qtlpoly (version 0.2.4)

profile_qtl: QTL profiling

Description

Generates the score-based genome-wide profile conditional to the selected QTL.

Usage

profile_qtl(
  data,
  model,
  d.sint = 1.5,
  polygenes = FALSE,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)
# S3 method for qtlpoly.profile
print(x, pheno.col = NULL, sint = NULL, ...)

Value

An object of class qtlpoly.profile which contains a list of results for each trait with the following components:

pheno.col

a phenotype column number.

stat

a vector containing values from score statistics.

pval

a vector containing p-values from score statistics.

qtls

a data frame with information from the mapped QTL.

lower

a data frame with information from the lower support interval of mapped QTL.

upper

a data frame with information from the upper support interval of mapped QTL.

Arguments

data

an object of class qtlpoly.data.

model

an object of class qtlpoly.model containing the QTL to be profiled.

d.sint

a \(d\) value to subtract from logarithm of p-value (\(LOP-d\)) for support interval calculation, e.g. \(d=1.5\) (default) represents approximate 95% support interval.

polygenes

if TRUE all QTL but the one being tested are treated as a single polygenic effect, if FALSE (default) all QTL effect variances have to estimated.

n.clusters

number of parallel processes to spawn.

plot

a suffix for the file's name containing plots of every QTL profiling round, e.g. "profile" (default); if NULL, no file is produced.

verbose

if TRUE (default), current progress is shown; if FALSE, no output is produced.

x

an object of class qtlpoly.profile to be printed.

pheno.col

a numeric vector with the phenotype column numbers to be plotted; if NULL, all phenotypes from 'data' will be included.

sint

whether "upper" or "lower" support intervals should be printed; if NULL (default), only QTL peak information will be printed.

...

currently ignored

Author

Guilherme da Silva Pereira,

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").

Qu L, Guennel T, Marshall SL (2013) Linear score tests for variance components in linear mixed models and applications to genetic association studies. Biometrics 69 (4): 883–92.

Examples

  # \donttest{
  # 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)

  # Build null model
  null.mod = null_model(data, pheno.col = 1, n.clusters = 1)

  # Perform forward search
  search.mod = search_qtl(data = data, model = null.mod,
w.size = 15, sig.fwd = 0.01, n.clusters = 1)

  # Optimize model
  optimize.mod = optimize_qtl(data = data, model = search.mod, sig.bwd = 0.0001, n.clusters = 1)

  # Profile model
  profile.mod = profile_qtl(data = data, model = optimize.mod, d.sint = 1.5, n.clusters = 1)
  # }