SynthesisMolecular psychiatry2026
Genome-wide meta-analyses of non-response to antidepressants provide insights into underlying molecular genetics and suggest potential pharmacotherapies.
Synthesis in Molecular psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Understanding antidepressant change patterns in the UK Biobank.The British journal of psychiatry : the journal of mental science · 2026Article
- The relevance of phenotypic definition in treatment resistant forms of major depressive disorder: a narrative review.Frontiers in pharmacology · 2026Review
Corrections and comments
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Authors and funding
27 authors.
Funding
Abstract
Antidepressants exhibit a considerable variation in efficacy, and increasing evidence suggests that individual genetics contribute to antidepressant treatment response. Here, we combined data on antidepressant non-response measured using rating scales for depressive symptoms, questionnaires of treatment effect, and data from electronic health records, to increase statistical power to detect genomic loci associated with non-response to antidepressants in a total sample of 135,471 individuals prescribed antidepressants (25,255 non-responders and 110,216 responders). We performed genome-wide association meta-analyses, genetic correlation analyses, leave-one-out polygenic prediction, and bioinformatics analyses for genetically informed drug prioritization. We identified one novel locus (rs1106260) associated with non-response to selective serotonin reuptake inhibitors (SSRIs), and one novel locus (rs60847828) associated with non-response to SSRIs and serotonin-norepinephrine reuptake inhibitors (SNRIs) and showed significant polygenic prediction in independent samples. Genetic correlation analyses show positive associations between non-response to antidepressants and most psychiatric traits, and negative associations with cognitive traits and subjective well-being. In addition, we investigated drugs that target proteins likely involved in mechanisms underlying antidepressant non-response, and shortlisted drugs that warrant further replication and validation of their potential to reduce depressive symptoms in individuals who do not respond to first-line antidepressant medications. These results suggest that meta-analyses of GWAS utilizing real-world measures of treatment outcomes can increase sample sizes to improve the discovery of variants associated with non-response to antidepressants.
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Registered trials
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