United efforts: Combining neuroimaging and genetics to unravel the human brain

Several recent developments have created a unique opportunity for scientists to advance our understanding of the heritable influences on brain structure and function. The availability of large-scale neuroimaging and genotype data enable the search across the entire genome for variants that explain small individual differences in a wide range of brain characteristics derived from different MRI modalities. Translating the identified variants into mechanistic hypotheses through in silico annotation can increase our biological understanding of brain structure and function. Comparing the genetic results with those of conditions that are affected by structural and functional alterations in the brain can potentially reveal a shared cause and improve our understanding of how such conditions emerge. In this thesis, I therefore presented several studies aimed to improve understanding into the genetic architecture and mechanistic underpinnings of neuroimaging-derived phenotypes and the genetic overlap with brain-related conditions.

Specifically, in Chapter 2, I focus on describing the genetic architecture of cerebellar volume. Because cerebellar volume, as well as the neurodevelopmental and neurodegenerative disorders it is associated with, are substantially heritable, the genetic determinants of cerebellar volume may improve our insight into these disorders. This study aims to investigate the convergence of cerebellar volume genetic associations in close detail. In Chapter 3, I aim to provide insight into a fundamental question in neuroscience from a genetics viewpoint: the structure-function relationship of brain network connectivity. I focus on seven well-described networks that have been associated with psychiatric and neurological conditions in the neuroimaging literature. I describe the genetic determinants of connectivity within seven brain functional networks and their underlying white matter connections and estimate the genetic overlap between their functional and structural connectivity patterns. In Chapter 4, I use the GWAS results from Chapter 3 and brain volume phenotypes such as used in Chapter 2 together with molecular endophenotypes to explore local genetic correlation with major depressive disorder (MDD). I aim to elucidate on plausible (brain) mechanisms through which genetic variants influence MDD and provide additional biological context to any detected gene-sets. I focus on genes with correlations between MDD and multiple levels of function, as these genes might be particularly interesting for functional follow-up. Lastly, in Chapter 5, I shed light on the consequences of a key genetic concept – pleiotropy – in the context of multimodal neuroimaging. By applying a multivariate GWAS design, I aim to describe the extent to which pleiotropy is present across brain characteristics derived from different neuroimaging modalities (structural, functional and diffusion MRI) and which biological processes are unique to or shared

Together, the studies included in this thesis provide insight into the heritability of neuroimaging-derived phenotypes, generate hypotheses about putative mechanisms of function, and genetic overlap with brain-related conditions. The main implications of this thesis are further discussed below, followed by potential future directions.