setGenes: Set genes

Description

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

Usage

setGenes(params, dfGenes)

Arguments

params

a LandsepiParams object

dfGenes

a data.frame containing gene parameters. It can be defined manually, or, alternatively, with loadGene.

Value

a LandsepiParams object.

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),
time_to_activ_exp: expected delays to resistance activation (for APRs),
time_to_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),
fitness_cost: fitness penalties paid by pathogen genotypes fully adapted to the considered resistance genes on host 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.

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

geneName	efficiency	time_to_activ_exp	time_to_activ_var	mutation_prob	Nlevels_agressiveness	fitness_cost	tradeoff_strength	target_trait
MG1	1	0	0	1e-07	2	0.5	1	IR
QTL1	0.5	0	0	0.0001	10	0.74	1	LAT

Examples

Run this code

# NOT RUN {
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
# }