Decoding the genetics of complex traits
Authors:   , Werme Josefin Eivor Maria Salmine Anette 
Published: September 2024

The field of complex trait genetics has seen tremendous progress in the last few decades. This has been largely thanks to the advent of affordable high-throughput technology which has permitted the hypothesis-free interrogation of genetic variants across the entire genome, as well as massive data collection efforts and worldwide collaborations having enabled sufficient sample sizes for the relevant genetic signals to be detected. Along with these advances, another crucial factor has been the development of novel statistical methods that allow the detection of biologically interpretable patterns from the genetic associations.

What has been particularly striking about this field is how the technological advancements drastically outpaced the development of appropriate statistical tools, leading to a situation where there was lots of data available, yet a great difficulty extracting meaningful information therefrom. A large part of this thesis was aimed at addressing this issue, via the development of a novel tool dedicated to local genetic correlation analysis (chapter 3), the optimisation, evaluation, and improvement of existing approaches for gene-environment interaction and transcriptome-wide association (chapters 2 and 4), as well as the application of these methods in ways which elucidate the genetic mechanisms underlying two mental health phenotypes (chapters 2 and 5).

In chapter 2, we evaluated and optimised an analysis paradigm used to perform genome-wide gene-environment interaction studies (GWEIS), which we applied to neuroticism. This allowed us to perform the largest GWEIS for a mental health phenotype to date, involving a total of 25 different environments. We subsequently performed functional follow-up analyses with the results from our GWEIS with the goal of elucidating genes and biological pathways that may be sensitive to environmental influence. In chapter 3, we developed LAVA, a tool dedicated to local genetic correlation (rg) analysis. Beyond addressing the issues with existing bivariate approaches to local rg, it provided the opportunity to perform multivariate local rg analysis involving several phenotypes at once. This allowed for the evaluation of conditional genetic relationships within user defined segments on the genome, permitting entirely novel questions to be answered. In chapter 4, we outlined the conceptual and statistical issues with state-of-the-art methods used for transcriptome-wide association studies (TWAS). TWAS aims to infer the relationship between a phenotype and the genetically regulated component of gene expression when the phenotype and gene expression data have been measured in separate samples. However, due to the issues outlined in this chapter, we show that traditional TWAS approaches are unsuited to this aim. We also show how local rg analysis provides a viable alternative, and consequently updated the LAVA framework in order to facilitate TWAS. Finally, chapter 5 utilises the developments of chapters 3 and 4, employing local rg analysis to highlight the biological paths through which genetic variants may influence depression. This was done via the analysis of endophenotypes across multiple levels of function: gene expression and splicing in various brain, endocrine and immune related tissues, the morphology of several cortical and subcortical brain regions, and the structural and functional connectivity within different brain networks.

An important theme of this thesis, in addition to the development, optimisation, and application of statistical methods, was the integration of multiple sources of data. Chapter 2 focused on the integration of environmental and genome-wide genetic data; chapter 3 integrated genetic data from higher-order phenotypes across several domains; chapter 4 focused on the integration of eQTL data with GWAS; and chapter 5 integrated endophenotypes to unravel the biological component of depression. In all of these instances, the overall goal was to extract more information from the genetic association signals to say something about the aetiology and underlying biology of the analysed phenotypes.