| Title: | DeepPheWAS: an R package for phenotype generation and association analysis for phenome-wide association studies |
| Journal: | Bioinformatics |
| Published: | 6 Feb 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36744935/ |
| DOI: | https://doi.org/10.1093/bioinformatics/btad073 |
| URL: | https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad073/49101508/btad073.pdf |
| Citations: | 10 (10 in last 2 years) as of 8 Aug 2024 |
Publication 7959
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Abstract
SUMMARY: DeepPheWAS is an R package for phenome-wide association studies that creates clinically curated composite phenotypes and integrates quantitative phenotypes from primary care data, longitudinal trajectories of quantitative measures, disease progression and drug response phenotypes. Tools are provided for efficient analysis of association with any genetic input, under any genetic model, with optional sex-stratified analysis, and for developing novel phenotypes.</p>
AVAILABILITY AND IMPLEMENTATION: The DeepPheWAS R package is freely available under GNU general public licence v3.0 from at https://github.com/Richard-Packer/DeepPheWAS.</p>
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</p>
16 Authors
- Richard J Packer
- Alex T Williams
- William Hennah
- Micaela T Eisenberg
- Nick Shrine
- Katherine A Fawcett
- Willow Pearson
- Anna L Guyatt
- Ahmed Edris
- Edward J Hollox
- Mikko Marttila
- Balasubramanya S Rao
- John Raymond Bratty
- Louise V Wain
- Frank Dudbridge
- Martin D Tobin
1 Application
| Application ID | Title |
|---|---|
| 43027 | Phenome-wide association studies to inform drug development |
Enabling scientific discoveries that improve human health