructor genome contains many radiated ace-like genes that vary in copy number and amino acid sequence. In this work, we characterise copy number and target-site mutation variation at the canonical ace and ace-like genes and test for potential associations with organophosphate insensitivity. This was achieved through comparisons of whole-genome pool-seq data from alive and dead mites following organophosphate exposure. RESULTSA combination of increased copy number and target-site mutations at the canonical ace was associated with organophosphate insensitivity in H. destructor. Resistant populations were segregating for G119S, A201S, F331Y at the canonical ace. A subset of populations also had copy numbers of canonical ace >2, which potentially helps over-express proteins carrying these target-site mutations. Haplotypes possessing different copy numbers and target-site mutations of the canonical ace gene may be under selection across H. destructor populations. We also detected some evidence that increases in copy number of radiated ace-like genes are associated with organophosphate insensitivity, which might suggest potential roles in sequestration or breakdown of organophosphates. CONCLUSIONDifferent combinations of target-site mutations and (or) copy number variation in the canonical ace and ace-like genes may provide non-convergent ways for H. destructor to respond to organophosphate selection. However, these changes may only play a partial role in organophosphate insensitivity, which appears to have a polygenic architecture.

evolutionary biology↗

bioRxiv2022

The redlegged earth mite draft genome provides new insights into pesticide resistance evolution and demography in its invasive Australian range

Genomic data provide valuable insights into pest management issues such as resistance evolution, historical patterns of pest invasions and ongoing population dynamics. We assembled the first reference genome for the redlegged earth mite, Halotydeus destructor (Tucker, 1925), to investigate adaptation to pesticide pressures and demography in its invasive Australian range using whole-genome pool-seq data from regionally distributed populations. Our reference genome comprises 132 autosomal contigs, with a total length of 48.90 Mb. We observed a large complex of ace genes, which has presumably evolved from a long history of organophosphate selection in H. destructor and may contribute toward organophosphate resistance through copy number variation, target-site mutations, and structural variants. In the putative ancestral H. destructor ace gene, we identified three target-site mutations (G119S, A201S, and F331Y) segregating in organophosphate resistant populations. Additionally, we identified two new para sodium channel gene mutations (L925I and F1020Y) that may contribute to pyrethroid resistance. Regional structuring observed in population genomic analyses indicates that gene flow in H. destructor does not homogenise populations across large geographic distances. However, our demographic analyses were equivocal on the magnitude of gene flow; the short invasion history of H. destructor makes it difficult to distinguish scenarios of complete isolation vs. ongoing migration. Nonetheless, we identified clear signatures of reduced genetic diversity and smaller inferred effective population sizes in eastern vs. western populations, which is consistent with the stepping-stone invasion pathway of this pest in Australia. These new insights will inform development of diagnostic genetic markers of resistance, further investigation into the multifaceted organophosphate resistance mechanism, and predictive modelling of resistance evolution and spread.

genomics↗

bioRxiv2022

Incorporating selection pressures into risk predictions of chemical resistance evolution in pest organisms

Chemical resistance in pest organisms threatens global food security and human health, yet resistance issues are mostly dealt with reactively. Predictive models of resistance risk provide an avenue for field practitioners to implement proactive pest management but require knowledge of factors that drive resistance evolution. Despite the importance of chemical selection pressure on resistance evolution, poor availability of chemical usage data has limited the use of a general multi-species measure of selection pressure in predictive models. We demonstrate the use of pesticide product registrations as a predictor of resistance status and potential proxy of chemical selection pressure. Pesticide product registrations were obtained for 427 USA and 209 Australian agricultural arthropod pests, for 42 and 39 chemical Mode of Action (MoA) groups, respectively. We constructed Bayesian logistic regressions of resistance status as a function of the number of pesticide product registrations and two ecological traits, phagy and voltinism. Our models were well-supported with demonstrated power to discriminate between resistant and susceptible observations in both USA and Australian species sets using cross-validation. Importantly, we observed strong support for a positive association between pesticide products and resistance status. Our work expands the horizon for proactive management by quantitatively linking a proxy for selection pressure on pest species to different chemical MoAs. This proxy for selection pressure can be combined with ecological information to predict the resistance risk in agricultural pests. Because pesticide product registrations can typically be derived from publicly available data, we believe there is broad applicability to other agricultural pests such as weeds and fungi, and to other geographical regions beyond the USA and Australia.

evolutionary biology↗

bioRxiv2022

DAPC of genotype data only requires the first k - 1 PC axes for k populations

Discriminant analysis of principal components (DAPC) has become a popular method for visualising population structure due to its simplicity, computational speed, and freedom from demographic assumptions. Despite the popularity of DAPC, there has been little discussion on best practise. In this work, I provide guidelines for standardising the use of DAPC in studies of population genetic structure. An often-overlooked fact is that DAPC generates a model describing the genetic differences among a set of populations defined by a researcher. I demonstrate that appropriate parameterisation of this model is critical for obtaining biologically meaningful results. I show that the number of leading PC axes used as predictors of among population differences, paxes, should not exceed the k - 1 biologically informative PC axes that are expected for k effective populations in a genotype dataset. This k - 1 criterion for paxes selection is more appropriate compared to the widely used proportional variance criterion, which often results in a choice of paxes >> k - 1. DAPC parameterised with no more than the leading k - 1 PC axes is: (1) more parsimonious; (2) captures maximal among-population variation on biologically relevant predictors; (3) less sensitive to unintended interpretations of population structure; and (4) more generally applicable to independent sample sets. Assessing model fit should be routine practise and can aid interpretation of population structure when implementing DAPC. Additionally, it is imperative that researchers clearly articulate their study goals, that is, testing a priori expectations versus studying de novo inferred populations. Distinguishing between these goals is important because it dictates whether a researchers results can be treated as a test of the hypothesis that significant genetic differences exist among populations. Defining populations a priori (before observing the genotype data) constitutes a true hypothesis test, but populations defined de novo (after observing the genotype data) cannot be used to test this hypothesis due to issues with circularity. The discussion and practical recommendations provided in this work provide the molecular ecology community a roadmap for applying DAPC to their genotype datasets.

genetics↗

bioRxiv2019

genomalicious: serving up a smorgasbord of R functions for population genomic analyses

Turning SNP data into biologically meaningful results requires considerable computational acrobatics, including importing, exporting, and manipulating data among different analytical packages and programming environments, and finding ways to visualise results for data exploration and presentation. I present GENOMALICIOUS, an R package designed to provide a selection of functions for population genomicists to simply, intuitively, and flexibly, guide SNP data through their analytical pipelines, within and outside R. At the core of the original GENOMALICIOUS workflow is the conversion of genomic variant data into a data.table object. This provides a useful way of storing large amounts of data in an intuitive format that can be easily manipulated using methods unique to this object class. Over time, GENOMALICIOUS has grown to cater to a range of analyses in population structure and demography, adaptive evolution, quantitative traits, and phylogenetics. Researchers using pooled allele frequencies, or individually sequenced genotypes, are sure to find functions that accommodate their tastes in GENOMALICIOUS. The simplicity and accessibility of pipelines in GENOMALICIOUS may also serve as a useful tool for teaching basic population genetics and genomics in an R environment. The source code and a series of tutorials for this package are freely available on GitHub

bioinformatics↗