GWASLab is a Python library and CLI tool for comprehensive processing, quality control, harmonization, visualization, and downstream analysis of GWAS summary statistics. The core object is the `Sumstats` class, which wraps a pandas DataFrame with metadata, status codes, and methods for common operations such as basic QC (`basic_check()`), genome build inference, rsID assignment, allele harmonization, liftover, and format conversion (TSV, VCF, LDSC, PLINK, etc.). The architecture follows a modular design: a pure-calculation `algorithm` subpackage (numpy/scipy only) for effect-size conversions, fine-mapping (ABF), meta-analysis, and power calculations; a `qc` layer for pipeline orchestration with a decorator (`@with_logging`) that enforces column validation, tool availability, and logging; a `hm` layer for variant lookup and harmonization using VCF/BCF and caching (Parquet); a `viz` layer with dozens of plot types (Manhattan, QQ, regional, Miami, forest, phenogram, Sankey) using matplotlib; and an `extension` layer for integrating external tools like LDSC, PRS-CS, SuSiE, and GWAS Catalog API. The CLI (`gwaslab_cli`) exposes subcommands for all major workflows. External dependencies include `pysam`, `polars`, `pyarrow`, `h5py`, `adjustText`, and system tools (`bcftools`, `plink`, `R`). The library is actively developed with extensive test coverage (200+ test files) and bundled reference data for HapMap3, chain files, and cytoband annotations.
Pheweb is a full-stack web application for building interactive PheWAS (Phenome-Wide Association Study) websites from GWAS summary statistics. It provides a command-line pipeline to ingest association files, annotate variants with rsIDs and genes, compute Manhattan and QQ plots, and generate a searchable, browsable web interface with LocusZoom region views, phenotype correlations, and top hits tables. The architecture is a Flask monolith with a JavaScript frontend using D3.js, jQuery, and Bootstrap. While the codebase is well-organized for its domain and includes some reusable components (e.g., a simple flat-file indexer and a CFFI matrix builder), it suffers from significant technical drift: outdated dependencies (Flask 1.1, jQuery 1.12, Bootstrap 3.3.7, D3 v5), deprecated Python patterns, and a successor project (PheWeb2) explicitly recommended in the README. The project is functionally complete but in maintenance mode, with limited test coverage and several known bugs documented in the changelog.
MetaXcan is a command-line and library framework for transcriptome-wide association studies (TWAS). It integrates GWAS summary statistics or individual-level genotype data with gene expression prediction models to compute gene-level association statistics. The core methods implemented are S-PrediXcan (summary-based), PrediXcan (individual-level), MultiXcan, and S-MultiXcan (multi-tissue meta-analysis). The architecture consists of Python scripts that orchestrate data processing steps (e.g., parsing GWAS, aligning alleles, computing z-scores) using a core library (`metax`) with modules for genotype reading, GWAS loading, prediction model management, and covariance matrix handling. The system is designed for bioinformatics researchers and requires external reference data (e.g., GTEx models, 1000 Genomes LD). While functional, the codebase exhibits significant technical debt: Python 2 compatibility remnants, deprecated modules, hardcoded URLs, and limited test coverage. The GUI component (Tkinter) is unmaintained. The repository is hybrid, containing both application scripts and data processing pipelines, but the dominant character is a software tool for running TWAS analyses.