forget999 / focus

FOCUS: Fine-mapping Of CaUsal gene Sets

FOCUS

FOCUS (Fine-mapping Of CaUsal gene Sets) is software to fine-map transcriptome-wide association study statistics at genomic risk regions. The software takes as input summary GWAS data along with eQTL weights and outputs a credible set of genes to explain observed genomic risk.

This approach is described in,

Probabilistic fine-mapping of transcriptome-wide association studies. Nicholas Mancuso, Malika K. Freund, Ruth Johnson, Huwenbo Shi, Gleb Kichaev, Alexander Gusev, and Bogdan Pasaniuc. Nature Genetics 51, 675-682 (2019).

Installing

The easiest way to install is with pip:

pip install pyfocus --user

Check that FOCUS was installed by typing

focus --help

If that did not work, and pip install pyfocus --user was specified, please check that your local user path is included in $PATH environment variable. --user location and can be appended to $PATH by executing

export PATH=`python -m site --user-base`/bin/:$PATH

which can be saved in ~/.bashrc or ~/.bash_profile. To reload the environment type source ~/.bashrc or ~/source .bash_profile depending where you entered it.

Alternatively you can download the latest repo and then use setuptools:

git clone https://github.com/bogdanlab/focus.git
cd focus
python setup.py install

We currently only support Python3.6+.

A conda-forge recipe that should simplify installation is currently underway.

Example

Here is an example of how to perform LDL fine-mapping while prioritizing predictive models from adipose tissues:

focus finemap LDL_2010.clean.sumstats.gz 1000G.EUR.QC.1 gtex_v7.db --chr 1 --tissue adipose --out LDL_2010.chr1

This command will scan LDL_2010.clean.sumstats.gz for risk regions and then perform TWAS+fine-mapping using LD estimated from plink-formatted 1000G.EUR.QC.1 and eQTL weights from gtex_v7.db.

Please see the wiki for more details on how to use focus and links to database files.

Notes

Version 0.6.10: Fixed bug where weight database allele mismatch with GWAS broke infererence.

Version 0.6.5: Fixed bug in newer versions of matplotlib not accepting string for colormaps. Fixed legend bug in plot. Fixed bug that mismatched string and category when supplying custom locations.

Version 0.6: Fixed bug where only one of the two alleles was reversed complemented breaking alignment. For now these instances are dropped. Added option --use-ens-id for FUSION import to indicate the main model label is an Ensembl ID rather than HGNC symbol.

Version 0.5: Plotting sorts genes based on tx start. Various bugfixes that limited the number of queried SNPs and plotting when using newer matplotlib.

Version 0.4: Added FUSION import support.

Version 0.3: Initial release. More to come soon.

Software and support

If you have any questions or comments please contact [email protected]

For performing various inferences using summary data from large-scale GWASs please find the following useful software:

1. Association between predicted expression and complex trait/disease FUSION and PrediXcan
2. Estimating local heritability or genetic correlation HESS
3. Estimating genome-wide heritability or genetic correlation UNITY
4. Fine-mapping using summary-data PAINTOR
5. Imputing summary statistics using LD FIZI