remim2: Random-effect multiple interval mapping (REMIM)

In qtlpoly: Random-Effect Multiple QTL Mapping in Autopolyploids

Random-effect multiple interval mapping (REMIM)

Description

Automatic function that performs REMIM algorithm using score statistics.

Usage

remim2(
  data,
  pheno.col = NULL,
  w.size = 15,
  sig.fwd = 0.01,
  sig.bwd = 1e-04,
  score.null = NULL,
  d.sint = 1.5,
  polygenes = FALSE,
  n.clusters = NULL,
  n.rounds = Inf,
  plot = NULL,
  verbose = TRUE
)

Arguments

data	an object of class qtlpoly.data.
pheno.col	a numeric vector with the phenotype columns to be analyzed or printed; if NULL (default), all phenotypes from 'data' will be included.
w.size	the window size (in centiMorgans) to avoid on either side of QTL already in the model when looking for a new QTL, e.g. 15 (default).
sig.fwd	the desired score-based significance level for forward search, e.g. 0.01 (default).
sig.bwd	the desired score-based significance level for backward elimination, e.g. 0.001 (default).
score.null	an object of class qtlpoly.null with results of score statistics from resampling.
d.sint	a d value to subtract from logarithm of p-value (LOP-d) for support interval calculation, e.g. d=1.5 (default) represents approximate 95% support interval.
polygenes	if TRUE all QTL already in the model 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.
n.rounds	number of search rounds; if Inf (default) forward search will stop when no more significant positions can be found.
plot	a suffix for the file's name containing plots of every algorithm step, e.g. "remim"; if NULL (default), no file is produced.
verbose	if TRUE (default), current progress is shown; if FALSE, no output is produced.
sint	whether "upper" or "lower" support intervals should be printed; if NULL (default), only QTL peak information will be printed.

Value

An object of class qtlpoly.remim 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.
lower	a data frame with information from the lower support interval of mapped QTL.
upper	a data frame with information from the upper support interval of mapped QTL.

Author(s)

Guilherme da Silva Pereira, [e-mail], Getúlio Caixeta Ferreira, [e-mail]

References

Kao CH, Zeng ZB, Teasdale RD (1999) Multiple interval mapping for quantitative trait loci. Genetics 152 (3): 1203–16.

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

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 = remim2(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)
  

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