plot_profile {qtlpoly}

Logarithm of P-value (LOP) profile plots

Description

Plots profiled logarithm of score-based P-values (LOP) from individual or combined traits.

Usage

plot_profile(
  data = data,
  model = model,
  pheno.col = NULL,
  sup.int = FALSE,
  main = NULL,
  legend = "bottom",
  ylim = NULL,
  grid = FALSE
)

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.

sup.int

if TRUE, support interval are shown as shaded areas; if FALSE (default), no support interval is show.

main

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

legend

legend position (either "bottom", "top", "left" or "right"); if NULL, no legend is shown.

ylim

a numeric value pair supplying the limits of y-axis, e.g. c(0,10); if NULL (default), limits will be provided automatically.

grid

if TRUE, profiles will be organized in rows (one per trait); if FALSE (default), profiles will appear superimposed. Only effective when plotting profiles from more than one trait.

Value

A ggplot2 with the LOP profiles for each trait.

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

profile_qtl, remim

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)

  # Plot profile
  plot_profile(data = data, model = remim.mod, grid = FALSE)