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212 The APA Publishing Textbook of Mood Disorders, Second Edition
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control subjects (Cooper et al. 2011; Sebat et al. 2007). These CNVs have large effect
sizes, with odds ratios ranging from 2 to 50. A more recent, larger study of 20,000
schizophrenia case subjects and 20,000 control subjects (Marshall et al. 2017) revealed
genome-wide significance for eight CNVs that were overrepresented among the case
subjects, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2,
proximal 16p11.2, and 22q11.2. A global enrichment analysis yielded an odds ratio of
1.11 (P=5.7×10
–15
), providing high-quality evidence that rare CNVs contribute to
schizophrenia risk.
In a study of de novo CNVs in a cohort of 788 parent-child trios (n= 185 for subjects
with bipolar disorder, n=177 for schizophrenia, and n=488 for control subjects), Mal
hotra et al. (2011) reported that frequencies of de novo CNVs were significantly
higher in subjects with bipolar disorder compared with control subjects (OR 4.8; P=
0.009). De novo CNVs were more common among subjects with bipolar disorder with
an age at onset younger than 18 (OR 6.3 [1.7–22.6]; P=0.006). Malhotra and colleagues
also noted a significant enrichment of de novo CNVs in subjects with schizophrenia
(OR 5.0; P=0.007).
In contrast to Malhotra et al. (2011), Grozeva et al. (2010) reported no evidence for
an increased large CNV burden among 1,697 subjects with bipolar disorder and 2,806
nonpsychiatric control subjects, all of United Kingdom or European ancestry. Green
et al. (2016), in a study of large (>100,000 base pairs) CNVs in 2,600 patients with bi
polar disorder, reported that three such CNVs (duplications at 1q21.1, deletions at
3q29, and duplications at 16p11.2) previously associated with schizophrenia (Sebat et
al. 2007) were also associated with bipolar disorder. However, only the statistical significance of the duplications at 16p11.2 survived correction for a genome-wide search
for CNVs.
In a study of short (<100 base pairs) and long (>100 base pairs) CNVs across four
cohorts of patients with RUP, Zhang et al. (2019) reported an excess of short CNVs but
no excess of long CNVs. Short deletions were also modestly enriched for high-confidence “enhancer regions.” Because enhancer regions are thought to bind transcription factors (which regulate mRNA production), such results suggest abnormalities
of gene regulation rather than changes in amino acid sequences of proteins. CNVs in
these enhancer genomic regions may cause abnormal amounts of mRNA (either increased or decreased), thereby elevating risk for RUP. Charney et al. (2019) noted that
CNVs were increased among individuals with a schizoaffective disorder diagnosis,
but not among those with a bipolar disorder diagnosis. Depending on the inclusion
criteria for CNV studies of bipolar disorder, this diagnostic gray area may help resolve
some contradictory studies. Kendall et al. (2019) conducted a large study of CNVs in
approximately 466,000 individuals from the UK Biobank. Those with CNVs had a
modestly increased risk (odds ratio of 1.34) of a depression diagnosis, using three different definitions, including a diagnosis given by a physician. This small risk increase
may indicate that studies of fewer patients may not have had sufficient power.
It is evident that CNVs, documented as more frequent in schizophrenia, autism
spectrum disorder, and developmental disabilities, are marginally, if at all, increased
in bipolar disorder (Charney et al. 2019) and RUP. Large-scale studies of smaller
CNVs in these disorders are expected as whole-genome sequencing is completed in
more case subjects. The roles of these shorter CNVs in risk for bipolar disorder and
RUP remain to be fully established.
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Conclusions Regarding Recurrent
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Unipolar Disorder
213 Genetics of Mood Disorders
It is clear that RUP risk is polygenic, with hundreds of common alleles contributing
to overall risk. The total h
mately 30% of total h
RUP. In this scenario, given the heterogeneity of an RUP diagnosis, it is unlikely that
a PRS for RUP will be clinically useful in confirmation of diagnosis or prediction of
course of illness or response to a distinct class of antidepressants.
2
of RUP is approximately 35%, and the SNP h2 is approxi-
2
. Thus, SNP data explain approximately 10% of the total risk for
Genetic Counseling
Families frequently seek advice regarding genetic risk for mood disorders, and heritability is sufficiently substantial that most persons with bipolar disorder or RUP
have affected first- or second-degree relatives. Couples from families that have been
severely affected may have concerns about reproduction and may request counseling
regarding the risks to their children. Although important advances have been made
through GWASs and sequencing, DNA data (including PRSs) are not currently used
to predict risk, but these may be employed in the future. Most current mood disorder
DNA data are derived from common SNPs, which capture only 20%–30% of the total
2
h
(Lee et al. 2013) and only 5%–25% of the total phenotypic variance for mood disorders. At present, there is no indication for genetic testing of people at risk for bipolar
disorder by virtue of an affected first-degree relative or other positive family history.
No diagnostic or therapeutic advantage can be gained by applying a bipolar disorder
PRS to a person suspected of or confirmed as having bipolar disorder. Such a PRS
does not identify optimal pharmacotherapy or predict course of illness for an affected
individual.
Family studies of mood disorders (e.g., Gershon et al. 1982; Weissman et al. 1984)
form the basis for estimates of family risk, and several tables of risks have been pub
lished. It is important to take a careful family history, because the risks from studies
must be interpreted in light of the specific family being counseled. It is strongly rec
ommended that medical records of affected family members be reviewed whenever
possible. Family lore and medical record evidence may not coincide. A high familial
load suggests a greater risk, as does bilinear transmission (both prospective parents
have a personal or family history of bipolar disorder and RUP).
When counseling family members who request risk assessment, emphasize both
the recurrent course and the treatable nature of the mood disorder. Individual family
members may interpret the available evidence quite differently due, in part, to personal experience, and the psychiatrist’s sensitivity to this issue is an essential aspect of
genetic counseling for psychiatric disorders. Another key aspect of genetic counseling
for psychiatric disorders should include education about the signs and symptoms, as
well as recommendations for consultation should such signs and symptoms appear.
Providing website URLs for organizations that promote evidence-based evaluation
and treatment information (e.g., the International Bipolar Foundation, Depression and
Bipolar Support Alliance, Brain and Behavior Research Foundation, National Alliance
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214 The APA Publishing Textbook of Mood Disorders, Second Edition
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for the Mentally Ill) is strongly recommended. This is particularly important in this era
of widely available misinformation.
Conclusion
Family, twin, and adoption studies of bipolar disorder and RUP consistently indicate
that both disorders have substantial heritable components of risk, with bipolar dis
order having a higher h
plicated by several independent groups in the GWAS reports on bipolar disorder. A
picture of bipolar disorder emerges as a polygenic disorder with hundreds of riskincreasing alleles, a substantial fraction of which may be shared by other psychiatric
disorders, including schizophrenia and RUP. The genetic architecture of RUP is similar, with hundreds of risk-increasing alleles and a greater contribution (compared
with that of bipolar disorder) from environmental factors. Lower h
predict greater difficulty in detecting common risk alleles of small effect.
The most recent GWAS reports provide a wealth of data on more than 100 genes
containing risk alleles for bipolar disorder and RUP. However, this knowledge does
not permit rapid translation to improved diagnosis and treatment. Current state-of
the-art genetic counseling should rely on estimates of common family history configurations (e.g., one or two affected parents), because no current evidence indicates that
adding DNA estimates of risk (e.g., through a PRS) produces more predictive data.
2
(~50%) than RUP (~30%). Dozens of alleles have been im-
2
in RUP may also
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CHAPTER 13
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Epigenetics
of Mood Disorders
Melissa P. H. Miller, M.Sc.
Caroline Symcox, B.Sc.
Frances A. Champagne, Ph.D.
Advances in molecular biology have generated novel perspectives within
the study of mood disorders, with particular relevance to understanding the mecha
nisms that contribute to altered neurobiological structure and function. Epigenetic
mechanisms have been increasingly explored, broadly within the context of health
and development and more specifically as a molecular interface between the environmental factors that confer physical and psychiatric disease risk and the neural and
behavioral outcomes associated with disease. In the case of mood disorders, epigen
etic variation in the form of DNA methylation, posttranslational histone modifications, and expression of noncoding RNAs is being increasingly explored in humans
and builds on an expansive foundation of studies in animal models that illustrate the
role of epigenetics in depressive-like behavior (Peña and Nestler 2018), the epigenetic
effects of pharmacological agents used in the treatment of mood disorders (Vialou et
al. 2013), and the impact of stress and early life adversity on epigenetic variation in
the brain (Alyamani and Murgatroyd 2018).
In this chapter, we describe the current state of knowledge on the role of epigenetics in mood disorders, focusing on research in humans that indicates epigenetic
variation in the brain and in peripheral tissues associated with depression and bipolar disorder; epigenetic effects of pharmacological treatments used in depression and
bipolar disorder; and the role of stress- and trauma-induced epigenetic variation in
predicting the emergence of mood disorders.
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Peripheral and Brain Epigenetic Markers
of Depression
The regulation of gene expression is a dynamic process involving a broad range of
factors that interact with DNA to either increase or decrease transcription (Cheung et
al. 2000; Feng and Fan 2009; Meller et al. 2015). Characterization of variation in DNA
methylation, posttranslational histone modifications, and noncoding RNAs has illustrated the role of these epigenetic mechanisms in gene regulation and also implicates
these mechanisms as either etiologically relevant or as biomarkers for mood disor
ders. Within this growing literature, variation in DNA methylation within candidate
genes or across the genome is most prevalent, primarily due to the stability of DNA
methylation and the reliability of methods of assessing differential DNA methylation
in the human genome. This literature includes epigenetic analyses of peripheral tissues, such as blood, buccal cells, and saliva, and analyses of postmortem brain tissue.
Given the role of epigenetic mechanisms in shaping the differentiation of cell types,
interpretation of the “meaning” of epigenetic variation within peripheral tissues is
challenging. Peripheral epigenetic variation associated with mood disorders may be a
useful biomarker, but it is unclear whether these epigenetic biomarkers would also be
evident in the brain or relevant in the altered brain function associated with moodrelated outcomes. Analysis of epigenetic variation in the human brain is limited to
postmortem brain tissue of individuals with a history of mood disorder. Although this
epigenetic variation could be implied to be etiologically relevant, the confounding
effects of treatment, emotional and behavioral phenotypes, and a broad range of lim
itations associated with molecular analyses in postmortem tissue (Ferrer et al. 2008)
need to be carefully considered. Despite these caveats, the analysis of epigenetic vari
ation in the periphery and brain, particularly when coupled with genetic and environment data, has the potential to identify novel pathways and treatment approaches
in mood disorders.
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Peripheral DNA Methylation Biomarkers of Depression
In addition to a growing literature on genome-wide analyses of DNA methylation in
human tissues in cohorts of patients versus control subjects, much of the established
work is focused on candidate genes selected based on their role in stress responsivity
(e.g., NR3C1), neural plasticity (e.g., brain-derived neurotrophic factor [BDNF]), or
neurotransmitter pathways implicated in depression (e.g., SLC6A4). The NR3C1
gene, which encodes for the glucocorticoid receptor, is implicated in stress reactivity
through the role of hippocampal glucocorticoid receptor expression in negative feed
back within the hypothalamic-pituitary-adrenal axis (Sapolsky et al. 1985). In a longitudinal study, increased DNA methylation within the NR3C1 gene in DNA extracted
from blood samples was associated with current and follow-up depression as assessed by the Geriatric Mental State Schedule (Kang et al. 2018). Differential DNA
methylation of NR3C1 has also been observed in younger patient populations. In
blood samples from patients with major depressive disorder (MDD) and healthy control subjects, DNA methylation of NR3C1 was significantly positively associated with
hippocampal volume within the MDD group, as assessed by MRI (Na et al. 2014).
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This type of analysis provides useful insight into the potential relationship between
epigenetic biomarkers observed in the periphery and brain structures implicated in
the pathology of depression.
Genes involved in neuroplasticity, such as BDNF, are also implicated in depression, and analyses of DNA extracted from saliva indicate that decreased BDNF DNA
methylation is associated with higher depression scores in adults (Song et al. 2014).
However, in geriatric populations, an association has been found between increased
BDNF DNA methylation in promoters I and IV and depression at baseline, as well as
chronic late-life depression (Januar et al. 2015). Similar to NR3C1, peripheral DNA
methylation of BDNF is associated with reduced cortical thickness among patients
with MDD (Na et al. 2016), indicating the predictive utility of this biomarker in psy
chiatric phenotypes.
Analysis of DNA methylation in blood within a large cohort of elderly adults
(N=1,863) indicated that increased DNA methylation of the angiotensin-converting
enzyme (ACE) gene, which is a regulator of the stress response via vascular function
(Tikellis and Thomas 2012), was inversely correlated with cortisol levels (Lam et al.
2018). Although ACE DNA methylation did not significantly correlate with depres
sion in this sample, the relationship between ACE DNA methylation was moderated
by genetic polymorphisms in ACE. These findings highlight the importance of incor
porating both genetic and epigenetic analyses in the study of depression and complement a growing literature on gene-by-environment interactions in predicting altered
epigenetic states (Liu et al. 2008).
Twin studies offer a methodologically more rigorous approach for incorporating
analyses of genetics and shared environment in the relationship between DNA meth
ylation and mood. In a study of 84 monozygotic twin pairs, DNA methylation within
the serotonin transporter gene (SLC6A4), which is implicated in depression risk, was
associated with scores on the Beck Depression Inventory–II using a matched twinpair analysis and adjusting for lifestyle variables (Zhao et al. 2013).
Genome-wide analyses of DNA methylation in blood samples using the Infinium
®
HumanMethylation450 BeadChip (Illumina 450K) array indicate differential DNA
methylation between World Trade Center first responders with a current diagnosis of
MDD and those without a current or past MDD diagnosis (Kuan et al. 2017). However, even with a sample size of 473 participants, these effects did not survive false
discovery rate correction. A similar lack of differential DNA methylation between
MDD and healthy control subjects has been observed in monozygotic twin studies
(examining concordant and discordant twin pairs), although increased variability in
blood DNA methylation is observed in subjects with MDD (Byrne et al. 2013), a find
ing that was replicated in an additional cohort of monozygotic twin pairs (CórdovaPalomera et al. 2015).
A critical variable that may contribute to the reduced capacity to detect epigenetic
effects in genome-wide analyses is medication status. Profiling of DNA methylation
in the blood leukocytes of medication-free patients with MDD and healthy controls
revealed that patients with MDD have significantly reduced DNA methylation
within 363 cytosines and that patients and control subjects can be distinguished based
on these epigenetic markers (Numata et al. 2015). In a community sample of 100
adults, analyses of DNA methylation using the HumanMethylation17 BeadChip array indicated increased and decreased DNA methylation at genomic loci associated
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with a lifetime history of depression (Uddin et al. 2011). A meta-analysis of epigenome-wide association studies (EWAS) likewise indicated an association between
DNA methylation at several genomic loci and the severity of depressive symptom
atology (Story Jovanova et al. 2018).
Genome-wide DNA methylation analysis can also be used to assess biological aging,
by comparing accelerated “epigenetic age” relative to chronological age based on the
DNA methylation status of approximately 300 genes (Horvath 2013). Epigenetic age
acceleration is associated with exposure to stress (Wolf et al. 2018) and with mortality
rates (even when controlling for chronological age) (Fransquet et al. 2019). Analyses
of DNA from blood samples of patients with depression compared with healthy con
trol subjects indicate significant epigenetic age acceleration in depressed patients, with
increasing symptom severity associated with increased epigenetic age acceleration
(Han et al. 2018). This effect was also replicated in postmortem brain tissue samples
of patients with depression and healthy control subjects, confirming the lack of tissue
specificity for this particular epigenetic measure (Han et al. 2018; Horvath 2013). Importantly, epigenetic age acceleration can be altered through intervention (Brody et
al. 2016) and lifestyle factors (Quach et al. 2017), which may be a promising supple
ment to pharmacological treatments of depression.
DNA Methylation Variation in the Brain in Depression
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In some cases, MDD-associated epigenetic variations in the periphery overlap with
those in the brain (Oh et al. 2015). In a comparison of patients with MDD and healthy
control subjects, differential DNAmethylation within the prefrontal cortex was found
to overlap with loci found to be differentially methylated in the blood (Aberg et al.
2020). The differential methylation of genes implicated in depression is more evident
with increasing age (McKinney et al. 2019), possibly indicating disruption to molecular processes that control gene regulation.
An important consideration in these postmortem brain studies is the overlap between MDD and suicidality. Suicidal behavior may be associated with a unique epigenomic profile within the brain in comparison with healthy control subjects and
patients with MDD without suicidal behavior. Research designs that can disentangle
the MDD-specific DNA methylation profiles will be critical to generating fine-tuned
hypotheses about the epigenetics of depression.
Histones and Noncoding RNA in Depression
Posttranslational histone modifications are a dynamic mechanism of gene regulation
and are themselves regulated by enzymatic processes that add and remove chemical
tags to histone proteins. A comparison of blood samples from subjects with MDD and
healthy control subjects indicates increased expression of histone deacetylase
(HDAC), an enzyme that removes the acetyl group from histone proteins during a depressive state but not during remission (Hobara et al. 2010), in the blood of patients
with MDD. Similarly, differential HDAC expression in patients with MDD is normalized following 8 weeks of treatment with paroxetine (Iga et al. 2007). Within the brain,
histone acetylation of histone 3 (H3) at lysine 14 (H3K14ac) within the nucleus accumbens is increased in patients with MDD compared with healthy control subjects
(Covington et al. 2009). Histone trimethylation within the synapsin I (SYN1) gene
221 Epigenetics of Mood Disorders
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(which is involved in neurotransmitter release and synaptic plasticity) is increased in
the prefrontal cortex of patients with MDD (Cruceanu et al. 2013).
Noncoding RNAs regulate transcription and translation through direct interactions
with DNA, RNA, and proteins (Sato et al. 2011). Within the epigenetics literature, mi
croRNAs (miRNAs) have emerged as critical regulators of gene function during development and in response to stress and other environmental exposures (Allen and
Dwivedi 2020; Lema and Cunningham 2010). Compared with healthy control sub
jects, patients with depression have elevated levels of serum miR-132 and miR-182 (Li
et al. 2013). These miRNAs may play a significant role in regulating BDNF RNA and
protein levels, with implications for neuroplasticity. Although other miRNAs have
been explored as peripheral biomarkers of depression, inconsistencies among studies
limit the generalizability of their findings (Yuan et al. 2018). Within the prefrontal cortex, expression of miR-1202 is decreased in patients with MDD compared with
healthy control subjects (Lopez et al. 2014). Within human cell lines, miR-1202 expres
sion was found to increase following a 2-week treatment with citalopram or imipramine (Lopez et al. 2014). These findings suggest that increased understanding of the
role of miRNAs in depression can potentially identify novel therapeutic targets.
Brain and Peripheral Epigenetic Markers
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of Bipolar Disorder
Similar to the work on depression, epigenetic analyses have been increasingly incorporated into the study of bipolar disorder, with the focus on both candidate gene and
whole-genome characterization of the epigenome in both the periphery and postmor
tem brain tissue. A critical question is whether a unique epigenomic signature is associated with different clinical diagnoses within the mood disorders. Although robust
clinical epigenetic markers have yet to emerge, the potential of this approach remains
and will benefit from prospective study designs and analyses that determine the rela
tionship between specific epigenetic markers and symptom profiles.
Peripheral DNA Methylation Biomarkers
in Bipolar Disorder
Within peripheral blood DNA samples of patients with bipolar disorder compared
with healthy control subjects, DNA methylation within the long interspersed nuclear
element-1 (LINE-1), a transposable element within the human genome, is significantly
decreased (Li et al. 2018). DNA methylation within LINE-1 has been demonstrated to
be highly correlated with genome-wide DNA methylation levels. Consistent with
these LINE-1 findings, global DNA hypomethylation has been observed in blood
samples of patients with bipolar disorder compared with healthy control subjects
(Murata et al. 2020). Analyses of DNA methylation levels in genes that have significant overlap between bipolar disorder and schizophrenia (based on genome-wide
association studies) indicate that although some overlap in DNA methylation between these disorders occurs (e.g., hypomethylation of FAM63B), unique DNA meth-
ylation patterns differentiate these candidate genes (e.g., hypermethylation of
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