4.2 Article

Interaction between serotonin transporter gene variants and life events predicts response to antidepressants in the GENDEP project

Journal

PHARMACOGENOMICS JOURNAL
Volume 11, Issue 2, Pages 138-145

Publisher

SPRINGERNATURE
DOI: 10.1038/tpj.2010.14

Keywords

depression; antidepressive agents; pharmacogenetics; serotonin transporter; gene-environment interaction; stressful life events

Funding

  1. European Commission [LSHB-CT-2003-503428]
  2. United Kingdom National Institute for Health Research of the Department of Health
  3. Medical Research Council [G9817803B] Funding Source: researchfish

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There is substantial inter-individual variation in response to antidepressants, and genetic variation may, in part, explain these differences. For example, there is evidence to suggest that variation in the serotonin transporter gene (SLC6A4) predicts response to selective serotonin reuptake inhibitors (SSRIs). Environmental factors such as the occurrence of stressful life events before treatment may also be important. One prior report suggests that both factors interact in predicting response to antidepressants. GENDEP, a prospective part-randomized pharmacogenomics trial, collected longitudinal data on the outcome of 811 patients with major depression undergoing treatment with either an SSRI (escitalopram) or a tricyclic antidepressant (nortriptyline). Life events experienced over 6 months preceding treatment were measured using a List of Threatening Experiences Questionnaire, and several polymorphisms in the serotonin transporter gene (SLC6A4) have been genotyped including the serotonin transporter-linked polymorphic region (5-HTTLPR). Stressful life events were shown to predict a significantly better response to escitalopram but had no effect on response to nortriptyline. Variation in the 5-HTTLPR and another polymorphism in the gene, STin4, significantly modified these effects. Gene-environment interactions including life events may therefore be important not only in the aetiology of depression, but also in predicting response to antidepressant medication. The Pharmacogenomics Journal (2011) 11, 138-145; doi:10.1038/tpj.2010.14; published online 9 March 2010

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