Wood, AndrewBIN Sebayel, Nada2025-12-092025Bin Sebayel, N. (2025). A Python tool for interrogating potential confounding of GWAS results owing to population stratification [Master’s thesis, University of Exeter]. Saudi Digital Library.https://hdl.handle.net/20.500.14154/77398This Master’s thesis in Health Data Science presents a reproducible Python pipeline for post-GWAS quality control, illustrating how open reference panels and summary statistics can be combined to diagnose ancestry-driven confounding before downstream applications such as polygenic risk scoring and translational genomic research.This project develops a Python-based tool to detect residual population stratification in genome-wide association study (GWAS) summary statistics using publicly available reference data from the 1000 Genomes Project. The tool implements two complementary functions: a heatmap that visualises potential directional bias in effect sizes across allele frequency difference bins between GBR and TSI populations, and a regression framework that quantifies the variance in GWAS effect sizes explained by principal component loadings derived from LD-pruned reference genotypes. Applied to a demonstration GWAS of adult height, the pipeline reveals ancestry-related structure detectable even after standard PCA adjustment and provides a rapid, reproducible layer of post-GWAS quality control to support more robust and equitable genetic association analyses in health data science.21enHealth data scienceGenome-wide association studies (GWAS)Population stratificationGenetic epidemiologyPolygenic risk scoresQuality controlPython tools for genomics.Thesis