metaGE (version 1.2.2)

metaGE.fit: Meta-analysis procedure: Fixed or Random effect.

Description

Quantitative trait loci detection via Fixed or Random effect meta-analysis GWAS procedure.

Usage

metaGE.fit(Data, MatCorr, Method, NA.omit = TRUE, DropZScores = FALSE)

Value

The dataset Data with supplementary columns:

• PVALUE: The pvalue of the MA test,

• Mu: Estimate of the mean marker effect,

• Tau: Estimate of the variance of the marker effect, for the Random model only,

• the Zscores for each environment if DropZScores = FALSE.

Arguments

Data

A dataset containing the estimated marker effect and its associated pvalue of each marker (in rows) in each environment (in columns), as obtained from metaGE.collect().

MatCorr

The inter-environments correlation matrix. Can be computed using metaGE.cor().

Method

A string specifying the method to be performed: either "Fe" or "Re".

NA.omit

A boolean specifying whether the markers with some NA values should be removed. (TRUE by default)

DropZScores

A boolean specifying whether the Zscores should be dropped from the dataset or not.(FALSE by default)

Details

Different tests may be performed:

• Fixed Effect (Fe), to identify markers with a stable effect across environments.

• Random Effect (Re), to identify markers whose effects may be unstable across environments.

Examples

require(dplyr)
# Import the data
data("metaData")

# Compute the inter-environment correlation matrix
matCorr <- metaGE.cor(metaData, Threshold = 0.8)

# Fixed Effect
FeDF <- metaGE.fit(metaData, matCorr, Method = "Fe")
head(FeDF %>% select(CHR, POS, MARKER, Mu, Tau, PVALUE))

# Random Effect
ReDF <- metaGE.fit(metaData, matCorr, Method = "Re")
head(ReDF %>% select(CHR, POS, MARKER, Mu, Tau, PVALUE))