viprs

Variational Inference of Polygenic Risk Scores

https://github.com/shz9/viprs

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    Found CITATION.cff file
  • codemeta.json file
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  • Scientific vocabulary similarity
    Low similarity (11.0%) to scientific vocabulary

Keywords

gwas medicine pgs polygenic-risk-scores prs variational-inference
Last synced: 6 months ago · JSON representation ·

Repository

Variational Inference of Polygenic Risk Scores

Basic Info
Statistics
  • Stars: 28
  • Watchers: 4
  • Forks: 2
  • Open Issues: 0
  • Releases: 4
Topics
gwas medicine pgs polygenic-risk-scores prs variational-inference
Created over 5 years ago · Last pushed 10 months ago
Metadata Files
Readme Changelog License Citation

README.md

viprs: Variational Inference of Polygenic Risk Scores

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viprs is a python package that implements variational inference techniques to estimate the posterior distribution of variant effect sizes conditional on the GWAS summary statistics. The package is designed to be fast and accurate, and to provide a variety of options for the user to customize the inference process. Highlighted features:

  • The coordinate ascent algorithms are written in C/C++ and cython for improved speed and efficiency.
  • The code is written in object-oriented form, allowing the user to extend and experiment with existing implementations.
  • Different priors on the effect size: Spike-and-slab, Sparse mixture, etc.
  • We also provide scripts for different hyperparameter tuning strategies, including: Grid search, Bayesian optimization, Bayesian model averaging.
  • Easy and straightforward interfaces for computing PRS from fitted models.
  • Implementation for a wide variety of evaluation metrics for both binary and continuous phenotypes.

Helpful links

Owner

  • Name: Shadi
  • Login: shz9
  • Kind: user

PhD Student at McGill University

Citation (CITATION.md)

If you use `viprs` in your research, please cite the following paper(s):

> Zabad, S., Gravel, S., & Li, Y. (2023). **Fast and accurate Bayesian polygenic risk modeling with variational inference.** 
The American Journal of Human Genetics, 110(5), 741–761. https://doi.org/10.1016/j.ajhg.2023.03.009

## BibTeX records

```bibtex
@article{ZABAD2023741,
    title = {Fast and accurate Bayesian polygenic risk modeling with variational inference},
    journal = {The American Journal of Human Genetics},
    volume = {110},
    number = {5},
    pages = {741-761},
    year = {2023},
    issn = {0002-9297},
    doi = {https://doi.org/10.1016/j.ajhg.2023.03.009},
    url = {https://www.sciencedirect.com/science/article/pii/S0002929723000939},
    author = {Shadi Zabad and Simon Gravel and Yue Li}
}
```

GitHub Events

Total
  • Create event: 2
  • Release event: 2
  • Issues event: 14
  • Watch event: 7
  • Issue comment event: 16
  • Push event: 16
  • Fork event: 1
Last Year
  • Create event: 2
  • Release event: 2
  • Issues event: 14
  • Watch event: 7
  • Issue comment event: 16
  • Push event: 16
  • Fork event: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 13
  • Total pull requests: 0
  • Average time to close issues: 6 months
  • Average time to close pull requests: N/A
  • Total issue authors: 9
  • Total pull request authors: 0
  • Average comments per issue: 2.23
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 6
  • Pull requests: 0
  • Average time to close issues: about 1 month
  • Average time to close pull requests: N/A
  • Issue authors: 5
  • Pull request authors: 0
  • Average comments per issue: 2.5
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • MuhammadMuneeb007 (3)
  • biostatShao (2)
  • nlapier2 (2)
  • shz9 (2)
  • xinyu-c9 (1)
  • sanket-desai (1)
  • EtienneNtumba (1)
  • rkarlssonlinner (1)
  • ekhar17 (1)
  • HUST-341 (1)
Pull Request Authors
Top Labels
Issue Labels
enhancement (1) bug (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 295 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 8
  • Total maintainers: 1
pypi.org: viprs

Variational Inference of Polygenic Risk Scores (VIPRS)

  • Versions: 8
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 295 Last month
Rankings
Dependent packages count: 6.6%
Stargazers count: 19.5%
Average: 21.8%
Forks count: 30.5%
Dependent repos count: 30.6%
Maintainers (1)
Last synced: 7 months ago

Dependencies

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