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General Genetics of Bipolar Disorder 

General Genetics of Bipolar Disorder
General Genetics of Bipolar Disorder

John I. Nurnberger

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Research into the genetics of major medical disorders is important because it provides a direct window into the biology of illness. The actions of specific single genes are revealed in the developmental expression of specific proteins in the brain and other organs; the expression and control of the expression of these proteins provides a substrate for the development of disease. In recent years, it has become clear that psychiatric disorders, like other common medical conditions, result from complex genetic factors and interactions. That is, for the major disorders that we study such as depression, drug and alcohol dependence, schizophrenia, and bipolar disorder, multiple genes are involved. This conclusion has dramatically affected the methods that we use in psychiatric genetics.

At the same time, a scientific revolution has occurred in the field of genetics with the advent of molecular biological techniques. It is now possible to study DNA variants directly, and to study these variants within the context of examining the entire genome rather than single genes. Using these techniques, genes influencing risk for many neuropsychiatric diseases have been identified. Initially, Mendelian single-gene conditions, such as Huntington's disease, were studied and resolved; in the last few years, research into conditions with complex genetic pathophysiology, such as alcohol dependence and schizophrenia, has informed multi-gene models underlying illness expression. Some of this work has been facilitated by the study of endophenotypes or biologic vulnerability markers. Technological gains now permit investigation of genetic effects in complex behavioral disorders such as bipolar disorder that involve complex, multi-gene interactions with environmental risk factors to lead to these conditions. In this chapter, we review various findings within these and other genetic investigations that inform the potential molecular substrate of bipolar disorder.

Clinical Epidemiology: Twin, Family, and Adoption Studies

Clinical epidemiology permits determination of how a condition distributes within a population and, consequently, whether it clusters in families. These techniques provide starting points to decide whether a condition might have a genetic basis. Three types of population genetic studies are typically conducted to ascertain whether a particular human phenomenon is substantially genetically influenced:

Family Studies

Family studies can answer three critical questions concerning the inheritance of a disorder:

  1. 1. Are relatives of an affected subject at increased risk for the disorder compared to relatives of comparison (unaffected) subjects?

  2. 2. What other disorders may share a common genetic vulnerability with the phenomenon in question?

  3. 3. Can a specific mode of inheritance be discerned?

A family study typically begins with a proband (i.e., an index patient or case), whose relatives are then studied. Family studies in mood disorder have continually demonstrated aggregation of illness in relatives (Tsuang & Faraone, 1990). For example, in a study at the National Institute of Mental Health (NIMH) Intramural Research Program, 25% of relatives of bipolar probands were found to have bipolar or unipolar illness (we use the latter term interchangeably with major depression in this chapter), compared with 20% of relatives of unipolar probands and 7% of relatives of healthy subjects (Gershon et al., 1982). In the same study 40% of the relatives of schizoaffective probands demonstrated mood disorder at some point in their lives (Table 9.1, Table 9.2, Figure 9.1). These data demonstrate increased risk for mood disorders in relatives of patients with mood disorders. These studies also suggested that the various forms of mood disorder appear to be related in a hierarchical way; namely, relatives of schizoaffective probands more often have schizoaffective illness than probands of other mood disorders. However relatives of schizoaffective disorder are more likely to have bipolar or unipolar illness than schizoaffective disorder, so transmission of symptom complexes (i.e., syndromes) is not necessarily pure. Similarly, relatives of bipolar probands are more likely to express bipolar disorder than relatives of other probands, yet they exhibit major depression more often than bipolar illness. Some of these relationships are illustrated graphically in Figure 9.1.

Table 9.1 Lifetime Risk for Major Affective Disorder in Different Groups

General Population

Relatives of probands with depression

Relatives of probands with bipolar illness

Relatives of probands with acute schizoaffective Disorder

Children of two ill parents

Identical twin ill





50% +


Table 9.2 Lifetime Risk for Bipolar Disorder in Different Groups


Relatives of probands with depression

Relatives of probands with bipolar disorder

Relatives of probands with acute schizoaffective disorder

Identical twin with bipolar disorder






Figure 9.1 Risk of mood disorders among relatives of probands with different specific mood-disorder diagnoses and relatives of healthy subjects (data from Gershon et al, 1982).

Figure 9.1
Risk of mood disorders among relatives of probands with different specific mood-disorder diagnoses and relatives of healthy subjects (data from Gershon et al, 1982).

The familial nature of mood disorders has been recognized for centuries, but recent evidence suggests that the rates of these mood disorders in the population may be changing. Specifically, a birth cohort effect has been observed in recent family studies: There is an increasing prevalence of mood disorder among persons born in more recent decades (especially since 1970) in comparison to those born earlier.. The cohort effect appears to be true for schizoaffective and bipolar disorders as well as major depression (Gershon et al., 1989). The cohort effect appears among relatives at risk to a greater degree than in the general population, an observation that may be ascribed to a gene-by-environment interaction. The critical environmental variable(s) have not yet been identified.

In order to clarify these obviously complex relationships among the various mood disorders, investigators have tried to identify clinical characteristics that might define more homogeneous subgroups, presumably identifying a more circumscribed genetic risk. For example, age at onset may be useful for this purpose because early onset probands have increased morbid risk of illness in relatives in comparison to probands with later onset illness in most data sets (Faraone et al., 2004). Other subphenotypes, such as affective cycling frequency and the presence of comorbid anxiety or substance use disorders, have also been studied, as we will discuss later in this chapter. In terms of the questions that may be answered by family studies, bipolar disorder is clearly a familial condition. Other diagnoses that may aggregate in relatives of bipolar I disorder probands include bipolar II disorder, major (unipolar) depression, and schizoaffective disorder, bipolar type; this question of familial aggregation is treated in more detail later in this chapter (“The Mood Disorders Spectrum”). In terms of mode of inheritance, segregation analyses of family data (a comparison of the actual distribution of illness within families to the distribution that would be predicted under specific genetic models) has generally favored multi-factorial inheritance (Nurnberger & Berrettini, 1998), which implies a mixture of multiple genetic and environmental factors acting together to cause illness, rather than the impact of a single gene, as occurs in Huntington's disease.

Twin studies

Twin studies are based on the fact that monozygotic (MZ) or identical twins represent a natural experiment in which two individuals have exactly the same genes. This relationship is in contrast to dizygotic (DZ) or fraternal twins who share 50% of their genes and are no more genetically similar than any pair of siblings Consequently, characteristics which are under genetic control should be more concordant (similar) in monozygotic than dizygotic twins.

Twin studies for major mood disorders show consistent evidence for heritability. A commonly used formula to calculate heritability (often designated H2) is the Holzinger Index:

H2 = (% MZ concordance – % DZ concordance)/(100 – % DZ concordance).

On average, in mood disorders, monozygotic twin pairs show diagnostic concordance 65% of the time and dizygotic twin pairs demonstrate diagnostic agreement 14% of the time (Nurnberger & Berrettini, 2000). When divided by polarity, twin probands with bipolar illness show about 80% concordance (Bertelsen et al., 1977; 1979). Bienvenu et al. (2010) summarize three recent twin studies of bipolar disorder and calculated a heritability of 85%. This implies that about 85% of the variance in whether a person in the population will experience bipolar disorder is explained by genetic factors. This rate of heritability in bipolar disorder is higher than other psychiatric illnesses and most complex medical conditions.

Adoption Studies

Adoption studies provide a natural experiment to separate the effects of environment from those of genes. These types of studies are particularly relevant for complex disorders that are likely to involve multiple genes interacting with the environment, such as bipolar disorder. In adoption studies the risk for the disorder may be evaluated among four combinations of adoptees and adoptive and biological relatives: the adoptive and biological relatives of affected adoptees (probands) and the adoptive and biological relatives of comparison (unaffected) adoptees. If the disorder is heritable, one should find an increased risk among the biological relatives of affected probands, compared to the other three groups of relatives. One can also compare risk for illness in adopted-away children of ill parents versus adopted-away children of well parents. Several adoption studies have been performed examining these relationships in mood disorders generally and bipolar disorder specifically: The results have been consistent with hypotheses that genetic effects significantly explain illness variance in cases and families (Nurnberger & Berrettini, 1998). Namely, bipolar probands growing up in an adoptive family will have more biological relatives with mood disorder than control probands growing up in an adoptive family. One of the more striking examples of adoption studies in mood disorders was the excess of suicide found in the biological relatives of adoptees with depression compared to control adoptees, with no excess of suicide in adoptive relatives of either group (Wender et al., 1986). This finding suggests that even relatively specific behavioral symptoms may be significantly influenced by genetic effects.

In summary, genetic epidemiologic studies of bipolar illness provide strong evidence not only for familiality, but also for a predominance of genetic effects in the etiology of the disorder. These genetic effects, though, are complex, and one should not expect to identify single genes that explain a major portion of the variance. Moreover, since monozygotic twins do not show a 100% concordance rate of bipolar disorder, environmental factors clearly also contribute to the risk of developing bipolar illness (Table 9.2).

Clinical Epidemiology Key Points:

  • Mood disorders run in families and bipolar disorder is strongly heritable

  • Twin and adoption studies suggest that the phenomenology of bipolar disorder is under strong genetic control, but environmental factors are also relevant

  • Mood disorder inheritance is hierarchical, meaning that more severe disorders (e.g. Bipolar I and Schizoaffective Disorder Bipolar Type) tend to confer more risk in relatives although in all groups of relatives major depression is the most common expression of illness

  • Population rates of mood disorders seem to be increasing, particularly in relatives of persons with mood disorders

The Mood Disorders Spectrum

A spectrum of disorders generally refers to a set of disorders related by family studies or that share a number of overlapping symptoms. As noted in the previous discussion in this chapter, if you examine relatives of bipolar I probands, you will find an excess of not only bipolar I disorder, but also bipolar II, major (unipolar) depression, and schizoaffective disorder—bipolar type. In this section, we examine these disorders that appear to be genetically related to bipolar I disorder in more detail.

Bipolar I Disorder: Bipolar I disorder is so-called classic manic-depressive illness and is defined by the occurrence of mania, but in most cases also includes episodes of major depression that are typically more frequent than manic episodes.

Bipolar II disorder: Bipolar II disorder is defined by the occurrence of hypomania plus at least one episode of major depression. Hypomania shares the same symptoms as mania, but is distinguished from it by not being as severe in terms of functional impairment. Bipolar II disorder appears to be genetically related to both Bipolar I disorder and unipolar disorder on the basis of family studies. There is also some evidence in such studies for an excess of bipolar II disorder in relatives of bipolar II probands (Heun & Meier, 1993), suggesting that there may be some genetic specificity for this condition. Consistent with that notion, bipolar II disorder tends to be a stable lifetime diagnosis; that is, patients with bipolar II do not frequently convert to bipolar I disorder, at least among adults (Coryell et al., 1995).

Major Depression: Major depression is also known as unipolar disorder, as noted previously. This condition is highly familial, especially in early onset cases, and it is also related to bipolar I disorder. The classic twin studies that we reviewed typically included both unipolar and bipolar cases. Unipolar depression is the most common psychiatric illness among the relatives of probands with bipolar disorder (Tsuang & Faraone, 1990), although it is also quite common in the general population.

Rapid Cycling: Rapid-cycling represents a clinical course subtype of both bipolar I and II disorders. It has been the subject of great theoretical and clinical interest, as it can be very difficult to treat. Typically, rapid cycling is defined as four or more episodes in a year. Rapid cycling tends to be temporally limited, and may be related to environmental factors; for example, a link with thyroid pathology has been proposed. Rapid cycling may arise from heritable factors which might produce aggregation within families (Saunders et al., 2008, but see Nurnberger et al., 1988). Rapid switching of mood, which is related to rapid cycling, also appears to be familial (MacKinnon et al., 2003).

Unipolar mania: By definition, patients who experience unipolar mania (i.e., never develop major depression) meet criteria for bipolar I disorder, but their course of illness is atypical. Nonetheless, this relatively uncommon group is not distinguishable from other bipolar I patients on the basis of family pattern of illness (Nurnberger et al., 1979).

Cyclothymia: This is a condition of repetitive high and low mood swings that do not meet criteria for full manic or depressive episodes. Cyclothymia often does not require clinical intervention, but may be genetically related to bipolar disorder (Akiskal & Pinto, 1999). Cyclothymia has been considered a personality disorder, rather than an Axis I condition by some investigators.

Schizoaffective Disorder: Schizoaffective disorder is differentiated from psychotic mood disorders by the persistence of psychosis for at least two weeks in the absence of prominent mood symptoms, and from schizophrenia by the occurrence of significant mood symptoms throughout a substantial portion of the course of illness. It is further subdivided into bipolar and depressive subtypes based on whether mood symptoms include mania or only depression, respectively. This group of patients has an increase in mood disorder and an increase in schizophrenia in relatives, which suggests a genetic relationship to both conditions. This group may have the highest genetic load (total risk for mood disorder or schizophrenic illness in relatives) of any diagnostic category (Gershon et al., 1988). More specifically, those patients with schizoaffective disorder, bipolar type may carry genes related to both bipolar illness and schizophrenia. Patients with schizoaffective disorder, depressed type also confer risk for both chronic psychosis and mood disorder to relatives but have less overall genetic load than those with schizoaffective disorder, bipolar type.

Schizophrenia: In recent years, an overlap in chromosomal linkage areas and vulnerability genes between bipolar I disorder and schizophrenia has been identified (International Schizophrenia Consortium, 2009), especially in genes related to glutamate neurotransmission. These findings will be discussed in more detail later in this chapter.

Eating disorders: Family studies of anorexia and bulimia have generally found an excess of mood disorder in relatives. Relatives of anorexics may have a similar risk for mood disorders to that of relatives of bipolar probands (Nurnberger & Berrettini, 2000).

Attention-deficit hyperactivity disorder (ADHD): Children with ADHD appear to have increased depression in their relatives. Some studies, but not others, report increased risk of ADHD in the offspring of probands with bipolar disorder, which has led to hypotheses that ADHD may be a premorbid expression of bipolar illness (see summary in Nurnberger et al., 2011). The co-occurrence of bipolar disorder and attention deficit disorder has been proposed to be a potentially distinct familial illness (Faraone et al., 1997; Faraone & Wilens, 2003).

Alcohol Dependence: There may be overlapping vulnerability traits between alcohol dependence and mood disorders. Alcohol dependence is commonly comorbid with unipolar depression and especially, bipolar disorders; likewise persons with primary alcohol problems have an increased risk for comorbid mood disorders. There is some evidence that alcoholism with mood disorder may itself aggregate within families (Nurnberger et al., 2007).

Since family and genetic studies are particularly sensitive to the correct identification of affected individuals (i.e., cases), identifying appropriate diagnostic boundaries is a critical component of identifying genetic associations. Unfortunately, behavioral symptoms across different diagnostic conditions are often continuous, rather than distinct, making case identification a challenge at times. The use of spectrums may help to alleviate some of this problem by suggesting a variable phenotype within specific genetic models that can be used to identify gene effects. However, more research in the validity of this proposed mood spectrum is needed to maximize benefit from this approach.


An endophenotype is a biological characteristic that may substitute for a diagnosis in a genetic analysis. The advantage is that an endophenotype may be closer to the underlying pathophysiology of the illness, and therefore may be more easily demonstrated to be linked or associated with specific genetic markers. Sometimes an endophenotype is also easier to define using objective criteria. Endophenotypes may be important clues to the underlying biological mechanisms of illness. The term endophenotype was first used in this context by Gottesman (see Hasler et al., 2006). Criteria for an endophenotype have been derived from those proposed by Gershon and Goldin (1986):

  1. 1. The endophenotype must be associated with illness in the general population.

  2. 2. The endophenotype should be a stable, state-independent characteristic; that is, it should be observable even when the patient is in partial or complete remission.

  3. 3. The endophenotype should be heritable.

  4. 4. The endophenotype should segregate with illness within families.

Gottesman has also called attention to the following criterion:

  1. 5. Among family members of a proband with the endophenotype, the endophenotype should occur at a higher rate than it does in the general population.

Comments on selected endophenotypes for bipolar disorder are included in Table 9.3. A limitation of many of the endophenotypes studied so far is that they are difficult to measure and unlikely to be applied to large samples (see Scalability). However, they are certainly suitable for candidate gene studies. Some of the brain imaging phenotypes, such as amygdala activation in fMRI studies, may now be appropriate for larger-scale testing. The reader is directed to the review by Hasler et al. (2006) for a more extended discussion.

Table 9.3 Possible endophenotypes that have been identified in bipolar disorder

Marker and Primary Reference

Current Status



REM sleep induction by cholinergic drugs (Sitaram et al., 1980)

likely confirmed


Index of muscarinic cholinergic sensitivity

White matter hyperintensities on MRI

(Altshuler et al., 1995)

Heritability not clear


Well replicated

Amygdala activation on fMRI

(Strakowski et al., 2005)

Needs confirmation in euthymic subjects; heritability is unclear


Anatomically specific

Hippocampal size (Hallahan et al., 2011)

Inconsistent results


Anatomically specific

Response to tryptophan depletion

(Delgado et al., 1991)

True for unipolar patients; not clear if relevant for bipolar disorder


Index of serotonergic sensitivity

Response to sleep deprivation

(Wehr et al., 1987)

Heritability is not clear; Neurobiology not clear


Index of circadian rhythms disturbance

Melatonin suppression by light

(Lewy et al., 1985)

Needs replication in euthymic subjects


Index of circadian rhythms disturbance

The Mood Disorders Spectrum:

  • Mood disorders may exist in a spectrum of genetic liability and phenotypic expression

  • Primary conditions within this spectrum include:

    • Bipolar I Disorder

    • Bipolar II Disorder

    • Major (Unipolar) Depression

    • Schizoaffective Disorder, Bipolar Type

  • Other conditions mentioned above, such as schizophrenia and alcohol dependence, may also have some genetic overlap with the mood disorders.

Epigenetic Studies/Gene Expression Studies

Epigenetics is the study of biological modifiers of DNA transcription. The most common mechanisms studied thus far are DNA methylation and chromatin remodeling. Methylation of DNA generally prevents transcription of a particular gene. Chromatin (the protein framework supporting DNA in the nucleus) may exist in an active state (allowing transcription) or an inactive state (preventing transcription). Various stimuli, including environmental events, may be responsible for epigenetic changes that turn genes on or off. Of course, substantial additional gene regulation occurs at the RNA level, much of which may be captured by gene expression studies that measure RNA directly.

Epigenetic mechanisms have not been demonstrated to be critical in clinical studies of traditional psychiatric disorders to date. Differential methylation does appear to be important however in Prader-Willi syndrome, which includes mental retardation and sometimes mood disorders as part of the clinical picture. This condition is related to imprinting on 15q; the DNA segment that is transcribed for this chromosomal region is generally the segment from the father. The mother's DNA from that region tends to be methylated and not transcribed. In Prader-Willi there is deletion of the father's DNA in that region, so neither segment is functional.

Two animal models are of some interest. One has been described by Eric Nestler and includes differential methylation (and perhaps chromatin remodeling) in social defeat, with susceptible mice demonstrating decreased BDNF and cyclic AMP response element binding protein (CREB), and consequently presumably decreased neuronal growth. This effect is preventable with chronic antidepressant treatment (Benton et al., 2006). The other model (studied by Frances Champagne at Columbia) involves maternal licking/grooming in rodents. Low licking/grooming is associated with increased methylation of the estrogen receptor promoter in the offspring, decreased production of that receptor, and many behavioral changes suggesting greater responsivity to stress (but also increased sexual interest and more offspring). The most interesting aspect of this model is that differential methylation appears to be transmitted from the offspring to their offspring (i.e. the F2 generation) as well. This finding is an unusual instance of apparent inheritance of acquired characteristics, or an example that would seem to suggest a variation on the discredited theories of Lamarck and Lysenko (Champagne, 2008).

Ogden et al. (2004) have summarized gene expression data related to bipolar disorder. They used a convergent approach that integrated human brain gene expression data with results from a pharmacologic mouse model. This approach identified several candidate genes including DARPP-32 (dopamine- and cAMP-regulated phosphoprotein of 32 kDa) located at 17q12, PENK (preproenkephalin) located at 8q12.1, and TAC1 (tachykinin 1, substance P) located at 7q21.3. These findings suggest that genes associated with the experiences of pleasure and pain in animals may play a role in emotional expression in humans. Le-Niculescu et al. (2009) expanded this work by including data from genome-wide association studies as well as post-mortem gene expression studies and expression studies in lymphocytes (and other lines of evidence including animal models). The candidate genes determined to be most likely involved in bipolar disorder pathogenesis were ARNTL, BDNF, ALDH1A1, and KLF12.

Epigenetic and Gene Expression Studies:

  • Epigenetics involves study of the effects of DNA modification on the expression of phenotype

  • Epigenetic mechanisms have not been identified in mood disorders to date

  • Some animal models suggest that methylation of DNA may be relevant to affective behaviour in rodents

  • Gene expression studies have suggested several genes that may be involved in bipolar disorder including DARPP-32, PENK, TAC1, ARNTL, BDNF, ALDH1A1 and KLF12

High-Risk Studies

High-risk studies are of great use for testing endophenotypes and for observing the predictive utility of risk gene variants. High-risk studies in bipolar disorder typically involve evaluating children, who may or may not be ill, of bipolar parents and comparing them to children of healthy parents. When performed longitudinally, these studies are also useful for observing developmental changes of expression in the phenotype of bipolar disorder. More offspring of bipolar patients than healthy subjects have a diagnosed Axis I disorder. Offspring of bipolar parents may be more prone to respond to dysphoric feeling states and by disinhibitory behavior than children of healthy parents (Nurnberger et al., 1988). Recent studies of offspring at risk for bipolar disorder have identified children with anxiety disorders or externalizing disorders as being at increased risk for development of adolescent mood disorder (Duffy et al., 2009; Nurnberger et al., 2011). The reader is referred to chapter 5 of this textbook for a more detailed discussion of findings that have been reported from high-risk studies.

Linkage Studies

At any given genetic locus, each individual carries two copies (alleles) of the DNA sequence that defines that locus. One of these alleles is inherited from the mother and the other is from the father. If two genetic loci are close to each other on a chromosome, their alleles tend to be inherited together (not independently) and they are known as linked loci. During meiosis, crossing over (also known as recombination) can occur between homologous chromosomes, thus accounting for the observation that alleles of linked loci are not always inherited together.

The rate at which crossing over occurs between two linked loci is directly proportional to the distance separating them on the chromosome. In fact, the genetic distance between two linked loci is defined in terms of the percentage of recombination between the two loci (this value is known as theta). Loci that are far apart on a chromosome will have a 50% chance of being inherited together and consequently are not linked. Therefore, the maximum value for theta is 0.5, which represents random association, whereas the minimum value is 0, implying that two loci are so close that they are virtually always linked. Linkage analysis is a method for estimating theta for two or more loci. The probability that two loci are linked is the probability that theta < 0.5, while the probability that the two loci are not linked is the probability that theta = 0.5. This relationship is typically represented by a LOD (logarithm of the odds ratio) score for a family or set of families, which is defined by the following formula:

LOD score = (log10 probability of theta < 0.5)/(probability of theta = 0.5)

Although it is possible to perform such calculations by hand, LOD scores are usually calculated using computer programs such as GENEHUNTER or Merlin. Since a LOD score is a log value, scores from different families can be summed. For complex conditions collections of affected sibling pairs may be studied rather than large families. A LOD score of 1.0 indicates that linkage is 10 times more likely than non-linkage. For simple genetic conditions, a LOD score of 3 or greater is evidence for linkage, while a score of -2 or less is sufficient to exclude linkage for the sample studied. For disorders with more complex forms of inheritance (including most psychiatric disorders), a higher positive LOD score is required (3.6 for definite linkage and 2.2 for suggestive linkage) to protect against chance associations being interpreted as meaningful findings. See Lander and Kruglyak (1995) for further discussion on this topic.

Linkage for bipolar disorder (LOD scores of > 3.6 in single studies or equivalent evidence) has been demonstrated on (chromosome and arm) 4p, 6q, 8q, 13q, 18p, 18q, and 22q. Other areas are “close” to significant, including 12q, 21q, and Xq (Hayden & Nurnberger, 2006). Meta-analyses have supported linkage on 6q and 8q (McQueen et al., 2005), and 13q and 22q (Badner & Gershon, 2002). A large single sample analysis implicated 16p using a dominant model (Ross et al., 2008). It is clear that multiple chromosomal regions are linked to bipolar disorder; presumably all of these contain a specific gene or (more likely) a number of genes that influence vulnerability to the disorder

Linkage studies:

  • Identify whether a gene marker occurs more often with illness within families than would be expected by chance, suggesting that the chromosomal region includes one or more risk-related genes

  • A LOD score is one measure of linkage typically used in research reports; other measures include the NPL or non-parametric linkage score

  • Linkages for bipolar disorder have been identified at chromosomes 4p, 6q, 8q, 13q, 16p, 18p, 18q, & 22q

Association/Candidate Gene Studies

Studies of numerous candidate gene studies have been reported in the literature for bipolar disorder. Several genes have emerged with replicated findings or positive meta-analyses from multiple studies (Tables 9.4 and 9.5). In this section, we will review some of the more promising findings. References in text and table are not exhaustive, but feature the largest studies and meta-analyses.

Table 9.4 Single genes related to bipolar disorder in genome-wide association studies (GWAS)






Ferreira et al., 2008; Schulze et al., 2008; Smith et al., 2009



Ferriera et al., 2008



Cichon et al., 2011

Table 9.5 Candidate genes related to bipolar disorder in multiple studies




G72/G30 (DAOA)


Hattori et al., 2003; Chen et al., 2004; Bass et al., 2009



Sklar et al., 2002; Neves-Perreira et al., 2002; Liu et al., 2008



Binder et al., 2009; Willour et al., 2009



Millar et al., 2005; Thomason et al. 2005



Cho et al. 2005 (meta-analysis)



Preisig et al., 2000; Fan et al., 2010 (meta-analysis)



Chen et al., 2011 (meta-analysis)



Lopez et al., 2007; Harvey et al., 2007; Cichon et al., 2008

Candidates identified in genome-wide association studies:

Ankyrin 3 (ANK3): The first gene identified in a major psychiatric disorder using GWAS methods was ankyrin 3 (Ferreira et al., 2008; Schulze et al., 2008; Smith et al., 2009) This gene codes for a structural membrane protein related to sodium channels. Sodium transport has been reported to be abnormal in studies of bipolar disorder and major depression since the 1960s (El-Mallakh & Huff, 2001).

The calcium channel gene CACNA1C reached genome-wide significance in the report of Ferreira et al. (2008). Recent data show CACNA1C with the most significant association results for any gene in a 16,000 subject consortium analysis of bipolar GWAS data (Psychiatric GWAS Consortium Bipolar Disorder Working Group, submitted for publication).

NCAN was recently identified by a large international consortium studying bipolar illness and using GWAS methods (Cichon et al., (2011). it codes for an extracellular matrix glycoprotein. In the mouse, this is localized in cortical and hippocampal brain areas.

Candidates identified in multiple individual studies:

G72 or D-Amino Acid Oxidase Activator (DAOA): This gene (together with G30) is one of two implicated together in association studies on chromosome 13q. The gene G30 is a DNA sequence which is reverse transcribed within G72. The association with bipolar disorder was first identified by Hattori et al. (2003) after work by Chumakov and colleagues (2002) in schizophrenia. It has been supported by several other independent groups (Chen et al., 2004, Williams et al., 2006), but the implicated variants have not always been the same (see meta-analyses by Detera-Wadleigh and McMahon, 2006, Shi et al., 2008, and Muller et al., 2011). The function of DAOA is to oxidize serine, which is a potent activator of glutamate transmission via a modulatory site on the NMDA (n-methyl-d-aspartate) receptor. Inadequate DAOA function might be hypothesized to lead to problems in modulating the glutamate signal in areas of the brain such as the prefrontal cortex, which are likely to be involved in the expression of bipolar disorder (please see chapter 2 for detailed discussions of bipolar neuroanatomy). Existing evidence from animal studies suggests that glutamate antagonists may have antidepressant effects, and that depression may be associated with inadequate modulation of glutamate neurotransmission. A subsequent report, however, emphasized the role of G72 in dendritic arborization rather than serine oxidation (Kvajo et al., 2008). Dendritic arborization could be relevant for underlying findings of prefrontal and other regional brain abnormalities observed in bipolar disorder.

Brain-derived Neurotrophic Factor (BDNF): This gene is a candidate based both on position (11p14, near reported linkage peaks in several family series) and function (as a neuronal growth factor, it is implicated in several recent hypotheses of depression and bipolar mood disorder—see Verhagen et al., 2010). Polymorphisms in BDNF have shown significant association in three independent reports in family-based data, but not in several case-control series. Two reports suggested association in child/adolescent onset bipolar disorder, and two additional series show association in rapid-cycling subgroups of bipolar patients. A meta-analysis was positive (Fan & Sklar, 2008). However a population study (Petryshen et al., 2010) has shown significant ethnic variation in the most widely studied variant (the val66met promoter polymorphism), and this effect must be carefully considered in case/control studies. Several studies have shown that antidepressant administration is associated with increased central BDNF levels in experimental animals, and administration of BDNF itself has been associated with antidepressant-like activity. Depression has been postulated to be associated with decreased neurogenesis in the hippocampus, which is dependent on neurotrophic factors, including BDNF. Mood stabilizing medications used in bipolar disorder are thought to have neuroprotective effects that may be mediated through BDNF expression. The val/met polymorphism appears to be directly functional in the brain, as variation is associated with hippocampal activity and memory function (Hariri et al., 2003). It is also associated with HPA reactivity (Goodyer et al., 2010; Dougherty et al., 2010; Vinberg et al., 2009; Alexander et al., 2010; Shalev et al., 2009)

FKBP5: Binder (2009) reviewed evidence suggesting a role for FKBP5 in glucocorticoid receptor sensitivity, and also evidence for alleles at this locus being involved with bipolar and unipolar mood disorders. Our collaborative group (Willour et al., 2009) has participated in a positive family-based association study of SNPs in this gene and bipolar disorder as well.

Disrupted in Schizophrenia 1 (DISC1): This gene, located on chromosome 1q, was identified in a Scottish family with a genetic translocation and with multiple cases of psychiatric disorders, primarily schizophrenia. However DISC1 variants were associated with mood disorders in family members as well. Later studies in an independent series of bipolar patients in Scotland were positive for association (Thomson et al., 2005). A study in Wales of schizoaffective patients showed a linkage peak in the same chromosomal location. This gene is expressed in multiple brain regions, including the hippocampus, where it is differentially expressed in neurons. It is associated with microtubules that may contribute to cortical structural development; in mice, disruption of DISC1 leads to abnormal neuronal migration in the developing cerebral cortex. DISC1 appears to interact with phosphodiesterase 4B, which may play a role in mood regulation (Millar et al., 2005).

5HTT (SLC6A4), MAOA, COMT: These three genes have been shown in meta-analyses to be associated with bipolar disorder, even though no strong effects were shown in any one study. The effect size for each appears to be in the range of 10%–20% increase in risk. Each of these genes has been shown to be associated with other behavioral phenotypes, and each has been reported to interact with environmental factors to increase risk for specific disorders (major depression, antisocial personality disorder, and schizophrenia respectively). Note that a second meta-analysis of COMT and bipolar disorder was not positive (Craddock et al., 2006).

Tryptophan hydroxylase (TPH1 and 2): These two enzymes catalyze the first and rate-limiting, step of serotonin synthesis. TPH1 is peripherally expressed and TPH2 is brain-expressed. Both genes contain variants that have been associated with bipolar disorder in several, but not all, studies (Roche & McKeon 2009).

Other candidate genes:

P2RX7 (aka P2X7, P2X7R): This gene on 12q24 was identified in a French-Canadian case-control series following linkage studies using large pedigrees from the same population (Barden et al., 2006). It codes for a calcium-stimulated ATPase. The association was also seen in a German series of patients with major depression (Lucae et al., 2006), but not in a large UK series (Green et al., 2009). Its relevance for bipolar disorder remains somewhat uncertain.

GRK3: GRK3 is the only candidate gene identified using animal model studies, specifically a mouse model employing methamphetamine. The original gene expression studies were followed by association studies in several samples as well as expression studies in human lymphoblasts (Barrett et al., 2003; 2007). Independent replication has not yet occurred. This gene participates in down-regulation of G protein coupled receptors.

As genetic techniques continue to improve, and as we better understand possible endophenotypes underlying the expression of bipolar disorder, it is likely that some of these genes will turn out to play a significant role in conferring risk for bipolar disorder, whereas others will be only peripherally involved, or will be false positives. However, the examples discussed in this section demonstrate the promise of this approach.

Association/Candidate Gene Studies:

  • Candidate gene studies have identified several genes that may be involved in the expression of bipolar disorder

  • None of the currently identified candidate genes demonstrate large effect sizes, suggesting multiple genes combine to accumulate risk of bipolar disorder

  • Candidate genes include: DAOA, BDNF, Ankyrin 3, DISC1, 5HTT, MAOA, TPH1 & 2, P2RX7,GRK3, NCAN, and CACNA1C

  • These candidate genes are involved with neural development and structure, monoamine regulation, and sodium and calcium channel regulation

The Development of Genome-wide Association Studies (GWAS)

Genome-wide association studies (GWAS) were introduced in 2006. They were made possible by chip technology in which up to 2.5 million SNPs may be tested within a single experiment. This methodology enables examination of virtually every gene in the genome with multiple SNPs, and, because of linkage disequilibrium (the fact that nearby variants tend to be transmitted together within a population), even detection of variation some distance from the actual SNP tested (GAIN Collaborative Research Group, 2007). The major limitation of GWAS studies is in interpreting the data, since the number of simultaneous tests is massive, and requires statistical corrections that are complex, since not all events are independent due to linkage disequilibrium in the population as noted above. The presently accepted standard is a p value of < 5x10e-8 for a SNP association with illness in a GWAS study (Altshuler et al., 2008), based on empirical probability of a type I error. Since the effect size of variants associated with psychiatric disorders is generally quite small (odds ratios of 1.1–1.2 are the norm), achieving p values that meet this threshold requires very large sample sizes. Complex traits such as height, and risk for type II diabetes have now been analyzed extensively with GWAS methods, but success required samples in the tens of thousands or even hundreds of thousands (Lango Allen et al., 2010). These samples are achievable now only by extensive collaboration involving multiple sites, usually from international sources. Each set of cases should be matched with controls from a similar ethnic background because of the extensive variation in SNP genotype frequencies on the basis of ancestry. This ethnic variability is generally assessed formally using multidimensional scaling (MDS) or a similar method.

GWAS methods have now proven to be useful in psychiatric disorders, with several loci meeting stringent criteria in both schizophrenia and bipolar disorder (Ripke et al, 2011; Psychiatric GWAS Consortium Bipolar Disorder Working Group, 2011). Several of the loci described previously are the product of GWAS investigations (e.g., ANK3, CACNA1C, NCAN).

GWAS datasets have also been used for additional studies that extend the reach of the association methodology: polygenic score analyses and pathway analyses. The polygenic score method was introduced in neuropsychiatric disorders by Shaun Purcell as part of the International Schizophrenia Consortium (2009) report on GWAS findings in an initial dataset. The idea is to assign a score to each risk allele (i.e. the variant of the gene that is more common in cases than in controls) that is even nominally associated with disease (using a weighting factor based on the ratio of allele frequency in cases to allele frequency in controls) and then add the scores for each individual based on the number of risk alleles that individual carries. The risk alleles from one population may be tested to see whether they predict illness in a second population. In the ISC paper, risk scores for a group with schizophrenia successfully predicted illness in a second population with schizophrenia, and also in a separate population with bipolar disorder, but not in groups with several other medical conditions. This suggested substantial genetic overlap between the schizophrenia and bipolar disorder samples.

Pathway analyses start with the premise that multiple genes (each one explaining a small portion of the overall genetic variance) are involved in the predisposition for complex neuropsychiatric disorders and that it will be more parsimonious and heuristic to explain their effects in terms of the biological pathways that they participate in rather than considering them individually. SNPs that show evidence for association (even though not meeting the stringent criteria discussed in the section Association/Candidate Gene Studies in this chapter) are considered markers for genes that they reside in or are very close to. The gene lists generated in this manner are compared with canonical pathways or gene lists designated in bioinformatic databases. Commonly used databases for this purpose include Gene Ontology (GO, or KEGG (, or the proprietary database Ingenuity ( Statistical analysis may be conducted at the pathway level, usually correcting for gene size, which varies over several orders of magnitude. Schork has published a report on pathways in bipolar disorder (Torkamani et al., 2008) and several other reports are in preparation. Using this approach, Torkamani et al. (2008) suggested that regulation of dopamine signaling represents a significant risk pathway for bipolar disorder.

Genome-wide Association Studies (GWAS):

  • GWAS examines the entire human genome, looking for regions associated with a condition

  • GWAS studies are beginning to identify genes that are related to bipolar disorder, such as ANK3, CACNA1C and NCAN

  • Pathway analyses have been developed to help interpret large GWAS

Sequencing Studies

Sequencing studies have been initiated in a number of major psychiatric disorders including bipolar disorder. Sequencing (also referred to as re-sequencing) now uses next-generation methods that are many times cheaper and more efficient than the common PCR-based methods in use several years ago. The two strategies generally employed are whole genome sequencing and exome sequencing, the former involving determination of every base pair in a subject's genome and the latter involving just the ∼2% of the genome that is directly transcribed or in known regulatory regions. An important variable in sequencing endeavors is the “read frequency” or the number of times that an area is analyzed for sequence information. Up to 30x coverage may be necessary to identify some rare mutations precisely, but 8x may be sufficient to identify most variants. The major advantage of sequencing over GWAS is that sequencing is better for identifying rare variants (e.g., less than 1% frequency in cases), some of which are anticipated to have large effects on illness vulnerability.

Analysis of sequence data presents currently unsolved computational problems, since there are 3x10e9 data points per person, including several hundred thousand rare variants per person (Ng et al., 2008); each of us appears to carry 250–300 loss-of-function variants in annotated genes and 50-100 variants previously implicated in inherited disorders (1000 Genomes Project Consortium, 2010). How does one identify the truly pathogenic variants within these huge datasets? Current studies have relied on lists of genes previously reported to be associated with the disorder in question, as well as strategies of collapsing different variants within single genes or even single regions. We expect that statistical methods will evolve quickly in this area to help answer these questions and strengthen the value of sequencing methods for defining the genetics of bipolar, and other, disorders.

Copy Number Variation (CNV)

Studies of copy number variation (CNVs) have been ongoing for several years in neuropsychiatric disorders. CNVs are cytogenetic abnormalities that are too small to resolve using microscopic examination of the chromosomes, but still large enough to involve hundreds or thousands of base pairs. They are, therefore, mini-duplications or deletions of genetic material. They have been found to be widespread in healthy individuals, but have also been reported to be concentrated in areas of possible significance for autism (Pinto et al., 2010), intellectual disability (Morrow, 2010), and schizophrenia (Walsh et al., 2008). They may either be inherited or de novo, and the de novo events have appeared to be of more importance, at least for the childhood onset disorders. De novo status is demonstrated by examination of the parents’ genomes and confirmation of the absence of the event in them.

Rare CNVs were reported to be elevated in a study by Zhang et al. (2009) in subjects with bipolar disorder from the NIMH Genetics Initiative Database. A subsequent study showed increased CNVs in patients with bipolar disorder who had early onset (< 21), but not patients with later onset (Priebe et al., 2011). However other studies have not seen an elevation in CNVs (Grozeva et al., 2010). In order for these reports to be biologically meaningful, the identification and confirmation of specific loci, or genes, involved in the putative increased CNV burden in bipolar illness, will be necessary. One study has implicated 16p11.2 (McCarthy et al., 2009) and one study has reported increased CNVs in the GSK3beta gene (Lachman et al., 2007) in bipolar disorder. CNVs may now be detected using dedicated microchips, and thus these studies are expected to be more commonly performed in the future.

Pharmacogenetics of Lithium Response

Genetic evidence may be useful not only to identify pathophysiology and prediction of risk for illness, but also in identification of treatment mechanisms and prediction of treatment response. In a series of studies, Grof and his coworkers have described a method for strict classification of lithium responders and nonresponders among bipolar patients (Turecki et al., 2001). Their evidence suggests that lithium response is familial and probably genetic. Data from their group (Turecki et al., 2001) suggests linkage of lithium response to an area on chromosome 15q.

Specific genetic associations have not yet been identified and replicated for lithium response. Perlis et al. (2009) have published a genomewide association study of lithium response from the STEP-BD dataset but did not report unambiguous signals. Additional GWAS in this area are awaited.

Genetic Counseling

The lifetime risk (also described as age-corrected morbid risk) for severe (incapacitating) mood disorder in the general population is about 7%. Risk is increased to about 20% in first-degree relatives of unipolar depressed probands, and 25% in first-degree relatives of bipolar probands. Risk is about 40% in relatives of schizoaffective patients. The risk to offspring of two affected parents is in excess of 50% (Figure 9.1 and Tables 1 & 2; data from the family study described in Gershon et al., 1982). Overall prevalence figures appear to be rising in recent years, but more so in relatives of patients than in the general population (keeping at about a 3:1 ratio).

First episodes of bipolar illness almost always occur before age 50. Fully 50% of subjects with bipolar disorder develop an initial episode (either depressive or manic) prior to age 20 (see Figure 9.2).; for unipolar depression, the median onset age would be 25. This age distribution should be considered when assessing risk. For example, an unaffected 40-year-old son of a bipolar parent has already passed through most of the age at risk, and thus, his risk is substantially less than 25% to develop major mood disorder. An estimate of ∼2% would be more accurate in this case.

Figure 9.2 The cumulative age of onset for mania and depression in bipolar and unipolar mood disorders (data from the NIMH Genetics Initiative Study as described in Dick et al, 2003).

Figure 9.2
The cumulative age of onset for mania and depression in bipolar and unipolar mood disorders (data from the NIMH Genetics Initiative Study as described in Dick et al, 2003).

The subject of genetic counseling is discussed in greater detail elsewhere (Nurnberger and Berrettini, 1998) Nurnberger and Beirut (2007; Smoller et al, 2008). It is anticipated that genotypic methods will be adapted for use in genetic counseling in the coming years. Such methods are not yet clinically applicable. Most experts feel that genotypic screening for persons with multifactorial disorders would still be premature; however some products are already on the market, and it seems likely that the predictive power of such methods will approach clinical utility within the next decade. As our understanding of the genetics of bipolar disorder evolves, it is likely that we will first be able to define specific subgroups within the larger bipolar population, then develop tests to make diagnoses and, ultimately, use the information from these genetic analyses to improve treatments. As we integrate genetic studies with neuroimaging, treatment trials and other research methods, our ability to impact the lives of our bipolar patients should substantially improve.


Some sections of this chapter were modified from a chapter by Drs. Nurnberger, Wade Berrettini, and A. Niculescu in The Medical Basis of Psychiatry, H. Fatemi and P. Clayton, Saunders Publishers, 2008, and were used with permission.


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