https://github.com/soedinglab/b-lore
Bayesian multiple logistic regression for GWAS meta-analysis
https://github.com/soedinglab/b-lore
Last synced: 8 months ago
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Bayesian multiple logistic regression for GWAS meta-analysis
- Host: GitHub
- URL: https://github.com/soedinglab/b-lore
- Owner: soedinglab
- License: gpl-3.0
- Created: 2017-07-06T14:16:22.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2025-08-20T19:45:54.000Z (10 months ago)
- Last Synced: 2025-10-10T08:34:13.040Z (8 months ago)
- Language: C++
- Size: 21.6 MB
- Stars: 17
- Watchers: 3
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Bayesian LOgistic REgression
A tool for meta-analysis in GWAS using Bayesian multiple logistic regression
## Description
B-LORE is a command line tool that creates summary statistics from multiple logistic regression on GWAS data,
and combines the summary statistics from multiple studies in a meta-analysis.
It can also incorporate functional information about the SNPs from other external sources.
Several genetic regions, or loci are preselected for analysis with B-LORE.
### Key features
1. Association probability: B-LORE outputs probabilities of the input genetic loci being statistically associated with the phenotype.
2. Finemapping: B-LORE also outputs the probability of each SNP being statistically associated with the phenotype.
3. Leverage functional genomic data as a prior probability to improve prioritization.
4. Models data with logistic regression, and is suited for case/control studies.
5. Combines information over all SNPs in a locus with multiple regression.
## Installation
B-LORE is written in python and C++. To run B-LORE, you will need
- python version 3.4 or higher,
- the Python packages for scientific computing NumPy and SciPy.
- C++ compiler
To use B-LORE, you have to download the repository and compile the C++ shared libraries:
```
git clone https://github.com/soedinglab/b-lore.git
cd b-lore
make
```
The `Makefile` uses `g++` by default, which you can change depending on the compiler available on your system.
## Input files
For calculating summary statistics, it uses the following file formats as input:
1. Genotype files in [Oxford format](http://www.stats.ox.ac.uk/~marchini/software/gwas/file_format.html), for all loci of interest (e.g. Locus001.gen, Locus002.gen, etc.).
2. Sample file in [Oxford format](http://www.stats.ox.ac.uk/~marchini/software/gwas/file_format.html) (e.g. study1.sample)
For meta-analysis, it uses the following input:
1. Output files B-LORE summary statistics.
2. List of loci to be analyzed. This is a single file containing 2 columns with no header. The first column lists the name of the loci (e.g. Locus001, Locus002, etc.) and the second column is a binary number (1 or 0) indicating if it is a SNP locus (1) or a covariate locus (0). [Note: The summary statistics at each study outputs this file]
2. (Optional) Functional genomics data, separately for each locus.
Each feature file contains 2 parts:
(a) a header line detailing the names of the columns in the file, and
(b) a line for each SNP detailing the information for that SNP.
The columns are tab-separated.
The annotation tracks are present from column 4 onwards.
The first 3 columns are:
- RSID: must have the same SNP identifier as in the genotype files
- CHR: chromosome number
- POS: base-pair position of the SNP.
## Usage
### Quick start
- Clone the repository
- `cd example`
- `tar -zxvf input.tar.gz`
This will create an example input folder, with genotypes at 20 loci for 3 populations, a sample file for each population and ENCODE data for the 20 loci.
- `./commands.sh` to run B-LORE on the 3 populations to generate summary statistics, followed by a meta-analysis.
### Command line arguments
An executable file to run B-LORE is provided as `bin/blore`. This can used as follows:
```
blore [--help] [COMMAND] [OPTIONS]
```
There are 2 commands for B-LORE:
- `--summary` : for creating summary statistics of individual studies.
- `--meta` : for meta-analysis from summary statistics of multiple studies.
Each of these 2 commands takes different options, as described below.
#### blore --summary [OPTIONS]
Create summary statistics of individual studies. Valid options are:
Option | Description | Priority | Default value
:--- | :--- |:--- | :--
‑‑gen *filename(s)* | Input genotype file(s), all loci should have separate genotype files and specified here (wildcards allowed) | Required | --
‑‑sample *filename* | Input sample file | Required | --
‑‑pheno *string* | Name of the phenotype as it appears in the header of the sample file| Optional | `pheno`
‑‑regoptiom | If specified, the variance of the regularizer will be optimized, otherwise it will be N(0, σ2) where σ is specified by `--reg` | Optional | --
‑‑reg *float* | Value of the standard deviation (σ) of the regularizer | Optional | 0.01
‑‑pca *int* | Number of principal components of the genotype to be included as covariates | Optional | 0
‑‑cov *string(s)* | Name of covariate(s) as they appears in the header of the sample file, multiple covariates can be specified as space-separated strings | Optional | None
‑‑out *directory* | Name of the output directory where summary statistics will be created | Optional | directory of the genotype files
‑‑prefix *string* | Prefix for the summary statistics files | Optional | `_summary`
#### blore --meta [OPTIONS]
Perform meta-analysis from summary statistics of multiple studies. Valid options are:
Option | Description | Priority | Default value
:--- | :--- |:--- | :--
‑‑input *filename* | Input file containing list of loci to be analyzed together | Required | --
‑‑statdir *filename(s)* | Input directory of B-LORE summary statistics | Required | --
‑‑feature *filename(s)* | Input file(s) for genomic feature tracks | Optional | --
‑‑params *floats* | Initial values of the hyperparameters, requires 4 space-separated floats corresponding to βπ μ σ σbg| Optional | 0.01 0.0 0.01 0.01
‑‑muvar | If specified, μ will be optimized, otherwise it will be fixed to the initial value (default 0) | Optional | --
‑‑zmax *int* | Maximum number of causal SNPs allowed | Optional | 2
‑‑out *directory* | Name of the output directory where result files will be created | Optional | current directory
‑‑prefix *string* | Prefix for the meta-analysis output files | Optional | `_meta`
#### Example
- Clone the repository
- `cd example`
- `tar -zxvf input.tar.gz`
This will create an example input folder, with genotypes at 20 loci for 3 populations, a sample file for each population and ENCODE data for the 20 loci.
View `commands.sh` in your favorite editor to see the commands, and execute `./commands.sh` to run B-LORE on the 3 populations to generate summary statistics, followed by a meta-analysis.
## Citation
- Saikat Banerjee, Lingyao Zeng, Heribert Schunkert and Johannes Soeding (2017). [Bayesian multiple logistic regression for case-control GWAS.](https://doi.org/10.1101/198911 "B-LORE method details") bioRxiv.
## License
B-LORE is released under the GNU General Public License version 3. See LICENSE for more details. Copyright Johannes Soeding and Saikat Banerjee.
## Contact
[Saikat Banerjee](https://www.mpibpc.mpg.de/staff/42459)