simulate_qtl function

Simulations of multiple QTL

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, ...)

Arguments

• 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.
• ...: currently ignored

Returns

An object of class qtlpoly.sim which contains a list of results with the same structure of class qtlpoly.data.

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)

  # Simulate new phenotypes
  sim.dat = simulate_qtl(data = data, n.sim = 1)
  sim.dat

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

Author(s)

Guilherme da Silva Pereira, gdasilv@ncsu.edu