Understanding the Aetiology of Metabolic Diseases and Related Phenotypes through Human Genetics
type: CreativeWork
description: A key objective in human genetics is to elucidate how inherited genetic variations contribute to the phenotypic variations and disease susceptibility in the population. Whole-exome sequencing (WES) is a pivotal approach for uncovering gene-disease associations by analysing rare variants that can lead to significant changes in protein function, thereby establishing clear links between protein functions and diseases. Another important method, Mendelian randomisation (MR), leverages genetic variants as natural experiments to investigate potential causal relationships between modifiable risk factors and diseases. This thesis used both approaches to explore the genetic basis of metabolic diseases. Using the extensive WES and phenotyping data from approximately 450,000 participants in the UK Biobank (UKBB), I performed several studies on rare variant burden tests. I first conducted a comprehensive computational experiment to evaluate various rare variant selection strategies for gene-based burden tests. The results informed the development of the analytical pipeline and the selection of computational tools, with the aim of maximising the discovery power of disease-associated genes. I then applied the gene-burden test to identify protein-coding genes associated with type 2 diabetes (T2D) that showed heterogeneous effects between biological sexes. Four T2D risk-associated genes were identified, three of which demonstrated evidence of sex-specific effects. Although these findings were not replicated in a smaller independent cohort, they provided novel hypotheses for further investigations. Studying rare variants offers valuable insights into the genetic architecture of common complex diseases, helping to bridge the gap between monogenic and polygenic disease models. I investigated the contribution of rare variants in obesity susceptibility, focusing on nine genes initially identified in patients with severe early-onset obesity. This work first showed that rare variants previ…
abstract: A key objective in human genetics is to elucidate how inherited genetic variations contribute to the phenotypic variations and disease susceptibility in the population. Whole-exome sequencing (WES) is a pivotal approach for uncovering gene-disease associations by analysing rare variants that can lead to significant changes in protein function, thereby establishing clear links between protein functions and diseases. Another important method, Mendelian randomisation (MR), leverages genetic variants as natural experiments to investigate potential causal relationships between modifiable risk factors and diseases. This thesis used both approaches to explore the genetic basis of metabolic diseases. Using the extensive WES and phenotyping data from approximately 450,000 participants in the UK Biobank (UKBB), I performed several studies on rare variant burden tests. I first conducted a comprehensive computational experiment to evaluate various rare variant selection strategies for gene-based burden tests. The results informed the development of the analytical pipeline and the selection of computational tools, with the aim of maximising the discovery power of disease-associated genes. I then applied the gene-burden test to identify protein-coding genes associated with type 2 diabetes (T2D) that showed heterogeneous effects between biological sexes. Four T2D risk-associated genes were identified, three of which demonstrated evidence of sex-specific effects. Although these findings were not replicated in a smaller independent cohort, they provided novel hypotheses for further investigations. Studying rare variants offers valuable insights into the genetic architecture of common complex diseases, helping to bridge the gap between monogenic and polygenic disease models. I investigated the contribution of rare variants in obesity susceptibility, focusing on nine genes initially identified in patients with severe early-onset obesity. This work first showed that rare variants previ…
author: Jia, Yiran
language: eng
keywords: human genetics, metabolic diseases
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/42ba98a5-3309-4a6a-8b64-0cb42d474a4d/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2024-08-20
date modified: 2026-07-22T22:24:20Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.113722