RESEARCH ARTICLE
Polygenic risk scores for major depressive
disorder and neuroticism as predictors of
antidepressant response: Meta-analysis of
three treatment cohorts
Joey Ward
ID
1
*, Nicholas Graham
1
, Rona J. Strawbridge
ID
1,2
, Amy Ferguson
1
,
Gregory Jenkins
3
, Wenan Chen
4
, Karen Hodgson
5
, Mark Frye
3
, Richard Weinshilboum
3
,
Rudolf Uher
6
, Cathryn M. Lewis
5
, Joanna Biernacka
3
, Daniel J. Smith
1
1 Institute of Health And Wellbeing, University of Glasgow, Glasgow, Scotland, 2 Department of Medicine
Solna, Karolinska Institutet, Stockholm, Sweden, 3 Mayo Clinic, Rochester, MN, United States of America,
4 St. Jude Children’s Research Hospital, Memphis, TN, United States of America, 5 King’s College London,
London, England, 6 Dalhousie University, Halifax, Canada
* joey.ward@glasgow.ac.uk
Abstract
There are currently no reliable approaches for correctly identifying which patients with major
depressive disorder (MDD) will respond well to antidepressant therapy. However, recent
genetic advances suggest that Polygenic Risk Scores (PRS) could allow MDD patients to
be stratified for antidepressant response. We used PRS for MDD and PRS for neuroticism
as putative predictors of antidepressant response within three treatment cohorts: The
Genome-based Therapeutic Drugs for Depression (GENDEP) cohort, and 2 sub-cohorts
from the Pharmacogenomics Research Network Antidepressant Medication Pharmacoge-
nomics Study PRGN-AMPS (total patient number = 760). Results across cohorts were
combined via meta-analysis within a random effects model. Overall, PRS for MDD and neu-
roticism did not significantly predict antidepressant response but there was a consistent
direction of effect, whereby greater genetic loading for both MDD (best MDD result, p <
5*10–5 MDD-PRS at 4 weeks, β = -0.019, S.E = 0.008, p = 0.01) and neuroticism (best neu-
roticism result, p < 0.1 neuroticism-PRS at 8 weeks, β = -0.017, S.E = 0.008, p = 0.03) were
associated with less favourable response. We conclude that the PRS approach may offer
some promise for treatment stratification in MDD and should now be assessed within larger
clinical cohorts.
Introduction
Major Depressive disorder (MDD) is a leading cause of disability worldwide [1]. Antidepres-
sants such as Selective Serotonin Reuptake Inhibitors (SSRIs) are first line treatments for
MDD but up to one third of patients do not respond satisfactorily [2, 3]. There are currently
no robust methods for predicting whether an individual patient will respond well to SSRIs and
PLOS ONE | https://doi.org/10.1371/journal.pone.0203896 September 21, 2018 1 / 8
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OPEN ACCESS
Citation: Ward J, Graham N, Strawbridge RJ,
Ferguson A, Jenkins G, Chen W, et al. (2018)
Polygenic risk scores for major depressive disorder
and neuroticism as predictors of antidepressant
response: Meta-analysis of three treatment
cohorts. PLoS ONE 13(9): e0203896. https://doi.
org/10.1371/journal.pone.0203896
Editor: Toni-Kim Clarke, University of Edinburgh,
UNITED KINGDOM
Received: June 19, 2018
Accepted: August 29, 2018
Published: September 21, 2018
Copyright: © 2018 Ward et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The GENDEP genetic
and phenotype data is available from GENDEP -
http://gendep.iop.kcl.ac.uk/. The ISPC genetic and
phenotype data is available from the ISPC - https://
www.pharmgkb.org/page/ispc.
Funding: This work received support from Royal
College of Physicians of Edinburgh JMAS Sims
Fellowship, http://www.rcpe.ac.uk/college/jmas-
sim-fellowship, UKRI Innovation- HDR-UK
Fellowship (Grant MR/S003061/1 to Dr Rona J
there is often a lag period of several weeks before clinical response, making decisions on
switching to a different class of antidepressant difficult. Individual genetic variation may dic-
tate likelihood of response to SSRIs [4] and, as such, stratifying patients into sub-groups based
on genetic profiles may allow for more efficient targeting of treatment.
Polygenic risk scoring (PRS) [5] is a method which allows an individual’s genetic loading
for a trait to be calculated using genome-wide single nucleotide polymorphism (SNP) data and
the output of genome-wide association study (GWAS) summary statistics from another study
of the same or related phenotype. As current GWAS results do not capture the full extent of
genetic effects on any given trait, typically a series of scores are created at different association
p-value cut offs, allowing for the capture of more variance than that explained by only
genome-wide significant loci. Additionally, as the underlying genetic architecture of the trait is
unknown creating a range of scores can allow for the optimum p value threshold to be deter-
mined, should one detect a significant correlation.
It has been shown that a PRS can be of clinical use in predicting traits in independent sam-
ples. For example, for coronary heart disease, PRS improved the 10 year risk prediction in
those over age 60 [6]. PRS approaches can also predict response to treatment, as demonstrated
recently with an association between PRS for schizophrenia and less favourable response to
lithium in bipolar disorder [7]. Here we test the hypothesis that PRS for MDD and PRS for
neuroticism are associated with less favourable response to SSRIs, specifically citalopram and
its active S-enantiomer escitalopram, in patients with MDD. Neuroticism is of particular inter-
est in this regard because it has a known association with both serotonergic neurotransmission
[8] and response to antidepressants [9, 10], and those with higher phenotypic neuroticism are
less likely to respond as well to antidepressant therapy [11].
The analysis investigated three cohorts, GENDEP, AMPS-1 and AMPS-2 separately and
then combine the results via meta-analysis.
Methods
Cohort descriptions, genotyping and imputation
The Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics
Study (PGRN-AMPS) is a study of citalopram/escitalopram for treatment of MDD performed
at the Mayo Clinic. An initial batch of 530 subjects (N = 499 subjects of European ancestry that
passed quality control) was genotyped for a pharmacogenomics GWAS of SSRIs [12]. An addi-
tional 229 patients recruited in the PGRN-AMPS were subsequently genotyped for the Inter-
national SSRI Pharmacogenomics Consortium (ISPC) GWAS [13]. Depressive symptoms
were assessed on the Hamilton Depression Rating Scale (HAMD) with a maximum score of
51, a scale developed to rate both the psychiatric as well as the psychomotor and somatic symp-
toms of the condition[14]. Full genotyping and imputation of these cohorts (here referred to
as AMPS-1 and AMPS-2) have been described previously [12, 13].
Genome Based Therapeutic Drugs for Depression (GENDEP) is a cohort of 868 individuals,
recruited from across Europe, treated with two classes of antidepressants: escitalopram (an
SSRI) and nortriptyline (a tricyclic antidepressant). For the purposes of this study, only those
patients in GENDEP treated with an SSRI were assessed (n = 267). Depressive symptoms were
assessed on the 10-item Montgomery-Asberg Depression Rating Scale (MADRS) with a maxi-
mum score of 60, with measurements taken weekly for 12 weeks from baseline. MADRS differs
from HAMD in that it focuses exclusively on the psychiatric symptoms only and not the
accompanying psychomotor and somatic symptoms of MDD [14]. Full genotyping and impu-
tation methodology in GENDEP is described in previous reports [15].
Does genetic loading for major depressive disorder and neuroticism impact the efficacy of anti-depressants?
PLOS ONE | https://doi.org/10.1371/journal.pone.0203896 September 21, 2018 2 / 8
Strawbridge), MRC Doctoral Training Programme
(Grant MR/K501335/1 to Ms Amy Ferguson),
National Institute for Health Research (NIHR)
Biomedical Research Centre at South London and
Maudsley NHS Foundation Trust and King’s
College London to Prof Cathryn Lewis.The funders
had no role in study design, data collection and
analysis, decision to publish, or preparation of the
manuscript.
Competing interests: The authors have declared
that no competing interests exist.
Principal component generation and PRS construction
Principal genetic components were derived using PLINK. For all models the top 4 principal
components were used as covariates in the model to account for hidden population structure.
To ensure that an ethnically homogeneous sample was used in the AMPS-1 and AMPS-2
cohorts those whose Principal genetic components 1 to 4 were outside two standard deviations
from the mean were excluded as outliers.
PRS were constructed via PLINK [16] with SNP weights based on outputs from the Smith
et al. (2016) neuroticism GWAS [17] and the “probable MDD” phenotype of Howard et al
(2018) MDD GWAS from UK Biobank[18]. SNPs were filtered by MAF < 0.01, HWE
p<1
10
−6
and imputation score < 0.8 before Linkage Disequilibrium (LD) clumping. SNPs
were clumped using LD parameters of r
2
>0.05 in a 500kb window. Selection of SNPs for each
clump was based on which SNP had the lowest p value. If 2 SNPs in a clump had the same P
value the SNP with the largest beta coefficient was selected. The scores generated were average
scores with no-mean-imputation flag. Six profile scores were created for each trait using p
value cut offs of p < 5
10
−8
, p < 5
10
−5
, p < 0.01, p < 0.05, p < 0.1 and p < 0.5. Risk scores
were then standardised to mean = 0, SD = 1[19].
Due to low numbers and therefore the potential for noise within outcome data, instead of
assessing change in outcomes across the full range of polygenic scores we chose to investigate
only the difference between the extreme ends of the PRS scale. To do this, we split the stan-
dardised scores into quintiles and looked at the difference between the top and bottom quintile
of each PRS p-value cut off within each cohort. For the GENDEP cohort the top and bottom
quintile from each centre was selected to account for variation between recruitment centres. It
is also important to note that an individual may be in the top quintile for one PRS P-value cut-
off but not in another. As such, the two fifths of individuals used in each regression will change
depending on the PRS p value cut off used.
Phenotype definition
For all three cohorts the primary outcome of interest was percentage change in depression
score from baseline at four weeks. This was calculated by subtracting the score at four weeks
from baseline, and dividing this difference by the score at baseline. A secondary outcome at
eight weeks was also assessed, calculated using the same method. To be included in the analy-
sis, an individual had to have a score recorded at baseline, four weeks and eight weeks.
Statistical modelling
Modelling was performed in R using the lm function. All models were adjusted for age, sex
and the first 4 principal components. The GENDEP models were additionally adjusted for
recruitment centre which was treated as a factor variable. The R
2
for the PRS term of the
model was derived using the methodology described in Selzam et al[20]. Due to the results
being largely null we did not perform any correction for multiple testing.
Meta-analysis
A random effects Meta-analysis was performed using the rma.uni function of the metaphor
package with method set to “REML”[21].
Results
Demographic and clinical characteristics of the three cohorts (GENDEP, AMPS-1, and AMPS-2)
are presented in Table 1. The percentage female and age range of the three cohorts were broadly
Does genetic loading for major depressive disorder and neuroticism impact the efficacy of anti-depressants?
PLOS ONE | https://doi.org/10.1371/journal.pone.0203896 September 21, 2018 3 / 8
similar. The scores at baseline, 4 week and 8 week time points in AMPS-1 and AMPS-2 show a sim-
ilar trend with a similar percentage drop at 4 and 8 week time points. The baseline scores of the
GENDEP cohort are higher than in AMPS-1 and AMPS-2 due to the cohort being scored using
MADRS and not HAMD as is the case with AMP-1 and AMPS-2. At Both the 4 week and 8 week
time point the GENDEP cohort showed a smaller percentage reduction than in the AMPS-1 and
AMPS-2. This difference may be explained by the differing depression measures picking up on dif-
fering aspects of MDD, differing healthcare settings and levels of severity at baseline. The within
cohort drop from baseline at both 4 and 8 weeks was statistically significant for all three cohorts.
For neuroticism PRS in GENDEP, AMPS-1 and AMPS-2 the number of SNPs in each risk
score were similar between cohorts across all p-value cut-offs (S1 Table). For the MDD risk
scores the number of SNPs were similar between cohorts in the lower p value thresholds but
diverged at the higher p value cut-offs. These differences arise mainly due to the differences in
imputation coverage and the differing ethnicities and their impact on LD block estimation.
Individual study analyses
The results of all the individual study analyses can be found in S2–S4 Tables. Two of the mod-
els returned nominally significant results, both of which were in the AMPS-2 cohort (Table 2).
They were neuroticism p < 0.5 PRS at four weeks (β = -0.04, p = 0.02) and neuroticism p < 0.5
at eight weeks (β = -0.039, p = 0.03). Of particular note is the R
2
of the PRS term of the signifi-
cant models which accounts for approximately 10% of the variance. Note, however, that these
results would not pass correction for multiple testing.
Although we were unable to reject the null hypothesis in the rest of the models, a clear
majority (56 of 72 models) identified beta coefficients in the same direction of effect (greater
loading for MDD or neuroticism associated with a smaller percentage drop in depression
score). Of the 16 positive beta coefficient models, ten were from GENDEP MDD PRS models,
three were from GENDEP neuroticism PRS model, two were from AMPS-1 neuroticism PRS
models and one was an AMPS-2 MDD PRS models (S2–S4 Tables).
Meta-analysis
Two of the 24 meta-analyses were nominally significant: MDD p < 5
10
−5
PRS at four weeks
(β = -0.02, p = 0.009, I
2
= 0); and neuroticism p<0.1 PRS at eight weeks (β = -0.017, p = 0.03,
Table 1. Demographic and clinical characteristics.
Cohort Total
N
N used per
regression
N female of
total N (%)
Age of total
N, mean
(SD)
Baseline
score
, mean
(SD)
4 week
score
, mean
(SD)
8 week
score
, mean
(SD)
% drop in mean score at
4 weeks from mean score
at baseline
% drop in mean score at
8 weeks from mean score
at baseline
AMPS-1 357 142 229 (64.1) 40.9 (13.5) 22 (4.88) 11.9 (6.7) 8.83 (5.92) 46 60
AMPS-2 138 55 85 (61.6) 40.1 (13.6) 21.2 (5.14) 12 (5.84) 9.14 (6.41) 43 57
GENDEP 265 106 170 (64.2) 42.3 (11.8) 28.3 (6.16) 18.7 (8.2) 14.2 (8.89) 34 50
score rating is HAMD for AMPS-1 and AMPS-2 and MADRS for GENDEP.
https://doi.org/10.1371/journal.pone.0203896.t001
Table 2. Nominally significant individual PRS models (AMSP-2 cohort).
predictor Time point
(weeks)
p Beta SE T Test stat R
2
Neuroticism p<0.5 4 0.019 -0.044 0.018 -2.42 0.1
Neuroticism p<0.5 8 0.029 -0.039 0.017 -2.26 0.08
https://doi.org/10.1371/journal.pone.0203896.t002
Does genetic loading for major depressive disorder and neuroticism impact the efficacy of anti-depressants?
PLOS ONE | https://doi.org/10.1371/journal.pone.0203896 September 21, 2018 4 / 8
I
2
= 0) (Fig 1). Neither of these results would survive correction for multiple testing. The direc-
tion of effect in all of the meta-analyses was negative (greater genetic loading for MDD and
neuroticism associated with a smaller percentage drop in depression score at both four and
eight weeks; S5 Table. The forest plots of all other meta-analyses are provided as supplemen-
tary material (S1–S4 Figs).
Discussion
Our goal was to assess the extent to which PRS for MDD and PRS for neuroticism were associ-
ated with response to SSRIs in patients with MDD. Although most of the findings were null,
there was a direction of effect where higher PRS for MDD and higher PRS for neuroticism
were associated with less favourable response to SSRIs. It is likely that our analyses were
under-powered–replication in larger datasets will therefore be of interest. We estimate that a
training sample of approximately 10,000 and a target sample of 5,000 individuals would give
60% power in a PRS of 100,000 SNPs that explain 10% of the variance in the training sample
[22]. For the two AMPS-2 nominally significant results the R
2
values of approximately 10%,
suggesting that these PRSs could potentially be useful clinically.
This work diverges from previous analyses in these cohorts which have focused on GWAS
and candidate gene analyses to identify genetic loci that associate with antidepressant response
with the exception of Garcia-Gonzalez et al[23]. However, the outcome is markedly different
to the outcome used here. It is possible that the use of PRS is advantageous for clinical use over
these methods as it allows for a whole-genome approach instead of focusing on specific SNPs,
genes or regions. An individual’s response to antidepressants is likely to be influenced by
many genetic factors and, as such, candidate gene methodologies will fail to capture polygenic
influences. An additional strength of this work is that all three cohorts systematically assessed
treatment response at comparable time-points and in the context of the use of the same class
of antidepressants, namely SSRIs.
Limitations
Apart from the issue of low power, our methodology was one in which only the extreme ends
of genetic loadings were considered. This makes it difficult to translate the findings into a gen-
eral population setting and routine clinical practice. Further work is needed to assess genetic
loadings for MDD and neuroticism within the general population and how these relate to the
clinical cohorts described here. The use of different depression rating scales between GENDEP
Fig 1. Forest plot of nominally significant meta-analyses. A) p < 5
10
−5
MDD-PRS at 4 weeks, B) p<0.1 Neuroticism-PRS at 8 weeks.
https://doi.org/10.1371/journal.pone.0203896.g001
Does genetic loading for major depressive disorder and neuroticism impact the efficacy of anti-depressants?
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