qtlpoly (version 0.2.4)

breeding_values: Prediction of QTL-based breeding values from REMIM model

Description

Computes breeding values for each genotyped individual based on multiple QTL models

Usage

breeding_values(data, fitted)
# S3 method for qtlpoly.bvalues
plot(x, pheno.col = NULL, ...)

Value

An object of class qtlpoly.bvalues which is a list of results for each trait containing the following components:

pheno.col

a phenotype column number.

y.hat

a column matrix of breeding value for each individual.

A ggplot2 histogram with the distribution of breeding values.

Arguments

data

an object of class qtlpoly.data.

fitted

an object of class qtlpoly.fitted.

x

an object of class qtlpoly.bvalues to be plotted.

pheno.col

a numeric vector with the phenotype column numbers to be plotted; if NULL, all phenotypes from 'data' will be included.

...

currently ignored

Author

Guilherme da Silva Pereira,

References

Pereira GS, Gemenet DC, Mollinari M, Olukolu BA, Wood JC, Mosquera V, Gruneberg WJ, Khan A, Buell CR, Yencho GC, Zeng ZB (2020) Multiple QTL mapping in autopolyploids: a random-effect model approach with application in a hexaploid sweetpotato full-sib population, Genetics 215 (3): 579-595. tools:::Rd_expr_doi("10.1534/genetics.120.303080").

See Also

read_data, fit_model

Examples

  # \donttest{
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob) #5,7
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data = data, model = remim.mod, probs = "joint", polygenes = "none")

  # Predict genotypic values
  y.hat = breeding_values(data = data, fitted = fitted.mod)
  plot(y.hat)
  # }