Allele reference panels

Comparing result files with an standard reference panel is the most important part of the QC process. This reference is used to check the alleles in the datasets and to ensure they are all in the same configuration (same strand, same coded alleles) in the post-QC data.

We have created databases from the most popular refernece panel (e.g. HapMap, 1000G, HRC) which are available from our website. Database files are in SQLite format and can be downloaded as a compressed file.

Some reference panels include more than one population. The target population which should be set as a parameter in the configuration file before running the algorithm.

The header-translation table

The column names used in the input may differ between files (e.g. one file uses EFFECT_ALLELE where another uses CODED_ALLELE). This file is a table of possible column names and their standard translations. A sample file with common names is provided in the package.

A sample file including common terms is provided as part of the package and could be used as a template. The file contains a two-column table, with the left column containing the standard column-names and the right the alternatives.

Configuration file

An INI file is used to configure the parameters for running the algorithm. See the manual for details.

Key-names and section-names should not be edited or renamed. Otherwise the algorithm will not work properly.

A sample file is included in the package which should be used as a template. File paths and QC parameters are set according to comments and examples in the file.

Step 1: make sure the package is installed correctly

After installation, try loading the package with the following command. You might see some warnings but the package should load successfully without any errors.

require(GWASinspector)

Step 2: check R environment

Local machine and R environment can be explored by running the following function.

system.check()

This will also inform you if pandoc, kableExtra, xlsx packages and Java library are available. These are not required for running GWASinspector, but allow for Excel and HTML formatted outputs.

Step 3: download the standard allele-frequency reference datasets

Standard allele-frequency reference datasets are available from our website. The database file should be decompressed and copied in the references folder [dir_references parameter of the config file].

This package supports both Rdata and SQLite database files (the later is recommended).

Step 4: get the header-translation table

A copy of this file can be copied to a local folder by running the below command. This is a text file which includes most common variable/header names and can be edited according to user specifications. This file should be copied in the references folder [dir_references parameter of the config file].

The default name of this file is alt_headers.txt.

header_translations field should be edited in the configuration file accordingly if this name is changed by user.

get.headerTranslation('/path/to/referenceFolder') # copy the file to selected folder

Step 5: get the configuration file

It is recommended to set a working directory at first.

setwd('/path/to/workingDirectory')

The configuration file is in plain text format and is used for setting the desired parameters for running the algorithm. A template file can be copied to local folder by running the following command.

The default name of this file is config.ini which can be changed by user.

get.config(getwd()) # copy the file to selected folder

Step 6: modify the parameters in the configuration file

Please refer to the configuration file or package manual for full detail of parameters.

Step 7: run the QC function

GWASinspector uses the S4 object system of R to conduct the QC. Parameters are configured using a configuration (ini) file, which is imported into R through setup.inspector and turned into an object of the Inspector class. To perform the QC, process the object with run.inspector.

A quick scan of the results can be performed via result.inspector, but the primary outcome of the QC are the log files and graphs generated by run.inspector. An exhaustive log file indicating the progress and possible warnings is also saved which can be used for localization of any problems during this run.

Example:

## load the package
require(GWASinspector)

## import the QC-configuration file 
job <- setup.inspector("/home/user/config.ini")

## check the created instance
## input result files that will be inspected are also displayed
job

## run the algorithm 
job <- run.inspector(job)

## check the results
## comprehensive report and result file are already saved in the output folder
result.inspector(job)