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University of Cambridge

Genetic Determinants of Human Susceptibility to Infections: A Multi-faceted Exome-based Analysis in the UK Biobank

Abstract

dc:description.abstract

Genetic factors play a crucial role in determining susceptibility to infections and shaping immune responses, yet this area remains understudied in genome-wide association studies (GWAS), particularly in the context of exome sequencing. This thesis begins with a comprehensive literature review, which explores how advancements in sequencing technologies and analytical methods have revolutionized our understanding of genetic contributions to complex traits, including infectious diseases. The literature review provides a critical foundation for the thesis by examining the methodological intricacies of GWAS and their applications in infectious disease research. It highlights the importance of complementary statistical approaches, such as fine-mapping and colocalisation analyses, in refining GWAS findings and uncovering causal variants. The review also delves into the emerging field of pharmacogenomics, setting the stage for investigating genetic influences on drug responses, particularly in the context of infectious diseases. Building on this comprehensive background, the thesis addresses existing knowledge gaps through three interconnected studies utilising UK Biobank datasets. These studies investigate the relationships between genetics, exomes, and immune responses, focusing on serological antibody responses to common pathogens, broader phenotypic effects of infection-related genetic variants, and pharmacogenetic interactions in COVID-19 outcomes. The first research chapter employed whole exome analysis to identify 30 genetic variants independently associated with immune responses to six common pathogens. Fine-mapping revealed 30 causal exonic variants, demonstrating the potential of exome sequencing in uncovering rare variants related to infection-related traits. The second research chapter conducted a phenome-wide association study (PheWAS), revealing 304 significant associations between 25 genetic variants and 98 diverse phenotypic traits. Colocalisation analyses identified 36 instances of shared genetic effects across multiple traits, highlighting the potential pleiotropic effect between genetics, immune responses, and various health outcomes. The final research chapter extended the research into pharmacogenetics, focusing on SARS-CoV-2-related outcomes. It uncovered a robust association between the DPYD c.85T>C (*9A) haplotype and increased COVID-19 susceptibility and hospitalisation risk, with significant interactions observed with dexamethasone and ritonavir. This thesis contributes novel insights into the genetic underpinnings of immune responses and infection susceptibility. By leveraging advanced genomic techniques and large-scale datasets, it identifies specific genetic variants influencing immune function, reveals their broader phenotypic impacts, and uncovers important pharmacogenetic interactions. These findings enhance our understanding of host-pathogen interactions at the genetic level and offer potential avenues for improving infectious disease management and treatment strategies.

Degree

thesis:*
Name dc:type.qualificationname
Doctor of Philosophy (PhD)
Level dc:type.qualificationlevel
Doctoral
Grantor dc:publisher.institution
University of Cambridge
Year dc:date.issued
2024

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Zhu, Zhengyang
Advisor dc:contributor.advisor
  • Inouye, Michael

Subjects

dc:subject × 5

Rights

dc:rights
Language dc:language
eng

Identifiers

dc:identifier.*
DOI dc:identifier.doi
https://doi.org/10.17863/CAM.120507
OAI identifier oai:identifier
oai:www.repository.cam.ac.uk:1810/387900

Chain of custody

source
Harvested from
Cambridge University
Base URL
api.repository.cam.ac.uk/server/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Zhu, Zhengyang. Genetic Determinants of Human Susceptibility to Infections: A Multi-faceted Exome-based Analysis in the UK Biobank. Doctoral thesis, University of Cambridge, 2024. https://doi.org/10.17863/CAM.120507