The brain self-organises slowly over time, being shaped by both endogenous and exogenous factors. This process results in a unique system for each person, with neuronal circuits shaped by an individual’s genetic background and experience. This thesis aims to better understand these multifactorial gene-environment-outcome relationships, and how they relate to human development. Using multiple data types from the Avon Longitudinal Study of Parents and Children (ALSPAC) combined with a diverse methodological approach, I highlight compounding environmental factors which longitudinally interact to predict developmental outcomes, generate meaningful and valid measures of polygenic propensity, and consider the ways the early environment and polygenic heritability influence and interact to influence specific features of the structural connectome, features that are essential for coherent neural communication and brain function.

In the first empirical chapter, I use structural equation modelling to assess whether maternal mental health longitudinally mediates associations between the early socioeconomic status (SES) and key developmental outcomes (i.e., cognitive ability and child mental health). I show that maternal mental health mediates these relationships, and primarily during the first year of life. In the second empirical chapter, I employ polygenic score (PGS) analysis to generate valid and meaningful PGSs for cognitive ability and educational attainment, with the PGSs for cognitive ability ultimately taken forward in the proceeding empirical chapters. The third empirical chapter incorporates measures of socioeconomic status, PGSs for cognitive ability, and diffusion tract imaging data to explore how these data types are associated with global and local features of the connectome. This was made possible by employing graph theory metrics and partial least squares regression analysis. I demonstrate the early child environment and the genome influence the structure of the brain across at least three local metrics of network connectivity (i.e., node strength, degree, and clustering coefficient), with node strength playing a particularly significant role. Drawing on local node strength and generalised linear modelling, the fourth and final empirical chapter considers how measures of SES and polygenic propensity interact to influence developmental outcome and specific features of the connectome that fall under the rich-club framework (i.e., rich-club, feeder, and peripheral nodes, and rich-rich, rich-feeder, and peripheral connection types). Here I show both SES and PGSs to interact to influence developmental outcome, as well as node and connection type. In particular, PGS and the SES-by-PGS interaction appear to relate to the connectivity of a rich-club of highly interconnected nodes most strongly, but these variables do this by shaping so-called ‘feeder’ connections. Finally, the links between polygenic propensity and connection strength are patterned by gene expression, being strongest across connections that span regions with moderate levels of expression similarity.

This thesis provides new insights made possible by employing a diverse methodological approach in combination with a rich prospective longitudinal dataset. The findings show how it is possible to span multiple levels of analysis within a contemporary developmental science framework, to consider how factors operate at a population level in terms of behaviour, environment, heritability, and brain organisation.