estQTLeffects: Calculate QTL effects at each position across the genome

In qtlcharts: Interactive Graphics for QTL Experiments

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))

qtlcharts documentation built on Jan. 8, 2022, 1:06 a.m.