View source: R/breeding_values.R
breeding_values | R Documentation |
Computes breeding values for each genotyped individual based on multiple QTL models
breeding_values(data, fitted)
## S3 method for class 'qtlpoly.bvalues'
plot(x, pheno.col = NULL, ...)
data |
an object of class |
fitted |
an object of class |
x |
an object of class |
pheno.col |
a numeric vector with the phenotype column numbers to be plotted; if |
... |
currently ignored |
An object of class qtlpoly.bvalues
which is a list of results
for each trait containing the following components:
pheno.col |
a phenotype column number. |
y.hat |
a column matrix of breeding value for each individual. |
A ggplot2 histogram with the distribution of breeding values.
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
, fit_model
# Estimate conditional probabilities using mappoly package
library(mappoly)
library(qtlpoly)
genoprob4x = lapply(maps4x[c(5)], calc_genoprob) #5,7
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 = data, model = remim.mod, probs = "joint", polygenes = "none")
# Predict genotypic values
y.hat = breeding_values(data = data, fitted = fitted.mod)
plot(y.hat)
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