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Review
. 2025 Jun;18(6):e70255.
doi: 10.1111/cts.70255.

Comprehensive Characterization of Antidepressant Pharmacogenetics: A Systematic Review of Studies in Major Depressive Disorder

Caroline W Grant  1 , Karina Delaney  1 , Linsey E Jackson  2 , Justin Bobo  1 , Leslie C Hassett  3 , Liewei Wang  1   4 , Richard M Weinshilboum  1   4 , Paul E Croarkin  5 , Melanie T Gentry  5 , Ann M Moyer  6 , Arjun P Athreya  1   4   5
Affiliations
Review

Comprehensive Characterization of Antidepressant Pharmacogenetics: A Systematic Review of Studies in Major Depressive Disorder

Caroline W Grant et al. Clin Transl Sci. 2025 Jun.

Abstract

Pharmacogenetics is a promising strategy to facilitate individualized care for patients with Major Depressive Disorder (MDD). Research is ongoing to identify the optimal genetic markers for predicting outcomes to antidepressant therapies. The primary aim of this systematic review was to summarize antidepressant pharmacogenetic studies to enhance understanding of the genes, variants, datatypes/methodologies, and outcomes investigated in the context of MDD. The secondary aim was to identify clinical genetic panels indicated for antidepressant prescribing and summarize their genes and variants. Screening of N = 5793 articles yielded N = 390 for inclusion, largely comprising adult (≥ 18 years) populations. Top-studied variants identified in the search were discussed and compared with those represented on the N = 34 clinical genetic panels that were identified. Summarization of articles revealed sources of heterogeneity across studies and low rates of replicability of pharmacogenetic associations. Heterogeneity was present in outcome definitions, treatment regimens, and differential inclusion of mediating variables in analyses. Efficacy outcomes (i.e., response, remission) were studied at greater frequency than adverse-event outcomes. Studies that used advanced analytical approaches, such as machine learning, to integrate variants with complimentary biological datatypes were fewer in number but achieved higher rates of significant associations with treatment outcomes than candidate variant approaches. As large biological datasets become more prevalent, machine learning will be an increasingly valuable tool for parsing the complexity of antidepressant response. This review provides valuable context and considerations surrounding pharmacogenetic associations in MDD which will help inform future research and translation efforts for guiding antidepressant care.

Keywords: MDD; major depressive disorder; pharmacogenetics.

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Conflict of interest statement

Previous Presentation: An abstract by Caroline Grant et al., summarizing preliminary findings from this work was presented as a poster at the annual meeting of the American Society of Clinical Pharmacology and Therapeutics (ASCPT), in Atlanta, GA, 2023.

Drs. L.W. and R.M.W. are co‐founders of and stockholders in OneOme LLC. Dr. P.E.C. has received research grant support from Neuronetics Inc., NeoSync Inc., and Pfizer Inc. He has received grant in‐kind (equipment, supply, and genotyping support for research studies) from Assurex Health, Neuronetics Inc., and MagVenture Inc. Dr. P.E.C. has served as a consultant for Engrail Therapeutics, Myriad Neuroscience, Procter & Gamble, and Sunovion. All other authors declared no competing interests for this work.

Figures

FIGURE 1
FIGURE 1
Study selection following Prisma 2020 flow diagram for systematic reviews.
FIGURE 2
FIGURE 2
Treatments studied. N lists the number of studies evaluating each treatment in pharmacogenetic contexts. Figure excludes N = 7 studies with non‐pharmacological treatments (transcranial magnetic stimulation (TMS), transcranial direct current stimulation (TDCS), theta‐burst stimulation (TBS), electroconvulsive therapy). MAOI, Monoamine oxidase inhibitor; SNRI, Serotonin‐norepinephrine reuptake inhibitor; SSRI, Selective serotonin reuptake inhibitor; TCA, Tricyclic antidepressant.
FIGURE 3
FIGURE 3
Study duration (weeks). Count represents the final timepoint of evaluation (one per study). This does not reflect all reported outcome time points, as some studies may have reported pharmacogenetic associations at multiple timepoints.
FIGURE 4
FIGURE 4
Data types used in associating genomic variants with antidepressant outcomes. Count represents the number of studies using each respective datatype (light color: No significant association; dark color: Significant association). Significance was defined by the study authors.
FIGURE 5
FIGURE 5
Variant‐outcome associations of top studied variants. Variants studied in the context of outcomes to antidepressant therapies were separated according to whether they were studied in the context of (A) efficacy outcomes or, (B) adverse‐event outcomes. Size of circle represents the number of studies while color intensity represents the proportion of significant associations. Counts are representative of the number of analyses, not independent studies.
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