setGenes: Set genes

Description

Updates a LandsepiParams object with parameters associated with resistance genes and pathogen adaptation.

Usage

setGenes(params, dfGenes)

Value

a LandsepiParams object.

Arguments

params: a LandsepiParams object
dfGenes: a data.frame containing gene parameters. It can be defined manually, or, alternatively, with loadGene.

Details

dfGenes is a data.frame of parameters associated with each resistance gene and with the evolution of each corresponding pathogenicity gene. Columns of the dataframe are:

geneName: names of resistance genes,
target_trait: aggressiveness components ("IR", "LAT", "IP", or "PR") targeted by resistance genes,
efficiency: resistance gene efficiencies, i.e. the percentage of reduction of the targeted aggressiveness component (IR, 1/LAT, IP and PR),
age_of_activ_mean: expected delays to resistance activation (for APRs),
age_of_activ_var: variances of the delay to resistance activation (for APRs),
mutation_prob: mutation probabilities for pathogenicity genes (each of them corresponding to a resistance gene),
Nlevels_aggressiveness: number of adaptation levels related to each resistance gene (i.e. 1 + number of required mutations for a pathogenicity gene to fully adapt to the corresponding resistance gene),
adaptation_cost: fitness penalties paid by pathogen genotypes fully adapted to the considered resistance genes on all hosts,
relative_advantage: fitness advantages of pathogen genotypes fully adapted to the resistance genes on hosts carrying these genes, relative to those that do not carry these genes,
tradeoff_strength: strengths of the trade-off relationships between the level of aggressiveness on hosts that do and do not carry the resistance genes.
recombination_sd: standard deviation of the normal distribution used for recombination of quantitative traits during sexual reproduction (infinitesimal model)

The data.frame must be defined as follow (example):

geneName	efficiency	age_of_activ_mean	age_of_activ_var	mutation_prob	Nlevels_agressiveness	adaptation_cost	relative advantage	tradeoff_strength	target_trait	recombination_sd
MG1	1	0	0	1e-07	2	0.5	0.5	1	IR	0.27
QTL1	0.5	0	0	0.0001	10	0.74	0.74	1	LAT	0.27

Examples

Run this code

if (FALSE) {
simul_params <- createSimulParams()
gene1 <- loadGene(name = "MG 1", type = "majorGene")
gene2 <- loadGene(name = "MG 2", type = "majorGene")
genes <- data.frame(rbind(gene1, gene2), stringsAsFactors = FALSE)
simul_params <- setGenes(simul_params, genes)
simul_params@Genes
}