| estQTLeffects {qtlcharts} | R Documentation |
Calculate QTL effects at each position across the genome
Description
Calculates the effects of QTL at each position across the genome using Haley-Knott regression, much like [qtl::effectscan()], but considering multiple phenotypes and not plotting the results
Usage
estQTLeffects(cross, pheno.col = 1, what = c("means", "effects"))
Arguments
cross |
(Optional) Object of class '"cross"', see [qtl::read.cross()]. |
pheno.col |
Phenotype columns in cross object. |
what |
Indicates whether to calculate phenotype averages for each genotype group or to turn these into additive and dominance effects. |
Details
One should first run [qtl::calc.genoprob()]; if not, it is run with the default arguments.
The estimated effects will be poorly estimated in the case of selective genotyping, as Haley-Knott regression performs poorly in this case.
Value
list of matrices; each component corresponds to a position in the genome and is a matrix with phenotypes x effects
See Also
[iplotMScanone()], [qtl::effectscan()] [cbindQTLeffects()]
Examples
data(grav)
library(qtl)
grav <- reduce2grid(calc.genoprob(grav, step=1))
out <- estQTLeffects(grav, phe=seq(1, nphe(grav), by=5))