MetaXcan is a set of tools to perform integrative gene mapping studies. Almost all of the software here is command-line based.

S-PrediXcan is an extension of PrediXcan, that infers PrediXcan's results using only summary statistics. It is a component of MetaXcan. A manuscript describing S-PrediXcan and the MetaXcan framework with an application can be found here.

You can try our web version of the application here. You will need to create a user or log in using a Google account.

We have ran MetaXcan on over a 100 complex traits in publicly available GWAS summary statistics using models trained on GTEx data. An SQLite database can be downloaded here(1.3GB). An usage example in R is provided. If you plan to use that data in a publication, please be sure to take a look at that package's README and make sure you are compliant with the data usage restrictions.

The software is developed and tested in Linux and Mac OS environments. The main S-PrediXcan script is also supported in Windows.

To run S-PrediXcan, you need Python 2.7, with the following libraries:

• numpy (>=1.11.1)
• scipy (>=0.18.1)
• pandas (>=0.18.1)
• mock and sqlalchemy are needed for the unit tests.

To run PrediXcan and MulTiPrediXcan, you also need:

• patsy (>=0.5.0)
• statsmodels (>=0.8.0)
• h5py (>=2.7.1)
• h5py-cache (>=1.0.0)

R with ggplot and dplyr is needed for some optional statistics and charts.

software folder contains an implementation of S-PrediXcan's method and associated tools. The following scripts from that folder constitute different components in the MetaXcan pipeline:

MetaXcan.py
PrediXcan.py
MulTiXcan.py
SMulTiXcan.py

, although a typical user will only need MetaXcan.py. MetaXcan.py script contains the current implementation of S-PrediXcan. MulTiXcan.py and SMulTiXcan.py are the multiple-tissue methods.

The rest of the scripts in software folder are python packaging support scripts, and convenience wrappers such as the GUI.

Subfolder software/metax contains the bulk of Metaxcan's logic, implemented as a python module.

S-PrediXcan will calculate the gene-level association results from GWAS summary statistics. It supports most GWAS formats by accepting command line argument specifying data columns. Some precalculated data is needed, that must be set up prior to S-PrediXcan execution.

The gist of S-PrediXcan's input is:

• A Transcriptome Prediction Model database (an example is here)
• A file with the covariance matrices of the SNPs within each gene model (such as this one)
• GWAS results (such as these, which are just randomly generated). GWAS results can belong to a single file or be split into multiple ones (i.e. split by chromosome). You can specify the necessary columns via command line arguments (i.e. which column holds snps, which holds p-values, etc)

You can use precalculated databases, or generate new ones with tools available in PredictDB repository. GTEx-based tissues and 1000 Genomes covariances precalculated data can be found here.

(Please refer to /software/Readme.md for more detailed information)

The following example assumes that you have python 2.7, numpy, and scipy installed.

1. Clone this repository.

$ git clone https://github.com/hakyimlab/MetaXcan

2. Go to the software folder.

$ cd MetaXcan/software

3. Download sample data:

# You can click on the link above or type the following at a terminal
$ wget https://s3.amazonaws.com/imlab-open/Data/MetaXcan/sample_data/v0_5/sample_data.tar.gz

This may take a few minutes depending on your connection: it has to download approximately 200Mb worth of data. Downloaded data will include an appropiate Transcriptome Model Database, a GWAS/Meta Analysis summary statistics, and SNP covariance matrices.

Extract it with:

tar -xzvpf sample_data.tar.gz

4. Run the High-Level S-PrediXcan Script

./MetaXcan.py \
--model_db_path data/DGN-WB_0.5.db \
--covariance data/covariance.DGN-WB_0.5.txt.gz \
--gwas_folder data/GWAS \
--gwas_file_pattern ".*gz" \
--snp_column SNP \
--effect_allele_column A1 \
--non_effect_allele_column A2 \
--beta_column BETA \
--pvalue_column P \
--output_file results/test.csv

This should take less than a minute on a 3GHZ computer. For the full specification of command line parameters, you can check the wiki.

The example command parameters mean:

• --model_db_path Path to tissue transriptome model
• --covariance Path to file containing covariance information. This covariance should have information related to the tissue transcriptome model.
• --gwas_folder Folder containing GWAS summary statistics data.
• --gwas_file_pattern This option allows the program to select which files from the input to use based on their name. ...This allows to ignore several support files that might be generated at your GWAS analysis, such as plink logs.
• --snp_column Argument with the name of the column containing the RSIDs.
• --effect_allele_column Argument with the name of the column containing the effect allele (i.e. the one being regressed on).
• --non_effect_allele_column Argument with the name of the column containing the non effect allele.
• --beta_column Tells the program the name of a column containing -phenotype beta data for each SNP- in the input GWAS files.
• --pvalue_column Tells the program the name of a column containing -PValue for each SNP- in the input GWAS files.
• --output_file Path where results will be saved to.

Its output is a CSV file that looks like:

gene,gene_name,zscore,effect_size,pvalue,var_g,pred_perf_r2,pred_perf_pval,pred_perf_qval,n_snps_used,n_snps_in_cov,n_snps_in_model
ENSG00000150938,CRIM1,4.190697619877402,0.7381499095142079,2.7809807629839122e-05,0.09833448081630237,0.13320775358,1.97496173512e-30,7.47907447189e-30,37,37,37
...

Where each row is a gene's association result:

• gene: a gene's id: as listed in the Tissue Transcriptome model. Ensemble Id for some, while some others (mainly DGN Whole Blood) provide Genquant's gene name
• gene_name: gene name as listed by the Transcriptome Model, generally extracted from Genquant
• zscore: S-PrediXcan's association result for the gene
• effect_size: S-PrediXcan's association effect size for the gene
• pvalue: P-value of the aforementioned statistic.
• pred_perf_r2: R2 of tissue model's correlation to gene's measured transcriptome (prediction performance)
• pred_perf_pval: pval of tissue model's correlation to gene's measured transcriptome (prediction performance)
• pred_perf_qval: qval of tissue model's correlation to gene's measured transcriptome (prediction performance)
• n_snps_used: number of snps from GWAS that got used in S-PrediXcan analysis
• n_snps_in_cov: number of snps in the covariance matrix
• n_snps_in_model: number of snps in the model
• var_g: variance of the gene expression, calculated as W' * G * W (where W is the vector of SNP weights in a gene's model, W' is its transpose, and G is the covariance matrix)

S-PrediXcan supports a large number of input GWAS formats through command line arguments. By specifying the appropriate input file column name, S-PrediXcan will analize the file without extra need for input conversion. Input GWAS files can be plain text files or gzip-compressed.

For example, you can specify an effect allele column and a standard error column, or a pvalue column and an odds ratio column, or only a GWAS zscore column. S-PrediXcan will try to use the following (in that order) if available from the command line arguments and input GWAS file:

1. use a z-score column if available from the arguments and input file;
2. use a p-value column and either effect, odd ratio or direction column;
3. use effect size (or odd ratio) and standard error columns if available.

Check the Github's ' wiki for those that work best for your data, and interpreting the results. For example, if your GWAS has p-values that are too small (i.e 1e-350), then you should avoid specifying a p-value column because numerical problems might arise; you should use effect size and standard error instead.

Please see the following article in the wiki.

You also have the option of installing the MetaXcan package to your python distribution. This will make the metax library available for development, and install on your system path the main MetaXcan scripts.

You can install it from the software folder with:

# ordinary install
$ python setup.py install

Alternatively, if you are going to modify the sources, the following may be more convenient:

# developer mode instalation
python setup.py develop

PIP support coming soon-ish.

Issues and questions can be raised at this repository's issue tracker.

There is also a Google Group mail list for general discussion, feature requests, etc. Join if you want to be notified of new releases, feature sets and important news concerning this software.

You can check here for the release history.

Transcriptome Models are a key component of PrediXcan and S-PrediXcan input. As models are improved, sometimes the format of these databases needs be changed too. We only provide support for the very latest databases; if a user updates their repository clone to the latest version and MetaXcan complains about the transcriptome weight dbs, please check if new databases have been published here.

For the time being, the only way to use old transcriptome models is to use older versions of MetaXcan.

Check software folder in this repository if you want to learn about more general or advanced usages of S-PrediXcan, or MulTiXcan and SMulTiXcan.

Check out the Wiki for exhaustive usage information.

The code lies at

/software

New release and features coming soon!