pred_mar_eff | R Documentation |
Predict marker effects in a training population
pred_mar_eff(
genome,
training.pop,
method = c("RRBLUP", "BRR", "BayesA", "BL", "BayesB", "BayesC"),
n.iter = 1500,
burn.in = 500,
thin = 5,
save.at = "."
)
genome |
An object of class |
training.pop |
An object of class |
method |
The statistical method to predict marker effects. If |
n.iter, burn.in, thin |
Number of iterations, number of burn-ins, and thinning, respectively. See
|
save.at |
See |
The training.pop
must have phenotypic values associated with each entry.
The mean phenotype is used as training data in the model. Genotypic data (excluding
QTL) are used to predict marker effects.
The training.pop
with predicted marker effects.
# Simulate a genome
n.mar <- c(505, 505, 505)
len <- c(120, 130, 140)
genome <- sim_genome(len, n.mar)
# Simulate a quantitative trait influenced by 50 QTL
qtl.model <- matrix(NA, 50, 4)
genome <- sim_gen_model(genome = genome, qtl.model = qtl.model,
add.dist = "geometric", max.qtl = 50)
# Simulate the genotypes of eight founders
founder_pop <- sim_founders(genome, n.str = 8)
founder_pop <- sim_phenoval(pop = founder_pop, h2 = 0.5)
ped <- sim_pedigree(n.par = 2, n.ind = 100, n.selfgen = 2)
# Extract the founder names
parents <- indnames(founder_pop)
# Generate a crossing block with 5 crosses
cb <- sim_crossing_block(parents = parents, n.crosses = 5)
# Simulate the populations according to the crossing block
pop <- sim_family_cb(genome = genome, pedigree = ped, founder.pop = founder_pop,
crossing.block = cb)
# Use the founders as a training population for the progeny
# Predict marker effects
training.pop <- pred_mar_eff(genome = genome, training.pop = founder_pop)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.