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Using Genetics to Understand Pathways to Mental Disorders

Chair: Fabian Streit
Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University

Mantelabstract: Author: Fabian Streit

The symposium aims to demonstrate how large-scale genetic data can be used to identify pathways that might predispose individuals to mental disorders.
Julian Konzok uses genomic structural equation modeling to investigate causal risk factors for internalizing and externalizing mental disorders. He identifies childhood maltreatment as a universal risk factor while indicating alcohol consumption as a specific risk factor for the externalizing dimension, and physical activity as a specific protective factor for the internalizing dimension.
Javier Schneider Penate's study on the genetics of extinction learning used polygenic scores (PGS) in a well-characterized sample subjected to a fear conditioning paradigm. He shows that the functional connectivity between key brain regions mediates the relationship of genetic risk for anxiety disorders and PTSD with fear learning.
Helena Forstbach investigates the small-world properties of DTI brain networks, using data from the UK Biobank to examine genetic and phenotypic associations with small-world propensity. The results identify a genetic locus related to Alzheimer's disease and show associations with physical health measures and mental health variables.
Sebastian Markett focused on white matter tract integrity as a potential intermediate phenotype for depression. Analyzing data from the UK Biobank, the study found that depressive symptoms, genetic predisposition for depression, and adverse life events are linked to reduced white matter integrity.
Taken together, these presentations underscore the complex genetic and neurobiological underpinnings of mental disorders and highlight potential intermediate phenotypes through which genetic and environmental risks might affect mental health.