META

META is a program for the meta analysis of genome-wide association studies. The program is designed to synthesizing the evidence from different association studies. Particularly, the program is able to work seamlessly with the output of SNPTEST [1] . This program was used in the meta analysis of the genome-wide association studies of smoking.

Home	Input File Formats	Output Summaries	Running META	Options	Perl Script
Contributors	Download	Version History	References	Contact Information

Contributors (top)

The following people have contributed to the development of the software for META:

Jason Liu, Jonathan Marchini

Download (top)

Pre-compiled versions of the program and example files can be downloaded from the links below. We've supplied both static and dynamic versions of the Linux executables. If you intend to run META on a machine running an old kernel then you probably want to use the dynamic version. If you have any problems getting the program to work on your machine please contact me.

Platform	File
Linux (x86_64) Static Executable	meta_v1.3.1_x86_64_static.tgz
Linux (x86_64) Dynamic Executable	meta_v1.3.1_x86_64_dynamic.tgz
Linux (x86_64) Static Executable	meta_v1.3.2_x86_64_static.tgz
Linux (x86_64) Dynamic Executable	meta_v1.3.2_x86_64_dynamic.tgz

Please fill out the registration form to receive emails about updates to this software.

To unpack the files use a command like

tar zxvf meta_vX.X_x86_64.tgz

This will create an executable called META and a directory /example that contains the following example data files

example1.txt	A result of study containing information mention in Input File Formats.
example2.txt	A sample file as exmple1.txt.

Version History (top)

Input File Formats (top)

META reads plain text files at input, with each line of each file represeting the information of a SNP. Although the format is quite flexible, following column names must be provided:

rsid	SNP id.
pos	Base-pair position of SNP.
allele_A	non-coded allele (a.k.a non-effect allele, non-reference allele).
allele_B	coded allele (a.k.a effect allele, reference allele).
info	imputation quality score (RSQR_HAT column in MACH; INFO column in PLINK; PROPER_INFO column in SNPTEST).
P_value	p-value of each SNP.
beta	effect size of each snp.
se	standard error of effect size.

Some other columns, e.g. chr which is chromosome number (1-22), and coded_af which is the coded allele frequencies, can be aslo provided. If the chr column is specified, the input file can contain SNPs from difference chromosomes, otherwise, SNPs are assumed to come from the same chromosome. Note that for --method 3 (z-statistics combination method), beta and se are not required, only the direction of effect size is needed. An example of input file is given below ( this is not a real data set):

META can use the output files of SNPTEST as its input files because all the information mentioned above is already included in the output of SNPTEST. See SNPTEST Mode for how to read them and here for the details of output of SNPTEST.

Output Summaries (top)

The output file of META contains a line for each SNP and there is a header line which specifies the contents of each column. The following table give a description of each of the entries in this file.

chr	Chromosome number (if you specified the chromosome in input files)
rsid	SNP id (taken from input files).
pos	Base-pair position of SNP (taken from input files).
allele_A	non-coded allele (taken from input files).
allele_B	coded allele (taken from input files).
P_value	combined p-value.
beta	combined effect size.
se	combined standard error of effect size.
Q	Cochran's Q statistics.
P_heterogeneity	p-value for heterogeneity.
I2	percentage of total variation across studies that is due to heterogeneity.
P_cohort_1, ..., P_cohort_n	p-values of cohort 1, ..., cohort n.

Running META (top)

To run META and see the parameters it requires, simply type:

./meta

META will read gzipped or non-compressed files at input. Output files will be gzipped if the main input data file is gzipped.

Following command gives a simplest use of META:

./meta --method 1 --cohort example1.txt example2.txt --output meta.txt

or if the input files are gzipped:

Following command gives a simplest use of META:

./meta --method 1 --cohort example1.txt.gz example2.txt.gz --output meta.txt

Three differnt meta analysis methods are coded. Here 1 means to use inverse variance method based on the fixed-effects model. For other methods, see Options. Hence, this will combine the p-values at each SNP in exampl1.txt and exmple2.txt, saving the result into meta.txt. The SNPs in the output file is a union of SNPs in the input files. So the number of cohorst used to combine p-values at each SNP would be different, as some SNPs only can be found only in some cohorts (due to different genotyping platforms, imputation quality, etc)

Threshold Imputation Quality (top)

Specify sample size of each cohort (top)

Specify genomic control lambda for each cohrt (top)

Select SNPs of interest (top)

Select a subregion of SNPs (top)

Select Best SNPs (top)

SNPTEST Mode (top)

Options (top)

A complete set of options is given in the following table :

Parameters	Type	Description
--method	Number (1 to 3)	Three different methods used to combine p-values: 1 = inverse variance method (based on fixed-effects model); 2 = inverse variance mehtod (based on random-effects model); 3 = z-statistics combination method (based on fixed-effects model).
--cohort	Files	A vector of formatted files.
--output	File	Output file.
--snptest	File	Optional, use the output of SNPTEST as input files.
--sample-size	Numbers	Optional, a vector of sample sizes for each cohort. To use z-statistics combination method (method = 3), sample sizes have to be given.
--lambda	Numbers	Optional, a vector of genomic control lambdas for each cohort.
--threshold	Number (between 0 and 1)	Optional, define a threshold of imputation quality score (between 0 and 1), default value = 0.5.
--rsid	RSIDs	Optional, RSIDs of SNP of interest.
--interval	Two numbers	Optional, define a subset of SNPs by position (in basepairs) in the range start ≤ position ≤ end.
--top-snp	Number	Optional, define the number of most significant SNPs that will be output.

Perl Script (top)

A perl script called meta.pl is also provided to ease the use of META. Once you have downloaded, you can type "./meta.pl --help" in the terminal for a brief help or type "perldoc meta.pl" for full message. A simple use of the script is given below, when the cohort files example1.txt and example2.txt are stored in examples/cohorts1/ (note that there is no "chr" information in the input files):

References (top)

Contact Information (top)

If you have any questions regarding the use of this program please send an email to Dr Jason Liu (jsliu < at > stats < dot > ox <dot > ac < dot > uk)