Leveraging Samples with Diverse Ancestries to Better Understand the Genetic and Molecular Architecture of Complex Traits and Improve Polygenic Risk Scores

Abstract

Large-scale genome-wide association studies (GWAS) have identified numerous genetic variants associated with human complex traits and diseases. However, the vast majority of these studies have been conducted in individuals of European ancestry, which has led to missed opportunities for biological discovery in non-European ancestry populations. Moreover, this imbalance could result in unequal benefits of precision medicine, as polygenic risk scores (PRS) based on large-scale genetic studies in European populations have high predictive power of clinical outcomes in European samples but poor predictive power in non-European samples. A key insight from genetic discovery is the widespread pleiotropy of common variants associated with disease, which has been underutilized in elucidating distinct genetic mechanisms underlying multiple diseases. A fundamental challenge in genetics is connecting disease-associated variation to biological mechanisms, as individual variants exert modest effects and often have unclear functional consequences. While aggregated associations across a disease can provide insights, the presence of numerous independent mechanisms has limited the interpretability of gene enrichment strategies. In Chapter 1, I conduct GWAS for 36 quantitative traits in a large Korean cohort, a major biobank effort that broadens the population diversity of genetic studies in East Asia. I identify 301 novel genetic loci, compare the genetic architecture across East Asian and European ancestry populations, and pinpoint novel causal variants through statistical fine-mapping. In Chapter 2, I develop multi-ancestry PRS for venous thromboembolism (VTE) using European and African ancestry samples. By evaluating the prediction performance of these models across diverse ancestry groups, I demonstrate that multi-ancestry PRS for VTE outperform population-specific PRS, particularly in African ancestry populations with smaller GWAS sample sizes. In Chapter 3, I investigate genetic drivers of heterogeneity in thyroid cancer pathophysiology using a multi-ancestry GWAS meta-analysis. By leveraging cross-trait associations of 66 independent thyroid cancer-associated variants across 151 phenotypes, I identify five distinct mechanistic clusters of thyroid cancer, each representing biologically meaningful pathways and robust associations with disease outcomes in an independent dataset.