The impact of antigen presentation pathway genetic variation on infectious disease phenotypes

Thesis

The antigen presentation pathway (APP) plays a pivotal role in adaptive immunity by processing and presenting pathogen-derived peptides to T cells, thereby shaping immune responses to infection. Genetic variation within the APP, including highly polymorphic human leukocyte antigen (HLA) genes, endoplasmic reticulum aminopeptidases (ERAP1 and ERAP2), transporter associated with antigen processing (TAP) genes, and proteasome subunits, has been implicated in susceptibility to infectious diseases. However, the complex genetic architecture of the APP, charac- characterised by strong linkage disequilibrium, epistatic interactions, and population-specific variation, presents major challenges for identifying causal mechanisms.

My thesis addresses these challenges through three integrated aims. First, we develop a scalable bioinformatics pipeline to systematically extract and characterise genetic features of the APP across multiple cohorts. Second, we introduce a novel Bayesian statistical framework based on the regularised horseshoe prior, implemented via a maximum a posteriori (MAP) estimator, to enable robust fine-mapping of genetic associations in high-dimensional, correlated settings. Third, we apply this framework to jointly model the main and interaction effects among APP components in relation to infectious disease phenotypes.

By integrating pathway-wide genetic features within a flexible Bayesian framework, this work advances our understanding of how genetic variation in the APP shapes immune response diversity and infectious disease outcomes. These findings provide a foundation for future mechanistic and translational studies aimed at linking molecular diversity to immune function and clinical phenotypes.

Authors: Shen, Q

• DOI: 10.5287/ora-yryzbn6ma
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: genetics; HLA; antigen presentation pathway; Bayesian inference
• Subjects: Bioinformatics