metaGE.collect {metaGE}

Collect the results of GWAS data from different files

Description

This function merges files containing the summary statistics of GWAS in different environments (one file per environment).

Usage

metaGE.collect(
  FileNames,
  VariableNames,
  MinFreq = 0,
  DropDuplicates = TRUE,
  Verbose = FALSE,
  NA.rmv = TRUE
)

Arguments

FileNames

A list containing the file paths to merge (one trait only) or a list of such lists

VariableNames

A named list containing the column names in the original files corresponding to the variables below:

• MARKER, CHR, POS, EFFECT, EFFECT_SE, PVAL,

• (optional: FREQ, ALLELE0, ALLELE1, WEIGHT) or a list of such lists.

MinFreq

A numeric value allowing to filter markers based on the maf. (optional)

DropDuplicates

A boolean indicating whether duplicate markers should be removed or not. (TRUE by default)

Verbose

A boolean indicating whether progression messages should be printed or not. (FALSE by default)

NA.rmv

A boolean should the NA be removed or not (TRUE by default)

Details

Each file MUST contain the variables below:

• MARKER = marker,

• CHR = the chromosome ,

• POS = the position of the marker on the chromosome,

• EFFECT = the mean effect of the marker,

• EFFECT_SE = the standard error of the mean effect,

• PVAL = the pvalue of the mean effect. Each file might contain the variables:

• FREQ = MAF

• ALLELE0

• ALLELE1

• WEIGHT

Value

A list of:

• Data -> a tibble containing all the columns of interest of all the files from FileNames,

• RemovedMarkers -> same kind of tibble, but containing the markers that have been removed due to unclear allele coding.

Examples

require(dplyr)
require(tibble)
require(stringr)
RepData <- system.file("extdata", package = "metaGE")
# Get the complete list of association files
File.list <- list.files(RepData ,full.names = TRUE) %>%
            tibble(Names = .) %>%
            mutate(ShortNames = Names %>%
                  str_remove(pattern = paste0(RepData,"/")) %>%
                  str_remove(pattern = "_DF.txt"))  %>%
            select(ShortNames,Names) %>%
            deframe
###Build the dataset
## First provide the list of variable names
Names.list <- list(MARKER="Marker_Name",
                  CHR="Chromosome",
                  POS="Marker_Position",
                  FREQ="Maf",
                  EFFECT="SNP_Weight",
                  PVAL="Pvalue",
                  ALLELE0="Allele1",
                  ALLELE1="Allele2")

MinFreq <- 0.07

## Now collect
metaData <- metaGE.collect(File.list, Names.list,MinFreq = MinFreq)