optimize_qtl: Model optimization

In qtlpoly: Random-Effect Multiple QTL Mapping in Autopolyploids

Model optimization

Description

Tests each QTL at a time and updates its position (if it changes) or drops the QTL (if non-significant).

Usage

optimize_qtl(
  data,
  offset.data = NULL,
  model,
  sig.bwd = 0.05,
  score.null = NULL,
  polygenes = FALSE,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.optimize'
print(x, pheno.col = NULL, ...)

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.
model	an object of class qtlpoly.model containing the QTL to be optimized.
sig.bwd	the desired score-based p-value threshold for backward elimination, e.g. 0.0001 (default).
score.null	an object of class qtlpoly.null with results of score statistics from resampling.
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 optimization round, e.g. "optimize" (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.optimize to be printed.
pheno.col	a numeric vector with the phenotype columns to be printed; if NULL, all phenotypes from 'data' will be included.
...	currently ignored

Value

An object of class qtlpoly.optimize 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.

Author(s)

Guilherme da Silva Pereira, [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. \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.

Zou F, Fine JP, Hu J, Lin DY (2004) An efficient resampling method for assessing genome-wide statistical significance in mapping quantitative trait loci. Genetics 168 (4): 2307-16. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1534/genetics.104.031427")}

See Also

read_data, null_model, search_qtl

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 model
  null.mod = null_model(data = 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)
  

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