1. The NCAM1 gene set is linked to depressive symptoms and their brain structural correlates in healthy individuals.
- Author
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Petrovska J, Coynel D, Fastenrath M, Milnik A, Auschra B, Egli T, Gschwind L, Hartmann F, Loos E, Sifalakis K, Vogler C, de Quervain DJ, Papassotiropoulos A, and Heck A
- Subjects
- Adolescent, Adult, Anisotropy, Brain diagnostic imaging, Brain Mapping, Collagen metabolism, Diffusion Tensor Imaging, Female, Genetic Association Studies, Humans, Male, Psychiatric Status Rating Scales, White Matter diagnostic imaging, White Matter pathology, Young Adult, Brain pathology, CD56 Antigen genetics, Collagen genetics, Depression genetics, Depression pathology, Polymorphism, Single Nucleotide genetics
- Abstract
Depressive symptoms exist on a continuum, the far end of which is found in depressive disorders. Utilizing the continuous spectrum of depressive symptoms may therefore contribute to the understanding of the biological underpinnings of depression. Gene set enrichment analysis (GSEA) is an important tool for the identification of gene groups linked to complex traits, and was applied in the present study on genome-wide association study (GWAS) data of depression scores and their brain-level structural correlates in healthy young individuals. On symptom level (i.e. depression scores), robust enrichment was identified for two gene sets: NCAM1 Interactions and Collagen Formation. Depression scores were also associated with decreased fractional anisotropy (FA) - a brain white matter property - within the forceps minor and the left superior temporal longitudinal fasciculus. Within each of these tracts, mean FA value of depression score-associated voxels was used as a phenotype in a subsequent GSEA. The NCAM1 Interactions gene set was significantly enriched in these tracts. By linking the NCAM1 Interactions gene set to depression scores and their structural brain correlates in healthy participants, the current study contributes to the understanding of the molecular underpinnings of depressive symptomatology., (Copyright © 2017 Elsevier Ltd. All rights reserved.)
- Published
- 2017
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