null_model2 {qtlpoly}

Null model

Description

Creates a null model (with no QTL) for each trait.

Usage

null_model2(
  data,
  offset.data = NULL,
  pheno.col = NULL,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)

Arguments

data

an object of class qtlpoly.data.

offset.data

a data frame with the same dimensions of data$pheno containing offset variables; if NULL (default), no offset variables are considered.

pheno.col

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

n.clusters

number of parallel processes to spawn.

plot

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

verbose

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

Value

An object of class qtlpoly.null 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 (NULL at this point).

Author(s)

Guilherme da Silva Pereira, [e-mail], Gabriel de Siqueira Gesteira, [e-mail]

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. 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.

See Also

read_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)

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