qb.covar {qtlbim}R Documentation

Examine GxE effect of covariates on main genetic effects.

Description

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

Usage

qb.covar(qbObject, element = "add", covar = 1, chr, ...)
summary.qb.covar(object, percent = 5, digits = 3, ...)
print.qb.covar(x, ...)
plot.qb.covar(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.
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.ssg.uab.edu/qtlbim

See Also

qb.mcmc

Examples


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

[Package qtlbim version 1.6.0 Index]