covar: Examine GxE effect of covariates on main genetic effects.

In byandell/qtlbim: QTL Bayesian Interval Mapping

Description

Compare main effects with GxE effects to address correlation of estimates.

Usage

qb.covar(qbObject, element = "add", covar = 1, adjust.covar, chr, ...)
## S3 method for class 'qb.covar'
summary(object, percent = 5, digits = 3, ...)
## S3 method for class 'qb.covar'
print(x, ...)
## S3 method for class 'qb.covar'
plot(x, percent = 5, cex, include.zero = TRUE, ...)

Arguments

qbObject	Object of class qb.
object	Object of class qb.covar.
x	Object of class qb.covar.
element	Main effect to examine ("add" or "dom").
covar	Index to covariates used in MCMC samples.
adjust.covar	Adjustments to covariates. Default is NA, which adjusts by covariate mean values. Values are assumed to be in order of fixed covariates.
chr	Subset of chromosomes as integer vector.
percent	Percentile (0 to 100) for summaries.
digits	Number of significant digits to print.
cex	Character expansion for plots (default decreases with MCMC sample size).
include.zero	Include zero values in plot when TRUE.
...	Arguments passed through to inherited routines.

Details

The diagonal dark green line of points on plots by chromosome indicate adjustment for covariates that have not been centered. Main effects are generally less correlated with GxE when covariates are first centered to have mean zero.

Value

Objects of class qb.covar have three columns: main effect, GxE effect and chromosome. Summary objects have eight columns, three for main effect and GxE (mean, lower and upper percentile), followed by correlation and p-value. Summaries are done by chromosome.

Author(s)

Brian S. Yandell

References

http://www.qtlbim.org

See Also

qb.mcmc

Examples

data(qbExample)

temp <- qb.covar(qbExample)
summary(temp)
plot(temp)

byandell/qtlbim documentation built on Dec. 19, 2021, 12:47 p.m.