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Antidepressant treatment response is modulated by genetic and environmental factors and their interactions

Dávid Kovacs12*, Xénia Gonda123, Péter Petschner12, Andrea Edes12, Nóra Eszlari12, György Bagdy12 and Gabriella Juhasz124

Author Affiliations

1 Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, 1089 Budapest, Hungary

2 MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, 1089 Budapest, Hungary

3 Department of Clinical and Theoretical Mental Health, Kutvolgyi Clinical Center, Semmelweis University, 1125 Budapest, Hungary

4 Neuroscience and Psychiatry Unit, School of Community Based Medicine, Faculty of Medical and Human Sciences, The University of Manchester, UK and Manchester Academic Health Sciences Centre, M13 9PT Manchester, UK

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Annals of General Psychiatry 2014, 13:17  doi:10.1186/1744-859X-13-17

Published: 13 June 2014


Although there is a wide variety of antidepressants with different mechanisms of action available, the efficacy of treatment is not satisfactory. Genetic factors are presumed to play a role in differences in medication response; however, available evidence is controversial. Even genome-wide association studies failed to identify genes or regions which would consequently influence treatment response. We conducted a literature review in order to uncover possible mechanisms concealing the direct effects of genetic variants, focusing mainly on reports from large-scale studies including STAR*D or GENDEP. We observed that inclusion of environmental factors, gene-environment and gene-gene interactions in the model improves the probability of identifying genetic modulator effects of antidepressant response. It could be difficult to determine which allele of a polymorphism is the risk factor for poor treatment outcome because depending on the acting environmental factors different alleles could be advantageous to improve treatment response. Moreover, genetic variants tend to show better association with certain intermediate phenotypes linked to depression because these are more objective and detectable than traditional treatment outcomes. Thus, detailed modeling of environmental factors and their interactions with different genetic pathways could significantly improve our understanding of antidepressant efficacy. In addition, the complexity of depression itself demands a more comprehensive analysis of symptom trajectories if we are to extract useful information which could be used in the personalization of antidepressant treatment.