fit_model: Fits multiple QTL models

In guilherme-pereira/QTLpoly: Random-effect multiple QTL mapping in autopolyploids

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
)

## S3 method for class 'qtlpoly.fitted'
summary(x, 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.
x	an object of class qtlpoly.fitted to be summarized.
pheno.col	a numeric vector with the phenotype column numbers to be summarized; if NULL, all phenotypes from 'data' will be included.

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. http://doi.org/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. http://doi.org/10.1534/genetics.120.303080.

See Also

read_data, remim

Examples

  ## Not run: 
  # load raw data
  data(maps)
  data(pheno)

  # estimate conditional probabilities using mappoly package
  library(mappoly)
  genoprob <- lapply(maps, calc_genoprob)

  # prepare data
  data <- read_data(ploidy = 6, geno.prob = geno.prob, pheno = pheno, step = 1)

  # perform remim
  remim.mod <- remim(data = data, w.size = 15, sig.fwd = 0.01, sig.bwd = 0.0001,
    d.sint = 1.5, n.clusters = 4, plot = "remim")

  # fit model
  fitted.mod <- fit_remim(data=data, model=remim.mod, probs="joint", polygenes="none")
  
## End(Not run)

guilherme-pereira/QTLpoly documentation built on Oct. 10, 2021, 10:22 p.m.