simulate_qtl: Simulations of multiple QTL
In guilherme-pereira/QTLpoly: Random-effect multiple QTL mapping in autopolyploids

Description Usage Arguments Value Author(s) References See Also Examples

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 `qtlpoly.data`.
`mu`	simulated phenotype mean, e.g. 0 (default).
`h2.qtl`	vector with QTL heritabilities, e.g. `c(0.3, 0.2, 0.1)` for three QTL (default); if `NULL`, only error is simulated.
`var.error`	simulated error variance, e.g. 1 (default).
`linked`	if `TRUE` (default), at least two QTL will be linked; if `FALSE`, QTL will be randomly assigned along the genetic map. Linkage is defined by a genetic distance smaller than the selected `w.size`.
`n.sim`	number of simulations, e.g. 1000 (default).
`missing`	if `TRUE` (default), phenotypes are simulated with the same number of missing data observed in `data$pheno`.
`w.size`	the window size (in centiMorgans) between two (linked) QTL, e.g. 20 (default).
`seed`	integer for the `set.seed()` function.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.sim` to be printed.
`detailed`	if `TRUE`, detailed information on linkage groups and phenotypes in shown; if `FALSE`, no details are printed.

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. http://doi.org/10.1534/genetics.120.303080.

read_data

  ## Not run: 
  # load raw data
  data(maps)
  data(pheno)

  # estimate conditional probabilities using mappoly package
  library(mappoly)
  genoprob <- lapply(maps, calc_genoprob)

  # prepare data
  data <- read_data(ploidy = 6, geno.prob = genoprob, pheno = pheno, step = 1)

  # simulate new phenotypes
  sim.dat <- simulate_qtl(data = data, n.sim = 1000)
  
## End(Not run)