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https://github.com/jean997/GWASBrewer
Simulate GWAS data from an arbitrary DAG
https://github.com/jean997/GWASBrewer
Last synced: about 2 months ago
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Simulate GWAS data from an arbitrary DAG
- Host: GitHub
- URL: https://github.com/jean997/GWASBrewer
- Owner: jean997
- License: gpl-3.0
- Created: 2022-08-24T23:09:09.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-12T19:50:14.000Z (2 months ago)
- Last Synced: 2024-11-12T20:32:17.468Z (2 months ago)
- Language: R
- Homepage: https://jean997.github.io/GWASBrewer
- Size: 85 MB
- Stars: 7
- Watchers: 3
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-complex-trait-genetics - GWASBrewer
README
## Welcome
Welcome to `GWASBrewer`, a flexible tool for simulating realistic GWAS summary statistics for one or many traits from a wide range of scenarios.
This package was previously called `simGWAS`. Congratulations on finding our new home.
## Simulate Realistic GWAS Summary Statistics for One or Multiple Traits
The `GWASBrewer` package simulates GWAS summary statistics. The main function in the package is `sim_mv`. Get started with the "Simulating Data" vignette.
Briefly, `GWASBrewer` can simulate data with the following features.
- Data an be produced for continuous traits with user supplied linear causal relationships.
- GWAS for multiple traits may have overlapping samples.
- Data can be generated with or without LD. One realistic LD pattern is supplied as built-in data.
- GWAS for the same trait can be replicated with different sample sizes, LD patterns, and allele frequencies (see the "Resampling and Re-Scaling.." vignette)
Data generated by `GWASBrewer` can be used for testing a variety of methods including heritability estimation,
Mendelian randomization, genetic correlation estimation, colocalization, fine mapping etc.
This package is under active development. Some features that may be added in the future include
- Support for binary traits
- Ability to add confounding effects
## Installation
```
devtools::install_github("jean997/GWASBrewer", build_vignettes = TRUE)
browseVignettes("GWASBrewer")
```
Note that the "Simulating Data" vignette requires the following packages which will not be
installed automatically:
+ DiagrammeR (use `install.packages`)
Generating individual level data using `resample_inddata` (see "Resampling and Rescaling..." vignette) requires the `hapsim` package which will not be installed automatically. This can be installed with
```
install.packages("hapsim")
```