qtl_effects: QTL allele effect estimation

In guilherme-pereira/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.
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).

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. http://doi.org/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

  ## Not run: 
  # load raw data
  data(maps)
  data(pheno)

  # estimate conditional probabilities using mappoly package
  library(mappoly)
  genoprob <- lapply(maps, calc_genoprob)

  # prepare data
  data <- read_data(ploidy = 6, geno.prob = genoprob, pheno = pheno, step = 1)

  # perform remim
  remim.mod <- remim(data = data, w.size = 15, sig.fwd = 0.01, sig.bwd = 0.0001,
    d.sint = 1.5, n.clusters = 4, plot = "remim")

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

  # estimate effects
  est.effects <- qtl_effects(ploidy = 6, fitted = fitted.mod)
  plot(est.effects)
  
## End(Not run)
  

guilherme-pereira/QTLpoly documentation built on Oct. 10, 2021, 10:22 p.m.