qtlcharts (version 0.16)

estQTLeffects: 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.

Value

list of matrices; each component corresponds to a position in the genome and is a matrix with phenotypes x 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.

See Also

[iplotMScanone()], [qtl::effectscan()] [cbindQTLeffects()]

Examples

# NOT RUN {
data(grav)
library(qtl)
grav <- reduce2grid(calc.genoprob(grav, step=1))
out <- estQTLeffects(grav, phe=seq(1, nphe(grav), by=5))

# }