RUA Presentation

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Everything you are going to need is in the attachment with all the instructions and don’t forget to do as it asks. Its article I need one slides PowerPoint. Please just need one slide on part “G-Discuss your conclusion”. I need one slide and a side note to explain the slide in detail with references.

RN326 Mental Health, July 2021 Session

RUA Group PPT Presentation

Each group will prepare a Power Point (PPT) Presentation utilizing Scholarly Nursing Research/Journal Articles that have been approved by the Faculty (See Course Calendar for due date and Presentation date)

Please submit your articles via permalink attachment if the article is from Chamberlain library

for approval

prior to developing your PowerPoint.

If the article
is not from Chamberlain Library, download the article and send it via email attachment for approval [DO NOT SUBMIT LINK, COPY AND PASTE IS NOT ACCEPTED].

Note that you will be presenting to a
Focus Group that need to learn about the disorder. Each group will utilize information collected from the Scholarly Articles to develop the Power Point Presentation. Additional resources may be used. Your Course Textbook must be used as one of your resources/references.

Discuss the following in your Presentation/PPT:

· A brief introduction of your assigned disorders

· A brief introduction of the scholarly article’s topic and explain why it is important to mental health nursing.

· b. Cite statistics to support the significance of the topic.

· c. Summarize the article; include key points or findings of the article.

· d. Discuss how you could use the information for your practice; give specific examples.

· e. Identify strengths and weaknesses of the article.

· f. Discuss whether you would recommend the article to other colleagues.

· g. Discuss your conclusion.

Include an APA title page [include Group #, your group topic, and names of group member] and a reference page; include
in‐text citations (use citations whenever paraphrasing, using statistics, or quoting from the article).

Please refer to your APA Manual as a guide for in‐text citations and sample references page.
Additionally, include speaker notes in each of your slides.

Each member of the group must participate in the presentation to receive the point.

You can use a

3 x 5 index card
note for your presentation. Do not read from your notes, PPT, or articles during your presentation, the index card only serve as a reference. Reading to your audience from your note or PPT without expanding on the information will cost the group 3% deduction from your total points.

Each student must submit a copy of his/her group PPT in the grade book.

Dress Code: Semi-Business attire or Your Clinical Uniform (If your group decide to wear Clinical Uniform, every member must wear Clinical Uniform, the same apply if your group decide to wear Semi-business attire – i.e. all member must wear semi-business attire).

Grading Rubric: Criteria are met when the student’s application of knowledge demonstrates achievement of the outcomes for this assignment. Please see RUA Guidelines in Canvas.

Points for this Assignment: 50

image1.emf

Molecular Psychiatry (2020) 25:544–559
https://doi.org/10.1038/s41380-019-0634-7

EXPERT REVIEW

The genetics of bipolar disorder

Francis James A. Gordovez1,2 ● Francis J. McMahon 1

Received: 29 April 2019 / Revised: 22 November 2019 / Accepted: 11 December 2019 / Published online: 6 January 2020
This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020

Abstract
Bipolar disorder (BD) is one of the most heritable mental illnesses, but the elucidation of its genetic basis has proven to be a very
challenging endeavor. Genome-Wide Association Studies (GWAS) have transformed our understanding of BD, providing the
first reproducible evidence of specific genetic markers and a highly polygenic architecture that overlaps with that of
schizophrenia, major depression, and other disorders. Individual GWAS markers appear to confer little risk, but common variants
together account for about 25% of the heritability of BD. A few higher-risk associations have also been identified, such as a rare
copy number variant on chromosome 16p11.2. Large scale next-generation sequencing studies are actively searching for other
alleles that confer substantial risk. As our understanding of the genetics of BD improves, there is growing optimism that some
clear biological pathways will emerge, providing a basis for future studies aimed at molecular diagnosis and novel therapeutics.

Introduction

The genome-wide association studies (GWAS) era has
transformed our understanding of bipolar disorder (BD). Ten
years ago, BD was considered a distinct, highly heritable
disorder for which genes of major effect had eluded detection
by linkage studies but were expected to be found eventually.
Now, numerous common genetic markers have been found
by GWAS, none of which confers major risk for disease, and
many of which overlap with markers associated with schi-
zophrenia or major depression. A few higher-risk associations
have also been identified, involving rare copy number variants
(CNVs) that are usually not inherited. Now, BD can be
regarded as a point on a spectrum of risk, ranging from major
depression to schizophrenia. Despite this substantial progress,
most of the inherited risk for BD remains unexplained, sug-
gesting that there is still much to learn about the genetics of
BD. In this review, we will summarize the key developments
in BD genetics over the past decade and frame some open
questions that will need to be addressed by future studies

before we can fully realize the promise of “genomic medi-
cine” in the diagnosis and treatment of BD.

The phenotype

Common

BD is among the most common of major mental illnesses,
with prevalence estimates in the range of 1–4% [1]. How-
ever, since the diagnosis rests on reports of subjective
symptoms that can be subtle, diagnosed cases probably
represent the tip of an iceberg of very common disturbances
in mood and behavior that blend imperceptibly into the
clinical realm. Genetic studies have focused almost entirely
on individuals who can be easily diagnosed by interview or
are already in treatment, which undoubtedly provides an
incomplete picture. Imagine trying to describe the genetics
of hypertension by studying only stroke patients.

Varied clinical features

The genetic complexity of BD is belied by its complex and
varied clinical presentation [2]. Although the first episode of
major depression or mania typically begins between ages 18
and 24 [3], earlier or later onset cases are not rare. Episodes
can be frequent or separated by many years, and some
patients experience rapid cycling with a period of hours or
days [4]. Comorbid anxiety [5, 6] and substance abuse [7, 8]
are common, and psychotic features are often a component

* Francis J. McMahon
mcmahonf@mail.nih.gov

1 Human Genetics Branch, National Institute of Mental Health
Intramural Research Program, Department of Health and Human
Services, National Institutes of Health, Bethesda, MD, USA

2 College of Medicine, University of the Philippines Manila, 1000
Ermita, Manila, Philippines

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http://crossmark.crossref.org/dialog/?doi=10.1038/s41380-019-0634-7&domain=pdf

http://orcid.org/0000-0002-9469-305X

mailto:mcmahonf@mail.nih.gov

of mood episodes, particularly manias. Interepisode periods
can be completely symptom-free or beset with chronic
depressive or manic symptoms. Some people suffer only
from manias, although this is uncommon [9]. Mixed states
are frequent, as are periods of prolonged, treatment-resistant
depression [2]. With such protean manifestations, it seems
likely that what we now call BD may ultimately be resolved
into dozens of biologically distinguishable disease entities.

Many studies have examined the familiality of clinical
features in BD. Age at onset [10], psychotic symptoms
[11, 12], frequency of manic and depressive episodes [13],
and polarity (mania or depression) at onset [14] are all
highly familial, while comorbid anxiety and substance
abuse are less so [15]. Below we will address some of the
genetic signals that may help explain these patterns.

High risk of suicide

Many studies have pointed to a high risk of suicide in BD
[16–20]. On average, about 15% of people diagnosed with
BD die of suicide [21], a number that has remained dis-
couragingly stable for decades. Several small studies have
reported that suicide may be especially common in some
families with BD [18, 22, 23], suggesting specific genetic
or shared environmental factors, but these have so far
remained elusive.

Cycling as a distinct trait

Signs and symptoms of BD are so wide-ranging that they
can be seen, in part, in just about every major psychiatric
disorder. This makes for challenging differential diagnosis,
one of the reasons that it has proven more difficult to
accumulate very large samples of BD than schizophrenia,
autism, or major depression. The one very distinctive trait
seen in everyone with BD is cycling: episodic elevations
and depressions of mood and behavior, separated by periods
of relative or complete euthymia [4]. This is such a core
feature of BD as currently conceived that we will probably
not consider the genetics of BD to be solved until the
genetic mechanism of cycling itself has been elucidated.

Response to lithium

Another relatively distinctive clinical feature of some peo-
ple with BD is the response to lithium. Indeed about one-
third of people diagnosed with BD will experience a dra-
matic improvement in the frequency and severity of mood
episodes while receiving lithium, and another third with be
at least somewhat improved [24]. Lithium is also the only
drug shown to exert a protective effect against suicide in
BD [17, 19, 20, 25]. No other major mental illness shows
this kind of specific response to lithium, suggesting that

genetic risk factors unique to BD are in some way related to
the pharmacodynamics of lithium and that biologically
meaningful subtypes of BD may be identifiable, at least in
part, by response to lithium therapy. A few GWAS of
lithium response have been published, but the results so far
are divergent [26–29]. Some recent studies using cellular
models lend support to the view that lithium-responsive BD
carries a distinct neurobiological signature [30–32].

Genetic epidemiology

Before the era of molecular genetics, much of our etiologic
understanding of BD rested upon the methods of genetic
epidemiology. Family studies demonstrated that BD runs in
families, with a 10–15% risk of mood disorder among first-
degree relatives of people with BD, but could not distin-
guish the effects of shared environment from those of
shared genes [33]. Twin studies showed that much of the
shared familial risk could indeed be explained by shared
genes, with heritability estimates on the order of 70–90%
[33]. Adoption studies lent further support to a largely
genetic etiology, since BD was elevated only in the biolo-
gical parents of adult adoptees with the illness [33]. Despite
the strong and consistent evidence in favor of a genetic
etiology; however, segregation analyses could not find a
clear, Mendelian pattern of transmission, tending instead to
favor more complex models of inheritance [34].

Assortative mating

Assortative mating refers to nonrandom mating among
individuals in a population [35]. People with similar phe-
notypes may be more likely to mate or may selectively
avoid potential mates with other phenotypes. A number of
studies over the past decades have demonstrated varying
degrees of assortative mating in BD, with an increased rate
of matings between individuals with BD and those with BD,
major depression, alcoholism, or other phenotypes [35–43].
Recent, large population-based studies have found similar
patterns of assortative mating across psychiatric and other
traits, including height [44], activity level [45], emotional
intelligence [46], and educational and social status [47].

Such substantial rates of assortative mating are likely to
have a major impact on the genetic landscape of BD but are
often not considered in studies of the disorder. Theoreti-
cally, assortative mating can lead to accumulation of risk
alleles in subsequent generations, with consequent increases
in rates or severity of illness across generations of a family,
a phenomenon known as anticipation [48]. Assortative
mating across traits can also induce genetic correlations and
comorbidity between the traits in offspring, but these are not
likely to persist in the face of random mating by subsequent

The genetics of bipolar disorder 545

generations [49]. Assortative mating does not appear to
effect heritability estimates by twin studies but may con-
tribute to underestimates of heritability by empirical rela-
tionship methods based on SNP arrays [50]. This is because
individuals drawn from populations with nonrandom mat-
ing will tend to share more risk alleles than would be
expected based on their overall genetic relatedness.

Risk loci

Initial searches for risk loci depended on a very limited set
of genetic methods, chiefly genetic linkage analysis
[14, 51, 52]. However, since linkage methods do not work
well in the face of complex patterns of inheritance, linkage
studies of BD failed to produce definitive, replicable find-
ings [53]. A similar problem faced linkage studies of most
other common, complex traits.

Candidate genes

In an attempt to overcome the limitations of linkage
methods, many researchers tried to find genetic markers that
were chosen on the basis of their proximity to genes that
encoded proteins of known neurobiological importance,
such as the serotonin transporter [54]. Unfortunately, this
candidate gene strategy was largely unsuccessful. This is
because the selection of candidate genes with a high-prior
probability of involvement in BD proved to be quite diffi-
cult. Most candidate gene studies of BD also suffered from
the same biases due to small sample size and undetected
genetic mismatch between cases and controls that bedeviled
other such studies of a variety of common traits [55]. While
meta-analyses do tend to support a small contribution from
at least a few well-studied candidates, including the ser-
otonin transporter, SLC6A4 [56–59], d-amino acid oxidase,
DAOA [58, 60–62], and brain-derived neurotrophic factor
[58, 63–70], the most reliable association evidence has
come from GWAS.

GWAS

Genome-wide association studies, wherein large numbers of
genetic markers spanning the genome are tested for asso-
ciation with a trait, typically in large, case–control samples,
have so far been the most successful strategy for identifying
genetic variants associated with BD. Since the first BD
GWAS appeared in 2007 [71], almost 20 such studies have
been published. Most have focused on typical case defini-
tions of bipolar I disorder [26, 72–83], but some have
examined clinical subtypes such as schizoaffective disorder
[84], bipolar II [85], or BD in the context of personality [86]
or other traits. The most recent published GWAS, based on

~50 K cases, detected 30 genome-wide significant loci, of
which 20 were newly identified [87].

Genome-wide significant loci reported to date are sum-
marized in Table 1. As with most other common traits, risk
loci are numerous, most of the lead SNPs are noncoding,
and odds ratios are small (1.1–1.3). Although many of the
loci have been implicated by several studies, only a few loci
can be resolved to single genes [88, 89] based on current
information, so it is still too early to make firm conclusions
about specific risk genes underlying most GWAS loci. As
functional genomic data accumulates, convergent findings
are expected to point toward specific risk genes and
pathways.

Convergent data so far highlight at least three genes.
ANK3, located on chromosome 10q21.2, was one of the
earliest genes to be implicated in BD by GWAS [72, 90–93].
Significant association has now been found between BD and
SNPs near ANK3 by several studies, and several of those
SNPs affect expression of ANK3 [90, 91, 94–96]. ANK3
encodes ankyrin B, a protein involved in axonal myelina-
tion, with expression in multiple tissues, especially brain
[97]. Numerous alternative transcripts exist, suggesting a
potential role for alternative splicing [98]. A conditional
knock-out mouse displays cyclic changes in behavior that
resemble BD and respond to treatment with lithium [99].
CACNA1C, located on chromosome 12p13, has also been
implicated by genome-wide significant SNP associations in
several studies of BD, along with schizophrenia and major
depression; some of the associated SNPs are also associated
with expression of CACNA1C in multiple tissues, including
brain [73, 74, 87, 100–103]. The gene encodes an L-type
voltage-gated ion channel with well-established roles in
neuronal development and synaptic signaling. Heterozygous
knockdown of the gene in mice alters a variety of behaviors
thought to reflect mood, but without a clear syndromic
resemblance to BD [102]. TRANK1, which resides on
chromosome 3p22, has been implicated by genome-wide
significant association with nearby SNPs in studies of BD
and schizophrenia [75–77, 104, 105]. TRANK1 encodes a
large, mostly uncharacterized protein, highly expressed in
multiple tissues, especially brain, and may play a role in
maintenance of the blood–brain barrier [106]. The expres-
sion of TRANK1 is increased by treatment with the mood
stabilizer valproic acid, and cells carrying the risk allele
show decreased expression of the gene and its protein [104].
Recent transcriptomic studies suggest that DCLK3 may be
another gene in the same 3p22 GWAS locus that contributes
to risk for both BD and schizophrenia [88, 107].

While each individual GWAS “hit” has only a small
effect on risk, polygenic risk scores that combine the
additive effects of many risk alleles (often hundreds or
thousands) can index substantially more genetic risk by
including variants that have so far escaped detection

546 F. J. A. Gordovez, F. J. McMahon

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The genetics of bipolar disorder 547

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S
N
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(s
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548 F. J. A. Gordovez, F. J. McMahon

individually at genome-wide significance [108]. Recent
studies that use the PRS strategy have shown that common
variation accounts for about 25% of the total genetic risk for
BD (less of the phenotypic variance), that PRS overlap
substantially between BD and schizophrenia, and that PRS
derived from large schizophrenia samples are associated
with increased rates of psychotic symptoms and decreased
response to lithium in BD [101, 105, 109].

Copy number variants (CNVs)

CNVs are stretches of DNA that occur in one (deleted),
three (duplicated) or more copies on a chromosome, rather
than the typical two copies expected in the diploid human
genome. Initially discovered by use of hybridization or SNP
array methods that could detect deletions and duplications
too small to be found reliably by cytogenetic methods, large
(30–1000 kb) CNVs have since been shown to play a major
role in neurodevelopmental disorders [110–116] and some
cases of schizophrenia [110, 117–123].

CNVs seem to play a smaller role in BD [124], but at
least two CNVs have been associated with BD in large,
case–control samples. The 650 kb duplication on chromo-
some 16p11.2 was initially described in a de novo study of
schizophrenia [125] and was later detected as a de novo
event in a proband with early-onset BD [126]. Genome-wide
significant evidence of association with BD is based on a
large meta-analysis of SNP array data, in which the dupli-
cation conferred an OR of 4.37 (95% CI: 2.12–9.00) [127].
This same study also found evidence of association with a
deletion on 3q29, but this fell short of genome-wide sig-
nificance [127]. Both of these CNVs have also been asso-
ciated with schizophrenia, autism, and intellectual disability
[128]. A reciprocal deletion in the 16p11.2 region is asso-
ciated with autism and ID [129, 130]. One recent study
found enrichment of genic CNVs in schizoaffective BD
[131]. Taken together, these findings suggest that the genetic
overlap between BD and schizophrenia extends beyond
common, low-risk alleles to rare alleles of larger effect.

Most published CNV studies to date have relied on
technologies that cannot reliably detect CNVs much below
~30 kb. As WGS and other technologies come to the fore,
we will doubtless find very large numbers of smaller CNVs
in the human genome. Many such smaller CNVs may also
be associated with various neurodevelopmental and adult
psychiatric disorders and may well be found to play an
important role in BD in the future.

Single nucleotide variants (SNVs) and and small
insertions/deletions (indels)

Next-generation sequencing (NGS) technology has enabled
a search for rare single nucleotide and small insertion/

deletion variants that are not represented in SNP arrays
[132, 133]. Such studies may uncover alleles conferring
greater risk than the common alleles detectable by GWAS,
but the lower allele frequencies and large number of
potential variants usually demand very large sample sizes,
often larger than those needed for GWAS [134].

A few early NGS studies have been published in BD and
several others are underway [135–138]. While the early stu-
dies lacked statistical power to demonstrate significant evi-
dence of association after correction for multiple testing, as
sample sizes grow significant findings may emerge. Ongoing
consortia efforts that aim to achieve larger sample sizes
through meta-analysis of multiple independent samples have
perhaps the best likelihood of success. Studies that leverage
the increased frequencies of otherwise rare alleles sometimes
seen in unusual populations [134, 139, 140] may also succeed
as sample sizes grow and sequencing technology improves.

Other studies have used NGS to sequence RNA
expressed in brain tissue obtained post-mortem from people
diagnosed with BD [107, 141, 142]. Such studies can
identify diagnosis-associated changes in gene expression,
inform efforts to fine-map GWAS loci to individual genes
[143], and potentially reveal other transcriptomic events
(such as alternative splicing [144]) that mediate risk of
inherited genetic variants.

Pathways

One way to deal with the substantial genetic heterogeneity
of illnesses like BD is to group implicated genes across
studies into pathways or networks of functionally related
genes. In this way, increased power to detect association
may follow if different alleles in different genes converge at
the level of gene sets. Several such pathway studies have
been published, with little apparent agreement so far
[85, 93, 145–150]. The multiplicity of implicated pathways
and probably reflects genetic heterogeneity, the relatively
small number of robust genetic associations found so far for
BD, and the still-challenging problem of assigning common
genetic markers found by GWAS to the appropriate gene or
genes. Calcium signaling is probably the most supported
pathway in BD to date. Calcium signaling has been impli-
cated by animal and ex vivo models of BD [90, 151, 152].
The most compelling genetic evidence for this pathway in
BD follows from the known function of the risk gene,
CACNA1C [73, 102, 103, 153]. Lithium is also theorized to
act by decreasing intracellular calcium signaling [154].

Pathways related to chronobiology and circadian rhythm
have long been suspected to play a role in BD. Sleep dis-
turbance is often reported by patients suffering from BD,
and changes in sleep schedule (as in transmeridian travel)
can provoke episodes in susceptible people [155–157].
Genes that influence entrainment of circadian rhythm to the

The genetics of bipolar disorder 549

light/dark cycle have been widely studied in BD, with some
nominally significant findings [141, 158, 159], but none of
these genes have so far been directly implicated by GWAS.
Mutations of the CLOCK gene, a canonical gene in the
circadian pathway, have been associated with mood dis-
turbance and sleep disorders [160].

Mitochondrial dysfunction, with resulting disturbance in
energy metabolism, has also long been theorized to play a
role in BD. Patients with some known mitochondrial dis-
orders also show increased rates of mood disturbances
consistent with depression or BD [161, 162]. There is also
some evidence of mitochondrial dysfunction in induced
pluripotent stem cell (iPSC)-derived neurons from BD
patients [163]. However, GWAS have failed to detect any
significant association between mitochondrial DNA poly-
morphisms and BD [164].

The pathway analyses of genes implicated in the most
recent BD GWAS highlight ion transport, neurotransmitter
receptors, insulin secretion, and endocannabinoid signaling,
which may provide novel targets for therapeutic develop-
ment [87].

Genetic architecture

Heritability

Twin studies have consistently demonstrated that most of
the individual difference in risk for BD is explained by
inherited genetic factors. Studies that compare monozygotic
with dizygotic twins have estimated values for narrow-sense
heritability of about 70% [165]. Some concern has been
raised that the traditional twin design may overestimate
heritability under specific circumstances that violate model
assumptions [166]. These include assumptions about
unbiased ascertainment, equivalence of environments
shared by MZ as compared to DZ twins, and potential gene-
environment correlations [165]. (Gene–gene and
gene–environment interactions, however important they
may be in BD, do not contribute to narrow-sense heritability
estimates [167]). Recent, population-based studies that do
not depend on the same assumptions as twin studies have
found very similar heritability estimates [168]. Thus, any
overestimation of heritability in the earlier twin studies is
likely to be small.

Recent methods allow estimates of heritability based on
distant kinds of relatedness that may exist in large,
case–control samples [169]. These methods rely on
empirical estimates of relatedness derived from sharing of
common alleles genotyped by SNP arrays. As has been
observed for most common, complex disorders, the SNP-
based heritability estimates for BD tend to range from
around 25–45% [78, 170]. This “heritability gap” or

“missing heritability” is not fully understood, but may
reflect imprecision in the method, overestimates of herit-
ability in twin studies (noted above), or a contribution of
rare variants not captured on SNP arrays.

Models of etiology and risk

We still lack good models that can bring together genetic and
other data heuristically. Four possibilities broadly consistent
with the available data come to mind, but others are hard to
rule out: (1) Two-hit model. Under this model, we imagine
that classes of risk factors interact nonadditively to determine
outcome, with combinations accounting for phenotypic dis-
tinctions [171]. For example, given two individuals with
similar polygenic risk burden, one might develop BD while
the other, exposed to a second hit from maternal influenza,
develops schizophrenia. (2) Multifactorial threshold model.
Under this model, there is a large but finite set of nonspecific
genetic and other risk factors, whose total dosage determines
specific phenotypes [172]. Thus, BD would occupy some
intermediate space, with more risk factors than depression but
fewer than schizophrenia. This is a more general version of
the two-hit model and fits best when each risk factor has a
small, additive effect on outcome. (3) Risk-resilience model.
Under this model, genetic differences might confer risk or
resilience, with the phenotypic outcome reflecting a delicate
balance of harmful and protective factors [173, 174]. Thus,
BD might result from genetic risk factors conferring, say,
unstable mood, nearly balanced by stable temperament, and
advantageous life circumstances. (4) Omnigenic Model.
Under this model, almost all genetic differences contribute in
some small way to risk (or resilience), while phenotypic
outcomes are determined largely by which genes are involved
and their relative importance in relevant cells and tissues
[175]. Thus, BD might result from genetic risk factors that
happen to impact genes that play an important role in cells
that underlie neural circuits involved in regulation of mood
and behavior.

It has been said that all models are wrong, but some are
useful. Each of these models has supporters and critics. The
two-hit model resonates with long-held theories of gene ×
environment interaction, but robust evidence of such inter-
actions has proven elusive [176–180]. The Omnigenic
Model has generated much recent debate, since it would
seem to imply that larger and larger GWAS cannot alone
solve complex traits. In any case, we clearly need more and
better ways to incorporate nongenetic risk factors into
models of etiology and risk prediction.

Genetic correlations

Genetic correlation refers to the degree to which two dis-
tinct traits share genetic influences (or more formally, the

550 F. J. A. Gordovez, F. J. McMahon

proportion of additive genetic variance—heritability—that
is shared [167]). Traditionally, estimated through laborious
twin and family studies, genetic correlation can now be
estimated much more easily from overlapping sets of
common SNPs genotyped in existing samples [181]. Such
studies have so far revealed many expected and some
unexpected genetic correlations with BD. In addition to the
substantial genetic overlap with schizophrenia that was
already apparent early in the GWAS era, significant genetic
correlations are observed between bipolar and major
depressive disorder [87, 182, 183], attention deficit hyper-
activity disorder [184], neuroticism [185], and borderline
personality disorder [86]. Small but significant genetic
correlations have also emerged between BD and educational
attainment [87], creativity [186], and leadership [187].
These findings lend support to the view that BD represents a
point on a spectrum of genetic risk, with quantitative rather
than categorical genetic differences underlying a range of
common disorders of mood, perception, and cognition
(Fig. 1).

Pharmacogenetics

Pharmacogenetic studies aim to use genetic information to
help match patients with the safest, most effective treat-
ments. Several pharmacogenetic studies have been per-
formed in patients with BD, but replicated findings have not
yet emerged. This may reflect the fact that many past studies
relied on a candidate gene design, while GWAS have not
generally been able to achieve sample sizes large enough to
detect variants of minor effect. The measurement of treat-
ment response in BD brings additional challenges, since the
episodic nature of the illness makes short-term assessments
of outcome unreliable.

Some promising findings have nevertheless emerged
from recent studies. The largest study to date, by the Con-
sortium on Lithium Genetics, carried out a GWAS of
lithium response in over 2000 individuals with BD who
were treated with lithium and systematically rated for
response. Significant association was detected with a set of
genetic variants within a noncoding region on chromosome
21 [27]. Another recent GWAS compared lithium-
responsive patients to healthy controls, revealing sig-
nificant association with a SNP near SESTD1 [188]. The
apparent lack of agreement between these two GWAS
studies probably reflects limited power to detect small
effects. One study in a highly selected set of Taiwanese
claimed a locus of major effect [28], but several well-
powered studies have failed to replicate this finding
[29, 189–191]. As sample sizes grow, it seems likely that
common loci influencing response to lithium or other drugs

will be identified. Larger samples may also enable PRS
derived from pharmacogenomic studies to illuminate path-
ways of drug response or help identify subgroups of patients
most likely to respond to a specific treatment regimen.

In contrast to studies of treatment response, those
focused on serious adverse events have detected strong and
reproducible signals for drugs that are sometimes used in
the treatment of BD. Patients exposed to carbamazepine
occasionally develop serious adverse cutaneous reactions
(ACR), such as Stevens–Johnson Syndrome. Genetic
association studies initially carried out in people of Asian
ancestry identified an HLA haplotype that conferred sub-
stantial risk of ACR after carbamazepine exposure [192].
Subsequent studies have confirmed this association also in
patients of European ancestry [193], albeit with a different
HLA haplotype. Other studies have identified additional,
apparently independent HLA haplotypes that predispose to
ACR after exposure to lamotrigine or phenytoin [194].
Based on these findings, HLA testing is advised in all
patients being considered for carbamazepine and may also
be informative for treatment decisions concerning other
anticonvulsants [195].

Genetics of clinical subtypes

It has long been assumed that the clinical diversity of BD
reflects, at least in part, differences in underlying risk
alleles. Limited statistical power has so far forestalled a
complete genetic dissection of the bipolar phenotype, but
several studies have found suggestive evidence of genetic

Fig. 1 Genetic and symptomatic relationships between bipolar and
some other psychiatric disorders. Shared heritability of bipolar dis-
order (BD) with schizophrenia (Scz), attention deficit disorder (ADD),
and major depressive disorder (MDD). Genetic correlation values were
extracted from Ref. [181].

The genetics of bipolar disorder 551

differences in bipolar cases with psychosis or catatonic
features, and in cases with bipolar II disorder
[84, 105, 196, 197]. One large study found a significant
positive correlation between genetic risk for schizophrenia
and psychotic episodes in patients with BD [84]. This same
study detected significant heritability, as estimated from
genome-wide SNP data, for psychotic features and suicide
attempts in BD.

Ongoing studies aim to go beyond clinical symptoms to
define subtypes of disease based on neuroimaging [198–
201], neurocognitive tests [202, 203], and EEG patterns
[201, 204, 205], as well as genetic markers. Such studies
hold promise for a future nosology of bipolar (and other
psychiatric) disorders that better reflects neurobiological
disease entities.

Future directions

Cellular phenotyping

The generation of iPSCs from patients allows for in vitro
evaluation of cell-autonomous traits that might be asso-
ciated with clinical diagnosis [206, 207]. Cellular mor-
phology, gene expression, and cellular functions are just
some of the phenotypes that can be analyzed using iPSC-
based cellular models. More complex models, such as 3D
organoids, can explore more macroscopic interactions
and might shed light on disorder-specific changes in
brain circuitry. So far, only a few published studies
have used iPSC derived from patients with BD
[104, 151, 163, 208, 209], but several studies are under-
way. Initial results suggest some differences in neurons
derived from patients with BD.

Reverse phenotyping

As we begin to identify genes that have a substantial
influence on risk (either collectively, as with PRS, or indi-
vidually, as with certain CNVs or rare variants), it may be
instructive to study individuals who carry substantial risk
but do not present in a psychiatric clinic. This approach,
dubbed “reverse phenotyping” [210] or “genetics-first”
[211, 212] has begun to bear fruit in studies of CNVs and
aneuploidies that confer high risk for ASD or schizophrenia
[116, 213–215]. These kinds of studies are needed for
accurate estimates of penetrance [110, 114, 216, 217] and
may also reveal an unheralded range of phenotypes related
to identified genetic risk factors [218, 219]. Longitudinal
studies of genetically high-risk individuals may also shed
light on protective or resilience factors and could provide
the basis for assessing the impact of primary prevention
strategies.

Drug development

The path from the identification of risk alleles to the
development of new drugs is complex and beyond the scope
of this review. Readers interested in exploring this topic
further should consult some recent reviews [220–222].

Clinical genetic testing

Genetic testing with utility for the diagnosis of BD or its
treatment is not on the horizon right now. Too little of the
risk is explained by current polygenic risk scores [170], and
known pathogenic CNVs are so far quite rare in BD
[124, 127]. However, some models suggest that PRS may
ultimately prove useful in psychiatric diagnosis as GWAS
samples reach sizes on the order of one million, at least for
those individuals with the highest risk allele burdens
[223, 224].

Genome-wide approaches help us navigate through the
complex genetic landscape in an unbiased manner. How-
ever, multiple testing means that GWAS can only detect
robust associations in large samples. Increasing the number
of samples through involvement of different sample col-
lection sites may improve power but can also introduce
substantial genetic heterogeneity. This could be due to the
innate genetic variability present across different popula-
tions and differences in ascertainment or clinical diagnosis
by different research groups. This challenge highlights the
need for further global-scale collaborations, standard prac-
tices of clinical assessment and phenotype characterization
across different groups, and genome-scale modeling that
can elucidate the biological impact of the many different
risk alleles that are detected in large, population-based
studies.

Conclusions

What emerges most clearly from molecular genetic findings
over the past decade is a concept of BD that includes several
features: (1) BD is a heterogeneous set of illnesses united by
the core clinical feature of cyclic elevation in mood and
activity, with substantial individual variation in depressive
and psychotic symptoms; (2) there is strong sharing of
weak, common genetic risk factors with schizophrenia and
major depression; (3) high-risk alleles also exist, but they
are rare and nonspecific, and there is so far no evidence for
monogenic forms of BD.

As a disease entity, BD may resemble stroke or type II
diabetes in the sense that several subclinical states create a
meta-stable condition that periodically erupts in symptoms.
For stroke, we understand that hypertension and cere-
brovascular disease create vulnerabilities that may present

552 F. J. A. Gordovez, F. J. McMahon

periodically with paralysis, language, or cognitive deficits.
And while there are rare, high-risk alleles that cause stroke,
most of the genetic risk resides in large numbers of common
alleles that each have a small impact on blood pressure,
vascular health, and coagulability [225]. This analogy
suggests that we need to identify the fundamental neuro-
biological processes that are most directly influenced by
common risk alleles and we should expect that these pro-
cesses are underway long before the first manic episode.
The analogy further suggests that secondary preventive
strategies will need to take aim at these underlying pro-
cesses, probably beginning at or around the time of the first
manic symptoms.

It remains to be seen whether genetic findings to date will
continue to coalesce into clear neurobiological pathways. If
they do, identification of new drug targets may be possible.
The advent of cellular modeling through iPSC technology
offers a new platform for screening large numbers of
potential new drug treatments, but the success of this
approach will depend heavily on the identification of robust
cellular phenotypes that reflect at least some of same the
genetic risk factors that predispose to bipolar or related
disorders. Meanwhile, even if single genes of large effect
remain elusive, it seems likely that polygenic approaches
incorporating numerous common risk alleles will continue
to be useful for research and may ultimately find modest
applications in some clinical settings. We have finally made
it through the first era of molecular genetics of BD, but the
road to new methods of diagnosis and treatment may well
remain long and uncertain.

Funding This study was supported by the Intramural Research Pro-
gram of the NIMH.

Compliance with ethical standards

Conflict of interest The authors declare that they have no conflict of
interest.

Publisher’s note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.

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