| Title: | PGS-Depot: a comprehensive resource for polygenic scores constructed by summary statistics based methods |
| Journal: | Nucleic Acids Research |
| Published: | 11 Nov 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37953384/ |
| DOI: | https://doi.org/10.1093/nar/gkad1029 |
| URL: | https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkad1029/53343581/gkad1029.pdf |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 8350
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Abstract
Polygenic score (PGS) is an important tool for the genetic prediction of complex traits. However, there are currently no resources providing comprehensive PGSs computed from published summary statistics, and it is difficult to implement and run different PGS methods due to the complexity of their pipelines and parameter settings. To address these issues, we introduce a new resource called PGS-Depot containing the most comprehensive set of publicly available disease-related GWAS summary statistics. PGS-Depot includes 5585 high quality summary statistics (1933 quantitative and 3652 binary trait statistics) curated from 1564 traits in European and East Asian populations. A standardized best-practice pipeline is used to implement 11 summary statistics-based PGS methods, each with different model assumptions and estimation procedures. The prediction performance of each method can be compared for both in- and cross-ancestry populations, and users can also submit their own summary statistics to obtain custom PGS with the available methods. Other features include searching for PGSs by trait name, publication, cohort information, population, or the MeSH ontology tree and searching for trait descriptions with the experimental factor ontology (EFO). All scores, SNP effect sizes and summary statistics can be downloaded via FTP. PGS-Depot is freely available at http://www.pgsdepot.net.</p>
8 Authors
- Chen Cao
- Shuting Zhang
- Jianhua Wang
- Min Tian
- Xiaolong Ji
- Dandan Huang
- Sheng Yang
- Ning Gu
1 Application
| Application ID | Title |
|---|---|
| 67665 | Developing scalable computational tools for understanding polygenic trait architectures in large-scale genome-wide association studies |
Enabling scientific discoveries that improve human health