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        "author": "Nicholas Mancuso, Ruth Johnson",
        "author_email": "nicholas.mancuso@med.usc.com, ruthjohnson@ucla.com",
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            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
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        "description": "FOCUS\n=====\nFOCUS (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. \n\nThis approach is described in,\n\n> [Probabilistic fine-mapping of transcriptome-wide association studies](https://www.nature.com/articles/s41588-019-0367-1). Nicholas Mancuso, Malika K. Freund, Ruth Johnson, Huwenbo Shi, Gleb Kichaev, Alexander Gusev, and Bogdan Pasaniuc. Nature Genetics 51, 675-682 (2019).\n\nInstalling\n----------\nThe easiest way to install is with pip:\n\n    pip install pyfocus --user\n\nCheck that FOCUS was installed by typing\n\n    focus --help\n\nIf that did not work, and `pip install pyfocus --user` was specified, please check that your local user path is included in\n`$PATH` environment variable. `--user` location and can be appended to `$PATH`\nby executing\n\n    export PATH=`python -m site --user-base`/bin/:$PATH\n\nwhich can be saved in `~/.bashrc` or `~/.bash_profile`. To reload the environment type `source ~/.bashrc` or `~/source .bash_profile` depending where you entered it.\n\nAlternatively you can download the latest repo and then use setuptools:\n\n    git clone https://github.com/bogdanlab/focus.git\n    cd focus\n    python setup.py install\n\n*We currently only support Python3.6+.*\n\n*A conda-forge recipe that should simplify installation is currently underway.*\n\nExample\n-------\nHere is an example of how to perform LDL fine-mapping while prioritizing predictive models from adipose tissues:\n\n    focus finemap LDL_2010.clean.sumstats.gz 1000G.EUR.QC.1 gtex_v7.db --chr 1 --tissue adipose --out LDL_2010.chr1\n\nThis 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`. \n\nPlease see the [wiki](https://github.com/bogdanlab/focus/wiki) for more details on how to use focus and links to database files.\n\nNotes\n-----\nVersion 0.6.10: Fixed bug where weight database allele mismatch with GWAS broke infererence.\n\nVersion 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.\n\nVersion 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.\n\nVersion 0.5: Plotting sorts genes based on tx start. Various bugfixes that limited the number of queried SNPs and plotting when using newer matplotlib.\n\nVersion 0.4: Added FUSION import support.\n\nVersion 0.3: Initial release. More to come soon.\n\nSoftware and support\n-----\nIf you have any questions or comments please contact nicholas.mancuso@med.usc.edu\n\nFor performing various inferences using summary data from large-scale GWASs please find the following useful software:\n\n1. Association between predicted expression and complex trait/disease [FUSION](https://github.com/gusevlab/fusion_twas) and [PrediXcan](https://github.com/hakyimlab/PrediXcan)\n2. Estimating local heritability or genetic correlation [HESS](https://github.com/huwenboshi/hess)\n3. Estimating genome-wide heritability or genetic correlation [UNITY](https://github.com/bogdanlab/UNITY)\n4. Fine-mapping using summary-data [PAINTOR](https://github.com/gkichaev/PAINTOR_V3.0)\n5. Imputing summary statistics using LD [FIZI](https://github.com/bogdanlab/fizi)\n\n\n",
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