pred_geno_val | R Documentation |
Predict genotypic values using genomewide markers
pred_geno_val(
genome,
training.pop,
candidate.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 |
candidate.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, which are then used to predict the
genotypic value of the individuals in the candidate.pop
.
The candidate.pop
with predicted genotypic values.
# 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
pop <- pred_geno_val(genome = genome, training.pop = founder_pop, candidate.pop = pop)
## Alternatively, predict marker effects first, then predict genotypic values
## This is faster.
training.pop <- pred_mar_eff(genome = genome, training.pop = founder_pop)
pop <- pred_geno_val(genome = genome, training.pop = founder_pop, candidate.pop = pop)
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