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.