fit_model: Fits multiple QTL models

In qtlpoly: Random-Effect Multiple QTL Mapping in Autopolyploids

Fits multiple QTL models

Description

Fits alternative multiple QTL models by performing variance component estimation using REML.

Usage

fit_model(
  data,
  model,
  probs = "joint",
  polygenes = "none",
  keep = TRUE,
  verbose = TRUE,
  pheno.col = NULL
)

## S3 method for class 'qtlpoly.fitted'
summary(object, pheno.col = NULL, ...)

Arguments

data	an object of class qtlpoly.data.
model	an object of class qtlpoly.profile or qtlpoly.remim.
probs	a character string indicating if either "joint" (genotypes) or "marginal" (parental gametes) conditional probabilities should be used.
polygenes	a character string indicating if either "none", "most" or "all" QTL should be used as polygenes.
keep	if TRUE (default), stores all matrices and estimates from fitted model; if FALSE, nothing is stored.
verbose	if TRUE (default), current progress is shown; if FALSE, no output is produced.
pheno.col	a numeric vector with the phenotype column numbers to be summarized; if NULL (default), all phenotypes from 'data' will be included.
object	an object of class qtlpoly.fitted to be summarized.
...	currently ignored

Value

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

pheno.col	a phenotype column number.
fitted	a sommer object of class mmer.
qtls	a data frame with information from the mapped QTL.

Author(s)

Guilherme da Silva Pereira, [e-mail]

References

Covarrubias-Pazaran G (2016) Genome-assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11 (6): 1–15. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pone.0156744")}.

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")}.

See Also

read_data, remim

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)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data=data, model=remim.mod, probs="joint", polygenes="none")
  

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