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Genome-wide gene-environment interaction in depression: A systematic evaluation of candidate genes
The childhood trauma working-group of PGC-MDD
Van Der Auwera, S., Peyrot, W. J., Milaneschi, Y., Hertel, J., Baune, B., Breen, G., Byrne, E., Dunn, E. C., Fisher, H., Homuth, G., Levinson, D., Lewis, C., Mills, N., Mullins, N., Nauck, M., Pistis, G., Preisig, M., Rietschel, M., Ripke, S., ... Grabe, H. (2018). Genome-wide gene-environment interaction in depression: A systematic evaluation of candidate genes: The childhood trauma working-group of PGC-MDD. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 177(1), 40-49. https://doi.org/10.1002/ajmg.b.32593, https://doi.org/10.1002/ajmg.b.v177.1
Gene by environment (GxE) interaction studies have investigated the influence of a number of candidate genes and variants for major depressive disorder (MDD) on the association between childhood trauma and MDD. Most of these studies are hypothesis driven and investigate only a limited number of SNPs in relevant pathways using differing methodological approaches. Here (1) we identified 27 genes and 268 SNPs previously associated with MDD or with GxE interaction in MDD and (2) analyzed their impact on GxE in MDD using a common approach in 3944 subjects of European ancestry from the Psychiatric Genomics Consortium who had completed the Childhood Trauma Questionnaire. (3) We subsequently used the genome-wide SNP data for a genome-wide case-control GxE model and GxE case-only analyses testing for an enrichment of associated SNPs. No genome-wide significant hits and no consistency among the signals of the different analytic approaches could be observed. This is the largest study for systematic GxE interaction analysis in MDD in subjects of European ancestry to date. Most of the known candidate genes/variants could not be supported. Thus, their impact on GxE interaction in MDD may be questionable. Our results underscore the need for larger samples, more extensive assessment of environmental exposures, and greater efforts to investigate new methodological approaches in GxE models for MDD.
