plot_qtl | R Documentation |
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
)
data |
an object of class |
model |
an object of class |
fitted |
an object of class |
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 |
drop.pheno |
if |
drop.lgs |
if |
A ggplot2 with dots representing the QTLs.
Guilherme da Silva Pereira, gdasilv@ncsu.edu
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1534/genetics.120.303080")}.
read_data
, remim
, fit_model
# 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)
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