Exploring Multivariate Gene-Environment Interactions: Models And Applications
Repository URI
Repository DOI
Change log
Authors
Abstract
Complex diseases are driven by multiple risk factors, including genetic variants, environmental exposures and interactions between the two. The advent of GWAS in 2005 and subsequent methodological advances have increased our knowledge of the genetic risk factors underpinning complex diseases. In addition, some research exploring genotype-environment interaction (G
In Chapter 2, I describe the structured linear mixed model (StructLMM), a novel computationally efficient multivariate G
In Chapter 3, I present an application of StructLMM, where I identify significant interaction effects with 64 lifestyle-based factors for BMI using the UK Biobank data. In addition, I show that the StructLMM association test can be used to identify loci with genotype-environment contributions. Subsequently, I explore characteristics of loci with significant interaction effects, including the fraction of the genetic variance that is explained by G
In Chapter 4, I apply the StructLMM interaction test to multiple cardiometabolic traits using the UK Biobank data, facilitating exploration of shared G
Taken together, the work in this thesis demonstrates the need and advantages of jointly modelling interaction effects at multiple environments, providing a new computationally efficient method to achieve this. Combined with the recent and ongoing generation of large biobanks, further research in this field has the potential to advance our understanding of complex traits and diseases.
Description
Date
Advisors
Stegle, Oliver