breeding_values {qtlpoly}

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 class 'qtlpoly.bvalues'
plot(x, pheno.col = NULL, ...)

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

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.

Author(s)

Guilherme da Silva Pereira, [e-mail]

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. doi:10.1534/genetics.120.303080.

See Also

read_data, fit_model

Examples

  
  # 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)