plot_qtl function

QTL heritability and significance plot

Creates a plot where dot sizes and colors represent the QTLs heritabilities and their p-values, respectively.

plot_qtl(
  data = data,
  model = model,
  fitted = fitted,
  pheno.col = NULL,
  main = NULL,
  drop.pheno = TRUE,
  drop.lgs = TRUE
)

Arguments

• data: an object of class qtlpoly.data.
• model: an object of class qtlpoly.profile or qtlpoly.remim.
• fitted: an object of class qtlpoly.fitted.
• pheno.col: the desired phenotype column numbers to be plotted. The order here specifies the order of plotting (from top to bottom.)
• main: plot title; if NULL (the default), no title is shown.
• drop.pheno: if FALSE, shows the names of all traits from pheno.col, even of those with no QTLs; if TRUE (the default), shows only the traits with QTL(s).
• drop.lgs: if FALSE, shows all linkage groups, even those with no QTL; if TRUE (the default), shows only the linkage groups with QTL(s).

Returns

A ggplot2 with dots representing the QTLs.

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, remim.mod, probs="joint", polygenes="none")

  # Plot QTL
  plot_qtl(data, remim.mod, fitted.mod)

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, fit_model

Author(s)

Guilherme da Silva Pereira, gdasilv@ncsu.edu