run_scanone: Do single-QTL scan.
In gact/shmootl: QTL Analysis Utilities for Yeast

Description Usage Arguments Details Author(s) References See Also

Read cross data from the specified cross input CSV file or HDF5 scan file, run a single QTL analysis using R/qtl scanone (Broman et al. 2003), and write the results of that scan to the specified HDF5 file.

run_scanone(infile = NA_character_, h5file = NA_character_,
  chr = character(), pheno = character(), model = c("normal", "binary",
  "2part", "np"), method = c("em", "imp", "hk", "ehk", "mr", "mr-imp",
  "mr-argmax"), n.perm = 1000L, n.cluster = 1L, alpha = NA_real_,
  fdr = NA_real_, threshold = NA_real_, step = 0,
  map.function = c("haldane", "kosambi", "c-f", "morgan"),
  error.prob = 1e-04, ci.function = c("lodint", "bayesint"), drop = 1.5,
  prob = 0.95, acovfile = NA_character_, icovfile = NA_character_)

`infile`	input cross CSV file
`h5file`	HDF5 scan file [required]
`chr`	sequences [default: all]
`pheno`	phenotypes [default: all]
`model`	phenotype model
`method`	method of QTL analysis
`n.perm`	number of permutations
`n.cluster`	number of threads
`alpha`	significance level for LOD threshold
`fdr`	FDR for LOD threshold
`threshold`	fixed LOD threshold
`step`	step size for genotype probabilities
`map.function`	genetic map function
`error.prob`	genotyping error rate
`ci.function`	QTL interval function
`drop`	LOD support interval drop
`prob`	Bayesian credible interval probability
`acovfile`	additive covariates file
`icovfile`	interactive covariates file

If the input cross contains enumerated genotypes, marker regression is performed regardless of the value of the method parameter.

In typical usage, LOD threshold stringency can be set through either the significance level (alpha), or the false-discovery rate (fdr), but not both. If neither is specified, a significance level alpha of 0.05 is used. The given stringency is then used to estimate a LOD threshold from scanone permutations.

This can be bypassed by setting a fixed LOD threshold, along with a nominal stringency (alpha or fdr), in which case permutations are skipped and the fixed LOD threshold is applied directly for assessing significance.

LOD interval estimation can be controlled with the 'ci.function' parameter: set to 'lodint' for LOD support intervals (adjusting stringency with the 'drop' parameter), or to 'bayesint' for Bayesian credible intervals (adjusting stringency with the 'prob' parameter). For more information on the QTL interval methods used, see functions 'lodint' and 'bayesint' in the R/qtl manual, as well as Section 4.5 of Broman and Sen (2009).

Thomas Walsh <tw164@protonmail.com>

Yue Hu

Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889-890. (PubMed)

Broman KW, Sen S (2009) A guide to QTL mapping with R/qtl. New York: Springer. (Website)

R/qtl manual

Other pipeline functions: run_annoqtl, run_digest, run_estimap, run_interptimes, run_makecross, run_makegeno, run_prep, run_pullmap, run_pushmap, run_recode, run_report, run_scantwo