Austin Health

Title
Identifying the Common Genetic Basis of Antidepressant Response.
Publication Date
2022-04
Author(s)
Pain, Oliver
Hodgson, Karen
Trubetskoy, Vassily
Ripke, Stephan
Marshe, Victoria S
Adams, Mark J
Byrne, Enda M
Campos, Adrian I
Carrillo-Roa, Tania
Cattaneo, Annamaria
Als, Thomas D
Souery, Daniel
Dernovsek, Mojca Z
Fabbri, Chiara
Hayward, Caroline
Henigsberg, Neven
Hauser, Joanna
Kennedy, James L
Lenze, Eric J
Lewis, Glyn
Müller, Daniel J
Martin, Nicholas G
Mulsant, Benoit H
Mors, Ole
Perroud, Nader
Porteous, David J
Rentería, Miguel E
Reynolds, Charles F
Rietschel, Marcella
Uher, Rudolf
Wigmore, Eleanor M
Maier, Wolfgang
Wray, Naomi R
Aitchison, Katherine J
Arolt, Volker
Baune, Bernhard T
Biernacka, Joanna M
Bondolfi, Guido
Domschke, Katharina
Kato, Masaki
Li, Qingqin S
Liu, Yu-Li
Serretti, Alessandro
Tsai, Shih-Jen
Turecki, Gustavo
Weinshilboum, Richard
McIntosh, Andrew M
Lewis, Cathryn M
Subject
Antidepressant response
Depression
GWAS
Genetics
MDD
Polygenic score
Type of document
Journal Article
OrcId
0000-0001-6548-426X
DOI
10.1016/j.bpsgos.2021.07.008
Abstract
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. 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. 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. 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.
Link
Citation
Biological psychiatry global open science 2022; 2(2): 115-126
Jornal Title
Biological psychiatry global open science

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