null_model2: Null model
In qtlpoly: Random-Effect Multiple QTL Mapping in Autopolyploids

null_model2

R Documentation

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, gdasilv@ncsu.edu, Gabriel de Siqueira Gesteira, gdesiqu@ncsu.edu

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. \Sexpr[results=rd]{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

  
  # 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)

qtlpoly documentation built on May 29, 2024, 2:14 a.m.