Data from: Dissecting the genetic architecture of a stepwise infection process
Hall, Matthew D, University of Basel
Routtu, Jarkko, University of Basel
Ebert, Dieter, University of Basel
Published Aug 19, 2020 on Dryad.
Cite this dataset
Hall, Matthew D; Routtu, Jarkko; Ebert, Dieter (2020). Data from: Dissecting the genetic architecture of a stepwise infection process [Dataset]. Dryad. https://doi.org/10.5061/dryad.tv2kb51
How a host fights infection depends on an ordered sequence of steps, beginning with attempts to prevent a pathogen from establishing an infection, through to steps that mitigate a pathogen’s control of host resources, or minimising the damage caused during infection. Yet empirically characterising the genetic basis of these steps remains challenging. Although each step is likely to have a unique genetic and environmental signature, and may, therefore, respond to selection in a specific way, events that occur earlier in the infection process can mask or overwhelm the contributions of subsequent steps. In this study, we dissect the genetic architecture of a stepwise infection process using a quantitative trait loci (QTL) mapping approach. We control for variation at the first line of defence against a bacterial pathogen and expose downstream genetic variability related to the host’s ability to mitigate the damage pathogens cause. In our model, the water-flea Daphnia magna, we found a single major effect QTL, explaining 64% variance, that is linked to the host’s ability to completely block pathogen entry by preventing their attachment to the host oesophagus; consistent with the detection of this locus in prior studies. In susceptible hosts allowing attachment, however, a further 23 QTL, explaining between 5 to 16% variance, were mapped to traits related to the expression of disease. The general lack of pleiotropy and epistasis for traits related to the different stages of the infection process, together with the wide distribution of QTL across the genome, highlights the modular nature of a host’s defence portfolio, and the potential for each different step to evolve independently. We discuss how isolating the genetic basis of individual steps can help resolve discussion over the genetic architecture of host resistance.
Pheno and genotypes used for QTL mapping
The phenotype file consisting of line means (i.e. phefile) and genotype file in R/qtl format (i.e. genfile).
Fitness of infected and uninfected individuals
Data for describing the phenotypic and genetic variability for the estimates of fitness traits underlying pathogen infection and exploitation