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Major depressive disorder (MDD) is a common psychiatric disorder and one of the leading causes of disability worldwide. MDD is moderately heritable (~30­40%), suggesting both genetic and environmental effects are influential. Several strands of evidence point to the possible presence of gene­environment correlations and gene­environment interaction effects in MDD, although findings to date have been relatively inconsistent. Recently, research using available genetic and environmental data on biological parents and offspring (trios), have shown that parental genetic nurturing effects are detectable using polygenic scores (PGSs) in more heritable traits such as educational attainment. Research findings also point to potential gene­by­trauma exposure interaction effects involved in MDD. It is evident that data limitations may result in power issues, confounding effects and biases, which impact reliable and accurate quantification of these effects, highlighting the need for methods that maximise statistical power and minimise bias and confounding when exploring these effects. This thesis aims to adapt existing statistical frameworks and use of data to explore gene­environment interplay effects, which are robust to the limitations of the available data. Here, two large population­scale datasets, the UK Biobank (N~150,000) and Generation Scotland: Scottish Family Health Study (N~2680 trios), were utilised to explore genome­by­trauma exposure interaction effects in depression, as well as parental genetic nurturing effects in a range of traits including MDD. The research aims of this thesis included (1) implementing models exploring parental genetic nurturing effects using available trio PGSs; (2) expanding these models to explore mechanisms of genetic nurturing effects with available parental phenotypic data, both addressed in chapter 2. Here, the quantification of parental genetic nurturing effects using trio PGSs was found to be reliable and robust to data limitations. However, expanding these models by including parental phenotypes resulted in confounded effects. Simulation analyses demonstrated that the confounding was induced by power issues associated with PGSs, highlighting the need for improved measures of genetic variance. The final research aim (3) was to explore genome­by­trauma exposure interaction effects using relationship matrices capturing genetic, trauma exposure and genome­by­trauma exposure interaction similarity between participants. Genomic relationship matrices utilised all available genetic data, and thus, served as an improved representation of genetic variance. Environmental relationship matrices utilised principal components, capturing underlying dimensions of trauma exposure. A substantial proportion of MDD variance was found to be attributed to genetic, trauma exposure and genome-­by-trauma interaction effects. However, little insight was inferred regarding the specificity and direction of trauma exposure involved in MDD manifestation, due to the difficulty in interpreting the underlying dimensions of trauma exposure. The two studies provide a strong rationale for the use of improved measures of genetic and environmental components of MDD. Specifically, findings from chapter 2 highlight the need for measures that capture a substantial proportion of genetic variance to explore these complex gene­environment interplay effects. Chapter 3 results demonstrated how leveraging all available genetic data can uncover substantial effects that were previously missed. Evaluations of study designs highlight how future work can incorporate omics (e.g. methylation) data to improve measures of environmental factors, which would aid translational interpretation of results obtained from these models. Methylation data could help identify additional trait associated genetic loci as well as their mode of action, and hence, specific drug targets; which may not have been previously identified by traditional analyses such as genome­wide association studies (GWAS) not containing the relevant genetic variants.