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Genome-wide association study of treatment-resistance in depression and meta-analysis of three independent samples

Research output: Contribution to journalArticle

Chiara Fabbri, Siegfried Kasper, Alexander Kautzky, Lucie Bartova, Markus Dold, Joseph Zohar, Daniel Souery, Stuart Montgomery, Diego Albani, Ilaria Raimondi, Dimitris Dikeos, Dan Rujescu, Rudolf Uher, Cathryn Mair Lewis, Julien Mendlewicz, Alessandro Serretti

Original languageEnglish
Pages (from-to)36-41
JournalBritish Journal of Psychiatry
Issue number1
Publication statusPublished - 23 Nov 2018


King's Authors


Treatment-resistant depression (TRD) is the most problematic outcome of depression in terms of functional impairment, suicidal thoughts and decline in physical health.
To investigate the genetic predictors of TRD using a genome-wide approach to contribute to the development of precision medicine.
A sample recruited by the European Group for the Study of Resistant Depression (GSRD) including 1148 patients with major depressive disorder (MDD) was characterised for the occurrence of TRD (lack of response to at least two adequate antidepressant treatments) and genotyped using the Infinium PsychArray. Three clinically relevant patient groups were considered: TRD, responders and non-responders to the first antidepressant trial, thus outcomes were based on comparisons of these groups. Genetic analyses were performed at the variant, gene and gene-set
(i.e. functionally related genes) level. Additive regression models of the outcomes and relevant covariates were used in the GSRD participants and in a fixed-effect meta-analysis performed between GSRD, STAR*D (n = 1316) and GENDEP (n = 761)
No individual polymorphism or gene was associated with TRD, although some suggestive signals showed enrichment in cytoskeleton regulation, transcription modulation and calcium signalling. Two gene sets (GO:0043949 and GO:0000183) were associated with TRD versus response and TRD versus response and non-response to the first treatment in the GSRD participants and in the meta-analysis, respectively (corrected P = 0.030 and P = 0.027).
The identified gene sets are involved in cyclic adenosine monophosphate mediated signal and chromatin silencing, two processes previously implicated in antidepressant action. They represent possible biomarkers to implement personalised antidepressant
treatments and targets for new antidepressants.

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