GWASEngine: A Pure Python Genome-Wide Association Study Analysis Engine

Abstract

We present GWASEngine, a complete genome-wide association study (GWAS) analysis pipeline implemented entirely in Python using only NumPy, SciPy, and scikit-learn. GWASEngine provides six analysis modules — quality control, association testing, LD clumping, polygenic risk score (PRS) computation, Bayesian fine-mapping, and LD Score Regression (LDSC) — without requiring PLINK, R, BOLT-LMM, REGENIE, or any other external compiled binaries. The entire pipeline runs on CPU and produces an interactive six-panel HTML dashboard. We demonstrate the engine on synthetic data (n=2,000, m=10,000 SNPs, h2_SNP=0.30, 20 causal variants), recovering key heritability estimates and generating publication-quality visualizations. GWASEngine enables researchers to run complete GWAS analyses in any Python environment with a single pip install and six lines of code.

1. Introduction

Genome-wide association studies (GWAS) have identified thousands of associations between genetic variants and complex traits. However, running a GWAS pipeline typically requires multiple software packages — PLINK for data management, R for visualization, specialized tools for LDSC and fine-mapping — creating substantial installation and reproducibility barriers.

GWASEngine eliminates these barriers by implementing the complete GWAS analysis workflow in pure Python, making it installable anywhere with pip and runnable on any CPU.

2. Methods

2.1 Quality Control

Sample-level QC includes call rate filtering, heterozygosity outlier detection, sex discordance checking, and relatedness removal using the KING-approximate method. Variant-level QC applies call rate, minor allele frequency (MAF), and Hardy-Weinberg equilibrium (HWE) filters. Population stratification is corrected via genetic principal components (PCA on genome-wide SNPs).

2.2 Association Testing

For quantitative traits, we use univariate linear regression per SNP with covariate residualization, which efficiently handles cases where the number of SNPs exceeds the number of samples (n < m). For binary traits, we implement Firth-penalized logistic regression via Newton-Raphson optimization to handle rare variants and separation issues.

2.3 LD Clumping

Linkage disequilibrium (LD) between SNPs is computed as r2 = correlation squared within 500kb windows. Gabriel's method identifies haplotype blocks. Clumping retains only the most significant SNP per LD block at r2 > 0.1.

2.4 Polygenic Risk Scores

We implement the Clumping + Thresholding (C+T) method and LDpred2-inspired Bayesian shrinkage for PRS computation. Effect sizes are optimized across a grid of p-value thresholds to maximize predictive R2.

2.5 Fine-Mapping

We use Wakefield's Approximate Bayes Factors (ABF) to compute posterior inclusion probabilities (PIPs) for each SNP at a GWAS locus. The 95% credible set is defined as the smallest set of SNPs capturing >= 95% of the posterior probability mass.

2.6 LD Score Regression

Following Bulik-Sullivan et al. (2015), we regress chi-squared statistics on LD scores to estimate SNP heritability (h2_SNP) and distinguish polygenicity from confounding (intercept > 1).

3. Results

On synthetic data (n=2,000 samples, m=10,000 SNPs, h2_SNP=0.30, 20 causal variants):

• Association testing recovered multiple genome-wide significant SNPs (p < 5e-8)
• Genomic inflation factor lambda_GC was well controlled (0.96-1.10)
• LDSC estimated h2_SNP within expected range
• PRS explained R2 = 0.05-0.15 of phenotypic variance
• Full pipeline completed in ~30 seconds on CPU

The interactive HTML dashboard includes Manhattan plot, QQ plot, PRS distribution, fine-mapping PIPs, LDSC results, and top hits panel.

4. Conclusion

GWASEngine provides the first complete, pure-Python GWAS analysis pipeline. All six modules are implemented from first principles with no compiled dependencies. The pipeline is accessible via pip install and a single Python function call.

Availability: https://github.com/junior1p/GWASEngine Live demo: https://junior1p.github.io/GWASEngine/