simulate_qtl | R Documentation |
Simulate new phenotypes with a given number of QTL and creates new object with the same structure of class qtlpoly.data
from an existing genetic map.
simulate_qtl(
data,
mu = 0,
h2.qtl = c(0.3, 0.2, 0.1),
var.error = 1,
linked = FALSE,
n.sim = 1000,
missing = TRUE,
w.size = 20,
seed = 123,
verbose = TRUE
)
## S3 method for class 'qtlpoly.simul'
print(x, detailed = FALSE, ...)
data |
an object of class |
mu |
simulated phenotype mean, e.g. 0 (default). |
h2.qtl |
vector with QTL heritabilities, e.g. |
var.error |
simulated error variance, e.g. 1 (default). |
linked |
if |
n.sim |
number of simulations, e.g. 1000 (default). |
missing |
if |
w.size |
the window size (in centiMorgans) between two (linked) QTL, e.g. 20 (default). |
seed |
integer for the |
verbose |
if |
x |
an object of class |
detailed |
if |
... |
currently ignored |
An object of class qtlpoly.sim
which contains a list of results
with the same structure of class qtlpoly.data
.
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
# 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)
# Simulate new phenotypes
sim.dat = simulate_qtl(data = data, n.sim = 1)
sim.dat
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