qtlpoly (version 0.2.4)

plot_sint: QTLs with respective support interval plots

Description

Creates a plot where colored bars represent the support intervals for QTL peaks (black dots).

Usage

plot_sint(data, model, pheno.col = NULL, main = NULL, drop = FALSE)

Value

A ggplot2 with QTL bars for each linkage group.

Arguments

data

an object of class qtlpoly.data.

model

an object of class qtlpoly.profile or qtlpoly.remim.

pheno.col

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

main

a character string with the main title; if NULL, no title will be shown.

drop

if TRUE, phenotypes with no QTL will be dropped; if FALSE (default), all phenotypes will be shown.

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, remim, profile_qtl

Examples

  # \donttest{
  # 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)

  # Plot support intervals
  plot_sint(data = data, model = remim.mod)
  # }