Major Depressive Disorder (MDD) is a disabling, common psychiatric disorder and the leading cause of global disability. A complex combination of genetic and environmental factors gives rise to MDD, although the exact aetiology has not been identified. Genome-wide association studies (GWAS) have established that MDD has a moderate heritability of approximately 37%. MDD has in the past also been associated with abnormalities of white matter microstructure, which represents the brain’s connectivity network. This network is also moderately heritable, providing rationale to investigate its relationship to MDD genetic risk.
Over recent years, there has been considerable progress in establishing genetic contributions to MDD. These advances can be harnessed, in combination with neuroimaging and epigenomics, to understand the neurobiology of the disorder. This has only recently become possible at sufficient scale with the availability of large publicly available datasets including genomic, epigenomic, and neuroimaging data.
In the current thesis, I therefore aimed to leverage genetic, epigenetic, and neuroimaging data in two large datasets, UK Biobank (N range: 6,400 – 14,800) and Generation Scotland: Scottish Family Health Study (N = 625). Specifically, I aimed to uncover links between white matter microstructure, as measured by fractional anisotropy and mean diffusivity, and (i) differential gene expression as indexed by expression quantitative trait loci (eQTLs) scores in chapter 2; here, decreased white matter integrity was found to be associated with 6 scores regulating genes previously reported to be implicated in neurological and neuropsychiatric disorders, while 2 scores regulating neurodevelopment-linked genes were associated with increased white matter integrity; (ii) MDD genetic risk stratified by the NETRIN1 Signalling Pathway, previously implicated in MDD, indexed by polygenic risk scores (PRS) in chapter 3; results indicated novel associations between the pathway-focussed PRS and decreased white matter integrity in thalamic radiations, as well as several association fibres, including superior and inferior longitudinal fasciculus; (iii) a novel wholegenome epigenetic risk score for MDD, which uncovered an association with MDD, but no significant associations with changes in white matter microstructure (chapter 4). The overall aim of the thesis was to use advanced genomic techniques to stratify
genetic function and risk and explore epigenetic risk for MDD in order to identify novel links to structural brain connectivity.
Overall, the three studies provide a strong rationale for integrating neuroimaging, genomic and epigenomic data. Specifically, findings in chapter 2 indicate the importance of DCAKD, SLC35A4, SEC14L4, SRA1, PLEKHM1, UBE3C, NMT1, and CPNE1, not previously found by conventional GWAS approaches. This suggests that integrating neuroimaging and genetic expression data may uncover novel associations that inform disease- or trait-specific genetic links to brain connectivity. Chapter 3 results provide a rationale for investigating the NETRIN1 Signalling Pathway and emphasise the role of thalamic connections in MDD within this biological pathway, indicating that novel associations with brain connectivity may be uncovered at a more focused level when stratifying MDD risk by biology. Finally, results from chapter 4 indicate that epigenetics play an important role in MDD risk, although further analysis including larger-scale epigenetic and neuroimaging data should be carried out to uncover the role of epigenetics in relation to brain phenotypes
