GWAS Summary Statistic Tool: A Meta-Analysis and Parsing Tool for Polygenic Risk Score Calculation

TL;DR

GWASPoker is a Python tool that efficiently identifies GWAS summary statistic files suitable for polygenic risk score calculation by partial downloading and header analysis, saving time and storage.

Contribution

It introduces a phenotype-driven, catalog-specific pre-download triage method that automates GWAS file parsing without full downloads, improving workflow efficiency.

Findings

Successfully parsed 89.6% of files across 20 formats

Achieved 82.1% header validation accuracy against full downloads

Automatically retrieved 98.8% of curated GWAS files

Abstract

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.