European Neuropsychopharmacology 100 (2025) 13–23
Available online 31 August 2025
0924-977X/© 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
REVIEW
Efficacy of pharmacogenetic (PGx)-guided antidepressant treatment on
functional outcomes and quality of life in adults with anxiety and affective
disorders: A systematic review and meta-analysis
Natalia E. Fares-Otero a,b,#,* , Monika Budde c,# , Jonathan Laatsch d, Mathias Harrer b,e ,
Teuntje Pelgrim f, Alexandra Philipsen d , Urs Heilbronner c , Eduard Vieta a,g,h,** ,
Roos van Westrhenen f,i,j, on behalf of the PSY-PGx Consortium1
a Department of Psychiatry and Psychology, Bipolar and Depressive Disorders Unit, Hospital Clínic, Institute of Neurosciences (UBNeuro), Fundacio Clínic per a la
Recerca Biomedica, Institut d’Investigacions Biomediques August Pi i Sunyer (FCRB-IDIBAPS), Barcelona, Catalonia, Spain
b Section for Evidence-Based Medicine in Psychiatry and Psychotherapy, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany
c Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
d Department of Psychiatry and Psychotherapy, University Hospital of Bonn, Bonn, Germany
e Department of Clinical, Neuro, and Developmental Psychology, WHO Collaborating Centre for Research and Dissemination of Psychological Interventions, Vrije
Universiteit Amsterdam, Amsterdam, the Netherlands
f Outpatient Clinic Pharmacogenetics, Parnassia Psychiatric Institute, Amsterdam, the Netherlands
g Faculty of Medicine and Health Sciences, University of Barcelona (UB), Barcelona, Catalonia, Spain
h Network Centre for Biomedical Research in Mental Health (CIBERSAM), Health Institute Carlos III (ISCIII), Barcelona, Catalonia, Spain
i Department of Psychological Medicine Institute of Psychiatry, Psychology and Neuroscience- King’s College London, London, UK
j St. John’s National Academy of Health Sciences, Bangalore, India
A R T I C L E I N F O
Keywords:
Functionality
Disability
Depression
Pharmacogenomics
Randomised control trial
Mental health
Intervention
Precision psychiatry
A B S T R A C T
We conducted a systematic review and meta-analysis to estimate the efficacy of pharmacogenetic (PGx)-guided
antidepressant treatment compared to treatment as usual (TAU) on functional outcomes and quality of life (QoL)
in people with anxiety and affective disorders. A PRISMA-compliant systematic search was performed up to 26/
06/2025 to identify relevant prospective, randomised controlled trials (RCTs) in seven databases. The revised
tool for Risk of Bias (RoB2) was used to assess the methodological qualities of the included studies (PROSPERO
CRD42024518683). Of 2774 records, six studies were included comprising 2285 adult patients (PGx group: n ˆ
1395, Mean age ˆ 48.14 years; 55.68 % females; TAU group: n ˆ 890, Mean age ˆ 47.83 years, 58.22 % fe-
males). Three studies were included in random-effect meta-analyses. In these, PGx-guided antidepressant
treatment significantly decreased functional disability, measured by the Sheehan Disability Scale/Inventory
(SDS/I), compared to TAU (k ˆ 3, Mean Difference ˆ -2.85, SE ˆ 1.32 [95 % CI: -5.44, -0.26], p ˆ .031). The
Hartung-Knapp adjustment of p-values yielded non-significant effects. Individually, one of these three studies
reported a significant effect of PGx-guided treatment on overall SDS score, one on SDI Perceived Social Support
partial score, and one no effect. Risk for bias was rated high for one study, with some concerns for the other five.
Due to the small number of included trials, our ability to conduct analyses of heterogeneity, moderators and
publication bias was limited. Nonetheless, our results suggest that PGx-guided antidepressant treatment may
improve functioning in people with anxiety and affective disorders
* Corresponding author at: Institute of Neurosciences (UBNeuro), University of Barcelona, FCRB-IDIBAPS, Barcelona, Catalonia, Spain.
** Co-corresponding author at: Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Catalonia, Spain.
E-mail addresses: nefares@recerca.clinic.cat (N.E. Fares-Otero), evieta@clinic.cat (E. Vieta).
# Joint first authorship
1 The on behalf of the PSY-PGx Consortium Collaboration are listed in Acknowledgements.
Contents lists available at ScienceDirect
European Neuropsychopharmacology
journal homepage: www.sciencedirect.com/journal/european-neuropsychopharmacology
https://doi.org/10.1016/j.euroneuro.2025.08.003
Received 23 April 2025; Received in revised form 31 July 2025; Accepted 2 August 2025
European Neuropsychopharmacology 100 (2025) 13–23
14
1. Introduction
With a lifetime risk of 15–18 % (Bromet et al., 2011) and 16.6 %
(Somers et al., 2006) respectively, depression and anxiety disorders are
common and severely impact daily life. Recurrence rates are high, often
requiring long-term treatments. In addition, comorbidity between these
disorders is common and associated with a worse course than either
disorder alone (Penninx et al., 2011).
Pharmacotherapy, particularly antidepressants, is a mainstay in
treating moderate-to-severe cases. However, treatment response and
tolerability vary greatly (Rush et al., 2006; Undurraga and Baldessarini,
2012), often leading to trial-and-error process that may ultimately fail.
In the Sequenced Treatment Alternatives to Relieve Depression
(STAR*D) trial, for example, only 40 % achieved remission after the first
step and over 30 % did not remit after four different medications (Rush
et al., 2006). Treatment resistance -defined as failure after two adequate
trials- remains a major challenge (Vieta and Colom, 2011).
To improve outcomes, pharmacogenetic (PGx) prescribing has
emerged based on genetic variants affecting drug metabolism (van
Westrhenen et al., 2020, 2021). Guidelines by the Dutch Pharmacoge-
netic Working Group (DPWG) (Beunk et al., 2024; Brouwer et al., 2022)
and the Clinical Pharmacogenetics Implementation Consortium (CPIC)
have published PGx-based prescription guidelines for antidepressants
recommended using CYP2C19 and CYP2D6 genotypes to guide drug
selection and dosing, see Bousman et al. for a comprehensive review
(Bousman et al., 2021). These genotypes determine enzyme activity,
classifying patients as ultrarapid to poor metabolisers.
Since 2017, multiple studies and 18 systematic reviews (Aboelbaha
et al., 2021; Fabbri et al., 2018; Health Quality Ontario, 2017; Ontario
Health (Quality), 2021; Peterson et al., 2017; Rosenblat et al., 2017) and
meta-analyses (Arnone et al., 2023; Bousman et al., 2019; Brown et al.,
2020, 2022; Bunka et al., 2023; Ielmini et al., 2022; Milosavljevic et al.,
2024; Rosenblat et al., 2018; Santenna et al., 2024; Skryabin et al., 2023;
Vilches et al., 2019; Wang et al., 2023) and one umbrella review
(Tesfamicael et al., 2024) have assessed PGx-guided antidepressant
treatment. Recent reviews report positive effects on response (Arnone
et al., 2023; Bunka et al., 2023; Milosavljevic et al., 2024; Skryabin
et al., 2023; Wang et al., 2023), remission (Arnone et al., 2023; Bunka
et al., 2023; Milosavljevic et al., 2024; Santenna et al., 2024; Skryabin
et al., 2023; Wang et al., 2023), and symptoms (Arnone et al., 2023;
Milosavljevic et al., 2024; Tesfamicael et al., 2024) though results on
tolerability remain inconsistent. For instance, no clear benefit was found
on adverse effects or discontinuation (Bunka et al., 2023; Milosavljevic
et al., 2024; Skryabin et al., 2023), except for fewer adverse reactions in
the PGx group compared to treatment as usual (TAU) (Santenna et al.,
2024). Only two meta-analyses reported number needed to treat (NNT):
NNT ˆ 8 for both response and remission (Bunka et al., 2023) as well as
NNT ˆ 21.1 for response and NNT ˆ 21.3 for remission (Milosavljevic
et al., 2024). Possible explanations for this quite substantial gap in NNTs
might be the partly different studies included in the meta-analyses due
to the time of literature search and specific inclusion criteria. Also, while
one study focused on outcomes at eight weeks (Milosavljevic et al.,
2024), the other used the longest post-treatment-initiation timepoint
available (Bunka et al., 2023, 2024). Overall, despite statistical signifi-
cance regarding efficacy, concerns remain regarding clinical relevance
(Heilbronner and van Westrhenen, 2024; Milosavljevic et al., 2024).
In addition to alleviating symptoms, restoring functioning and
quality of life (QoL) is central to recovery in individuals with depression
and anxiety. According to the DSM-5 (American Psychiatric Association,
2013), both disorders require the presence of functional impairment,
highlighting the relevance of outcomes beyond symptom reduction. The
Global Burden of Disease Study consistently ranks these conditions
among the leading causes of years lived with disability (YLDs) (Ferrari
et al., 2024). While symptom-based measures reflect disease-centred
outcomes, QoL captures patient-centred perspectives (Peipert et al.,
2025). Functional outcomes can refer to global functioning or specific
domains such as social, occupational, or physical functioning, whereas
QoL can encompass physical, psychological, social, economic, environ-
mental, or overall well-being.
Patients consistently prioritise regaining functioning and QoL as key
treatment goals (Lam et al., 2011; Zimmerman et al., 2006). Although
these outcomes remain underutilised in clinical trials, they provide
distinct and complementary information to symptom severity. Indeed,
depressive symptoms and functioning show only moderate correlations
and are not redundant (McKnight and Kashdan, 2009). Encouragingly, a
recent systematic review reported that, after cancer, mental disorders
are the second most frequently studied group in QoL research
(Haraldstad et al., 2019). Moreover, functioning and QoL can improve
with treatment (Grassi et al., 2020; Haraldstad et al., 2019), reinforcing
their importance as essential targets in clinical research and practice.
Impaired social, occupational and physical functioning can be accom-
panied by a more passive day-to-day life and might lead to increased
loneliness, which in turn can intensify symptoms of depression and
anxiety (Park et al., 2020).
Although the importance of functional outcomes and QoL is widely
recognised (Grassi et al., 2020; Haraldstad et al., 2019), the efficacy of
PGx-guided antidepressant treatment on functioning (social relations,
leisure, work) and QoL (social, environment) compared to TAU remains
unclear. This systematic review and meta-analysis sought to address this
gap by estimating the efficacy of PGx-guided antidepressant treatment
compared to TAU on functional outcomes and QoL in adults with anxiety
and affective disorders. We hypothesised that PGx-guided interventions
(vs. TAU) would lead to greater improvements. Collectively, this quan-
titative synthesis can inform clinical practice guidelines and strategies
for improving targeted interventions.
2. Methods
2.1. Protocol
The review protocol was registered on PROSPERO
(CRD42024518683) and follows the Preferred Reporting Items for Sys-
tematic Reviews and Meta-Analyses (PRISMA) 2020 (Page et al., 2021)
(see Appendices 1 and 2 in the Supplement), and the Enhancing the
Quality and Transparency of Health Research (EQUATOR) reporting
guidelines (Altman et al., 2008). A detailed report of all amendments to
the information provided at registration or in the study protocol is
presented in Appendix 3 of the Supplement. For a glossary of terms used
in this work, see Appendix 0 in the Supplement.
2.2. Search strategy and eligibility criteria
A systematic literature search using multiple terms for pharmaco-
genetics and RCTs (see the search strategy and terms in Appendix 4 in
the Supplement) was implemented up to 05/04/2024 (NEF-O, TP) on
PubMed (Medline), Embase, Scopus, PsycINFO, Cochrane Central Reg-
ister of Controlled Trials (CENTRAL), and Web of Science Core Collec-
tion (WoS), updated on 15/01/2025 (NEF-O) on Scopus, PsycINFO, and
WoS, and updated again up to 26/06/2025 on PubMed, Embase, and
ClinicalTrials.gov (NEF-O, MB) to identify RCTs on PGx-guided antide-
pressant treatments. Additionally, we systematically searched existing
reviews and meta-analyses in this field using the same databases and
manual search strategies to identify prior evidence.
A snow-balling approach (Wohlin et al., 2022) was applied to iden-
tify additional studies meeting the inclusion criteria in the reference list
of studies and relevant studies already included in previous systematic
reviews (Aboelbaha et al., 2021; Fabbri et al., 2018; Health Quality
Ontario, 2017; Ontario Health (Quality), 2021; Peterson et al., 2017;
Rosenblat et al., 2017), and meta-analyses (Arnone et al., 2023; Bous-
man et al., 2019; Brown et al., 2020, 2022; Bunka et al., 2023; Ielmini
et al., 2022; Milosavljevic et al., 2024; Rosenblat et al., 2018; Santenna
et al., 2024; Skryabin et al., 2023; Vilches et al., 2019; Wang et al.,
N.E. Fares-Otero et al.
European Neuropsychopharmacology 100 (2025) 13–23
15
2023), and an umbrella meta-analysis (Tesfamicael et al., 2024) were
cross referenced manually (NEF-O, MB, JL).
The software Endnote was used in the initial removal of duplicates,
and the software Rayyan QCRI (https://rayyan.qcri.org/) was used to
manage citations, remove duplicates, and screen abstracts and titles.
Titles and abstracts of articles were independently screened by NEF-O,
JL, TP. After excluding those not relevant, the full texts were indepen-
dently assessed for eligibility by the same authors. Discrepancies were
resolved through consensus.
Only prospective RCTs published in a peer-reviewed journal in any
language, examining the efficacy of PGx-guided antidepressant therapy
were considered for inclusion. According to the PICO framework,
studies were included if they: (i) (P) investigated adult individuals with
affective or anxiety disorder according to validated diagnostic criteria, e.
g., DSM (American Psychiatric Association, 2013) or ICD (World Health
Organisation, 2019); (ii) (I) conducted PGx-guided antidepressant
treatment (iii) (C) compared the PGx-guided intervention group with a
control group, receiving TAU or any other treatment; (iv) (O) assessed
the efficacy of functional outcomes, i.e., global and subdomains (e.g.,
social relationships, occupational functioning, independent living, lei-
sure), and/or QoL, i.e., global and subdomains (e.g., environmental,
social), or provided data that allowed effects to be calculated or on
request by the authors.
Studies were excluded if they: (i) were reviews, clinical cases, ab-
stracts alone, commentaries, letters to the editor, conference pro-
ceedings, study protocols, unpublished materials (preprints), or grey
literature (thesis); (ii) only recruited participants under age 18; (iii) only
investigated animals; (iv) only investigated individuals with psychotic
disorders; (v) had< 10 participants in each arm; (vi) were retrospective,
non-randomised designs.
2.3. Data extraction
Data were independently extracted by NEF-O, JL, and TP from
eligible studies and tracked in Microsoft Excel; discrepancies were
resolved through consensus.
Descriptive and clinical variables extracted included publication
year, geographical region, sociodemographic characteristics and study
endpoint (see full description of the extracted variables in Appendix 5 in
the Supplement). For continuous outcome variables the mean and SD or
the mean difference and SD (when the same instrument/measure
available) were extracted jointly with the number of people diagnosed
with affective and/or anxiety disorders. Corresponding authors were
contacted twice by email to retrieve additional information if needed.
2.4. Study outcomes
1) Functional outcomes: global functioning or specific functioning in
domains that are indicators of an individual’s ability to manage
everyday roles and responsibilities in key life areas, such as social,
and occupational functioning. These may include hospital admis-
sions, sick leave, employment or educational status, and interper-
sonal functioning (McKnight and Kashdan, 2009; Schwarz et al.,
2024).
2) Quality of life (QoL): subjective evaluation of overall well-being or
global QoL, including satisfaction with life across key domains such
as family life, environment, health, and financial independence
(Wilmer et al., 2021).
2.5. Risk of bias and certainty in the evidence assessment
Risk of bias assessment was independently conducted (MB and JL) by
using the revised Cochrane Rob2 tool (Sterne et al., 2019). Rated do-
mains included: (1) risk of bias arising from the randomisation process;
(2) risk of bias due to deviations from the intended interventions; (3) risk
of bias due to missing outcome data; (4) risk of measurement in the
outcome; and (5) risk of bias in the selection of the reported result. The
quality of included studies was rated as of high risk, some concerns, or
low risk related to the selection, performance, and outcome reporting
procedures used; disagreements were discussed with a third reviewer
(NEF-O) and resolved through consensus.
Certainty in the evidence for each outcome was assessed (NEF-O,
MB) using the Grading of Recommendations, Assessment, Development
and Evaluation (GRADE) system. GRADE uses a structured approach to
assign a rating of high, moderate, low, or very low certainty to the ev-
idence. Factors affecting certainty—study design, risk of bias, inconsis-
tency, indirectness, imprecision, and publication bias—are considered
in rating the certainty or confidence in evidence (Prasad, 2024). Dis-
agreements were resolved through consensus.
2.6. Statistical analyses
We conducted a meta-analysis when a minimum of three studies
(Higgins et al., 2024) were available. A random-effects model was
employed, with the between-study heterogeneity variance estimated
using the maximum-likelihood estimator (REML) (Viechtbauer, 2005).
We also applied Hartung-Knapp adjustments to the pooled effect and its
confidence interval as sensitivity analysis (Rover et al., 2015). If the
number of available effect sizes did not allow for a quantitative syn-
thesis, study findings were summarised and appraised qualitatively in a
narrative synthesis (Popay et al., 2006).
The mean difference (MD) was used as the effect size, measuring the
absolute mean difference between the two groups in a clinical trial. This
outcome measure estimates the amount by which the experimental
intervention changes the outcome on average compared with the control
(TAU) post-treatment. We also calculated standardised mean differences
(SMD) alongside the MD, as recommended by previous meta-
epidemiological studies (Heimke et al., 2024; Takeshima et al., 2014).
For one study (Papastergiou et al., 2021), the missing change score (MD)
had to be imputed (Furukawa et al., 2006) with values (Mean and SD
post-test) provided by authors.
As measures of heterogeneity, the percentage of variation not
attributable to sampling error was quantified using Higgins’ and
Thompson’s I2 statistic, and the between-study variance of the under-
lying distribution of true effect sizes was reported using the tau-squared
(τ2) statistic (Borenstein et al., 2017).
Meta-regression, subgroup analyses, and assessments of publication
bias were conducted only when a minimun of 10 studies were available
(Higgins and Thompson, 2004).
All the quantitative analyses were conducted in R version 4.2.1 using
the “meta” and “metafor” packages (Balduzzi et al., 2019; Harrer et al.,
2022; Viechtbauer, 2010); statistical significance was set at p < .05
(two-tailed). Forest plots were created to show results by means of MD
and SMD, although their heuristic value was necessarily limited given
the low number of included studies. An overview of the risk of bias
assessment was created with the “robvis” tool (McGuinness and Higgins,
2021).
3. Results
3.1. Study characteristics
A total of 2774 records were identified (2765 studies from databases,
and 9 studies from manual searches), of which 46 eligible studies (37
from data bases and 9 from manual searches) were full text screened.
After excluding 40 studies (32 from data bases and 8 from manual
searches), six studies were included in the qualitative and three studies
were included in quantitative syntheses (see the process of study se-
lection in detail in Fig. 1, the full list of included studies in Appendix 6,
and the full list of excluded studies in Appendix 7 in the Supplement).
The six studies included were conducted in Europe (n ˆ 2), North
America (n ˆ 2), Australia (n ˆ 1), and Asia (n ˆ 1), and published
N.E. Fares-Otero et al.
European Neuropsychopharmacology 100 (2025) 13–23
16
between 2015 and 2021. Sample sizes ranged from 49 to 1459,
compromising a total of 2285 adult participants of whom 56.67 % were
females (PGx group: n ˆ 1395, 55.68 % females; TAU group: n ˆ 890,
58.22 % females). The mean age of the total sample was 47.99 [range ˆ
41 –70] years.
For one study (Han et al., 2018), a corrigendum was published in
2020. In this manuscript, we always refer to the corrected version.
Five studies (Han et al., 2018; Perez et al., 2017; Rua~no et al., 2020;
Singh, 2015; van der Schans et al., 2019) were conducted in major
depressive disorder (MDD), one study was conducted in a mixed sample
with anxiety and MDD (Papastergiou et al., 2021).
In five of the six included studies, functional outcomes or QoL were
secondary outcomes measures. The study duration ranged from four
weeks to 6 months (see further descriptive characteristics of the
included studies in Table 1).
The included studies varied in both the number of psychotropic
medications prescribed and the manner in which this information was
reported. All studies that conducted statistical comparisons found no
significant differences in antidepressant treatment between study
groups (Han et al., 2018; Papastergiou et al., 2021; Perez et al., 2017;
Singh, 2015; van der Schans et al., 2019). Notably, van der Schans et al.
(2019) focused specifically on nortriptyline and venlafaxine, whereas
the other studies assessed a broader range of antidepressants. Rua~no
et al. (2020) reported on the most commonly prescribed psychotropic
medications in the overall sample but did not provide group-specific
data; in their sample, antidepressants were frequently augmented with
antipsychotics. Further details on psychotropic medication use across
studies are provided in Appendix 8 in the Supplement.
3.2. Qualitative synthesis
Functional outcomes. Five (Han et al., 2018; Papastergiou et al., 2021;
Perez et al., 2017; Rua~no et al., 2020; Singh, 2015) of the six included
studies assessed functional outcomes. Three studies (Han et al., 2018;
Papastergiou et al., 2021; Perez et al., 2017) assessed functional dis-
ability/impairment with the Sheehan Disability Scale/Inventory (SDS/I)
(Leon et al., 1997). The SDS is a composite of three self-rated items
designed to measure the extent to which three major domains in the
patient’s life are functionally impaired by psychiatric or medical
symptoms: work, social life/leisure activities, and family life/home
responsibilities.
Results from the studies using the SDS/SDI scores paint a mixed
picture. Han and colleagues did not find a significant effect of PGx-
guided treatment on functional disability (Han et al., 2018). Another
study (Papastergiou et al., 2021) on the other hand, reported a signifi-
cant effect of PGx-guided treatment on the overall SDS score, after six
months in a sample of patients with a diagnosis with MDD and/or
general anxiety disorder (GAD) (time*group interaction: F ˆ 6.25, p <
.001). About 58% of the sample fulfilled criteria for bothMDD and GAD.
In sensitivity analyses, the authors first selected those participants in the
PGx group whose prescribers accepted the recommendations regarding
dose adjustments, medication switches, addition of newmedication, and
medication adherence. In a second sensitivity analysis, those in both the
PGx and TAU groups, whose prescribers accepted the recommendations,
were selected. The results stayed statistically significant in both cases.
The third study (Perez et al., 2017) found a significant positive effect of
PGx-guided treatment on the SDI Perceived Social Support partial score
(p ˆ .048), but not on the SDI Disability and SDI Stress partial scores.
In all three studies using the SDS (Han et al., 2018; Papastergiou
et al., 2021) or SDI (Perez et al., 2017), the authors reported a signifi-
cantly larger effect of PGx-guided treatment compared to TAU on
symptom measures, such as depressive symptom reduction (Han et al.,
2018; Papastergiou et al., 2021) and response rate (Perez et al., 2017)
highlighting the discrepancies between symptom and functional
Fig. 1. PRISMA 2020 Flowchart outlining the study selection process.
N.E. Fares-Otero et al.
EuropeanNeuropsychopharmacology100(2025)13–23
17
Table 1
Sociodemographic and clinical characteristics of included studies.
Authors,
publication
year
Country/
region
N Diagnosis n
PGx
Sex PGx
( %
female)
Age PGx
[M(SD)]
n
TAU
Sex TAU
( %
female)
Age
TAU [M
(SD)]
Outcome Instrument Outcome
primary or
secondary
Study time
frame
Main results
Han et al. 2018 Korea/Asia 100 MDD 52 76.90 44.20
(16.10)
48 72.90 43.90
(13.80)
Functional
disability
SDS Secondary 8 weeks PGx-guided treatment appears to be a more effective and tolerable
option for managing MDD compared to TAU. Patients in the PGx
group showed significantly greater improvements in HAMD-17,
FIBSER, and some of the secondary outcomes (response rate, CGI-S,
GAD-7; p ˆ .014 –0.047), but not for PHQ-9, PHQ-15, SDS total
scores, and CGI-I. Additionally, fewer patients in the PGx group
discontinued treatment due to adverse events (30.8 %) compared to
the TAU group (50.0 %).
Papastergiou
et al. 2021
Canada/North
America
213 MDD,
GAD
105 73.33 41.90
(14.55)
108 75.93 43.46
(15.26)
Functional
disability
SDS Secondary 6 months
(26 weeks)
Participants with MDD or GAD randomised to receive PGx-guided
treatment reported greater improvements in depression severity
(PHQ-9) over a 6-month period compared to those randomised to
receive TAU. Participants receiving PGx-guided treatment also
reported greater improvements in two secondary outcomes, i.e.,
generalised anxiety (GAD-7) and in disability (SDS), over this period
compared to those receiving TAU. Treatment satisfaction did not
differ across groups. Significant results were not only statistically
robust but also clinically meaningful.
Perez et al.
2017
Spain/Europe 316 MDD 155 63.90 51.74
(12.05)
161 63.40 50.74
(13.12)
Functional
disability
SDI Secondary 12 weeks PGx-guided treatment resulted in significant improvement of MDD
patient’s response at 12 weeks, dependent on the number of
previously failed medication trials, but not on sustained response
during the study period. Burden of side effects was also significantly
reduced. Significant results favouring the PGx-guided treatment
group were found in clinician-rated CGI-S, all three FIBSER indices
and SATMED-Q total and partial scores, as well as the SDI Perceived
Social Support partial score, but not in patients rated CGI-S.
Rua~no et al.
2020*
USA/North
America
1459 MDD 982 51.30 NA 477 50.30 NA LOS and RAR 30
days after
discharge
Clinical
record
Primary and
Secondary
30 days
after
discharge
(4 weeks)
Results did not reveal differences in hospital LOS or RAR post
hospitalisation discharge between Group PGx-guided group and
standard therapy group, but potential confounders, such as comorbid
medical illness or lack of suitable community placement, may have
obscured the effects of PGx guidance.
Singh 2015* Australia/
Oceania
148 MDD 74 58.00 44.2 74 61.00 44.3 Proportion taking
sick leave and
number of sick
days due to
depression
Interview
with blinded
rater
Other** 12 weeks Patients receiving genetically guided prescribing had a 2.52-fold
greater likelihood of achieving remission. Participants in the PGx-
guided treatment group also had a significantly lower risk of taking
sick leave and, when necessary, experienced shorter durations of
leave. However, the strict exclusion of individuals with almost any
psychiatric comorbidities (e.g., bipolar, substance use, and
personality disorders) may limit the generalisability of these
findings.
Van der Schans
et al. 2019*
The
Netherlands/
Europe
49 MDD 27 51.90 70.20
(7.30)
22 63.60 68.50
(5.10)
QoL EQ5D-3 L /
EQ-VAS
Secondary 4 weeks Results did not support PGx CYP2D6 screening to accelerate dose
adjustment for nortriptyline and venlafaxine in older patients with
depression. No significant differences were observed in the MD
between visit 2 and visit 3 of the trial of self-reported functional
health status and general and disease specific QoL or severity of
depression.
Note: *studies fulfilling inclusion criteria but not included in meta-analysis; **not specified as an outcome in the registration (ACTRN12613001135707); n data is based on the Intention to Treat analyses (ITT) that
measured the intervention effect as randomised. Abbreviations: CGI-S ˆ Clinical Global Impression-Severity; EQ5D-3 L / EQ-VAS ˆ The 3-level version of EQ-5D (EQ-5D-3 L), including the EQ-5D descriptive system and
the EQ visual analogue scale (EQ VAS); FIBSER: Frequency, Intensity, and Burden of Side Effects Ratings; GAD ˆ Generalised anxiety disorder; LOS ˆ Length of Stay; MD ˆ Mean difference; MDD ˆ Major depressive
disorder; NA ˆ Not available; PGx ˆ Pharmacogenetics; PHQ ˆ Patient Health Questionnaire; QoL ˆ Quality of Life; RAR ˆ Re-Admission Rate; SATMED-Q: Treatment Satisfaction with Medicines Questionnaire; SDI/S:
Sheehan Disability Inventory/Scale; TAU ˆ Treatment As Usual.
N
.E.
Fares-O
tero
et
al.
European Neuropsychopharmacology 100 (2025) 13–23
18
measures. It is important to note, that none of these studies found sig-
nificant differences regarding SDS/SDI scores at baseline between
groups, which might have biased the results.
One study (Rua~no et al., 2020) reported functional outcomes in
terms of hospitalisation, including length of stay (LOS) and re-admission
rate (RAR) 30 days after discharge based on hospital electronic records,
therefore focusing on behavioural measures in contrast to the self-report
measures used in most of the included studies (n ˆ 4). This study also
differs to others in terms of the randomisation ratio, which was 2:1 to
PGx vs. TAU. Despite the large sample (nˆ 1459), no significant effect of
PGx-guided treatment was on LOS and the RAR 30 days after discharge
observed; 9.5 % of the whole sample were readmitted within 30 days of
discharge (Rua~no et al., 2020).
Finally, one study (Singh, 2015) reported on functional outcomes in
terms of sick leave, including either the proportion of participants taking
sick leave or the average number of days taken off from work or studies
due to depression. This study had a time frame of 12 weeks and found a
significant favourable effect of the PGx-guided treatment on both the
proportion of patients taking sick leave (PGx-guided: 4 % vs. TAU: 15 %)
and the average number of sick days, when the latter were needed
(PGx-guided: 4.3 days vs. TAU: 7.7 days).
Quality of life. One study (van der Schans et al., 2019) assessed QoL
via the self-rating scales EuroQol, including the 5D-3 L (EQ5D-3 L) and
the EuroQol visual analogue scale (EQ-Vas) (EuroQol Group, 1990). The
EQ-5D-3 L descriptive system describes patients health state and com-
prises the following five dimensions: mobility, self-care, usual activities,
pain/discomfort, and anxiety/depression. The study was conducted in a
sample of elderly patients (60 years or older) initiating with either
nortriptyline or venlafaxine. In contrast to the other studies described in
this review, only patients with the phenotype poor metaboliser, inter-
mediate metaboliser or ultra-rapid metaboliser were randomised to the
PGx or TAU group. There was an additional sample with the extensive
metaboliser phenotype, which was not randomised, and also received
TAU (n ˆ 57). Changes in QoL were compared between visit 2 (two
weeks after baseline) and visit 3 (four weeks after baseline) between all
three groups. No significant differences between the PGx and TAU
groups were reported regarding the effect on QoL, yet this study was
based on a small sample. At baseline, 27 and 22 individuals with a
deviating genotype were in the PGx and TAU groups respectively. The
non-randomised additional control group consisted of another 57 in-
dividuals with extensive metaboliser status.
3.3. Risk of bias and certainty in the evidence rating
One study (Han et al., 2018) was rated as high risk for bias due to
high risk in one domain, namely missing outcome data. It is important to
note that this study reported a higher overall dropout rate, and a higher
dropout associated with adverse events in the TAU group compared to
the PGx group. Since dropouts in a study can be associated with higher
symptom burden, which is correlated to functioning, we agreed there is
a risk that the missingness in the outcome (SDS) could depend on the
true value of the outcome. The remaining five studies had an overall
rating of some concerns (Papastergiou et al., 2021; Perez et al., 2017;
Rua~no et al., 2020; Singh, 2015; van der Schans et al., 2019). A source of
some concern for bias in the domain “deviations from intended in-
terventions” (ITT) was the fact that treating physicians could not be
blinded in any study. However, since all studies used either a behav-
ioural measure like hospital LOS and RAR 30 days after discharge
(Rua~no et al., 2020), blinded raters (Singh, 2015) or patient-reports
(Han et al., 2018; Papastergiou et al., 2021; Perez et al., 2017; van der
Schans et al., 2019) and participants were blinded in all studies, there is
low risk of bias in the assessment of the outcome (see Fig. 2).
The overall certainty of the evidence was very low. Results of the
GRADE rating are shown in Appendix 9 in the Supplement. In addition
to the risk of bias described above, there are other reasons for the low
certainty of evidence. Firstly, there are inconsistency indicated by the
high heterogeneity between studies (I2 ˆ 73.6 %) as well as imprecision
indicated by a large confidence interval in the meta-analysis. Secondly,
the low number of studies raise several issues. The generalisability of the
findings in clinical settings might be limited for all outcomes. For the
outcomes hospitalisation, sick leave, and QoL multiple domains could
not be rated because there was only one study per outcome. Publication
bias could not be assessed for any outcome because of the small number
of studies.
3.4. Meta-analyses
Random-effect meta-analyses showed that PGx-guided antidepres-
sant treatment significantly decreased SDS/I-measured functional
disability compared to TAU by 2.85 points on average (k ˆ 3; n ˆ 3; MD
ˆ  2.85, SE ˆ 1.32 [95 % CI  5.44,  0.26], p ˆ .031); with substantial
heterogeneity (I2 ˆ 70.46, τ2 ˆ 3.65, p ˆ .023). The results of Knapp-
Hartung analyses showed non-significant effect of PGx-guided treat-
ment on functional disability compared to TAU (MDˆ  2.85, SEˆ 1.26
[95 % CI  8.26, 2.58], p ˆ .151), see Fig. 3.
SDS/I outcomes were assessed post-treatment in all included studies,
at varying time points (Mean ˆ 15.3 weeks, SD ˆ 9.5; range: 8–26
weeks).
Similar results were obtained using SMD (SMD ˆ  0.33, SE ˆ 1.15
[95 % CI  0.62,  0.04], pˆ .026), heterogeneity (I2ˆ 66.8, τ2ˆ 0.04, p
ˆ .04). The results of Knapp-Hartung analyses also showed non-
significant effect of PGx-guided treatment on functional disability
compared to TAU (SMD ˆ  0.33, SE ˆ 1.14 [95 % CI  0.93, 0.27], p ˆ
.144), see Fig. 4.
4. Discussion
We conducted a systematic review and meta-analysis to explore the
efficacy of PGx-guided antidepressant treatment compared to TAU on
functional outcomes and QoL in adults with anxiety and affective dis-
orders. A total of six studies fulfilled the inclusion criteria, out of which
three used comparable outcome measures (SDS/SDI) and were eligible
for meta-analysis.
As a main result, PGx-guided antidepressant treatment significantly
decreased functional disability compared to TAU. However, heteroge-
neity was high, and the study time frame varied considerably. Notably,
we found that the direction of estimates was consistent, and a conven-
tional method showed results were statistically significant. However,
when we applied the Hartung-Knapp method (Jackson et al., 2017;
Rover et al., 2015), a more conservative method that accounts for un-
certainty in estimation of the between-study heterogeneity, this resulted
in a wider CI and non-significant result (despite individual studies sug-
gesting significant results). The small number of studies may lead to a
wide uncertainty in between-study heterogeneity. Nevertheless, this
result gives a first indication that PGx-guided treatment may also be
beneficial for functional outcomes compared to TAU.
We acknowledge that the effect size observed (SMD ˆ  0.33) is
considered small by Cohens benchmarks (Cohen, 1988). However, this
magnitude is consistent with the typical effect sizes seen in antidepres-
sant RCTs (0.3) (Cipriani et al., 2009; Wiesinger et al., 2023), which
have been argued to represent clinically meaningful change, especially
in domains such as functioning and QoL. Moreover, PGx-guided treat-
ment seeks to optimise response and minimise trial-and-error prescrib-
ing, so even modest enhancements in functional outcomes at the
individual level can lead to significant cumulative benefits at the pop-
ulation level (Hieronymus et al., 2020).
The low number of studies we found is in line with the observation
that functional and QoL measures are underrepresented in clinical trials
on depression compared to clinical symptom scales (Brockow et al.,
2004; McKnight and Kashdan, 2009). The three studies using the SDS
(Han et al., 2018; Papastergiou et al., 2021) or SDI (Perez et al., 2017),
paint a mixed picture. In all three studies, the authors reported a
N.E. Fares-Otero et al.
European Neuropsychopharmacology 100 (2025) 13–23
19
significantly larger effect of PGx-guided treatment compared to TAU on
depressive symptom reduction (Han et al., 2018; Papastergiou et al.,
2021) and response rate (Perez et al., 2017), but only one found a sig-
nificant effect on functional disability as measured by the overall SDS
score (Papastergiou et al., 2021) and one on the SDI Perceived Social
Support partial score (Perez et al., 2017). This discrepancy underlines
the notion that symptom reduction, even though often correlated with,
does not equate regaining of functioning and/or QoL. Future PGx trials
in psychiatry should focus more on functional and/or QoL outcomes.
Currently, PGx trials in psychiatry are underway that assess func-
tional outcomes, even though not as primary endpoints. One example is
the PSY-PGx clinical study (Pelgrim et al., 2024; van Westrhenen et al.,
2025), a PGx trial including patients with a depressive episode (MDD or
bipolar disorder), anxiety disorder, or psychotic disorder. In the
PSY-PGx study, general well-being and QoL is assessed with the EuroQol
5 Dimensions-5 levels questionnaire (EQ-5D-5 L) as well as psychosocial
functioning with the Functioning Assessment Short Test (FAST) (Rosa
et al., 2007). The primary endpoint is the RAS, the recovery assessment
scale as assessed by the patient (Law et al., 2012; Shanks et al., 2013).
Another example is the PANDORA trial, a PGx study in patients with
MDD (Minelli et al., 2021). In this study, psychosocial functioning is
assessed via the MINI-ICF-APP. A third study that is currently ongoing,
the PRESIDE (PhaRmacogEnomicS In DEpression) Trial (Saya et al.,
2023) uses the Assessment of Quality of Life (AQoL-4D) measure and
also assessed hospitalisations.
Only one of the outcomes that we analysed, was used in more than
one study. Interestingly, the study that found a significant effect of PGx-
guided treatment on the overall SDS score was the one with the longest
follow-up period, namely 6 months (Papastergiou et al., 2021). In their
review relating symptoms and functioning in depression, McKnight and
Kashdan conclude that functioning might “be less responsive to treat-
ment; thus, functional outcomes might lag behind symptom outcomes”
(McKnight and Kashdan, 2009). In one meta-analysis, PGx-guided
treatment had a positive effect on response and remission rates at weeks
8 and 12, but not after 4 and 24 weeks (Wang et al., 2023). However,
only two studies had follow-up measurements at 24 weeks, so it is not
clear, whether the non-significant finding is due to a limited sample size
or whether the favourable effect of PGx-guided treatment just dis-
appeared over time. Studies with longer duration of follow-up, such as
the PSY-PGx clinical study with a duration of 24 weeks (Pelgrim et al.,
2024) and symptom as well as functional outcomes are needed to
explore this further. In addition to the long follow-up period, it is
important to note that the study by Papastergiou and colleagues was the
only one including participants with a diagnosis of GAD in addition to
patients with MDD, many of whom (about 58 %) had both diagnoses
(Papastergiou et al., 2021), limiting the comparability of the results.
The negative findings regarding the effect of PGx guided treatment
on hospital LOS and the 30-day RAR (Rua~no et al., 2020), raises the
question what time period would be necessary to see effects on behav-
iour criteria such as a RAR. A recent publication of the PREPARE Study,
a trans-diagnostic PGx study with a follow-up time of up to 18 months,
reports a significantly shorter duration of initial hospitalisation, 40.5 %
less re-admissions, and a shorter duration of hospitalisation upon
readmission in patients with an actionable genotype belonging to the
PGx group compared to the TAU group (Skokou et al., 2024). Since this
study uses a cluster-randomised study design, it was excluded from our
review. Nevertheless, the results are promising. Behavioural criteria like
a RAR have the benefit that raters bias can be avoided but also come
with their own challenges. For example, in countries without fully
centralised hospital databases, it might be hard to track whether patients
have been hospitalised in another clinic. As Rua~no et al. report, they did
not assess whether patients had been hospitalised elsewhere during the
30-day period, which might have influenced the results (Rua~no et al.,
2020). Whether or not a patient is hospitalised might also differ between
countries, clinics within a country, or even depend on the treating
physician, which might make data like this less comparable than
standardised scales.
Singh reports a significant effect of PGx-guided treatment compared
to TAU both on the outcomes related to occupational functioning
(proportion of participants taking sick leave and average number of sick
days) and remission rates (Singh, 2015). These results are encouraging,
given that this outcome is highly relevant to patients and relatively easy
to assess. However, it should be interpreted with caution, because in this
study almost any psychiatric comorbidity was an exclusion criterion.
Given that comorbidities are common in depressive disorders, the re-
sults might not be generalisable.
As previously reported (Bunka et al., 2023), it is noteworthy that
only one study assessed the outcome QoL. The non-significant finding
might be due to the very small sample (n ˆ 49 randomised in total; n ˆ
57 in a non-randomised control group). Also, QoL was compared be-
tween two visits that were only two weeks apart, which might be too
short to detect any differences between the groups.
The main sources of potential bias in the included studies were
missing in the follow-up data that might depend on the true outcome,
since higher symptoms and poorer functioning can be associated with
higher dropout. Finally, the overall certainty of the evidence was very
low at this point, which is not surprising given the small number of
studies included. Also, in none of the studies the treating physician was
blinded, which –as we would like to acknowledge –is hard or even
impossible in a PGx trial. However, it still constitutes a risk of bias. The
certainty of evidence was further limited by the fact that neither
inconsistency nor imprecision could not be assessed for the three of the
Fig. 2. RoB2 Traffic plot for functional outcomes and QoL.
N.E. Fares-Otero et al.
European Neuropsychopharmacology 100 (2025) 13–23
20
six studies that reported unique outcomes. The meta-analysis revealed
high heterogeneity between the studies (inconsistency) and a large
confidence interval (imprecision).
4.1. Clinical implications
Due to the small number of studies, clinical implications can only be
drawn very cautiously at this point. Given the importance of functioning
and QoL for patients (Zimmerman et al., 2006), it would be desirable to
assess functional outcomes in addition to clinical symptoms in any
future RCTs on treatment efficacy, especially in PGx trials, to better
understand the relationship between disease- and patient-centred out-
comes as well as the treatments necessary to affect these. Therefore, it
would be ideal if response and remission criteria were based on both
types of outcomes. However, is should be carefully considered which
domains of functioning and QoL to assess with which instruments. There
needs to be a balance between covering domains important to the pa-
tient and feasibility in the clinical setting. Also, a high heterogeneity
among QoLmeasures in depression has been found (Peipert et al., 2025).
Our results give a first indication that PGx-guided treatment may also be
beneficial for functional outcomes compared to TAU.
4.2. Strengths and limitations
We performed an up-to-date systematic review, allowing the inclu-
sion of studies exploring the efficacy of PGx-guided antidepressant
treatment on functional/QoL outcomes in common mental disorders.
This is the first meta-analysis in the field of pharmacogenetics with a
functional outcome approach. Although there was some variability in
which outcomes were reported, the quality of the included studies was
fair, and three studies used the same standard and validated instrument
to assess functional disability. Other strengths of this study include the
rigorous methodology of the systematic search, study selection, and data
extraction performed by independent researchers.
Our work also includes some limitations. First, we performed meta-
analyses when data from at least three studies were available for a given
outcome (Higgins et al., 2024). However, given the small number of
studies in some comparisons, these results should be interpreted with
extra caution due to potential instability of the estimates and increased
risk of bias (Jackson and Turner, 2017). As the number of relevant
studies was limited, no meta-regressions or subgroup analyses could be
conducted, which precludes a reliable investigation of heterogeneity
and potential moderators. Due to the small number of studies (<10) no
publication bias was assessed (Higgins et al., 2024). Second, while the
exclusion of grey literature and unpublished studies may help reduce
heterogeneity in study quality, it also carries the risk of omitting rele-
vant findings and may introduce publication bias, favouring studies with
statistically significant or ‘positive’ results. Grey literature can offer
timely, practice-based, or otherwise unpublished evidence that com-
plements peer-reviewed publications. Conversely, the inclusion of such
data may introduce other forms of bias, particularly related to unclear
methodology or selective reporting (Boutron et al., 2023). Third, four of
the six studies used self-rating scales to assess functioning. While
self-rating scales have the two advantages that the patient’s perspective
is captured and patients are blinded to their treatment group, clinician
ratings and behavioural measures of functioning are needed to cover
more facets of functioning, including work, cognitive functions, leisure,
and social relationships. Forth, except for LOS (Rua~no et al., 2020), none
of the outcomes we considered were primary outcomes. Fifth, the sub-
stantial differences in trial duration (4 weeks - 6 months) may affect the
heterogeneity of the results. Lastly, we acknowledge that the overall
certainty of the evidence is very low at this point, which together with
the limitations above, highlights the need to study functional and QoL
outcomes in PGx trials.
5. Conclusion
Evidence relating to functional outcomes and QoL is particularly
limited and further investigation on the efficacy of PGx-guided antide-
pressant interventions on specific functional outcomes (social, leisure,
work) for people with anxiety and affective disorders is urgently needed.
Nonetheless, our results suggest that using PGx-guided antidepressant
treatment may benefit functioning and thus well-being in adult patients.
CRediT authorship contribution statement
Protocol registration: TP, NEF-O, EV, RvW. Term, Conceptualisation,
Methodology: NEF-O, TP, EV, RvW. Data collection, data curation: NEF-
O, MB, JL, TP. Writing –original draft: NEF-O, MB. Writing –reviewing&
editing: All the authors. Formal analysis, Software, Validation: NEF-O,
Fig. 3. Forest plot showing the efficacy of PGx-guided antidepressant treatment on functional disability in adults with anxiety and affective disorders (MD).
Fig. 4. Forest plot showing the efficacy of PGx-guided antidepressant treatment on functional disability in adults with anxiety and affective disorders (SMD).
N.E. Fares-Otero et al.
European Neuropsychopharmacology 100 (2025) 13–23
21
MH. Visualisation: NEF-O, MB, MH. Interpretation of the data: All the
authors. Investigation: NEF-O, MB, JL, TP. Supervision: NEF-O, EV,
RvW. Resources, funding acquisition: RvW, AP, UH, EV, The PSY-PGx
Consortium. All the authors approved the final version of the submit-
ted manuscript.
Data availability
NEF-O, MB, andMH have full access to all the data in the study. Open
materials: https://github.com/MathiasHarrer/meta-pharmacogenetics.
git
Funding
This study was supported by the European Unions Horizon 2020
Research and Innovation Program (PSY-PGx, EU.3.1.3. Treating and
managing disease, Grant agreement No. 945151). The work by NEF-O
was made possible also by the support of the Deutscher Akademischer
Austauschdienst (DAAD, No. 57740018). UH was also supported by the
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation,
project number 514201724). The funders had no role in the study
design, data collection, data analysis, data interpretation, or writing of
the report.
Declaration of competing interest
EV has received grants and served as a consultant, advisor or CME
speaker for the following entities: AB-Biotics, AbbVie, Adamed, Angel-
ini, BeckleyPsych, Biogen, Biohaven, Boehringer-Ingelheim, Celon
Pharma, Compass, Dainippon Sumitomo Pharma, Ethypharm, Ferrer,
Gedeon Richter, GH Research, Glaxo-Smith Kline, HMNC, Idorsia,
Janssen, Lundbeck, Luye Pharma, Medincell, Merck, Newron, Novartis,
Orion Corporation, Organon, Otsuka, Roche, Rovi, Sage, Sanofi-Aventis,
Sunovion, Takeda, Teva, and Viatris, outside of the submitted work.
RvW has received grants and served as advisor or speaker from/for the
European Union, World Psychiatric Association, the Medical Research
Council, Lundbeck, Illumina, Benecke, PsyFar, Baxter, Chipsoft, NVvP,
KNMP, KNMG, Oslo University, University Milano, King’s College Lon-
don, St. John’s National Academy of Health Sciences, Noord Holland
Noord, UMC Utrecht, Erasmus MC. The other authors report no financial
relationships with commercial interests.
Acknowledgements
We thank Dr. Jose Manuel Estrada Lorenzo for his assistance in the
search strategies design, literature searches and full-text retrieval.
The PSY-PGx Consortium
Kristian Kleine Schaarsa,b, Allan H. Youngc, Daniel Silmanc, Mario
Juruenac, Magnus Ingelman-Sundbergd, Marin Jukicd,f, Martien J.H.
Kasg, Raj Jagesarg, Ramona Moldovanh,i,j, Andrada Ciucah, Markus M.
Nothenk, Per Hoffmannk, Carina M. Matheyk, Laura L. Kilarskil, Noam
Shomronm, Erik Van der Eyckenn, Nigel Olisan, Thomas G. Schulzeo,p,q,
Therese van Amelsvoortr, Bea Campfortsr, Emma de Brabanderr, Moritz
J. Rossners, Sven P. Wicherts
a Department of Psychiatry, Parnassia Psychiatric Institute, Amster-
dam, The Netherlands
b Leiden University Medical Center, Leiden, The Netherlands
c Institute of Psychiatry, Psychology& Neurosciences, King’s College
London, London, United Kingdom
d Department of Physiology & Pharmacology, Karolinska Institute,
Stockholm, Sweden
f Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
g Groningen Institute for Evolutionary Life Sciences, Faculty of Sci-
ence & Engineering, University of Groningen, Groningen, The
Netherlands
h Department of Psychology, Babes¸-Bolyai University, Cluj-Napoca,
Romania
i Division of Evolution and Genomic Sciences, University of Man-
chester, Manchester, United Kingdom
j Manchester Centre for Genomic Medicine, Manchester University
Hospitals NHS Foundation Trust, Manchester, United Kingdom
k Institute of Human Genetics, University of Bonn, School of Medi-
cine and University Hospital Bonn, Bonn, Germany
l Department of Psychiatry and Psychotherapy, University Hospital
of Bonn, Bonn, Germany
m Faculty of Medical and Health Sciences, Tel Aviv University, Tel
Aviv, Israel
n Global Alliance of Mental Illness Advocacy Networks-Europe
(GAMIAN-Europe)
o Department of Psychiatry and Behavioral Sciences, Norton College
of Medicine, SUNY Upstate Medical University, Syracuse, NY, USA
p World Psychiatric Association, Geneva, Switzerland
q Institute of Psychiatric Phenomics and Genomics (IPPG), LMU
University Hospital, LMU Munich, Munich, Germany
r Mental Health and Neuroscience Institute, Maastricht University,
Maastricht, The Netherlands
s Systasy Bioscience GmbH, Munich, Germany
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.euroneuro.2025.08.003.
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