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Genomic Imaging

The working group „Genomic Imaging“ studies the influence of genetic factors on structure and function of the human brain. For this purpose, we combine cutting edge molecular genetic techniques, brain imaging and statistical as well as bioinformatics tools. Our research aims at understanding the molecular basis of phenotypic variability of the healthy and diseased brain. Further, genetic and epigenetic data support the formation of a multiscale and multimodal digital atlas of the human brain.

Schematic representation of basic „genomic imaging“ research strategiesFig. 1: Schematic representation of basic „genomic imaging“ research strategies: investigation of the influence of known genetic risk factors (derived from GWAS) on structural variability observed in the brain (left); identification of genetic factors contributing to variable brain structures (right).


Systematic identification of genetic factors influencing phenotypic variability. Imaging techniques have revealed remarkable structural and functional variability in the brain. Heritability estimates provide insights into the quantitative contribution that genetic factors have on the observed variability. To systematically search for common genetic variation that influences that variability, we employ genome-wide association studies (GWAS). For these studies, it is crucial to have large, well-characterized samples at hands, and we use the 1000Brains as well as samples available through collaborations. We are also involved in the international ENIGMA consortium.
Recently, the systematic study of the impact of rare genetic variants has become increasingly important, we employ whole exome and whole genome sequencing (next generation sequencing – NGS) for their identification.
The biostatistics and bioinformatics analysis of the data (both common and rare variants) is particularly challenging because most phenotypes are influenced by a complex interplay of many genetic factors. Apart from that, environmental factors also play a role. There is a growing need for the application of statistical approaches that can handle this enormous complexity, such as deep learning. This requires high performance computing (HPC) which is available at the Jülich Supercomputing Center (JSC).


Results of a GWAS to identify common genetic variationFig. 2: Results of a GWAS to identify common genetic variation associated with the volume of three brain regions (insulae, dACC) that were found to show gray matter loss in patients with different neuropsychiatric disorders (Goodkind, Eickhoff et al., 2015). The study sample comprised 3’259 probands (1000Brains, BiDirect, SHIP) and yielded an associated genomic region on chromosome 5.



ENIGMA3 study to identify common genetic variants influencing the structure of the human cortexAbb. 3: ENIGMA3 study to identify common genetic variants influencing the structure of the human cortex. The collaborative study comprised MRI data and genetic data of 51'238 individuals from 58 cohorts. A total of 140 genomic regions with a significant influence on surface area (SA) and 10 regions with a significant influence on cortical thickness (CT) were identified (Gratsby et al., submitted for publication).



Effect of known genetic risk factors on phenotype. Genetic studies in large cohorts of patients with neuropsychiatric and neurological diseases identify an increasing number of disease-associated genetic variants. We study the influence of such variants on the phenotypic variability observed in healthy individuals. For these studies, the availability of large, well-characterized cohorts of individuals derived from the normal population, such as the 1000Brains study, is of great value.

Influence of genetic variants associated with Alzheimer’s Disease on the cortical thickness of healthy probands (1000Brains)Fig. 4. Influence of genetic variants associated with Alzheimer’s Disease on the cortical thickness of healthy probands (1000Brains). The cumulative effect (genetic risk score) of 20 Alzheimer’s Disease markers was tested, grouped into different biological pathways they fall into. The tested variants have an influence on specific patterns of cortical thinning in healthy (older) individuals and may influence brain aging (Caspers, Röckner et al., submitted for publication).



Regional differences of gene expression and epigenetic patterns in the human brain. In the EU flagship Human Brain Project we study regional differences in gene expression and DNA methylation in post-mortem brain samples. Knowledge of such regional differences provides the basis for the recognition of gene groups and biological pathways that contribute to the functional differences. The data form an important level for the construction of a digital brain atlas.

Generation of bilateral maps of gene expression and DNA methylation patterns of the human brain. Fig. 5: Generation of bilateral maps of gene expression and DNA methylation patterns of the human brain.

Research cooperations


Department of Biomedicine, University of Basel
Department of Genomics, Life & Brain Center, University of Bonn
MIAC, Basel
The Human Brain Project
ENIGMA
PGC
Prof. Paolo Carloni, INM-9
Prof. Hans Grabe, SHIP
Prof. Ole Andreassen, NORMENT, University of Oslo
Prof. Stéphanie LeHellard, University of Bergen

Additional Information

Groupleader

Prof. Dr. rer. nat. Sven Cichon

Gebäude: 15.9, Raum: 4014

Institute for Neuroscience and Medicine (INM-1)
Forschungszentrum Jülich
52425 Jülich

Tel.: +49 (0)2461 61 2481 (Secretary: Stefanie Hennen)
Tel.: + 41 (0)61 265 36 47
s.cichon@fz-juelich.de, sven.cichon@unibas.ch

Management

Stefanie Hennen

Building: 15.9, Room: 3021

+49 2461 61-2481
 +49-2461 61-3483
s.hennen@fz-juelich.de

Janine Hucko

Building: 15.9, Room: 3020

+49 2461 61-6443
+49 2461 61-3483
j.hucko@fz-juelich.de

Address

Institut für Neurowissenschaften und Medizin (INM-1)
Forschungszentrum Jülich
52425 Jülich

Building: 15.9


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