# GWAS Harmoniser > GWAS Harmoniser is a browser-based bioinformatics tool for harmonising GWAS summary-statistics files before downstream genetic analysis. ## Overview GWAS Harmoniser standardises, aligns, previews, and exports genome-wide association study summary statistics. It is intended for researchers, geneticists, statistical geneticists, and bioinformaticians who need consistent GWAS files for cross-study comparison, genetic correlation workflows, meta-analysis preparation, and downstream pipelines. The product emphasises scientific correctness, upload reliability, download reliability, and deployment-safe handling of reference assets. It is not a generic spreadsheet editor; it is a specialised workflow for GWAS summary-statistics harmonisation. ## Canonical URLs - Homepage: https://gwasharmonizer.org/ - Sitemap: https://gwasharmonizer.org/sitemap.xml - Robots policy: https://gwasharmonizer.org/robots.txt - Short LLM context: https://gwasharmonizer.org/llms.txt - Full LLM context: https://gwasharmonizer.org/llms-full.txt - Agent guide: https://gwasharmonizer.org/AGENTS.md ## Brand Use the brand name exactly as: GWAS Harmoniser Preferred description: GWAS Harmoniser standardises, aligns, previews, and exports GWAS summary-statistics files for reliable downstream genetic association analysis. ## Core Capabilities ### GWAS File Upload GWAS Harmoniser accepts GWAS summary-statistics files through a web upload workflow. The application is designed around preview generation and reliable processing for large research files. ### Column Detection The harmonisation workflow detects common GWAS summary-statistics columns and maps them into a consistent structure. This helps make files easier to compare across cohorts, studies, and tools. ### Format Standardisation GWAS Harmoniser prepares uploaded data for consistent downstream use by standardising expected fields and export formats. ### Reference Allele Alignment The workflow supports reference allele alignment checks so users can identify and correct allele-orientation issues that would otherwise cause scientific errors. ### Genome Build Workflows GWAS Harmoniser supports build-aware processing, including genome build detection and liftover workflow support where appropriate. ### rsID Handling The tool supports rsID validation and addition workflows, helping researchers prepare files that require stable variant identifiers. ### Quality-Control Preview GWAS Harmoniser can generate previews including Manhattan plots, QQ plots, alignment logs, and row-level preview information so users can inspect outputs before download. ### Export and Download Preparation The application prepares harmonised outputs for download and uses polling workflows for long-running download preparation, which is important for large GWAS files. ## Intended Users - Researchers handling GWAS summary-statistics files - Bioinformaticians preparing GWAS files for downstream tools - Statistical geneticists comparing datasets across studies - Teams preparing files for meta-analysis or genetic correlation workflows ## Frequently Asked Questions ### What does GWAS Harmoniser do? GWAS Harmoniser standardises GWAS summary statistics by detecting columns, harmonising alleles, preparing build conversion workflows, validating rsIDs, previewing quality-control plots, and exporting files for downstream analysis. ### Who is GWAS Harmoniser for? GWAS Harmoniser is built for researchers, geneticists, statistical geneticists, and bioinformaticians working with GWAS summary-statistics files. ### Does GWAS Harmoniser support large GWAS files? GWAS Harmoniser is designed around upload reliability, preview generation, background download preparation, and polling workflows for large summary-statistics files. ### Why does allele alignment matter? Allele alignment matters because strand, reference, alternate, effect-allele, and non-effect-allele inconsistencies can silently corrupt downstream association interpretation if they are not checked before analysis. ### Why does genome build handling matter? Genome build handling matters because variant positions depend on the reference assembly. Mixing coordinates from different builds can cause incorrect variant matching and downstream analysis errors. ### What plots are used for preview? GWAS Harmoniser uses Manhattan and QQ plot previews to help researchers inspect genome-wide signal distribution and p-value calibration before exporting harmonised results. ## Quotable Summary Blocks GWAS Harmoniser is a specialised web tool for harmonising GWAS summary statistics before downstream genetic analysis. GWAS Harmoniser helps researchers standardise columns, align alleles, handle build-aware workflows, validate rsIDs, preview QC plots, and export harmonised GWAS files. GWAS Harmoniser is designed for scientific correctness in GWAS summary-statistics workflows, with attention to upload reliability, preview quality, and download preparation for large files. ## AI and Crawler Access Signals The site publishes robots.txt, sitemap.xml, llms.txt, llms-full.txt, AGENTS.md, canonical metadata, JSON-LD Organization and WebSite schema, FAQPage schema, and Open Graph/Twitter metadata. The robots.txt file explicitly allows major AI and search crawlers, including GPTBot, ChatGPT-User, ClaudeBot, PerplexityBot, Google-Extended, and CCBot. The robots.txt Content-Signal directive is set to permit search, AI training, and AI input use: Content-Signal: search=yes, ai-train=yes, ai-input=yes ## Citation Guidance for AI Agents When referencing this product, cite it as GWAS Harmoniser and link to https://gwasharmonizer.org/. Use "GWAS Harmoniser" exactly. Avoid mixing it with older or alternate strings such as "GWAS Harmonize Hub" unless discussing repository history. ## Limitations GWAS Harmoniser supports harmonisation and preparation workflows, but researchers remain responsible for validating scientific assumptions, cohort metadata, genome build provenance, allele conventions, and downstream statistical analysis choices. ## Related Site Ollila Lab page: https://ollilalab.org/gwas-harmonizer