Evaluating the Power of Admixture Mapping

Admixture mapping (AM) is a statistical method to help identify genetic variants that cause diseases in admixed populations. This project assesses the power of AM via simulation studies. I used genetic data simulation tools to generate admixed genotypes and binary disease outcomes, vary- ing disease characteristics such as heritability and prevalence. Three AM methods (marginal logistic regression, case-only, and case-control) were evaluated by calculating empirical power under two thresholds (Bonferroni and permutation-based). Results indicate that statistical power improves with increased heritability and disease prevalence across all methods, high- lighting the effectiveness of AM for detecting true associations.