qtl_effects: QTL allele effect estimation

In qtlpoly: Random-Effect Multiple QTL Mapping in Autopolyploids

Description

Computes allele specific and allele combination (within-parent) heritable effects from multiple QTL models.

Usage

qtl_effects(ploidy = 6, fitted, pheno.col = NULL, verbose = TRUE)

## S3 method for class 'qtlpoly.effects'
plot(x, pheno.col = NULL, p1 = "P1", p2 = "P2", ...)

Arguments

ploidy	a numeric value of ploidy level of the cross (currently, only 4 or 6).
fitted	a fitted multiple QTL model of class qtlpoly.fitted.
pheno.col	a numeric vector with the phenotype column numbers to be plotted; if NULL, all phenotypes from 'fitted' will be included.
verbose	if TRUE (default), current progress is shown; if FALSE, no output is produced.
x	an object of class qtlpoly.effects to be plotted.
p1	a character string with the first parent name, e.g. "P1" (default).
p2	a character string with the second parent name, e.g. "P2" (default).
...	currently ignored

Value

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

pheno.col	a phenotype column number.
y.hat	a vector with the predicted values.

A ggplot2 barplot with parental allele and allele combination effects.

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.

Kempthorne O (1955) The correlation between relatives in a simple autotetraploid population, Genetics 40: 168-174.

See Also

read_data, remim, fit_model

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  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, model=remim.mod, probs="joint", polygenes="none")

  # Estimate effects
  est.effects = qtl_effects(ploidy = 4, fitted = fitted.mod, pheno.col = 1)

  # Plot results
  plot(est.effects)
  
  

qtlpoly documentation built on Jan. 12, 2022, 5:06 p.m.