Molecular Psychiatry (2020) 25:544–559https://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 2020This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020

AbstractBipolar disorder (BD) is one of the most heritable mental illnesses, but the elucidation of its genetic basis has proven to be a verychallenging endeavor. Genome-Wide Association Studies (GWAS) have transformed our understanding of BD, providing thefirst reproducible evidence of specific genetic markers and a highly polygenic architecture that overlaps with that ofschizophrenia, major depression, and other disorders. Individual GWAS markers appear to confer little risk, but common variantstogether account for about 25% of the heritability of BD. A few higher-risk associations have also been identified, such as a rarecopy number variant on chromosome 16p11.2. Large scale next-generation sequencing studies are actively searching for otheralleles that confer substantial risk. As our understanding of the genetics of BD improves, there is growing optimism that someclear 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 hastransformed our understanding of bipolar disorder (BD). Tenyears ago, BD was considered a distinct, highly heritabledisorder for which genes of major effect had eluded detectionby linkage studies but were expected to be found eventually.Now, numerous common genetic markers have been foundby GWAS, none of which confers major risk for disease, andmany of which overlap with markers associated with schi-zophrenia or major depression. A few higher-risk associationshave also been identified, involving rare copy number variants(CNVs) that are usually not inherited. Now, BD can beregarded as a point on a spectrum of risk, ranging from majordepression 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 ofBD. In this review, we will summarize the key developmentsin BD genetics over the past decade and frame some openquestions 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 subjectivesymptoms that can be subtle, diagnosed cases probablyrepresent the tip of an iceberg of very common disturbancesin mood and behavior that blend imperceptibly into theclinical realm. Genetic studies have focused almost entirelyon individuals who can be easily diagnosed by interview orare already in treatment, which undoubtedly provides anincomplete picture. Imagine trying to describe the geneticsof hypertension by studying only stroke patients.

Varied clinical features

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

* Francis J. [email protected]

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

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

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of mood episodes, particularly manias. Interepisode periodscan be completely symptom-free or beset with chronicdepressive or manic symptoms. Some people suffer onlyfrom manias, although this is uncommon [9]. Mixed statesare frequent, as are periods of prolonged, treatment-resistantdepression [2]. With such protean manifestations, it seemslikely that what we now call BD may ultimately be resolvedinto dozens of biologically distinguishable disease entities.

Many studies have examined the familiality of clinicalfeatures 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 allhighly familial, while comorbid anxiety and substanceabuse are less so [15]. Below we will address some of thegenetic 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 withBD die of suicide [21], a number that has remained dis-couragingly stable for decades. Several small studies havereported that suicide may be especially common in somefamilies with BD [18, 22, 23], suggesting specific geneticor shared environmental factors, but these have so farremained elusive.

Cycling as a distinct trait

Signs and symptoms of BD are so wide-ranging that theycan be seen, in part, in just about every major psychiatricdisorder. This makes for challenging differential diagnosis,one of the reasons that it has proven more difficult toaccumulate very large samples of BD than schizophrenia,autism, or major depression. The one very distinctive traitseen in everyone with BD is cycling: episodic elevationsand depressions of mood and behavior, separated by periodsof relative or complete euthymia [4]. This is such a corefeature of BD as currently conceived that we will probablynot consider the genetics of BD to be solved until thegenetic 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 moodepisodes while receiving lithium, and another third with beat least somewhat improved [24]. Lithium is also the onlydrug shown to exert a protective effect against suicide inBD [17, 19, 20, 25]. No other major mental illness showsthis kind of specific response to lithium, suggesting that

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

Genetic epidemiology

Before the era of molecular genetics, much of our etiologicunderstanding of BD rested upon the methods of geneticepidemiology. Family studies demonstrated that BD runs infamilies, 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 ofshared genes [33]. Twin studies showed that much of theshared familial risk could indeed be explained by sharedgenes, with heritability estimates on the order of 70–90%[33]. Adoption studies lent further support to a largelygenetic etiology, since BD was elevated only in the biolo-gical parents of adult adoptees with the illness [33]. Despitethe strong and consistent evidence in favor of a geneticetiology; however, segregation analyses could not find aclear, Mendelian pattern of transmission, tending instead tofavor more complex models of inheritance [34].

Assortative mating

Assortative mating refers to nonrandom mating amongindividuals in a population [35]. People with similar phe-notypes may be more likely to mate or may selectivelyavoid potential mates with other phenotypes. A number ofstudies over the past decades have demonstrated varyingdegrees of assortative mating in BD, with an increased rateof matings between individuals with BD and those with BD,major depression, alcoholism, or other phenotypes [35–43].Recent, large population-based studies have found similarpatterns of assortative mating across psychiatric and othertraits, including height [44], activity level [45], emotionalintelligence [46], and educational and social status [47].

Such substantial rates of assortative mating are likely tohave a major impact on the genetic landscape of BD but areoften not considered in studies of the disorder. Theoreti-cally, assortative mating can lead to accumulation of riskalleles in subsequent generations, with consequent increasesin rates or severity of illness across generations of a family,a phenomenon known as anticipation [48]. Assortativemating across traits can also induce genetic correlations andcomorbidity between the traits in offspring, but these are notlikely to persist in the face of random mating by subsequent

The genetics of bipolar disorder 545

generations [49]. Assortative mating does not appear toeffect 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 becauseindividuals drawn from populations with nonrandom mat-ing will tend to share more risk alleles than would beexpected based on their overall genetic relatedness.

Risk loci

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

Candidate genes

In an attempt to overcome the limitations of linkagemethods, many researchers tried to find genetic markers thatwere chosen on the basis of their proximity to genes thatencoded proteins of known neurobiological importance,such as the serotonin transporter [54]. Unfortunately, thiscandidate gene strategy was largely unsuccessful. This isbecause the selection of candidate genes with a high-priorprobability of involvement in BD proved to be quite diffi-cult. Most candidate gene studies of BD also suffered fromthe same biases due to small sample size and undetectedgenetic mismatch between cases and controls that bedeviledother such studies of a variety of common traits [55]. Whilemeta-analyses do tend to support a small contribution fromat 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 hascome from GWAS.

GWAS

Genome-wide association studies, wherein large numbers ofgenetic 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 identifyinggenetic variants associated with BD. Since the first BDGWAS appeared in 2007 [71], almost 20 such studies havebeen published. Most have focused on typical case defini-tions of bipolar I disorder [26, 72–83], but some haveexamined 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, ofwhich 20 were newly identified [87].

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

Convergent data so far highlight at least three genes.ANK3, located on chromosome 10q21.2, was one of theearliest genes to be implicated in BD by GWAS [72, 90–93].Significant association has now been found between BD andSNPs near ANK3 by several studies, and several of thoseSNPs affect expression of ANK3 [90, 91, 94–96]. ANK3encodes ankyrin B, a protein involved in axonal myelina-tion, with expression in multiple tissues, especially brain[97]. Numerous alternative transcripts exist, suggesting apotential role for alternative splicing [98]. A conditionalknock-out mouse displays cyclic changes in behavior thatresemble BD and respond to treatment with lithium [99].CACNA1C, located on chromosome 12p13, has also beenimplicated by genome-wide significant SNP associations inseveral studies of BD, along with schizophrenia and majordepression; some of the associated SNPs are also associatedwith expression of CACNA1C in multiple tissues, includingbrain [73, 74, 87, 100–103]. The gene encodes an L-typevoltage-gated ion channel with well-established roles inneuronal development and synaptic signaling. Heterozygousknockdown of the gene in mice alters a variety of behaviorsthought to reflect mood, but without a clear syndromicresemblance to BD [102]. TRANK1, which resides onchromosome 3p22, has been implicated by genome-widesignificant association with nearby SNPs in studies of BDand schizophrenia [75–77, 104, 105]. TRANK1 encodes alarge, mostly uncharacterized protein, highly expressed inmultiple tissues, especially brain, and may play a role inmaintenance of the blood–brain barrier [106]. The expres-sion of TRANK1 is increased by treatment with the moodstabilizer valproic acid, and cells carrying the risk alleleshow decreased expression of the gene and its protein [104].Recent transcriptomic studies suggest that DCLK3 may beanother gene in the same 3p22 GWAS locus that contributesto risk for both BD and schizophrenia [88, 107].

While each individual GWAS “hit” has only a smalleffect on risk, polygenic risk scores that combine theadditive effects of many risk alleles (often hundreds orthousands) can index substantially more genetic risk byincluding variants that have so far escaped detection

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

Table

1Genetic

loci

associated

withBD.

Locus

LeadSNP(s)

Mapped

genes

eQTLgenes

References

1p31.1

rs4650608

None

IFI44L

Chen

etal.[76]

1q21.2

rs7544145

OTUD7B,RNU2-17P

ANP32E,MRPS21,PLEKHO1,HIST2H2AA3,

HIST2H2AA3,FCGR1A,RPRD2,SEMA6C,VPS45,SV2A,

HORMAD1,CTSS,APH1A

Stahlet

al.[87]

2q11.2

rs2271893,rs56361249,

rs57195239

MIR3127

ARID

5A,LMAN2L,CNNM4,ACTR1B

Chen

etal.[76],Charney

etal.[196],

Stahlet

al.[87]

2q24.3

rs17183814

SCN2A,CSRNP3,GALNT3

None

Stahlet

al.[87]

2q32.3

rs61332983

None

None

Stahlet

al.[87]

3p21.1-2

rs2251219,rs2302417,

rs7618915

TLR9,MIRLET7G,DNAH1

PCBP4,ALAS1,TWF2,LOC101929054,PPM1M,WDR82,

GLYCTK,MIR135A1,TNNC1,NISCH,STAB1,NT5DC2,

PBRM1,GNL3,GLT8D1,SPCS1,NEK4,ITIH

1,ITIH

3,

ITIH

4,ITIH

4-AS1,MUSTN1,TMEM110-M

USTN1,

TMEM110,BAP1,PHF7,SMIM

4,RNU6-856P,

RNU6ATAC16P,SNORD19B,SNORD19,SNORD69

McM

ahonet

al.[226],Chen

etal.[76],

Charney

etal.[196],Stahlet

al.[87]

3p22.2

rs6550435,rs9834970

DCLK3,TRANK1

TRANK1,RNU6ATAC4P,MLH1,LRRFIP2,GOLGA4

Chen

etal.[76],Mühleisen

etal.[77],

Houetal.[78],Charney

etal.[196],Ikeda

etal.[75],Stahlet

al.[87]

3q13.12

rs3804640

LINC01215

CD47,IFT57

Stahlet

al.[87]

4q32.2

rs11724116

FSTL5

Stahlet

al.[87]

5p15.31

rs148538395,rs17826816

ADCY2

Mühleisen

etal.[77],Stahlet

al.[87]

5q14.1

rs10035291

SSBP2

Stahlet

al.[87]

6q13

rs57970360

None

None

Stahlet

al.[87]

6q15

rs12201676

RNGTT,PNRC1,PM20D2

Wanget

al.[227]

6q16.1

rs12202969,rs1487441,

rs2388334

LOC101927314

Mühleisen

etal.[77],Houetal.[78],Stahl

etal.[87]

6q21

rs6568686

MFSD4B,REV3L,TRAF3IP2-AS1,TRAF3IP2,FYN

Fabbriet

al.[228]

6q25.2

rs1203233

SYNE1,SYNE1-AS1,RNA5SP223

Green

etal.[229],Charney

etal.[196]

6q27

rs1039002,rs10455979

PDE10A,RPS6KA2

Kerner

etal.[230],Stahlet

al.[87]

7p21.3

rs113779084

THSD7A,LOC102725191

Stahlet

al.[87]

7p22.3

rs4236274,rs4332037

MIR4655

MAD1L1,MRM2,ELFN1

Houet

al.[78],Ikedaet

al.[75]

7q22.3

rs73188321

SRPK2,PUS7

Stahlet

al.[87]

7q34

rs142673090

Stahlet

al.[87]

9p21.3

rs12553324

Houet

al.[78]

9q32

rs10513249

WHRN

Fabbriet

al.[228],Baum

etal.[79]

9q33.1

rs11789399

Wanget

al.[227]

The genetics of bipolar disorder 547

Table

1(continued)

Locus

LeadSNP(s)

Mapped

genes

eQTLgenes

References

10q21.2

rs10994299,rs10994318,

rs10994336,rs10994415,

rs4948418

ANK3

Ferreiraet

al.[73],Chen

etal.[76],

Mühleisen

etal.[77],Charney

etal.[196],

Stahlet

al.[87]

10q25.1

rs10884920,rs59134449

SORCS1,MXI1,SMNDC1

XPNPEP1,ADD3

Charney

etal.[196],Stahlet

al.[87]

11p15.4

rs6484218

AMPD3

Huanget

al.[183]

11q12.2

rs12226877,rs174576,rs28456

DKFZP434K028,MYRF,TMEM258,MIR611,FEN1,

FADS2,FADS1,MIR1908,FADS3,BEST1,LOC100507521

Ikedaet

al.[75],Stahlet

al.[87]

11q13.2

rs10896090

CATSPER1,GAL3ST3,TMEM151A

CST6,SNX32,PELI3,EIF1AD,CTSW,FIBP,RNASEH2C,

BANF1,SF3B2,CNIH

2,RAB1B,YIF1A,PACS1,KLC2

Stahlet

al.[87]

11q13.2

rs7122539

CST6,BBS1,BBS1,ZDHHC24,B4GAT1,SPTBN2,

C11orf80,CCDC87,CCS,LOC102724064,CTSF,RCE1,

PC,LRFN4

Stahlet

al.[87]

11q13.4

rs12575685

SHANK2

Stahlet

al.[87]

11q14.1

rs12290811,rs12576775

TENM4(O

DZ4),MIR708

Sklaret

al.[74],Mühleisen

etal.[77],

Ikedaet

al.[75]

12p13.33

rs10744560,rs4765913

CACNA1C-IT1,CACNA1C-IT2,

CACNA1C-A

S4,CACNA1C-IT3,

CACNA1C-A

S3

CACNA1C

Sklaretal.[74],Charney

etal.[196],Stahl

etal.[87]

12q13.12

rs10459221,rs1054442

KMT2D,RHEBL1,DHH

WNT10B,CACNB3,CCDC65,FKBP11,ARF3,

LOC105369758,DDN,PRKAG1,LMBR1L,TUBA1B

Houet

al.[78],Charney

etal.[196]

13q14.11

rs1012053

DGKH

Baum

etal.[79]

15q15.2

rs4447398

GANC,CAPN3,SNAP23,LRRC57,HAUS2,STARD9,

TTBK2,ADAL

Stahlet

al.[87]

15q25.3

rs139221256

Stahlet

al.[87]

16p12.2

rs420259

COG7,GGA2,EARS2,PALB2,DCTN5,PLK1,ERN2

Burtonet

al.[71],Jianget

al.[231]

16p13.2

rs11647445

GRIN

2A

Stahlet

al.[87]

17q12

rs2517959

MIR4728,MIEN1,GRB7

TCAP,ZPBP2,GSDMA,MED1,STARD3,IK

ZF3,

ORMDL3,PNMT,PPP1R1B,PGAP3,ERBB2,GSDMB

Houet

al.[78]

17q21.31

rs112114764

LOC105371789,RNU6-131P,TMUB2,

ATXN7L3

TMEM101,SLC25A39,RNU3P1,MPP2,UBTF,G6PC3,

HDAC5,C17orf53,ASB16,ASB16-A

S1

Stahlet

al.[87]

18q21.33

rs11557713

ZCCHC2

Stahlet

al.[87]

19p13.11

rs1064395,rs111444407

NCAN,RNU6-1028P,MIR640

RFXANK,GMIP,ZNF506,ZNF101,ATP13A1,BORCS8-

MEF2B,BORCS8,NDUFA13,TSSK6,TM6SF2,YJEFN3,

MAU2,GATAD2A,CILP2,LPAR2,HAPLN4,SUGP1

Cichonet

al.[232],Stahlet

al.[87]

19p13.13

rs4926298

NFIX

DNASE2,PRDX2,GCDH,SYCE2

Ikedaet

al.[75]

20q13.12

rs6130764,rs67712855

WFDC5,RBPJL

STK4-A

S1,MATN4,DNTTIP1,TNNC2,SYS1,TP53TG5,

SLPI,WFDC12,SEMG1,YWHAB,PABPC1L,STK4,

KCNS1,PI3

Stahlet

al.[87]

eQTLgenes

referto

genes

whose

expressionisassociated

withaSNPthat

isin

linkagedisequilibrium

withthelead

SNP(s)

548 F. J. A. Gordovez, F. J. McMahon

individually at genome-wide significance [108]. Recentstudies that use the PRS strategy have shown that commonvariation accounts for about 25% of the total genetic risk forBD (less of the phenotypic variance), that PRS overlapsubstantially between BD and schizophrenia, and that PRSderived from large schizophrenia samples are associatedwith increased rates of psychotic symptoms and decreasedresponse 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, ratherthan the typical two copies expected in the diploid humangenome. Initially discovered by use of hybridization or SNParray methods that could detect deletions and duplicationstoo small to be found reliably by cytogenetic methods, large(30–1000 kb) CNVs have since been shown to play a majorrole in neurodevelopmental disorders [110–116] and somecases of schizophrenia [110, 117–123].

CNVs seem to play a smaller role in BD [124], but atleast 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 ofschizophrenia [125] and was later detected as a de novoevent in a proband with early-onset BD [126]. Genome-widesignificant evidence of association with BD is based on alarge 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 adeletion 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 studyfound enrichment of genic CNVs in schizoaffective BD[131]. Taken together, these findings suggest that the geneticoverlap between BD and schizophrenia extends beyondcommon, low-risk alleles to rare alleles of larger effect.

Most published CNV studies to date have relied ontechnologies 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 CNVsin the human genome. Many such smaller CNVs may alsobe associated with various neurodevelopmental and adultpsychiatric disorders and may well be found to play animportant role in BD in the future.

Single nucleotide variants (SNVs) and and smallinsertions/deletions (indels)

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

deletion variants that are not represented in SNP arrays[132, 133]. Such studies may uncover alleles conferringgreater risk than the common alleles detectable by GWAS,but the lower allele frequencies and large number ofpotential 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 andseveral 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, assample sizes grow significant findings may emerge. Ongoingconsortia efforts that aim to achieve larger sample sizesthrough meta-analysis of multiple independent samples haveperhaps the best likelihood of success. Studies that leveragethe increased frequencies of otherwise rare alleles sometimesseen in unusual populations [134, 139, 140] may also succeedas sample sizes grow and sequencing technology improves.

Other studies have used NGS to sequence RNAexpressed in brain tissue obtained post-mortem from peoplediagnosed with BD [107, 141, 142]. Such studies canidentify 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 ofinherited genetic variants.

Pathways

One way to deal with the substantial genetic heterogeneityof illnesses like BD is to group implicated genes acrossstudies into pathways or networks of functionally relatedgenes. In this way, increased power to detect associationmay follow if different alleles in different genes converge atthe level of gene sets. Several such pathway studies havebeen published, with little apparent agreement so far[85, 93, 145–150]. The multiplicity of implicated pathwaysand probably reflects genetic heterogeneity, the relativelysmall number of robust genetic associations found so far forBD, and the still-challenging problem of assigning commongenetic markers found by GWAS to the appropriate gene orgenes. Calcium signaling is probably the most supportedpathway 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 inBD follows from the known function of the risk gene,CACNA1C [73, 102, 103, 153]. Lithium is also theorized toact by decreasing intracellular calcium signaling [154].

Pathways related to chronobiology and circadian rhythmhave 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 somenominally significant findings [141, 158, 159], but none ofthese genes have so far been directly implicated by GWAS.Mutations of the CLOCK gene, a canonical gene in thecircadian pathway, have been associated with mood dis-turbance and sleep disorders [160].

Mitochondrial dysfunction, with resulting disturbance inenergy metabolism, has also long been theorized to play arole in BD. Patients with some known mitochondrial dis-orders also show increased rates of mood disturbancesconsistent with depression or BD [161, 162]. There is alsosome evidence of mitochondrial dysfunction in inducedpluripotent stem cell (iPSC)-derived neurons from BDpatients [163]. However, GWAS have failed to detect anysignificant association between mitochondrial DNA poly-morphisms and BD [164].

The pathway analyses of genes implicated in the mostrecent BD GWAS highlight ion transport, neurotransmitterreceptors, 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 ofthe individual difference in risk for BD is explained byinherited genetic factors. Studies that compare monozygoticwith dizygotic twins have estimated values for narrow-senseheritability of about 70% [165]. Some concern has beenraised that the traditional twin design may overestimateheritability under specific circumstances that violate modelassumptions [166]. These include assumptions aboutunbiased ascertainment, equivalence of environmentsshared by MZ as compared to DZ twins, and potential gene-environment correlations [165]. (Gene–gene andgene–environment interactions, however important theymay be in BD, do not contribute to narrow-sense heritabilityestimates [167]). Recent, population-based studies that donot depend on the same assumptions as twin studies havefound very similar heritability estimates [168]. Thus, anyoverestimation of heritability in the earlier twin studies islikely to be small.

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

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

Models of etiology and risk

We still lack good models that can bring together genetic andother data heuristically. Four possibilities broadly consistentwith the available data come to mind, but others are hard torule out: (1) Two-hit model. Under this model, we imaginethat classes of risk factors interact nonadditively to determineoutcome, with combinations accounting for phenotypic dis-tinctions [171]. For example, given two individuals withsimilar polygenic risk burden, one might develop BD whilethe 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 nonspecificgenetic and other risk factors, whose total dosage determinesspecific phenotypes [172]. Thus, BD would occupy someintermediate space, with more risk factors than depression butfewer than schizophrenia. This is a more general version ofthe two-hit model and fits best when each risk factor has asmall, additive effect on outcome. (3) Risk-resilience model.Under this model, genetic differences might confer risk orresilience, with the phenotypic outcome reflecting a delicatebalance of harmful and protective factors [173, 174]. Thus,BD might result from genetic risk factors conferring, say,unstable mood, nearly balanced by stable temperament, andadvantageous life circumstances. (4) Omnigenic Model.Under this model, almost all genetic differences contribute insome small way to risk (or resilience), while phenotypicoutcomes are determined largely by which genes are involvedand their relative importance in relevant cells and tissues[175]. Thus, BD might result from genetic risk factors thathappen to impact genes that play an important role in cellsthat underlie neural circuits involved in regulation of moodand behavior.

It has been said that all models are wrong, but some areuseful. Each of these models has supporters and critics. Thetwo-hit model resonates with long-held theories of gene ×environment interaction, but robust evidence of such inter-actions has proven elusive [176–180]. The OmnigenicModel has generated much recent debate, since it wouldseem to imply that larger and larger GWAS cannot alonesolve complex traits. In any case, we clearly need more andbetter ways to incorporate nongenetic risk factors intomodels 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—thatis shared [167]). Traditionally, estimated through laborioustwin and family studies, genetic correlation can now beestimated much more easily from overlapping sets ofcommon SNPs genotyped in existing samples [181]. Suchstudies have so far revealed many expected and someunexpected genetic correlations with BD. In addition to thesubstantial genetic overlap with schizophrenia that wasalready apparent early in the GWAS era, significant geneticcorrelations are observed between bipolar and majordepressive disorder [87, 182, 183], attention deficit hyper-activity disorder [184], neuroticism [185], and borderlinepersonality disorder [86]. Small but significant geneticcorrelations have also emerged between BD and educationalattainment [87], creativity [186], and leadership [187].These findings lend support to the view that BD represents apoint on a spectrum of genetic risk, with quantitative ratherthan categorical genetic differences underlying a range ofcommon disorders of mood, perception, and cognition(Fig. 1).

Pharmacogenetics

Pharmacogenetic studies aim to use genetic information tohelp match patients with the safest, most effective treat-ments. Several pharmacogenetic studies have been per-formed in patients with BD, but replicated findings have notyet emerged. This may reflect the fact that many past studiesrelied on a candidate gene design, while GWAS have notgenerally been able to achieve sample sizes large enough todetect variants of minor effect. The measurement of treat-ment response in BD brings additional challenges, since theepisodic nature of the illness makes short-term assessmentsof outcome unreliable.

Some promising findings have nevertheless emergedfrom recent studies. The largest study to date, by the Con-sortium on Lithium Genetics, carried out a GWAS oflithium response in over 2000 individuals with BD whowere treated with lithium and systematically rated forresponse. Significant association was detected with a set ofgenetic variants within a noncoding region on chromosome21 [27]. Another recent GWAS compared lithium-responsive patients to healthy controls, revealing sig-nificant association with a SNP near SESTD1 [188]. Theapparent lack of agreement between these two GWASstudies probably reflects limited power to detect smalleffects. One study in a highly selected set of Taiwaneseclaimed 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 thatcommon loci influencing response to lithium or other drugs

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

In contrast to studies of treatment response, thosefocused on serious adverse events have detected strong andreproducible signals for drugs that are sometimes used inthe treatment of BD. Patients exposed to carbamazepineoccasionally develop serious adverse cutaneous reactions(ACR), such as Stevens–Johnson Syndrome. Geneticassociation studies initially carried out in people of Asianancestry identified an HLA haplotype that conferred sub-stantial risk of ACR after carbamazepine exposure [192].Subsequent studies have confirmed this association also inpatients of European ancestry [193], albeit with a differentHLA haplotype. Other studies have identified additional,apparently independent HLA haplotypes that predispose toACR after exposure to lamotrigine or phenytoin [194].Based on these findings, HLA testing is advised in allpatients being considered for carbamazepine and may alsobe informative for treatment decisions concerning otheranticonvulsants [195].

Genetics of clinical subtypes

It has long been assumed that the clinical diversity of BDreflects, at least in part, differences in underlying riskalleles. Limited statistical power has so far forestalled acomplete genetic dissection of the bipolar phenotype, butseveral studies have found suggestive evidence of genetic

Fig. 1 Genetic and symptomatic relationships between bipolar andsome 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 wereextracted from Ref. [181].

The genetics of bipolar disorder 551

differences in bipolar cases with psychosis or catatonicfeatures, and in cases with bipolar II disorder[84, 105, 196, 197]. One large study found a significantpositive correlation between genetic risk for schizophreniaand psychotic episodes in patients with BD [84]. This samestudy detected significant heritability, as estimated fromgenome-wide SNP data, for psychotic features and suicideattempts in BD.

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

Future directions

Cellular phenotyping

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

Reverse phenotyping

As we begin to identify genes that have a substantialinfluence on risk (either collectively, as with PRS, or indi-vidually, as with certain CNVs or rare variants), it may beinstructive to study individuals who carry substantial riskbut 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 andaneuploidies that confer high risk for ASD or schizophrenia[116, 213–215]. These kinds of studies are needed foraccurate estimates of penetrance [110, 114, 216, 217] andmay also reveal an unheralded range of phenotypes relatedto identified genetic risk factors [218, 219]. Longitudinalstudies of genetically high-risk individuals may also shedlight on protective or resilience factors and could providethe basis for assessing the impact of primary preventionstrategies.

Drug development

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

Clinical genetic testing

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

Genome-wide approaches help us navigate through thecomplex genetic landscape in an unbiased manner. How-ever, multiple testing means that GWAS can only detectrobust associations in large samples. Increasing the numberof samples through involvement of different sample col-lection sites may improve power but can also introducesubstantial genetic heterogeneity. This could be due to theinnate genetic variability present across different popula-tions and differences in ascertainment or clinical diagnosisby different research groups. This challenge highlights theneed for further global-scale collaborations, standard prac-tices of clinical assessment and phenotype characterizationacross different groups, and genome-scale modeling thatcan elucidate the biological impact of the many differentrisk alleles that are detected in large, population-basedstudies.

Conclusions

What emerges most clearly from molecular genetic findingsover the past decade is a concept of BD that includes severalfeatures: (1) BD is a heterogeneous set of illnesses united bythe core clinical feature of cyclic elevation in mood andactivity, with substantial individual variation in depressiveand psychotic symptoms; (2) there is strong sharing ofweak, common genetic risk factors with schizophrenia andmajor depression; (3) high-risk alleles also exist, but theyare rare and nonspecific, and there is so far no evidence formonogenic forms of BD.

As a disease entity, BD may resemble stroke or type IIdiabetes in the sense that several subclinical states create ameta-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 commonalleles that each have a small impact on blood pressure,vascular health, and coagulability [225]. This analogysuggests that we need to identify the fundamental neuro-biological processes that are most directly influenced bycommon risk alleles and we should expect that these pro-cesses are underway long before the first manic episode.The analogy further suggests that secondary preventivestrategies will need to take aim at these underlying pro-cesses, probably beginning at or around the time of the firstmanic symptoms.

It remains to be seen whether genetic findings to date willcontinue to coalesce into clear neurobiological pathways. Ifthey do, identification of new drug targets may be possible.The advent of cellular modeling through iPSC technologyoffers a new platform for screening large numbers ofpotential new drug treatments, but the success of thisapproach will depend heavily on the identification of robustcellular phenotypes that reflect at least some of same thegenetic risk factors that predispose to bipolar or relateddisorders. Meanwhile, even if single genes of large effectremain elusive, it seems likely that polygenic approachesincorporating numerous common risk alleles will continueto be useful for research and may ultimately find modestapplications in some clinical settings. We have finally madeit through the first era of molecular genetics of BD, but theroad to new methods of diagnosis and treatment may wellremain 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 ofinterest.

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

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