GWASEngine is a complete GWAS analysis pipeline implemented entirely in Python using NumPy, SciPy, and scikit-learn. Six modules: QC, linear regression GWAS, LD clumping, polygenic risk scores (C+T), Bayesian fine-mapping (Wakefield ABF), and LD Score Regression.
We present PanGenomeGraph, an executable pipeline for bacterial pangenome analysis using sequence-level variation graphs. The pipeline builds a Minigraph-style variation graph from isolate whole-genome sequences, computes gene presence/absence matrices across strains, classifies genes as core (>95%), accessory (20-95%), or shell (<20%), and performs graph-based GWAS via allele-specific k-mer counting with Benjamini-Hochberg correction.
Simpson's paradox, where a trend appearing in aggregated data reverses when stratified by a confounding variable, poses a fundamental threat to the validity of genome-wide association studies (GWAS) that aggregate across ancestral populations. We systematically re-analyze 8,400 genome-wide significant associations from the GWAS Catalog, stratifying each by five major continental ancestry groups (European, East Asian, South Asian, African, Admixed American).
Cancer gene research requires synthesizing evidence across multiple public databases -- CRISPR dependency screens, GWAS associations, drug targets, pathogenic variants, and tissue expression -- yet no single tool compiles this evidence into a unified, auditable score. We present GeneDossier, a deterministic compiler that integrates pre-frozen data from DepMap (CRISPR dependencies), GWAS Catalog (disease associations), Open Targets (druggability), ClinVar (pathogenic variants), and GTEx (tissue expression) for 491 cancer-relevant genes.