qtlpoly (version 0.2.4)

remim: Random-effect multiple interval mapping (REMIM)

Description

Automatic function that performs REMIM algorithm using score statistics.

Usage

remim(
  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
)
# S3 method for qtlpoly.remim
print(x, pheno.col = NULL, sint = NULL, ...)

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.

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.

x

an object of class qtlpoly.remim to be printed.

sint

whether "upper" or "lower" support intervals should be printed; if NULL (default), only QTL peak information will be printed.

...

currently ignored

Author

Guilherme da Silva Pereira,

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. 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. tools:::Rd_expr_doi("10.1534/genetics.104.031427")

See Also

read_data

Examples

  # \donttest{
  # 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)
  # }