GWAS-Insight: A Bioinformatics Pipeline for Genetic Association Analysis and Disease Susceptibility Inference

Abstract

The exponential growth of genomic data has revolutionized our understanding of the genetic architecture underlying complex diseases. Genome-wide association studies have successfully identified thousands of variant-trait associations, yet the translation of these statistical associations into biologically meaningful disease susceptibility models remains a profound computational challenge. Existing bioinformatics tools often require complex, fragmented workflows that hinder reproducibility and efficiency, particularly when processing biobank-scale datasets. In response, this paper introduces GWAS-Insight, a comprehensive, highly scalable bioinformatics pipeline designed to integrate raw genomic data quality control, advanced statistical association testing, and downstream disease susceptibility inference into a unified framework. By employing optimized memory mapping techniques and parallelized computing environments, GWAS-Insight significantly reduces computational overhead while maintaining rigorous statistical validity. The pipeline incorporates sophisticated modules for handling population stratification, cryptic relatedness, and multi-ancestry meta-analyses. Furthermore, GWAS-Insight features a dedicated susceptibility inference engine that leverages polygenic risk scoring and penalized regression models to predict individual disease risk. Through rigorous benchmarking on both simulated and real-world datasets, we demonstrate that GWAS-Insight outperforms conventional pipelines in both processing speed and memory efficiency. The platform provides researchers with an accessible, end-to-end solution that bridges the gap between raw genetic variants and clinical risk prediction, thereby accelerating the pace of discovery in precision medicine and complex trait genetics.