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GENETIC DISORDERS (F GOES, SECTION EDITOR)
Genetics of Depression: Progress at Last
Niamh Mullins
1
&Cathryn M. Lewis
1,2
Published online: 13 June 2017
#The Author(s) 2017. This article is an open access publication
Abstract
Purpose of Review We will describe the success of recent
genome-wide association studies that identify genetic variants
associated with depression and outline the strategies used to
reduce heterogeneity and increase sample size.
Recent Findings The CONVERGE consortium identified two
genetic associations by focusing on a sample of Chinese wom-
en with recurrent severe depression. Three other loci have
been found in Europeans by combining cohorts with clinical
diagnosis and measures of depressive symptoms to increase
sample size. 23andMe identified 15 loci associated with de-
pression using self-report of clinical diagnosis in a study of
over 300,000 individuals.
Summary The first genetic associations with depression have
been identified, and this number is now expected to increase
linearly with sample size, as seen in other polygenic disorders.
These loci provide invaluable insights into the biology of de-
pression and exciting opportunities to develop new bio-
markers and therapeutic targets.
Keywords Depression .Genetics .Genome-wide association
study .Heterogeneity .Polygenic
Introduction
Major depressive disorder (MDD) is a common psychiatric
illness and global public health problem [1]. It is the third
leading cause of years lived with disability worldwide and a
major contributor to early mortality from suicide [2].
Alleviating the burden of this costly disease is an important
priority; however, limited understanding of the biological ba-
sis of depression has hindered the development of novel treat-
ments and interventions.
Depression is a complex disorder with a heritability of 37%
estimated from twin studies [3]. Despite robust evidence for a
genetic component, identifying the specific genetic variants
involved in the disorder has been a major challenge.
Genome-wide association studies (GWAS) test differences in
allele frequencies between disease and control groups at mil-
lions of common single nucleotide polymorphisms (SNPs)
across the genome. These differences may be functionally
relevant to the disease or may represent loci which are trans-
mitted in linkage disequilibrium with a causative polymor-
phism. Early GWAS studies of MDD were not promising,
despite having sample sizes similar to successful studies for
other common diseases and traits, including psychiatric disor-
ders. In a GWAS of over 9000 clinically ascertained MDD
cases and 9000 healthy controls conducted by the Psychiatric
Genomics Consortium (PGC), no SNPs reached the genome-
wide significance threshold [4]. The CHARGE (Cohorts for
HeartandAgingResearchinGenomicEpidemiology)
Consortium conducted a GWAS of depressive symptoms in
This article is part of the Topical Collection on Genetic Disorders
*Niamh Mullins
niamh.mullins@kcl.ac.uk
1
MRC Social, Genetic andDevelopmental Psychiatry Centre, Institute
of Psychiatry, Psychology and Neuroscience, King’s College
London, London SE5 8AF, UK
2
Division of Genetics and Molecular Medicine, King’s College
London, London SE1 9RT, UK
Curr Psychiatry Rep (2017) 19: 43
DOI 10.1007/s11920-017-0803-9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
over 30,000 individuals which also failed to identify any ge-
netic associations [5].
Challenges of Depression Genetics
There are several reasons why identifying risk loci for MDD
has proven difficult. First, like most complex diseases, depres-
sion is a polygenic disorder arising from the combined effect
of many genetic variants with individually small effect sizes
[6]. Several sources provide evidence for the polygenic archi-
tecture of depression, despite a lack of genome-wide signifi-
cant loci. Polygenic risk scoring uses association statistics
from a discovery GWAS to weight the genotypes of individ-
uals in an independent test sample and sums these effects
across multiple SNPs into a polygenic risk score (PRS) [7].
Differences in PRS between cases and controls in the inde-
pendent sample show that the PRS is capturing genetic sus-
ceptibility that is predictive of disease status. PRS for MDD
generated from results of the PGC GWAS showed modest,
although significant prediction for depression in independent
samples (R
2
=0.6%,P<10
−6
), consistent with the presence of
small genetic effects that the original GWAS was underpow-
ered to detect at genome-wide significance [4]. SNP heritabil-
ity (h2
SNP) is the proportion of trait variance attributable to
common SNPs and reflects a greater overall genetic similarity
between cases than controls [8]. The h2
SNP of MDD in the PGC
GWAS was 0.21 (s.e. 0.021) [4,9], again confirming the
polygenic etiology of MDD. Large sample sizes are essential
to detect small individual genetic effects, and pooling samples
within research consortia has been key to the success of
GWAS on many human traits.
The second characteristic of MDD which poses challenges
to genetic analysis is its high lifetime prevalence of ~15% [1].
For a common disorder, the mean difference in phenotypic
liability between case and control groups is smaller, for both
unscreened and screened controls, and thus power to detect
allele frequency differences between them is reduced. Power
calculations show that samples 2.4-fold larger are needed for
GWAS of MDD compared with schizophrenia (prevalence
1%), to identify a variant that explains the same proportion
of risk [10]. Third, the heritability of MDD is modest, at 37%,
compared with other psychiatric disorders, meaning that risk
alleles are likely to have smaller effect sizes [3,11]. To ac-
count for this lower heritability, samples 4–5 times larger
would be required for MDD than schizophrenia to capture
an equal amount of genetic variance [10].
Finally, depression is a particularly heterogeneous disorder.
Some genetic heterogeneity is inherent to polygenicity; affect-
ed individuals may have different combinations of risk alleles
and unaffected individuals will also carry many of these var-
iants. But subphenotyping of the nine core symptoms of MDD
indicates that almost 1500 symptom combinations can fulfill
the diagnostic criteria and that two patients with a diagnosis of
MDD may not have a single symptom in common [12].
Subtypes of depression such as recurrence or early-onset
may be more heritable [3,13]. Another striking example of
heterogeneity is sex differences, with depression twice as
prevalent among women than men and twin studies indicating
that ~45% of the genetic liability to MDD is not shared be-
tween sexes [14–16]. Polygenic risk scoring methods also
enable us to look for genetic similarities across traits and sug-
gest that postpartum depression may be more genetically sim-
ilar to bipolar disorder, that typical depression shows more
pleiotropy with schizophrenia, and that atypical depression,
characterized by increased appetite and weight, additionally
shares genetic effects with BMI [17,18]. These findings to-
gether provide compelling evidence that depression is likely
composed of subtypes with differences in biological etiology
and a heterogeneous genetic architecture. Therefore, the suc-
cessful identification of genetic associations with MDD re-
quires either increased sample sizes or empirically driven ef-
forts to reduce heterogeneity. This review will outline recent
genetic studies on depression which have adopted such strat-
egies. Studies are described in detail, showing how each has
advanced our understanding of the genetic underpinnings of
depression, with summary information presented in Table 1.
CONVERGE Consortium
The CONVERGE (China, Oxford and Virginia
Commonwealth University Experimental Research on
Genetic Epidemiology) Consortium has collected a large de-
pression cohort with detailed clinical, genetic and environ-
mental data that is a powerful resource to dissect the etiology
of depression [19,20•]. The study aimed to ascertain a more
homogeneous sample by restricting the phenotype to recurrent
severe depression in women. Using low-coverage sequencing
of 5303 Han Chinese MDD cases and 5337 controls screened
to exclude MDD, two SNPs on chromosome 10 showed evi-
dence of association: one near the SIRT1 gene and the other in
an intron of LHPP [20•]. Both loci replicated in an indepen-
dent Chinese sample and the genetic signal at the SIRT1 locus
increased when further restricting the sample to melancholia, a
more severe subtype of MDD [20•]. This study demonstrates
the value of focusing on a homogeneous phenotype where
genetic effects should be larger and easier to detect, even at
the expense of a smaller sample size. SIRT1 is involved in the
biogenesis of mitochondria, which are the cell’s energy-
producing organelles. Supporting the genetic association, the
CONVERGE consortium report increased mitochondrial
DNA in MDD cases versus controls, with the amount of in-
crease positively correlated with stressors such as childhood
sexual abuse and lifetime adverse events [21].
43 Page 2 of 7 Curr Psychiatry Rep (2017) 19: 43
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Although these genetic associations are a considerable step
forward, the variants identified in individuals of East Asian
ancestry have low frequencies in populations of European
ancestry, and therefore no replication in the PGC depression
samples or other studies has been achieved [20•,22•]. The
trans-ancestry genetic correlation between the PGC and
CONVERGE GWAS results is ~0.3, indicating there are like-
ly population differences in the genetic etiology of MDD, a
finding with important implications for future studies [23].
Further comparison of the studies using genetic correlation
and polygenic risk scoring weakly supports an overlap of
SNP effects between the studies and strengthens when focus-
ing on female only and recurrent MDD cases from the PGC
[23]. This indicates that some of the genetic differences be-
tween the PGC and CONVERGE results may be due to dif-
ferences in the specific MDD phenotype studied.
Social Science Genetic Association Consortium
The Social Science Genetic Association Consortium
(SSGAC) has pursued the alternate strategy of increasing
sample size, by analyzing multiple cohorts with heteroge-
neous measures of depression [24•]. They utilized data
from two case-control studies of MDD: summary statistics
from the PGC GWAS (9240 MDD cases, 9519 healthy
controls) and dbGaP-accessible genotypes from the
GERA (Resource for Genetic Epidemiology Research on
Adult Health and Aging) study (7231 MDD cases, 49,316
controls) [4,25]. These clinical samples were meta-
analyzed with a GWAS on a measure of depressive symp-
toms in the UK Biobank, where adults in the general pop-
ulation were asked two questions about feelings of
unenthusiasm or disinterest and depression or hopelessness
in the past 2 weeks [26]. Combining these datasets resulted
in a sample of 180,866 individuals and found two genome-
wide significant associations with “depressive symptoms”
which replicated on look up in an independent depression
GWAS by 23andMe [24•]. One SNP is in the KSR2 (kinase
suppressor of ras 2) gene and the other is in the DCC gene,
which encodes a transmembrane receptor involved in axon
guidance. The h2
SNP for depressive symptoms from the total
sample was 0.04 (s.e. 0.004), which is considerably lower
than the estimates from clinically ascertained MDD sam-
ples (~0.2 in both the PGC and CONVERGE studies) [9,
27]. This may result from mixing heterogeneous measures
of depression which are influenced by different combina-
tions of genetic variants and the weak information on de-
pression symptoms from just two questions. Nevertheless,
the SSGAC attributes the success of their study to
exploiting the genetic correlation between clinical depres-
sion and depressive symptoms to combine studies and
Tab l e 1 Recent genome-wide association studies on depression
Study Year Total N Cases Controls Cohort Ancestry Depression phenotype GWAS hits Putative genes h
2
SNP
(s.e.)
PGC [4] 2013 18,759 9240 9519 European Lifetime MDD established using structured
clinical interviews
0–0.21 (0.021)
a
CHARGE [5] 2013 34,549 34,549 European Depressive symptoms in past weeks assessed
by questionnaires
0–Not calculated
CONVERGE [20•] 2015 10,640 5303 5337 East Asian Recurrent MDD in women 2 SIRT1,LHPP 0.21 (0.030)
SSGAC [24•] 2016 180,866 16,471 58,835 105,739 European Depressive symptoms in past 2 weeks assessed
by 2 questions; lifetime MDD
2KSR2,DCC 0.04 (0.004)
23andMe [22•] 2016 307,354 121,380 338,101 European Self-report of diagnosis or treatment for
major depression
17 TMEM161B-MEF2C,VRK2,L3MBTL2,NEGR1,
RERE,HACE1-LIN28B,SORCS3,OLFM4,PAX5,
MEIS2-TMCO5A,intergenic,RSRC1-MLF1,intergenic,
SLC6A15,NEGR1,KIAA0020-RFX3
0.06
b
CHARGE +
PGC [32•]
2016 70,017 9240 9519 51,258 European Depressive symptoms in past weeks assessed by
questionnaires; lifetime MDD
1FHIT 0.30 (0.040)
MDD major depressive disorder
a
On the liability scale, given a prevalence of 15%
b
In the discovery cohort, on the liability scale, given a prevalence of 25%
Curr Psychiatry Rep (2017) 19: 43 Page 3 of 7 43
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increase sample size [24•]. While such a strategy may in-
crease power for individual SNPs which influence both
clinical depression and depressive symptoms, it may dilute
associations for SNPs which only play a role in one phe-
notype and this has implications for replicating specific
associations in different samples.
23andMe
The direct-to-consumer genetic testing company 23andMe
(Mountain View, CA) also took the approach of increasing
sample size. They used self-report data from consumers who
participated in their research initiative and ascertained 75,607
individuals reporting previous clinical diagnosis or treatment
for major depression and 231,747 individuals reporting no
history of depression [22•]. They carried out meta-analysis
of these results with the PGCGWAS results and then analyzed
a replication sample of an additional 45,773 cases and
106,354 controls from 23andMe. A total of 17 independent
SNPs from 15 regions reached genome-wide significance af-
ter joint analysis over all three data sets (Table 1)[22•]. Two of
the loci were significant in both the meta-analysis and inde-
pendent replication sample. In a locus spanning MEF2C
(myocyte enhancer factor 2C) and TMEM161B (transmem-
brane protein 161B), two independent SNPs were significant.
MEF2C is a transcription factor which plays a role in synaptic
learning and memory and variants in the gene have been im-
plicated in epilepsy, mental retardation, and schizophrenia
[28–30]. The other locus encompasses the NEGR1 gene,
encoding neuronal growth regulator 1, which is involved in
neurite outgrowth [31].
The strategy of less intensive phenotyping used in this
study is a novel approach in psychiatric research, as cases
have traditionally been ascertained using structured clinical
interviews. To demonstrate the validity of the self-report mea-
sure, the authors calculated the genetic correlation between the
results from the 23andMe study and those from the PGC
GWAS. There was a high positive correlation of 0.72 (s.e.
0.09) between the results indicating common variant genetic
overlap [22•]. However the h2
SNP from the meta-analysis of the
23andMe discovery cohort and the PGC GWAS was 0.06,
showing a substantially lower genetic component than the
PGC h2
SNP estimate of 0.21 [9,22•]. This indicates that while
the phenotypes are genetically correlated, the genetic signal in
the 23andMe sample is likely weaker than in the PGC, which
could reasonably be due to some diagnostic misclassification.
The success of this 23andMe study in identifying genetic var-
iants at genome-wide significance shows that large sample
size can outweigh any reduction in power from additional
heterogeneity or limited clinical information. Genotyping is
now inexpensive compared with conducting detailed clinical
interviews and 23andMe’s light-phenotyping approach may
be more likely to attract the large number of participants re-
quired in the absence of high-quality phenotype information.
CHARGE Consortium and PGC
Depression can be conceptualized along a spectrum of sever-
ity from subthreshold or minor depression to MDD of varying
severity (e.g., mild, moderate, severe). Using a continuum
approach to depression may augment statistical power be-
cause sample size can be increased substantially and individ-
uals who fall anywhere along the phenotypic spectrum can be
included. This was the rationale for combining the results of
the CHARGE consortium GWAS of depressive symptoms
and the PGC GWAS on MDD [32•]. Depressive symptoms
were evaluated in individuals over 40 years old using validat-
ed questionnaires (mostly using the Center for
Epidemiological Studies Depression Scale CES-D), which fo-
cused on depressive symptoms in the previous weeks rather
than lifetime. This meta-analysis of a broad depression phe-
notype identified one genome-wide significant SNP, which
replicated in an independent sample comprising newly
ascertained MDD cases from the PGC and individuals
assessed for depressive symptoms from the Health and
Retirement Study [32•]. The SNP is located in an intron of
FHIT, which is expressed in several brain regions and encodes
a tumor suppressor protein also involved in oxidative stress
and the circadian clock [32•].
In this study, the genetic correlation (r
g
) between depres-
sive symptoms and MDD was 1.00 (s.e. 0.2) which supports
the concept of a depression continuum capturing similar ge-
netic underpinnings to a study of depression cases and con-
trols. Notably the h2
SNP of the broad depression phenotype was
0.3 (s.e. 0.04), which was greater than the h2
SNP of depressive
symptoms or MDD separately (0.04 (s.e. 0.01) and 0.21 (s.e.
0.02), respectively) [32•]. Testing the genetic correlation be-
tween different phenotypic measures before combining them
can be informative about heritability in the subsequent sample
and can be used to assess whether the sample size achieved
will be sufficient to outweigh any heterogeneity introduced.
Power and Study Design
The power of these studies to identify MDD-associated vari-
ants differs considerably by sample size and design. We cal-
culated the genotype relative risk (GRR) which the study had
50% power to identify (Table 1), assuming a multiplicative
model, allele frequency of 0.3, MDD prevalence of 15%, and
fully screened controls [33]. The power of 50% was chosen to
reflect the polygenic architecture of MDD, where many SNPs
43 Page 4 of 7 Curr Psychiatry Rep (2017) 19: 43
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of modest effect sizes contribute, and each study has low
power to detect a specific variant, but higher power to detect
a subset of SNPs having a pre-specified GRR. Using standard
power calculations, the 23andMe study would have 50% pow-
er to detect a variant with GRR 1.024, but the PGC MDD
study could only detect a GRR of 1.11. However, such power
calculations make simplistic assumptions about study design,
for example that selected participants are divided into MDD
cases and controls (defined as non-cases), with cases generally
being over-sampled from the population. In practice, studies
such as CONVERGE and some PGC MDD cohorts select
severe, recurrent cases of MDD and exclude any individuals
with mild to moderate depression. This selection of severe
cases and healthier controls with no history of depression in-
creases the power of the study by inducing a larger difference
in allele frequency between cases and controls. In contrast,
study power will be reduced by any misclassification of cases
and controls, which may be more likely in studies based on
self-report or limited phenotypic information at a single time
point.
Two of the studies listed in Table 1use a quantitative phe-
notype of the number of depressive symptoms (SSGAC,
CHARGE). The CHARGE study of 51,258 participants
would have 50% power to detect a variant accounting for
0.0058% of trait variance. A study of 180,000 participants,
similar to SSGAC, could detect a variant accounting for
0.017% of trait variance (with 50% power), but the SSGAC
study used only two questions on depressive symptoms, re-
ducing its power from this theoretical value.
The studies described here illustrate two approaches to dis-
sect the genetic contribution to depression: through a case-
control study of lifetime diagnosis of depression or using a
continuous measure of the count of depressive symptoms,
usually covering the previous 2 weeks. Although the time
scales for these measures differ, the genetic correlation be-
tween these measures is high, for example r
g
= 1 between
CHARGE and the PGC MDD study [32•]. The relationship
between the power of a case-control and continuous pheno-
type was derived by Yang et al. [34] and shows that a cohort
study with a continuous phenotype on Nindividuals has lower
power than a case-control study with N/2 cases and N/2 con-
trols when the disease prevalence is below 10%. This vali-
dates the design of studies such as CONVERGE, ascertaining
recurrent cases of MDD where the population prevalence in
China is already low at 3.6% [35]. In Western countries where
MDD prevalence is 15–20%, studies based on an underlying
quantitative trait may have higher power than an equivalently
sized case-control study.
Studies must balance the trade-off between gains in power
from increased sample size or reduced heterogeneity. As the
results of CONVERGE and the 23andMe studies show, both
approaches can be successful in identifying genetic variants
for depression, and researchers need to decide which strategy
maximizes the use of their resources. Since depression is a
common disorder, large sample sizes can be accrued through
consortia and inventive new methods such as leveraging elec-
tronic medical records, population biobanks, and online re-
cruitment. One limitation of mixing heterogeneous measures
of depression or less intensive phenotyping is that any associ-
ations discovered may be more difficult to interpret. But the
approach of increasing sample size can be used to find loci
whose role in MDD can then be dissected in follow-up sam-
ples with more detailed phenotypic data, even if these have
smaller sample size. Large samples with different depression
phenotypes will help to disentangle the genetic background of
different forms of depression.
Environment
While the focus of this review is on genetics, the role of the
environment in depression cannot be ignored, with twin stud-
ies showing that it accounts for 63% of the variance [3]. In
contrast to genetic associations, the environmental risk factors
are well-established and include social isolation, unemploy-
ment, and relationship stressors [36]. Childhood abuse or ne-
glect is one of the strongest environmental risk factors, more
than doubling the risk for depression in adult life [37]. Gene-
by-environment interactions (G×E) whereby genetic effects
are moderated by specific environmental factors have long
been postulated to play a role in depression. Most G×E re-
search has focused on candidate genes such as the serotonin
transporter promoter polymorphism (5-HTTLPR)interacting
with stressful life events or childhood trauma. Over a decade’s
worth of studies on this interaction has produced inconsistent
results, and recently, an extensive, pre-registered meta-analy-
sis concluded a lack of evidence for the 5-HTTLPR interaction
with environmental adversity [38•].
Since the genetic liability for depression is known to be
polygenic, studies have begun to test for interactions between
environmentalfactors and polygenic risk scores, which capture
the cumulative effect of many common variants in a single
measure. To date, two studies have reported no interaction
between PRS for MDD and adult stressful life events in the
etiology of depression [39,40]. Two studies have found sig-
nificant interactions between PRS for MDD and childhood
trauma, albeit in opposing directions [39,41]. The reason for
these discrepant results is unclear but further research is war-
ranted as the detection of G×E has implications for future re-
search strategies to identify genetic associations. In the
Netherlands Study of Depression and Anxiety (NESDA),
PRS had a stronger effect on MDD in individuals exposed to
childhood trauma, which suggests that focusing on exposed
individuals could render genetic effects larger, more homoge-
neous and easier to detect [41]. However, in the RADIANT
UK study, the effect of PRS on MDD risk was stronger in those
Curr Psychiatry Rep (2017) 19: 43 Page 5 of 7 43
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unexposed to childhood trauma, suggesting that more power
couldbeleveragedfromGWASbyfocusingonlyonindivid-
uals not exposed to trauma, as these MDD cases may have a
stronger genetic predisposition. In summary, the analysis of
cohorts with heterogeneous environmental exposures may also
contribute to the difficulty in identifying genetic associations
with MDD. Thus far, SNPs have been analyzed across average
environmental backgrounds in GWAS but reducing environ-
mental heterogeneity could be a valuable strategy to increase
genetic effect sizes. There is a need for depression samples
with good quality environmental data, which now can be more
expensive and difficult to attain than genotype data.
Conclusions
The first progress has been made towards identifying genetic
variants involved in MDD with studies amassing the critical
sample size necessary to reach an inflection point beyond
which the number of genetic associations is expected to in-
crease linearly with sample size [42•]. The critical goal of
GWAS is to identify the biological pathways underpinning
depression and even risk alleles with small effects could yield
enormous insights. As sample sizes continue to increase,
MDD GWAS will uncover more and more of the genetic
architecture of this debilitating disorder, as we have seen in
GWAS studies on schizophrenia [30]. The next challenge is to
establish the molecular mechanisms by which GWAS loci
mediate their effects and translate these into much-needed
new biomarkers and therapeutic targets. We have turned the
corner in identifying genetic variants for depression, and the
next few years will bring exciting opportunities to turn bio-
logical findings into clinical tools.
Acknowledgements This paper represents independent research part-
funded by the National Institute for Health Research (NIHR) Biomedical
Research Centre at South London and Maudsley NHS Foundation Trust
and King’s College London. The views expressed are those of the authors
and not necessarily those of the NHS, the NIHR, or the Department of
Health.
Compliance with Ethical Standards
Conflict of Interest Niamh Mullins and Cathryn Lewis each declare
that they have no conflict of interest.
Human and Animal Rights and Informed Consent This article does
not contain any studies with human or animal subjects performed by any
of the authors.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appro-
priate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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