Name: metacarpa
Owner: Wellcome Trust Sanger Institute - Human Genetics Informatics
Description: Fork of https://bitbucket.org/agilly/metacarpa
Created: 2015-09-29 15:09:22.0
Updated: 2015-09-29 15:09:57.0
Pushed: 2015-09-29 15:11:38.0
Homepage: null
Size: 1144
Language: C++
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This is the repository where the code for METACARPA lives.
As open data and data sharing policies increase among the scientific communities, more and more studies are expected to share their results in the coming years. This opens exciting perspectives for meta-analysis studies, which can aggregate such results in order to boost power and discover potential new genetic associations. When performing meta-analysis, particular care needs to be given to sample selection, because meta-analysed studies are supposed to contain independent samples only.However, due to privacy policies, researchers often do not publish raw genotype data, or if they do, they scramble sample IDs so that no genotype can be traced back to the actual person.
METACARPA is designed for meta-analysing genetic association studies with overlapping or related samples, when details of the overlap or relatedness are unknown. It implements and expands a method first described by Province and Borecki. In this article, the authors describe how to meta-analyse p-values, however what researchers are most interested in are effect sizes, i.e. the combined effect of a particular SNP across all studies. Lin and Sullivan describe how this can be done when the degree of overlap is known; METACARPA combines both these methods.
METACARPA is compiled as a static Linux x64 executable. Running it should be as simple as downloading this executable and running it.
The program is still under development and deployment has not yet been tested. Please email the author if you encounter any problems.
The src directory contains a single source file and Makefile. The Makefile is written for a specific environment and is not deployment-ready. If you plan to try and compile METACARPA, you will need to change at least the first three lines of the Makefile to add custom compiler paths. Please note that you will also need a copy of the Boost mathematical libraries on your machine (the binaries were compiled using Boost 1.55.0).
Here is a short summary of METACARPA's options.
CARPA ( ? - ? ): Meta-analysis in C++ Accounting for Relatedness using arbitrary Precision Arithmetic.
========================================================================================================
NB: All arguments mandatory except column arguments.
MATACARPA currently supports only one header line.
ons description :
help This help message.
[ --input ] arg Input file.
[ --output ] arg Output file.
[ --sep ] arg Input field separator. Don't forget to quote if
necessary. Output field separator is always \t.
[ --chr-col ] arg 1-based p-value column number.
[ --pos-col ] arg 1-based position column number.
[ --all-col ] arg 1-based column number for effect or reference allele.
[ --rsid-col ] arg 1-based column number for RSID or any other column that
you want to keep.
[ --pval-col ] arg 1-based p-value column number.
[ --beta-col ] arg 1-based beta column number.
[ --se-col ] arg 1-based beta-SE column number.
[ --size-col ] arg 1-based sample size column (if absent, sample sizes
will be assumed constant and should be appended to
input file names using a comma : -I
[FILENAME],[SAMPLE_SIZE]).
[ --id-col ] arg 1-based ID column number (must be unique - e.g.
chr:pos-A1-A2). If absent, chr:pos will be used.
[ --matrix ] arg Path to a METACARPA-generated correlation matrix array.
Although METACARPA scales well to large numbers of variants, you can LD-prune or randomly thin down your SNPs to accelerate the calculation of study correlations. This can be done, for example, using Plink. For more detailed estimations of minimum number of SNPs needed to get correct results, please refer to the Metacarpa simulation report.
METACARPA supports any fixed-delimiter input file that has the column it requires. You should specify the column indices that correspond to each of these columns using the command line arguments. For example:
./metacapa2 -I file1.assoc,1500 -I file2.assoc,2300 -O out.meta_analysis.txt -t '\t' -c 1 -q 2 -r 3 -i 3 -p 4 -b 5 -s 6
will run the meta-analysis on two studies with $1500$ and $2300$ samples respectively. If your input format contains the number of individuals genotyped at a particular position, remove the commas and sample sizes after the file name and replace by -n sample_col, where sample_col is the column number that contains this information.
METACARPA runs in two steps:
If you run meta-analyses for different traits, if you chunk your data or generally if you want to meta-analyse the same populations more than once, you can reuse the correlation matrix by using the -m argument. By default, METACARPA will always write the matrices it calculates to disk, they should appear as small *.matrix files in the running directory. If you point the -m argument to one of these files, METACARPA will skip calculation of the matrix and jump directly to the meta-analysis.
If you decide to chunk your data, please ensure that you calculate the correlation matrix on the whole, un-chunked set. You can then use that matrix on your chunked data for meta-analysis.
The matrix calculation step is the most costly part of the algorithm, but it has been designed to scale well to very large datasets such as sequencing data. A memory assignment of 1Gb should be more than enough for most cases.
For detailed analyses of this software's performance and comparisons with other methods, please refer to the Metacarpa simulation report.