The impact of low-coverage whole-genome sequencing on advancing genetic epidemiology studies in global populations

Thesis

Genetic epidemiology connects genomics with population health, offering insights into disease susceptibility, progression, and treatment outcomes. Understanding how genetic variation shapes these traits reveals biological mechanisms and informs clinical interventions. Yet, most existing knowledge stems from European, American, and select Asian populations, leaving major gaps in underrepresented regions where infectious diseases are most common. Expanding genomic studies to these populations is essential for scientific equity and a fuller understanding of human biology. Low-coverage whole-genome sequencing (lcWGS) offers a cost-effective, unbiased alternative to genotyping arrays and deep sequencing. LcWGS with imputation enables accurate variant discovery without ascertainment bias, enhancing sensitivity to population-specific and complex variants and making it especially valuable for large-scale global studies.

This thesis explores the potential of lcWGS in genetic epidemiology through applications in the context of global populations with two cohorts from Africa and Asia. Using samples from The Gambia, I benchmarked lcWGS performance in detecting genome-wide variants, HLA alleles, and structural variants, demonstrating its strong potential for comprehensive variant discovery. In addition, I developed strategies to further improve imputation accuracy by refining computational workflows and enhancing population representation. Then, I applied lcWGS in a genome-wide association study of Hepatitis C Virus infection in a Vietnamese cohort, identifying a regulatory variant of OSBPL2 that likely influences viral replication. Together, these findings demonstrate lcWGS as a scalable, accurate, and inclusive approach for global genetic studies.

Authors: Zhang, Z

• DOI: 10.5287/ora-4rxoybdn9
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Subjects: Bioinformatics; Genetic epidemiology