GWAS Summary Statistic Tool: A Meta-Analysis and Parsing Tool for Polygenic Risk Score Calculation
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911493984878592 |
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| author | Muneeb, Muhammad Ascher, David B. |
| author_facet | Muneeb, Muhammad Ascher, David B. |
| contents | Motivation: GWAS (genome-wide association study) summary statistic files are essential inputs for polygenic risk score (PRS) calculation. However, identifying suitable files across thousands of catalog entries typically requires downloading large datasets and manually inspecting their column structures, a process that is both time-consuming and storage-intensive.
Results: We present GWASPoker, a phenotype-driven, GWAS-Catalog-specific pre-download triage tool that scans candidate GWAS files for PRS column availability through partial downloads and header detection, without requiring full-file transfer. Analysing 60,499 records from the GWAS Catalog, 60,281 (99.6%) contained accessible download links, of which 54,026 (89.6%) were successfully partially downloaded and parsed across 20 file formats, yielding 724 unique header signatures. Across 13 phenotypes, 84 of 85 manually curated GWAS files (98.8%) were automatically retrieved and processed. Header validation against fully downloaded files showed exact agreement in 23 of 28 cases (82.1%).
Availability and implementation: GWASPoker is implemented in Python 3 and is freely available at https://github.com/MuhammadMuneeb007/GWASPokerforPRS under the MIT licence. Example outputs and documentation are provided in the repository. The tool was tested on Linux (HPC cluster) with Python 3.8 or later. The LLM-based code-generation step is entirely optional; a rules-based column-mapping template is provided for fully offline use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06756 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GWAS Summary Statistic Tool: A Meta-Analysis and Parsing Tool for Polygenic Risk Score Calculation Muneeb, Muhammad Ascher, David B. Quantitative Methods Motivation: GWAS (genome-wide association study) summary statistic files are essential inputs for polygenic risk score (PRS) calculation. However, identifying suitable files across thousands of catalog entries typically requires downloading large datasets and manually inspecting their column structures, a process that is both time-consuming and storage-intensive. Results: We present GWASPoker, a phenotype-driven, GWAS-Catalog-specific pre-download triage tool that scans candidate GWAS files for PRS column availability through partial downloads and header detection, without requiring full-file transfer. Analysing 60,499 records from the GWAS Catalog, 60,281 (99.6%) contained accessible download links, of which 54,026 (89.6%) were successfully partially downloaded and parsed across 20 file formats, yielding 724 unique header signatures. Across 13 phenotypes, 84 of 85 manually curated GWAS files (98.8%) were automatically retrieved and processed. Header validation against fully downloaded files showed exact agreement in 23 of 28 cases (82.1%). Availability and implementation: GWASPoker is implemented in Python 3 and is freely available at https://github.com/MuhammadMuneeb007/GWASPokerforPRS under the MIT licence. Example outputs and documentation are provided in the repository. The tool was tested on Linux (HPC cluster) with Python 3.8 or later. The LLM-based code-generation step is entirely optional; a rules-based column-mapping template is provided for fully offline use. |
| title | GWAS Summary Statistic Tool: A Meta-Analysis and Parsing Tool for Polygenic Risk Score Calculation |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2603.06756 |