Identifying the Common Genetic Basis of Antidepressant Response.

Abstract

Background:

Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction.

Methods:

Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA.

Results:

Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response.

Conclusions:

This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.

• Authors: Oliver Pain, Karen Hodgson, Vassily Trubetskoy, Stephan Ripke, Victoria S Marshe, Mark J Adams, Enda M Byrne, Adrian I Campos, Tania Carrillo-Roa, Annamaria Cattaneo, Thomas D Als, Daniel Souery, Mojca Z Dernovsek, Chiara Fabbri, Caroline Hayward, Neven Henigsberg, Joanna Hauser, James L Kennedy, Eric J Lenze, Glyn Lewis, Daniel J Müller, Nicholas G Martin, Benoit H Mulsant, Ole Mors, Nader Perroud, David J Porteous, Miguel E Rentería, Charles F Reynolds 3rd, Marcella Rietschel, Rudolf Uher, Eleanor M Wigmore, Wolfgang Maier, Naomi R Wray, Katherine J Aitchison, Volker Arolt, Bernhard T Baune, Joanna M Biernacka, Guido Bondolfi, Katharina Domschke, Masaki Kato, Qingqin S Li, Yu-Li Liu, Alessandro Serretti, Shih-Jen Tsai, Gustavo Turecki, Richard Weinshilboum, GSRD Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Andrew M McIntosh, Cathryn M Lewis
• Journal: Biological psychiatry global open science (Biol Psychiatry Glob Open Sci) Vol. 2 Issue 2 Pg. 115-126 (Apr 2022) ISSN: 2667-1743 [Electronic] United States
• PMID: 35712048 (Publication Type: Journal Article)
• Copyright: © 2021 The Authors.