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Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP

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Eleanor M Wigmore, Jonathan D Hafferty, Lynsey S Hall, David M Howard, Toni-Kim Clarke, Chiara Fabbri, Cathryn M Lewis, Rudolf Uher, Lauren B Navrady, Mark J Adams, Yanni Zeng, Archie Campbell, Jude Gibson, Pippa A Thomson, Caroline Hayward, Blair H Smith, Lynne J Hocking, Sandosh Padmanabhan, Ian J Deary, David J Porteous & 4 more Ole Mors, Manuel Mattheisen, Kristin K Nicodemus, Andrew M McIntosh

Original languageEnglish
JournalThe pharmacogenomics journal
Early online date31 Jan 2019
Publication statusE-pub ahead of print - 31 Jan 2019

King's Authors


Antidepressants demonstrate modest response rates in the treatment of major depressive disorder (MDD). Despite previous genome-wide association studies (GWAS) of antidepressant treatment response, the underlying genetic factors are unknown. Using prescription data in a population and family-based cohort (Generation Scotland: Scottish Family Health Study; GS:SFHS), we sought to define a measure of (a) antidepressant treatment resistance and (b) stages of antidepressant resistance by inferring antidepressant switching as non-response to treatment. GWAS were conducted separately for antidepressant treatment resistance in GS:SFHS and the Genome-based Therapeutic Drugs for Depression (GENDEP) study and then meta-analysed (meta-analysis n = 4213, cases = 358). For stages of antidepressant resistance, a GWAS on GS:SFHS only was performed (n = 3452). Additionally, we conducted gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis. We did not identify any significant loci, genes or gene sets associated with antidepressant treatment resistance or stages of resistance. Significant positive genetic correlations of antidepressant treatment resistance and stages of resistance with neuroticism, psychological distress, schizotypy and mood disorder traits were identified. These findings suggest that larger sample sizes are needed to identify the genetic architecture of antidepressant treatment response, and that population-based observational studies may provide a tractable approach to achieving the necessary statistical power.

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