permutations function

Fixed-effect interval mapping (FEIM) model permutations

Stores maximum LOD scores for a number of permutations of given phenotypes.

permutations(
  data,
  offset.data = NULL,
  pheno.col = NULL,
  n.sim = 1000,
  probs = c(0.9, 0.95),
  n.clusters = NULL,
  seed = 123,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.perm'
print(x, pheno.col = NULL, probs = c(0.9, 0.95), ...)

## S3 method for class 'qtlpoly.perm'
plot(x, pheno.col = NULL, probs = c(0.9, 0.95), ...)

Arguments

• data: an object of class qtlpoly.data.
• offset.data: a subset of the data object to be used in permutation calculations.
• pheno.col: a numeric vector with the phenotype columns to be analyzed; if NULL (default), all phenotypes from 'data' will be included.
• n.sim: a number of simulations, e.g. 1000 (default).
• probs: a vector of probability values in [0, 1] representing the quantiles, e.g. c(0.90, 0.95) for the 90% and 95% quantiles.
• n.clusters: a number of parallel processes to spawn.
• seed: an integer for the set.seed() function; if NULL, no reproducible seeds are set.
• verbose: if TRUE (default), current progress is shown; if FALSE, no output is produced.
• x: an object of class qtlpoly.perm to be printed or plotted.
• ...: currently ignored

Returns

An object of class qtlpoly.perm which contains a list of results for each trait with the maximum LOD score per permutation.

LOD score thresholds for given quantiles for each trait.

A ggplot2 histogram with the distribution of ordered maximum LOD scores and thresholds for given quantiles for each trait.

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)

  # Perform permutations
  perm = permutations(data = data, pheno.col = 1, n.sim = 10, n.clusters = 1)

References

Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping, Genetics 138: 963-971.

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

feim

Author(s)

Guilherme da Silva Pereira, gdasilv@ncsu.edu