HIGH-DIMENSIONAL ENDOPHENOTYPES

Several chapters of this thesis describe the analysis of high-dimensional (HD) imaging phe- notypes, which can potentially serve as better endophenotypes for genetic discoveries than using simple image-derived measures. While big data analytics approaches have facilitated scientific discoveries in fields such as genomics and imaging, cross-investigations between multiple big datasets has remained impractical. The goal of such analyses would be that by increasing data dimensionality it would be possible to more precisely study the complex relation between omics and neuroimaging data. Besides the biological relevance of such research, in this thesis we aimed to overcome fears of using HD imaging phenotypes in ge-

netic studies. In chapters 2.1, 5.2, even with quite a complex pipeline for data processing

and in a multicenter settings with different scanners, field strength, and acquisition protocols, we demonstrated that it is possible to develop a framework for HD data harmonization, and successfully performed imaging genetics analyses with HD imaging phenotypes. Applying this methodology in the context of population and family-based studies we showed that local gray matter density, represented on voxel level, is significantly heritable and varies widely. The clusters of highly heritable voxels are located in subcortical as well in cortical regions, which overlap with regions of high reproducibility. However, there are also regions with high reproducibility which are not heritable at all. These results implie that the voxels heritability follows some pattern due to the complexity of brain genetic architecture, not measurement bias or error. To support interpretation and visualization of the results of our work we made a special online portal: http://imagene.nl/heritability. Such online portals and tools in omics

and neuroimaging analysis are becoming increasingly adopted6-8 _{to provide the way for the}

research community to explore the results and data.

For subcortical shapes we observed the same complex patterns of genetic effects in chapter 2.2. Moreover, our analysis revealed that the genetic architecture of subcortical shapes goes beyond just volumetric measures and serves as a complimentary endophenotype. In our voxel based genetic analyses we showed that using the most heritable voxel can reduce the signal to noise ratio and gain more power in association analysis, compare to just gross measure- ments. For the shapes we demonstrated that exclusion of such HD endophenotype from the analysis would lead to missing a substantial part of information about the genetic architecture of subcortical structures.

In chapter 3.1 we demonstrated that although detecting significant genetic effects on the individual voxel requires large sample sizes, using such three-dimensional association maps

in combination with gene expression information may help to gain additional insight on how genes affect brain structure. We found that VBM association patterns overlap with some of the previously identified genes (CLU, SLC24A4, and MEF2C). We also showed that VBM analysis combined with expression data could provide evidence for new candidate genes in genetic loci, where the causal gene has not been strongly established by biological experi-

ments9_{. In chapter 3.2, I showed that such high-dimensional imaging phenotypes can not}

only be successfully applied to investigate the relation of brain anatomy at the voxel level with genetic data, but also with cognitive measures. Several clusters of voxels were found to be significantly associated with global cognition. Interestingly, each of these clusters was located within multiple anatomic regions, confirming that complex functions of human cognition are not accurately represented by arbitrarily defined anatomical brain regions.

There has been considerable, and very reasonable, criticisms related to the issue of decreas-

ing statistical power by increasing the number of tests when studying HD phenotypes10_{. There-}

fore imaging genetics studies have typically been limited to one or a few phenotypes/regions

of interest (ROI) based on a prior knowledge11-13_{. However, in chapter 5.2 we showed that}

such criticism must be taken with care and sufficient understanding of the origin of the HD phenotype. For gray matter density at the voxel level, we pointed out two important reasons why a HD approach may makes more sense. First, segmentation of biologically defined brain structures may aggregate too much functional information; therefore the more localized ge- netic effect on substructure may vanish when using an ROI, leading to less statistical power. Second, the effect may be distributed between several brain regions, and in such a case the analysis on preselected structures would provide uncomplete information about genetic in- fluence. Furthermore, our results showed that, due to HD nature of voxels, the genetic effect size on the single voxel is much bigger compared to classical non-HD phenotypes. Thus, the increased significance of the association readily overcomes the stricter correction threshold

that needs to be applied. The top found genetic variant in our study had a p-value of 10-87

which is far beyond any thresholds and explains about 3% of voxel density variation. FUTURE DIRECTIONS

The methodology and research results described in this thesis only address a small part of the questions and challenges which lie ahead for the imaging genetics field. Neuroscience and genomics are rapidly developing and an enormous amount of data and knowledge is accumulating. This opens unique opportunities for scientists to address questions which have challenged human conscience since ancient times. In this section I will discuss possible future directions, opportunities, and how I think different problems can be approached.