Melvin McInnis, M.D. Bipolar II disorder (BD II) is a heritable illness. Early family and twin studies showed that bipolar disorder runs in families, with the risk of developing bipolar disorder being between 5 and 10 times higher in first-degree relatives of affected individuals compared with the general population (Craddock and Sklar 2013). Estimates of the heritability of bipolar disorder are in the range of 85% (McGuffin et al. 2003), meaning that the vast majority of variance in susceptibility to bipolar disorder is likely due to genetic factors. The rationale for defining BD II as a distinct subtype of bipolar disorder rests at least in part on evidence showing high family loading for the BD II phenotype (Dunner et al. 1976). Indeed, when bipolar subtypes are considered, no differences in heritability are observed (Edvardsen et al. 2008), suggesting that heritability is comparable across bipolar I disorder (BD I) and BD II. A recent large comparative family aggregation study (Parker et al. 2018) showed that the prevalence of a bipolar disorder diagnosis in any family member of a bipolar disorder proband was similar for the BD I and BD II subsets (41% vs. 36%), adding further credence to this literature. Heritability provides an estimate of the proportion of the disorder that can be attributed to genetic variation but does not identify individual genes or combinations of genes that cause or increase susceptibility to a disorder. Recent advances in genetics, however, have begun to enable an exploration of these important questions. Psychiatric genetics is a specialty field that focuses on psychiatric disorders and studies patterns of inheritance with the ultimate goal of finding etiological mechanisms that cause disease. In this chapter I review the genetics of BD II. The review will highlight the challenges intrinsic to the study of complex human disease, including the controversy around the validity of the BD II phenotype and evolving research methodologies in psychiatric genetics. In this review I underscore the finding that BD II, like most other heritable psychiatric disorders, is not a single-gene disorder but likely a manifestation of complex polygenic risk interacting with environmental factors. I also examine methodological difficulties that have arisen in elucidating the genetics of BD II as the field has moved from small linkage studies to examination of large datasets. In the earlier studies, for example, small sample sizes limited conclusions that could be drawn about the contribution to risk of individual genes. In the current era of “big data,” different kinds of challenges arise. For instance, in order to accumulate large samples, genetic studies often include both BD I and BD II, making it difficult to make definitive statements about genetic variants specific to one subtype versus another. The matter is confounded further by arguments about the validity of the BD II phenotype. As discussed in Chapter 2 (“Diagnosing Bipolar II Disorder”), most would agree that BD II is a unique clinical entity that can be reliably identified in clinical settings (Regier et al. 2013; Simpson et al. 2002). Nevertheless, as with any diagnosis based solely on clinical evaluation, there are ongoing debates surrounding the validity of the BD II diagnosis (Ghaemi et al. 2002) that complicate the evaluation of genes and genetic markers within the genome for association with the clinical phenotype (Schrodi 2017). The goal of psychiatric genetics is to identify molecular mechanisms that will form the basis for the discovery of novel interventions. With few evidence-based interventions available to treat BD II, the tools of psychiatric genetics may play an especially important role in expanding the therapeutic armamentarium for this neglected psychiatric condition. This chapter lays the foundations for understanding basic principles of psychiatric genetics, especially as they apply to BD II, with the goal of preparing readers to understand new genetic studies as they become available. Genetics is a core discipline in the study of human disease that examines the transmission of traits (inheritance) from one generation to the next. It is a fascinating and rapidly evolving field (Harper 2008) with direct relevance to all life forms because it describes the fundamentals (content and mechanism) in transmission of essential biological information to the next generation. Scholars in the classical era (e.g., Hippocrates, Aristotle) appreciated the importance of observed hereditary features (Vinci and Robert 2005). Similarities of temperament and moods across generations were described in the seventeenth-century writings of Burton’s The Anatomy of Melancholy (Burton 1859). The modern understanding of genetics was established by Gregor Mendel, who described the laws of segregation and independent assortment, collectively known as Mendel’s laws of inheritance (Glass 1953). The law of segregation states that each individual has two copies of any given genetic unit and during meiosis the resulting gamete has one copy (allele); the law of independent assortment implies that the segregation of each allele is independent from other genetic units. These laws form the basis for the fields of both quantitative (population-based) and molecular (laboratory-based) genetics (Plomin et al. 2009). Genetic studies test the statistical association between a phenotype (what is clinically or otherwise observed) and the genotype (the genetic variant at a specific location on the genome). It is essential that these measures be accurate and consistently measured across all individuals in any given study. Capacity for increasingly sophisticated genetic analyses has surged with advances in the biochemistry and analytic methods in genetics (Mattson 2018). However, assessment of clinical phenotypes remains tied to clinical observations and based on clinician driven evaluations of study participants. Concerns about reliability and validity of clinical diagnoses, particularly in a class of disorders where there are no established biological markers associated with disease states (Scarr et al. 2015), has created stumbling blocks to advancing genetic research. For these reasons, debates about the validity of the BD II diagnosis have impacted our ability to elucidate the underlying genetics of the disorder. The genetics of psychiatric disorders was inspired by early observations of inheritance patterns among families of affected individuals. Kraepelin established the increased frequency of psychiatric illnesses among family members (Jablensky 2007), and Slater’s twin studies in affective disorder demonstrated heritability of BD (then referred to as “manic-depressive disorder”) (Slater 1936). More recent studies (Bertelsen et al. 1977; Gershon et al. 1982) have anchored the early observations with studies that are more systematic and epidemiologically based. What was not determined was exactly what was inherited and how it caused disease. The next generation of genetic research moved beyond observations of pedigrees and epidemiology to more sophisticated analytic techniques. It was heralded by emerging genetic mapping technologies (Donis-Keller et al. 1987), including reports of successful identification of genetic linkage in Huntington’s disease (Gusella et al. 1983) with subsequent characterization of the underlying gene (MacDonald et al. 1993). Major efforts were focused on identification of families with evidence of BD segregating among members in order to conduct this research. Initial reports were encouraging (Egeland et al. 1987), albeit with evidence of genetic heterogeneity (Hodgkinson et al. 1987). Many challenges accompanied these early linkage studies, including the small sample size, need for replication, and changes in diagnosis over time (Kelsoe et al. 1989). As the limitations of small sample sizes became apparent, association studies overtook genetic linkage analyses, and the genome-wide association study (GWAS) quickly became the standard approach in genetic analyses (Burmeister et al. 2008). GWASs are essentially case-control design studies that compare the frequency of a specific allele in a group of individuals (cases) with the phenotype or disorder under study to its frequency in a control group that does not have the disorder (Cantor et al. 2010). The genetic measure is the genotype at a specific genetic location along the genome that is known to vary, known as single nucleotide polymorphisms (SNPs). SNPs are variations in a single nucleotide within the genome. The human genome is composed of more than three billion nucleotides distributed over 23 pairs of chromosomes yielding an estimated 10 million SNPs. A typical GWAS study will use 200,000 to 2.4 million SNPs in the analysis. Because so many SNPs are tested in GWAS analyses, there are concerns about multiplicity and resultant need to correct for multiple testing. The overall accepted level of significance is 5×10–8; the results are represented in a so-called Manhattan plot to demonstrate the level of significance relative to other markers along the genome. Figure 6–1 shows a Manhattan plot from a GWAS study of more than 20,000 cases with bipolar disorder, compared with more than 30,000 without. Twenty-two individual loci that exceed threshold values for significance are identified (Stahl et al. 2017). Odds ratios of findings are typically low (<1.5), however, leading many to challenge the validity of the approach (Visscher et al. 2012); nevertheless, the initial association findings on the gene CACNA1C (Ferreira et al. 2008), whose product helps control calcium channel activity, led to the study of calcium metabolism in induced pluripotent stem cells (iPSC) (Chen et al. 2014), which in turn suggested a model of illness in bipolar disorder that is related to the instability of the calcium channels in the neuronal membrane (O’Shea and McInnis 2015). Thus, GWAS should be considered a screening tool, but one of fundamental importance. FIGURE 6–1.Manhattan plot. (see color plate 1) The Manhattan plot is a graphical portrayal of the significance of the genome-wide association study (GWAS), visualizing the human genome along the x-axis by placing consecutive chromosomes in a linear representation. In the figure above, the x-axis begins with the “top” of chromosome 1 and ends with the “bottom” of chromosome 22. In this figure, the X and Y chromosomes are not included. The y-axis shows the –log10 of the P value. The horizontal line represents the threshold for genome-wide significance (P<5×10–8). The genes of known significance to bipolar disorder are listed in black, and the new genes identified in this study (Stahl et al. 2017) are listed in blue. There have been many reports of association of genetic loci with BD. Most of these studies involved individuals with both BD I and BD II. An early association study yielded the finding of a variant of the calcium-volted gated channel gene (CACNA1C) (Ferreira et al. 2008) associated with the bipolar disorder phenotype. Presence of this variant was subsequently replicated in independent samples (Green et al. 2013b). Other loci emerged in large-scale GWAS studies, including ANK3, ODZ (Sklar et al. 2011), and SYN1, followed by other loci at the ANK3 locus (Ferreira et al. 2008) and SYN1 (Green et al. 2013a). GWAS studies of schizophrenia and other psychiatric disorders (major depressive disorder) found evidence of association with these loci as well (Cardno and Owen 2014; Green et al. 2010, 2013a). These findings led to several challenges of the categorical distinctions between illnesses, which were originally proposed by Kraepelin (Jablensky 2007) and continue to serve as the basis for modern psychiatric diagnosis (see Chapter 1, “A Neglected Condition”). These studies raise fundamental questions about whether multiple genes contribute to multiple disorders (Craddock and Owen 2005, 2007). This is an attractive concept, given that each specific gene contributes a very small amount of genetic risk, as estimated by the low odds ratio of each individual loci. This genetic overlap in psychiatric disorders was supported in a large-scale collaborative study wherein common genetic risk alleles were shared among psychiatric disorders, but less so among neurological diseases (Brainstorm Consortium et al. 2018). Because of the increasing number of genetic risk loci that have been reported (Brainstorm Consortium et al. 2018; Stahl et al. 2017), a novel approach in the analyses of the genetic data has emerged over the past decade: the polygenic risk score (Purcell et al. 2009). This method considers the evidence from a series of risk alleles and generates a risk score based on the relative presence of reported risk alleles. It compares a test cohort with an established cohort with a specific disease for the frequency of alleles associated with the disorder. These findings do not point to specific loci, but rather provide a measure of the weight of the genetic contribution based on the frequency of risk alleles found in the affected individuals being tested. This provides credibility to the genetic hypothesis. (For a review of the methods of study in the polygenic risk score, see Maier et al. 2018, and for a review of the findings see Mistry et al. 2018.) The rationale for defining BD II as a distinct subtype of bipolar disorder was supported by observations of an increased risk for BD II among relatives of BD II patients (Dunner et al. 1976). Early multigeneration pedigree studies supported these observations, showing high rates of BD II within a single family. Figure 6–2 shows an example of such a BD II pedigree (DePaulo et al. 1990). These observational studies paved the way to explore, at a molecular level, the genetics of BD II. FIGURE 6–2.Family with bipolar II disorder (BD II) segregating. In the earlier phase of psychiatric genetic analyses, considerable focus was placed on the identification of families with bipolar disorder segregating among them. A number of families were identified with BD II segregating among them, such as the pedigree above. The study of the genetics of BD II began with the observation that evidence of genetic linkage was driven to a large degree by the sharing of the paternal alleles among individuals with BD II (McMahon et al. 2001). This finding led to the first genome-wide association analysis limited to the BD II phenotype (Nwulia et al. 2007). This study included families in which multiple subtypes of bipolar disorder were segregating as well as an additional nine pedigrees wherein BD II alone was segregating in the affected members. This group, led by Ray DePaulo at Johns Hopkins University, had previously demonstrated the reliability of the BD II phenotype in a test-retest evaluation of mood disorder subjects and found a strong reliability among evaluators with similar training and background (Simpson et al. 2002). It had long been recognized that BD II is a common bipolar phenotype (Simpson et al. 1993), with epidemiological studies showing a prevalence of 0.4% (Merikangas et al. 2007). Bipolar disorder consistently shows a high degree of heterogeneity. The most common variants are BD I and BD II, and the distribution of genetic correlations between BD I and BD II suggests the heterogeneity between these two disorders is nonrandom and not a function of bipolar disorder being a complex disorder (Charney et al. 2017). The first GWAS focused on BD II (Nwulia et al. 2007) was limited by low power because of the small sample size, and no consistent finding was reported; the families wherein only the BD II phenotype was segregating did not support the findings in families wherein a combination of phenotypes (BD I and BD II) was found. This is consistent with heterogeneity (sadly, a concept that can be used to explain away many inconsistent findings), suggesting that differing risk loci are implicated. However, it must be recognized that the sample sizes were simply too small to render any definitive conclusions. It rapidly became clear that it was necessary to study cohorts with large numbers of affected and unaffected individuals and compare frequencies of alleles at loci throughout the genome in order to arrive at stable findings. Indicators were emerging suggesting that BD, like most psychiatric disorders, is an illness wherein many genetic variants contribute to the susceptibility. It is 1) heterogeneous (i.e., different genes contribute to the disorder) and 2) polygenic (i.e., many genes collectively contribute to the disorder), and 3) shows a complex pattern of inheritance (i.e., what is inherited and how it is inherited is not known and many dynamic genetic mechanisms [copy number variants, dynamic expansions, and variations at regulatory mechanisms, such as microRNA] may be contributing to the illness). It was further found that most loci that were found to contribute to the bipolar phenotype, such as the CACNA1C locus, were reported to have an odds ratio in the range of 1.1 to 1.15 (Ferreira et al. 2008). Despite this small odds ratio, the very large sample size permitted extraction of this significant finding. At the individual level, however, these findings are next to meaningless—that is, having the risk allele may or may not be of consequence in the development of the illness. At CACNA1C (a risk gene that has been frequently replicated), for example, the frequency of the risk allele in the affected individuals is 36%, and in the unaffected individuals the frequency is 34%. One of the major challenges in elucidating the genetics of BD II has been the ascertainment of large cohorts that include the affected phenotype. To increase the sample size of affected individuals with BD, BD II is often included in the cohort. Furthermore, to increase the numbers within the study cohort, collaborations were established and data combined. The Psychiatric Genomics Consortium (PGC) was formed, and tremendous efforts were made to integrate data from a wide range of genetic studies. Some of the studies included BD I and BD II, whereas others focused exclusively on BD I. In the combined datasets the default analyses combined BD I and BD II. None of the studies focused specifically on BD II. When combining these datasets, the PGC relied on the clinical diagnoses made by the study teams. Data were included from multinational research groups, which introduces confounding effects of different diagnostic practices at diverse sites. Site-to-site differences in diagnostic trends were found within even a relatively cohesive multisite U.S. study (Saunders et al. 2008). Diagnostic drift and geocultural trends are inevitable, complicating interpretation of these findings. Early family studies (Gershon et al. 1982), completed after the establishment of BD II as a separate diagnosis (Dunner et al. 1976), found that BD II and other forms of affective illness, such as BD I, major depressive disorder (MDD), and schizoaffective disorder, bipolar manic type, were common among families with a BD I proband. These findings were considered to be consistent with a spectrum of illness pattern in the broad category of BD and justified inclusion of BD II in the analyses of the affected phenotype. This led to defining an affectation status model that was assigned according to the categorical diagnosis and implied a hierarchy of severity. BD I and schizoaffective disorder, bipolar manic type were designated as ASMI, the most severe group. BD II was defined as ASMII, and MDD as ASMIII, reflecting declining levels of severity. Several studies on the National Institute of Mental Health Collaborative Genetics program in BD used this approach. Most of the findings that were limited to this small cohort (in the current “big data” climate) were inconclusive, but it was often found that the findings were typically stronger for the more severe ASMI model (Badner et al. 2012). Other studies simply excluded BD II from the analyses (Fullerton et al. 2015). It should also be noted that family studies have fallen out of favor in bipolar research; however, a recent family study of a large Brazilian family suggested different loci for the subtypes (such as BD II) of BD, resurrecting the question of different loci contributing selectively to specific subphenotypes (Diniz et al. 2017). Individual genetic variants have been studied between BD I and BD II and have been driven by etiologic hypotheses about biochemistry or neural circuitry. This approach is reminiscent of the candidate gene approach of the last century whereby enterprising young researchers (such as this author) would study and publish findings on a few genetic variants. Biases induced by low sample sizes undoubtedly skewed the results, which were notoriously difficult to replicate (Ogden et al. 2004). This cautionary note notwithstanding, differences in genotypic and phenotypic expression have been reported between BD I and BD II; the long allele of the serotonin transporter was associated with lower harm avoidance behaviors in BD I compared with BD II (Lu et al. 2012). Combining biochemistry assays of gene products and comparing BD I and BD II have suggested differential effects in BD I and BD II. Examples include progranulin (GRN), in which both disorders show lower plasma levels but the risk allele at GRN is found only in BD I (Galimberti et al. 2014). A recent report identified differences in neuro-oxidative stress markers (Maes et al. 2018). These findings are of interest because they provide a biological foothold for possibly explaining the underlying differences between BD I and BD II. However, these findings must be viewed with caution, because they may turn out to be a statistical curiosity, driven by random variation and small sample sizes. None of these differences between BD I and BD II among candidate genes have been replicated. The reader, therefore, is advised to consider these findings with a skeptic’s diminished enthusiasm and await replication before endorsing them in scientific debate. The era of “big data” has driven collaborative efforts of the PGC to include increasingly larger samples. A recent meta-analysis of 32 cohorts from 14 countries included 20,352 cases to compare with 31,358 unaffected controls. Only Caucasians were included in an attempt to minimize the potential heterogeneity caused by race and ethnic differences. Thirty loci were significant on a GWAS, of which 20 were novel (not reported in earlier studies). The primary analyses included BD I and BD II disorder; however, a secondary analysis focused on the subset of 3,421 individuals with BD II disorder in the combined dataset did not identify loci genome-wide that were significantly different from those in unaffected control subjects (Stahl et al. 2017). The relevance of the psychiatric diagnosis in the genetic analysis may be less than previously suggested. There has been evidence emerging over the past several years suggesting that there are genetic variants that contribute to susceptibility to psychiatric illnesses in general (i.e., genes associated with BD also associate with schizophrenia). A recent large-scale analysis that included all psychiatric and neurological disorders found a common thread of risk alleles contributing to most neuropsychiatric disorders, but not to the neurological and neurodegenerative disorders (Brainstorm Consortium et al. 2018). This is consistent with the observation that a general underlying susceptibility to psychiatric disorders is inherited, and additional mechanisms are needed to explain the specific nature of the illness. Analytic strategies that include comorbid illnesses or data that describe the course of illness may be useful in identifying specific genetic contributions to specific illness patterns within the bipolar phenotype. Analysis of the PGC data identified 28 subphenotypes related to illness pattern (age at onset and interview, number of episodes and hospitalizations, and comorbid features of illness and behaviors); within the bipolar phenotype, the presence of psychosis was the strongest classifier for the contributing risk of the polygenic set (Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium 2018). Both individuals with BD I and those with BD II were included in these studies to maximize the number of subjects in the cohort. The study of bipolar II disorder is among the more fascinating areas of human research. The disorder reaches across dimensional threads of human behaviors, emotions, and mood states, and defines qualitative and quantitative disease states that result in considerable disability and interference with personal, social, and vocational endeavors. It is also a very common BD phenotype (Simpson et al. 1993). From the initial description over 40 years ago, BD II has been controversial because of difficulties in reliable diagnosis and overlap with personality disorders (see Chapters 2, “Diagnosing Bipolar II Disorder,” and Chapter 3, “Interface Between Borderline Personality Disorder and Bipolar II Disorder,” respectively). However, a rigorous phenomenological approach to clinical diagnosis permits reliable diagnosis of this condition (Regier et al. 2013). In addition, there are tantalizing biological and genetic indicators suggesting that there may be unique biological factors that influence the development and manifestation of BD II. Current trends in genetics suggest that many psychiatric diseases, including BD II, may be the result of a broad-based susceptibility, on top of which other influencing factors at several levels contribute to development of disease. These factors include environment, behaviors, life experiences, personality and psychological factors, sleep and circadian influences, as well as responses to interventions (pharmacogenomics). Genetic architecture influences behaviors and personality, as well as many other elements of neuronal functioning. 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6
Genetics of Bipolar II Disorder
History of Genetics
What Is Genetics?
Why Is Phenotype Important?
Early Studies
Genome-Wide Association Studies
Genetics of Bipolar Disorder
Genetics of Bipolar II Disorder
Genome-Wide Association Studies
Findings From Large Cohort Studies
Affectation Status Model
Individual Genetic Variants
“Big Data” Era
Conclusion
KEY POINTS
References
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