학술논문
Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP
Document Type
Original Paper
Author
Wigmore, Eleanor M.; Hafferty, Jonathan D.; Hall, Lynsey S.; Howard, David M.; Clarke, Toni-Kim; Fabbri, Chiara; Lewis, Cathryn M.; Uher, Rudolf; Navrady, Lauren B.; Adams, Mark J.; Zeng, Yanni; Campbell, Archie; Gibson, Jude; Thomson, Pippa A.; Hayward, Caroline; Smith, Blair H.; Hocking, Lynne J.; Padmanabhan, Sandosh; Deary, Ian J.; Porteous, David J.; Mors, Ole; Mattheisen, Manuel; Nicodemus, Kristin K.; McIntosh, Andrew M.
Source
The Pharmacogenomics Journal. 20(2):329-341
Subject
Language
English
ISSN
1470-269X
1473-1150
1473-1150
Abstract
Antidepressants demonstrate modest response rates in the treatment ofmajor depressive disorder (MDD). Despite previous genome-wide association studies(GWAS) of antidepressant treatment response, the underlying genetic factors areunknown. Using prescription data in a population and family-based cohort (GenerationScotland: Scottish Family Health Study; GS:SFHS), we sought to define a measure of(a) antidepressant treatment resistance and (b) stages of antidepressant resistanceby inferring antidepressant switching as non-response to treatment. GWAS wereconducted separately for antidepressant treatment resistance in GS:SFHS and theGenome-based Therapeutic Drugs for Depression (GENDEP) study and then meta-analysed(meta-analysis n = 4213, cases = 358). For stagesof antidepressant resistance, a GWAS on GS:SFHS only was performed (n = 3452). Additionally, we conducted gene-setenrichment, polygenic risk scoring (PRS) and genetic correlation analysis. We didnot identify any significant loci, genes or gene sets associated with antidepressanttreatment resistance or stages of resistance. Significant positive geneticcorrelations of antidepressant treatment resistance and stages of resistance withneuroticism, psychological distress, schizotypy and mood disorder traits wereidentified. These findings suggest that larger sample sizes are needed to identifythe genetic architecture of antidepressant treatment response, and thatpopulation-based observational studies may provide a tractable approach to achievingthe necessary statistical power.