FOCUS & MA-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.

MA-FOCUS (Multi-Ancestry Fine-mapping Of CaUsal gene Sets) is an extension of FOCUS that leverages summary GWAS data with eQTL weights from multiple ancestries to increase the precision of credible sets for causal genes.

- We detest usage of our software or scientific outcome to promote racial discrimination.

FOCUS 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).

MA-FOCUS is described in:

Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies. Zeyun Lu^*, Shyamalika Gopalan^*, Dong Yuan, David V. Conti, Bogdan Pasaniuc, Alexander Gusev, Nicholas Mancuso. American Journal of Human Genetics VOLUME 109, ISSUE 8, P1388-1404, AUGUST 04, 2022.

^* indicates equal contribution

We greatly appreciate users cite both papers if they use our software.

Installation | Example | Version History | Support | Other Software

Installation

Before installing ma-focus, we recommend users to create a new conda environment:

conda create -n ma-focus python=3.8

Then activate the environment:

conda activate ma-focus

Users can download the latest repository and then use pip:

git clone https://github.com/mancusolab/ma-focus.git
cd ma-focus
pip install .

Get Started with Example

Data Preparation

We will use the white blood count (WBC) GWAS measured in European and African ancestry individuals from Chen et al. 2020 as an example. The eQTL weights are computed from LCLs of American European and African individuals in GENOA study (Shang et al. 2020). The LD reference is from the 1000 Genomes Project.

Users can download all the testing data from here.

FOCUS Example

Here is an example of how to perform fine-mapping :

focus finemap EA_WBC_new_munged_chr22.tsv.gz ./1000GP3/EUR/1000G.EUR.QC.allelesAligned.22 ea_fusion_genoa.db --chr 22 --prior-prob "gencode38" --locations 38:EUR --out ./focus_result

This command will scan EA_WBC_new_munged_chr22.tsv.gz for risk regions 38:EUR generated by LDetect on GRCh38 for European ancestry and then perform TWAS+fine-mapping using LD estimated from plink-formatted 1000G.EUR.QC.allelesAligned.22 and eQTL weights from ea_fusion_genoa.db GRCh38.

MA-FOCUS Example

Here is an example of how to perform multi-ancestry fine-mapping from European and African ancestry (just use : to concatenate):

focus finemap EA_WBC_new_munged_chr22.tsv.gz:AA_WBC_new_munged_chr22.tsv.gz ./1000GP3/EUR/1000G.EUR.QC.allelesAligned.22:./1000GP3/AFR/1000G.AFR.QC.allelesAligned.22 ea_fusion_genoa.db:aa_fusion_genoa.db --chr 22 --prior-prob "gencode38" --locations 38:EUR-AFR --out ./ma-focus_result

Note: --locations is a new required parameter compared to versions prior to v0.8. This parameter specifies the genomic regions to be fine-mapped. We recommend to use independent regions for single ancestry or multiple ancestries.

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

Version History

Version	Description
0.3	Initial release. More to come soon.
0.4	Added FUSION import support.
0.5	Plotting sorts genes based on tx start. Various bug fixes that limited the number of queried SNPs and plotting when using newer matplotlib.
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.
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.
0.6.10	Fixed bug where weight database allele mismatch with GWAS broke inference.
0.8	Added MA-FOCUS support. Added GWAS imputation using imp-G. Added additional choice for prior probability for causal genes (number of genes in the risk regions).
0.801	Added gencode_map_v38 and multiple LD block files in GRCh38. Fixed prior_prob bugs.
0.802	Fix the bug that .gitignore includes *.tsv so that gencode files couldn't be pushed to github.
0.803	Fix the bugs in pandas/numpy outdated version, intercept.
0.9	Convert to pyscaffold style, fix bugs and typos, and update documentation and read me.

Software and support

If users have any questions or comments, please contact Zeyun Lu (zeyunlu@usc.edu) and Nicholas Mancuso (nmancuso@usc.edu).

Feel free to use other software developed by Mancuso Lab:

• SuShiE: a scalable variational approach to perform SNP fine-mapping for molecular data across multiple ancestries.
• SuSiE-PCA: a scalable Bayesian variable selection technique for sparse principal component analysis
• twas_sim: a Python software to simulate TWAS statistics.
• FactorGo: a scalable variational factor analysis model that learns pleiotropic factors from GWAS summary statistics.
• HAMSTA: a Python software to estimate heritability explained by local ancestry data from admixture mapping summary statistics.
• Traceax: a Python library to perform stochastic trace estimation for linear operators.

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This project has been set up using PyScaffold 4.1.1. For details and usage information on PyScaffold see https://pyscaffold.org/.