Advances in technology enable increasing amounts of data collection from individuals for biomedical research. Such technologies, for example, in genetics and medical imaging, have also led to important scientific discoveries about health and disease. The combination of multiple types of high-throughput data for complex analyses, however, has been limited by analytical and logistic resources to handle high-dimensional data sets. In our previous EU Joint Programme–Neurodegenerative Disease Research (JPND) Working Group, called HD-READY, we developed methods that allowed successful combination of omics data with neuroimaging. Still, several issues remained to fully leverage high-dimensional multimodality data. For instance, high-dimensional features, such as voxels and vertices, which are common in neuroimaging, remain difficult to harmonize. In this Full-HD Working Group, we focused on such harmonization of high-dimensional neuroimaging phenotypes in combination with other omics data and how to make the resulting ultra-high-dimensional data easily accessible in neurodegeneration research.@en

General factors capturing the shared genetics in psychiatric (genomic p-factor) and cognitive traits (genomic g-factor), and more recently in resting-state functional magnetic resonance imaging-derived brain networks, have contributed to our increased understanding of the etiologies in their respective domains. Yet it remains unclear whether general factors can capture the three-way genetic overlap of psychopathology, cognition and brain function. Here we tested for the presence of this genetic overlap via genetic correlation analyses using summary statistics of genome-wide association studies of the p-factor (N = 162,151 cases and 276,846 controls), the g-factor (N = 269,867), and the two genomic factors estimated from the amplitude in resting-state functional magnetic resonance imaging-derived brain networks (N = 31,688). Unlike hypothesized, only the genetic correlation between the p-factor and the g-factor was significant. We conclude that specific functional brain network constructs may have more potential than their derived general dimensions to capture relevant genetic variation for cognition and psychopathology.@en