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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Affect Disord. Author manuscript; available in PMC 2017 August 10.
Published in final edited form as:
PMCID: PMC5552237
NIHMSID: NIHMS885432

The genetics of early-onset bipolar disorder: A systematic review

Abstract

Background

Early-onset bipolar disorder has been associated with a significantly worse prognosis than late-onset BD and has been hypothesized to be a genetically homogenous subset of BD. A sizeable number of studies have investigated early-onset BD through linkage-analyses, candidate-gene association studies, genome-wide association studies (GWAS), and analyses of copy number variants (CNVs), but this literature has not yet been reviewed.

Methods

A systematic review was conducted using the PubMed database on articles published online before January 15, 2015 and after 1990. Separate searches were made for linkage studies, candidate gene-association studies, GWAS, and studies on CNVs.

Results

Seventy-three studies were included in our review. There is a lack of robust positive findings on the genetics of early-onset BD in any major molecular genetics method.

Limitations

Early-onset populations were quite small in some studies. Variance in study methods hindered efforts to interpret results or conduct meta-analysis.

Conclusions

The field is still at an early phase for research on early-onset BD. The largely null findings mirror the results of most genetics research on BD. Although most studies were underpowered, the null findings could mean that early-onset BD may not be as genetically homogenous as has been hypothesized or even that early-onset BD does not differ genetically from adult-onset BD. Nevertheless, clinically the probabilistic developmental risk trajectories associated with early-onset that may not be primarily genetically determined continued to warrant scrutiny. Future research should dramatically expand sample sizes, use atheoretical research methods like GWAS, and standardize methods.

Keywords: Genetics, Early-onset, Bipolar disorder

1. Introduction

Bipolar disorder (BD) is a severe recurrent illness characterized by episodes of mania and depression (Goodwin and Jamison, 2007). Although it was once believe to primarily affect adults, evidence gathered since the 1980s has shown that it frequently begins in adolescence (Akiskal et al., 1985; Geller and Luby, 1997). This research also began to investigate the symptomatology of early-onset psychiatric disorders in comparison to adult-onset disorders (Akiskal et al., 1985). In the past 15 years, a number of studies have used admixture analysis to argue that bipolar disorder can be defined by two (Javaid et al., 2011; Kennedy et al., 2005; Kroon et al., 2013) or three distinct age-of-onset groups (Coryell et al., 2013; Lin et al., 2006; Severino et al., 2009; Tozzi et al., 2011). The early-onset group has typically been found to have an upper-bound of 18–22 years (Grigoroiu-Serbanescu et al., 2014). Early-onset BD has often been associated with a worse prognosis than late-onset BD, including more psychotic features, drug and alcohol abuse disorders, comorbidity with panic and obsessive–compulsive disorders, rapid cycling, lower lithium-response, and more suicide attempts (Cate Carter et al., 2003; Geoffroy et al., 2013; Grigoroiu-Serbanescu et al., 2014; Javaid et al., 2011; Lin et al., 2006; Perlis et al., 2004), although the specific clinical features associated with an early-onset have not always been consistent (e.g. Coryell et al., 2013; Ernst and Goldberg, 2004).

These findings have been taken as evidence that early-onset BD may have a qualitatively different genetic etiology than late-onset BD. Indeed, there is evidence that the age-of-onset is significantly correlated between siblings of a bipolar proband (Leboyer et al., 1998; Lin et al., 2006; O’Mahony et al., 2002), with heritability estimates for age-of-onset between 0.41 and 0.52 (Faraone et al., 2004; Visscher et al., 2001). Early-onset BD also appears to increase the morbidity risk of BD and other psychotic and affective disorders in relatives to a greater extent than late-onset BD, suggesting that early-onset BD may be a more heritable and severe form of the illness (Grigoroiu-Serbanescu et al., 2001, 2014; Schürhoff et al., 2000; Somanath et al., 2002; Taylor and Abrams, 1981). At least one large study, however, did not find significant familiality of age-of-onset for bipolar disorder (Schulze et al., 2006).

It is worth noting that these studies have often downplayed the role that environmental and developmental factors could play in shaping the clinical features of early-onset BD and the frequency of the disorder in relatives. For instance, one reason individuals who develop BD in adolescence will be more prone to long-term drug abuse than those who develop the disorder in middle age is the availability of narcotics, social pressures, and risk-taking behaviors that characterize adolescence. There do not have to be major genetic differences between early- and late-onset BD for this pattern to occur. A developmental psychopathology perspective could help explain how biological and environmental factors increase or decrease risk of bipolar disorder across the lifespan (Alloy et al., 2006; Klimes-Dougan et al., in press).

Over the past 15 years, a sizeable number of studies have investigated the genetics of early-onset BD, but there has not yet been an effort to review such studies (there have been two review of the genetics of childhood-onset BD; see Faraone et al., 2003; Mick and Faraone, 2009). The growing importance of Research Domain Criteria (RDoC) and “subphenotypes” in psychiatrics genetics research (Saunders et al., 2008) makes a review of this literature timely and all the more pertinent, given that early-onset BD has been one of the more studied subtypes of bipolar disorder. Moreover, as genetics research becomes an important source of therapeutic targets for psychopharmacology research, it is crucial to understand the relationship between early- and late-onset BD. Here, we systematically review the findings of these studies from four major methods of genetics research: linkage-analyses, candidate gene-association studies, genome-wide association studies (GWAS), and research on copy number variants (CNVs). We conclude by discussing the current status of the field and conclude by looking at future directions of genetics research on early-onset BD.

2. Methods

We conducted a systematic literature review of articles published (online) before January 15, 2015 and after 1990. Keyword searches were made using the PubMed database. For gene-linkage studies, we used the keywords “linkage AND bipolar disorder AND onset” which returned six papers that met our inclusion criteria. For candidate gene-association studies and genome-wide association studies we used the search terms “polymorphism AND bipolar disorder AND onset.” Due to the large number of studies on a variety of polymorphisms, we limited our discussion to candidate genes that have been investigated by at least two papers. Fifty-nine studies were ultimately included. For GWAS, we also used the terms “genome wide association AND bipolar disorder AND onset” and included two studies. For copy number variants (CNVS), we searched the terms “copy number AND bipolar disorder AND onset” and included six studies.

For inclusion, a study must have specifically looked for associations with age-of-onset or “early-onset” BD, and could not have looked at age-of-onset in a mixed mood disorder or mixed-psychosis population. Although we required that DSM criteria for BD be met (either DSM-III or DSM-IV), we did not place any restrictions on the types of BD included such as bipolar I disorder (BDI) or bipolar II disorder (BDII). Historically a range of criteria have been applied children and adolescent BD diagnosis (as reviewed by Klimes-Dougan et al., in press), but the genetic studies reviewed here typically applied a “narrow” DSM definition of BD.

Most genetics research has defined early-onset bipolar disorder as occurring before the age of 18–25 and meeting narrow DSM criteria. We were not strict about the specific cut-off age that a study used to define “early-onset” (e.g. onset before 18 years, 22 years, 25 years, etc.), so long as the study clearly defined its methods. We excluded studies that primarily reported on children or pre-adolescents because the research to date with children rarely uses narrow DSM criteria and the relationship between childhood-onset BD and adult BD is still disputed (see Leibenluft and Rich, 2008). Moreover, two valuable reviews on the genetics of childhood BD have already been compiled (Faraone et al., 2003; Mick and Faraone, 2009).

Three types of studies were included in this review, as each sheds light on a unique aspect of the genetic architecture of early-onset BD: how those with early-onset BD differ from controls, how they differ from individuals with a late-onset, and how genetic variants affect age-of-onset more generally. One type of study uses the presumed genetic homogeneity of the early-onset subtype as a way to identify susceptibility genes for BD in small samples. This research stems from the notion that the heterogeneity of BD has hindered genetics studies, even studies using thousands of participants. This branch of research compares an early-onset group to controls, often without comparing them to a late-onset BD group (e.g. GWAS). The second type of study has focused on determining the genetic differences between early- and late-onset BD and compares these two groups directly, often without a comparison to a control. Statistical analysis is typically used to sort patients into two or three separate onset-age groups, based on admixture analysis for the specific sample or on an arbitrary cut-off. The third type of study makes no effort to classify patients as early- or late-onset. Instead, it simply attempts to find an association between age-of-onset and a genetic variant. This type of analysis is often applied as an addendum to studies where age-of-onset data were collected for each participant.

Because this is a relatively new field, we did not set a minimum sample size or require the use of specific types of statistical analysis. Admittedly, the sample sizes of some studies were quite small, but we felt that it was useful to report results even if this research might be considered preliminary or have methodological limitations. We generally made note of the specific methodology used by each study, given that these parameters could influence the genetic heterogeneity of the sample and therefore the results of gene-associations. The small number of studies and the wide variance in methodology were two reasons that we did not attempt to meta-analyze or bundle these studies.

3. Linkage studies

Linkage analysis is a low-resolution molecular genetics technique that is able to implicate chromosomal regions that harbor variants with large phenotypic effects. More than 40 linkage studies have been conducted on BD. They have implicated regions on nearly every chromosome and findings have rarely been consistently or convincingly reproduced (Barnett and Smoller, 2009; Juli et al., 2012). Many researchers have interpreted these results as evidence that BD is not caused by common variants with large effect sizes, but by a large number of common variants of small effect size and/or very rare variants with much larger effect sizes (Craddock and Sklar, 2013; Gratten et al., 2014).

A small number of linkage studies have been conducted on early-onset BD, either on the disorder directly or by re-analyzing data from previous linkage studies on BD. Faraone et al. (2004) applied multipoint variance-components linkage analysis to the NIMH BD Genetics Initiative linkage study, consisting of 539 individuals in 97 families. They found that regions 12p, 14q, and 15q had minor (non-significant) associations with age-of-onset. Faraone et al. (2006) employed an ordered-subset analysis for early-onset BD (<20 years) on the same sample, which did not replicate the three previously-implicated regions. Chromosome 9q34 was implicated among the early-onset population, though this association was no longer significant after correcting for multiple comparisons. Lin et al. (2005) found that early age-of-onset (≤21 years) was associated with chromosomes 18p11.2 (LOD=2.83) and 21q22.13 (LOD=3.29) in a scan of 150 multiplex pedigrees with 874 individuals. Neither of these regions was implicated in a later study (Zandi et al., 2007), although this study did find a slight trend of linkage on 18p, given that the LOD increased from 0.0 to 1.03 when age-of-onset was included in the analysis. In this later study, Zandi et al. did not find any region significantly associated with an early-onset (≤21 years). No positive finding from any linkage study was significantly replicated in a second study.

Etain et al. (2006) performed a linkage scan on 87 sibling pairs in which a proband had an early-onset (≤21 years) of a mood disorder, with 29 sib-pairs having a proband with early-onset BD-I (the “narrow” phenotype). The study found three associations with the narrow early-onset group (3p14, 5q33, and 16q23) and six with the broad phenotype. These eight regions were evaluated in 51 new families in addition to the previous sample by Mathieu et al. (2010). When comparisons were made between early-onset and late-onset siblings, only the 2q14 region was significantly associated with the early-onset BD-I subtype. However, a candidate gene-association study by the same research group (Etain et al., 2010) found SNPs in the 20p12 region that were associated with the early-onset subtype, specifically synaptosomal-associated protein of 25 kDa (SNAP25).

Taken together, linkage studies on early-onset BD have returned conflicting results that have very rarely been replicated. However, as Mathieu et al. (2010) pointed out, these studies have employed noticeably different methodologies that may have contributed to the inconsistent findings, including types of statistical analyses, sample sizes, definitions of age-of-onset, inclusion of BD-II, and criteria for selecting affected families. These inconsistencies make it difficult to assess the results of current linkage studies. The fact that these regions have generally not been replicated by other candidate gene or GWAS may suggest that the vast majority of causal variants are still too small to show up in linkage analyses, even when using a more homogenous subtype. In this regard, linkage studies suggest that early-onset BD appears to have a similar genetic etiology as adult BD: many common polymorphisms with small effect and very rare variants with larger effects.

4. Candidate gene-association studies

Candidate gene-association studies detect associations among a small number of single nucleotide polymorphisms (SNPs) derived from a priori hypotheses about the neurobiological mechanisms of a disorder. Hundreds of candidate gene-association studies have been conducted on BD, with most focusing on genes related to serotonin, dopamine, glutamate, and brain-derived neurotrophic factor (BDNF) (Juli et al., 2012; Seifuddin et al., 2012). Like linkage analyses, these studies have primarily yielded inconsistent findings. A recent meta-analysis reviewed 487 candidate gene studies and found SNPs in only four genes with nominal significance before correcting for multiple comparisons: BDNF, DRD4, DAOA, and TPH1 (Seifuddin et al., 2012). None was significant after this correction. Neither were these polymorphisms significantly implicated in any of the 15 major genome-wide association studies (GWAS) on BD (Shinozaki and Potash, 2014). These negative results are not entirely surprising, given the substantial evidence that the majority of causal genetic variants in BD are common with very small effect sizes (OR=1.05 to 1.20) (Craddock and Sklar, 2013; Gratten et al., 2014). Large sample sizes are needed to detect an association, and even then, the polymorphism will only weakly increase risk of the disorder.

A sizeable number of candidate gene-association studies have been conducted on the early-onset subtype. Here, we report research only on polymorphisms and genes that have been investigated by at least two studies that met the criteria described above. However, we also describe single studies when they focused on biological systems or pathways that have been the subject of many early-onset BD gene-association studies (e.g., dopaminergic genes, oxidative stress, etc.). Candidate genes that have been investigated by only one study that nonetheless met our inclusion-criteria include: dysbindin-1 (DTNBP1); neurotrophic tyrosine kinase receptor 2 and 3 (NTRK2 and NTRK3); G72/G30 (DAOA); FYN kinase; the methylenetetrahydrofolate reductase (MTHFR); the nicotinic acetylcholine receptor; the norepinephrine transporter; G protein-coupled receptor 50 (GPR50); apolipoprotein E; the L-type calcium ion channel subunit CACNA1C; and the nuclear receptor NR2E1. Although candidate gene-association studies on early-onset BD have the advantage of using a subtype that possibly has a more homogenous presentation than mixed age-of-onset population, these studies have typically employed small populations (<300 cases) and very few positive findings have been independently replicated. Therefore, these results should be interpreted cautiously.

4.1. Brain-derived neurotrophic factor (BDNF)

Brain-derived neurotrophic factor (BDNF) regulates neuronal survival, differentiation, maintenance, synaptic plasticity, and axon growth (Huang and Reichardt, 2001). There is compelling evidence that BDNF is involved in the pathophysiology of BD, including involvement in lithium response (Rybakowski et al., 2005) and being inversely correlated to the severity of manic and depressive symptoms (reviewed in Scola and Andreazza, 2015; Tang et al., 2008). Most candidate gene-association studies on BDNF have focused on the valine to methionine substitution at codon 66, which has been associated with rapid cycling (Müller et al., 2006), smaller grey matter, and lithium responsiveness (see Wu et al., 2014). Surprisingly, no polymorphisms in BDNF have been significantly associated with BD in the largest meta-analysis of candidate gene-association studies (Seifuddin et al., 2012) or in the largest GWAS on BD (Shinozaki and Potash, 2014).

Tang et al. (2008) searched for two polymorphisms in BDNF, Val66Met (r6265) and C270T, in an early-onset (<18 years, n=67) and late-onset population (n=130). They found a significant increase in the Val allele in the early-onset population compared to the late-onset group, but no association between BDNF and BD as a whole. Rybakowski et al. (2003) found that the Val/Val genotype was significantly associated with an earlier onset than the Val/Met genotype, but the sample size was quite small (N=54) with only one individual with the Met/Met genotype. Conversely, Skibinska et al. (2004) found an association between the Met/Met genotype and early-onset BD (≤18 years; p=.03) but not for the alleles, but this study was limited by a very small early-onset population (n=28) and only 3 early-onset cases with Met/Met. Miller et al. (2013) also found that the Met allele was associated with a 35% earlier onset among individuals with childhood sexual abuse, but this association was not significant after regression analysis. Two larger studies did not find any association between Val66Met and age-of-onset: Kunugi et al. (2004) (N=519 cases) and Neves-Pereira et al. (2002) (N=283 cases). At least one study has implicated BDNF in pre-pubertal and early-adolescent BD (Geller et al., 2004), though a later study (N=170 cases) failed to replicate this association (Mick et al., 2009). Thus, the fact that the positive results have only come from very small studies suggests that these findings are false positives. Still, all of these studies have been fairly small. Larger samples are needed to convincingly confirm the involvement of BDNF in early-onset BD.

4.2. Dopamine receptor genes

The five dopamine receptors have been connected to cognitive impairments, memory and learning, reward and reinforcement, and stress response, and have been implicated in a range of psychiatric conditions, including alcoholism, schizophrenia, BD, ADHD, and depression (Brisch, 2014; Glatt et al., 2015; Noble, 2003). Dozens of studies have been conducted on the different dopamine receptor genes in adult BD, but no finding has remained significant in meta-analysis after Bonferroni correction (Kirov et al., 1999; Seifuddin et al., 2012). A small number of studies have evaluated the dopamine receptor genes in early-onset BD. Two studies have looked at the gene for the D1 receptor (DRD1), specifically the −48A/G polymorphism in DRD1, and age-of-onset in BD and schizophrenia. Among 380 BD cases, Dmitrzak-Weglarz et al. (2006) found that the G/G genotype was associated with a BD onset after 18 years and with BD-II, but not with schizophrenia or BD in general. Ni et al. (2002) not find an association between age of onset in BD and three DRD1 polymorphisms (−48A/G, −800T/C, and −1403T/C) in 286 BD trios.

Other studies have focused on the gene for the D2 receptor. Squassina et al. (2011) identified an association between the DRD2 TaqIA polymorphism and early-onset BD (≤21 years) in 300 BD cases. A study looking at 339 Polish BD cases found no association between BD and the DRD2 −141C ins/del polymorphism, including in 29 early-onset BD cases (<18 years; Leszczyńska-Rodziewicz et al., 2005). Serretti et al. (1999a) found an association between DRD2 and disorganized symptoms in a very small sample of early-onset BD (<25 years, n=13). Massat et al. (2002) found a significant association between DRD2 and BD in 358 cases that was also significant between early-onset BD (≤25 years) and controls. Thus, if DRD2 plays any role in the etiology of BD, it is likely a general risk factor, not one specific for early-onset. In this same study, the Ser9Gly polymorphism in DRD3 was not associated with BD, MDD, or with early-onset BD.

With regards to the gene for the D4 receptor, Gonçalves et al. (2012) found an association for the DRD4 variable number of tandem repeat (VNTR) 7-repeat allele and a 2.7 year earlier onset in BD (p=.03; N=274 BD subjects). This VNTR was also associated with a late onset in females with schizophrenia. The 7-repeat (7R) allele in exon 3 causes functional changes to the G-protein-coupled receptor, making it less responsive to dopamine (Gonçalves et al., 2012). By contrast, Serretti et al. (1999b) did not find any association between DRD4 exon 1 and 3 variants and early-onset BD (<25 years) in 210 BD-I and BD-II subjects. DRD5 has not been investigated by a (published) candidate gene-association study in an early-onset population. Finally, two studies have looked for an association between early-onset BD and polymorphisms for tyrosine hydroxylase (the rate-limiting enzyme in the biosynthesis of the catecholamines) but neither has found any significant association (Ho et al., 2000; Souery et al., 1999).

4.3. Catechol-O-methyltransferase (COMT)

Catechol-O-methyltransferase (COMT) is a methylation enzyme located on chromosome 22q11.2 involved in the catabolism of catecholamines (dopamine, epinephrine, and norepinephrine). The Val/Met polymorphism (rs4680) has been the primary subject of gene-association studies and has implicated in schizophrenia, anxiety disorder, and BD (Glatt et al., 2015). The Met allele results in 3-to-4 fold lower COMT activity, resulting in increased synaptic dopamine (reviewed in Brisch, 2014). Although COMT has not been implicated in the largest meta-analysis of gene-association studies, one meta-analysis found a significant association among Asians but not among Caucasians, showing that it may have a specific role only in certain ethnicities (Zhang et al., 2009).

Only a few studies have evaluated the role of COMT in the etiology of early-onset BD. In one study, Massat et al. (2011) found an association between the Val/Met polymorphism and early-onset BD (<18 years) in 147 cases. Associations with rs2075507 and rs4818 lost significance after correcting for multiple comparisons. Zhang et al. (2009) also found an association between the Val/Met SNP and early-onset BD (<20 years) that lost significance after the Bonferroni correction. Benedetti et al. (2011) did not find an association between the Val/Met polymorphism and age-of-onset in 163 BD-I individuals. Two candidate studies on COMT in pre-pubertal and early-adolescent BD also failed to find an association (Mick et al., 2009; Geller and Cook, 2000).

4.4. Serotonin transporter and receptor genes

Serotonin is one of the major neurotransmitters and has been implicated in a wide range of psychopathology, including BD and major depression (see Glatt et al., 2015; Mann, 1999). Although serotonin-related genes have been popular targets in candidate gene-association studies on BD, almost none have been validated by meta-analyses or GWAS (Seifuddin et al., 2012). A moderate number of studies have investigated the role of serotonin-related genes in early-onset BD with conflicting results.

The serotonin transporter returns serotonin from the synapse to the pre-synaptic neuron, effectively decreasing the amount of neurotransmitter in the synapse (see Glatt et al., 2015). Two polymorphisms in the serotonin transporter gene SLC6A4 have been the major targets of investigation: the variable-number tandem repeat (5-HTT-VNTR) and the 5-HTTLPR polymorphism in the promoter (44-bp insertion/deletion), both of which affect transcription of the gene (see Glatt et al., 2015). A recent meta-analysis involving 3778 cases and 4997 controls attempted to account for the heterogeneity of studies on 5-HTTLPR and found that the Short allele slightly increased risk of BD in European populations (OR=1.10; Jiang et al., 2013).

In a small sample (N=83), Yen et al. (2003) did not find an association between BD age of onset and genotype or allele frequencies of the 5-HTT-VNTR. Among 216 patients with BD, Bellivier et al. (2002) found that the 5-HTT-VNTR polymorphism was significantly associated with age-of-onset, but that the 5-HTTLPR polymorphism was not. In 88 BD cases, Gutierrez et al. (1998) did not detect any association between the VNTR or 5-HTTLPR polymorphisms, with BD in general or with age-of-onset. Mendlewicz et al. (2004) found no association for the 5-HTTLPR polymorphism with age-of-onset in BD or with BD in general (N=572 BD cases). By contrast, Pinto et al. (2011) did find a significant association between an earlier age of onset and the Short allele of the 5-HTTLPR polymorphism (p=.02) in 350 BD patients and 693 unaffected relatives. Manchia et al. (2010) and Mohammadi et al. (2015) also found an association for earlier-onset and the Short allele of the 5-HTTLPR polymorphism in 230 and 150 BD patients, respectively. Conversely, among 83 patients with BD, Serretti et al. (2004) found a trend towards the Long allele being associated with a lower age of onset (p=.07). Ospina-Duque et al. (2000) did not find a significant difference between the Short and Long alleles and age-of-onset in 85 BD cases, although the Short allele was overrepresented in early-onset cases (<21 years). Two other studies failed to find an association between pre-pubertal and early-adolescent BD and the 5-HTTLPR polymorphism (Geller and Cook, 1999; Mick et al., 2009).

Among other serotonin-related genes, Massat et al. (2007) found an association between early-onset BD (≤16 years) and Cys23Ser polymorphism of the serotonin 2C receptor gene (HTR2C). In 151 Kurdish–Iranian BD-I patients, however, Mohammadi et al. (2015) did not find a significant overrepresentation of the HTR2C Ser allele in early-onset cases (≤19 years). Massat, Souery et al. (2000) did not find an association between polymorphisms in the serotonin 2A receptor gene (HTR2A) and early-onset BD (≤25 years), or with BD in general. Ni et al. (2002) also failed to find a significant association between HTR2A and age-of-onset among 286 BD trios. Kim et al. (2014) found no association between the functional polymorphism C(−1019)G (rs6295) of the serotonergic 1A receptor (HTR1A) and BD, including subset analysis for age-of-onset. Only one study has studied early-onset BD and the gene for tryptophan hydroxylase 2 (TPH-2), which encodes the rate-limiting step in the biosynthesis of serotonin. In 190 BD-I patients, Grigoroiu-Serbanescu et al. (2008) found significant associations for four TPH-2 SNPs in an early-onset BD group (≤25 years, n=107), but not for BD in general. Taken together, there is little convincing evidence that serotonin-related genes play a significant role in the etiology of early-onset BD.

4.5. Genes related to immunity, inflammation, and oxidative stress

In the past 10 years, immune function, inflammation, and oxidative stress have been increasingly implicated in the etiology of psychiatric illnesses, including BD (e.g. Andreazza, 2012; Goldstein et al., 2009; Kim et al., 2007). Only a small number of studies have investigated these genes in relation to BD and early-onset BD, but this is a growing area of research.

The Toll-like receptors (TLR) are pattern recognition receptors that play an important role in innate immunity against microbial pathogens and trigger pro-inflammatory pathways (Moresco et al., 2011). Two recent papers have implicated the role of TLR-2 and TLR-4 in the pathogenesis of early-onset BD (<22 years). Oliveira et al. (2014) found two SNPs in the TLR4 gene that were associated with BD and early-onset BD (rs1927914 and rs11536891). Oliveira et al. (2014) investigated the presence of candidate polymorphisms in the TLR2 gene among 229 early-onset and 342 late-onset BD cases. The authors found that TLR2 rs3804099 TT and TLR2 rs4696480 TT were significantly associated with the early-onset BD group.

Xu et al. (2009) linked early-onset BD (≤24 years) to two haplotypes in the nonselective calcium ion permeable transient receptor potential melastatin type 2 gene (TRPM2) in 300 families with a bipolar spectrum disorder proband. This gene is involved in immune-system function, oxidative stress, and apoptosis (Nilius et al., 2007), and has been inconsistently implicated in adults with BD (McQuillin et al., 2006). In 183 BD subjects, Roh et al. (2007) failed to find any relationship between BD, age-of-onset, and the cytokine gene monocyte chemoattractant protien-1 (MCP-1), which is involved in immune function, the regulation of neurotransmission, and the regulation of other proteins implicated in BD, like NMDA receptor 1 and glycogen synthase kinase 3 (GSK-3) (see Roh et al., 2007).

Three studies have investigated the relationship between age-of-onset in BD and X-box binding protein 1 (XBP1), which encodes a transcription factor involved in the regulation of MHC class II genes, T cell genes, and the endoplasmic reticulum (ER) stress response. A large meta-analysis found a significant association between the XBP1 and BD among Asians, but not among BD in Caucasians (Cheng et al., 2014). Findings in the early-onset BD population have also been mixed: Kakiuchi et al. (2003) found an association with age-of-onset and XBP1, while Cichon et al. (2004) and Hou et al. (2004) failed to find an association.

Finally, two papers have investigated the relationship between early-onset BD and genes involved in oxidative stress, specifically the genes for the glutathione S-transferases. Glutathione S-transferases (GSTs) help neutralize reactive oxygen species, which can cause damage to biomolecules and lead to cell death. Oxidative stress has been implicated in the etiology of BD (Andreazza et al., 2008; Andreazza, 2012), and serum levels of glutathione have been associated with age-of-onset (Rosa et al., 2014). Mohammadynejad et al. (2011) investigated genes for GSTs in 228 patients with BD and found that neither GSTM1 nor GSTT1 were associated with risk for the disorder. However, a combination of the positive genotype for GSTM1 and the null genotype of GSTT1 significantly increased risk of BD among the early-onset, but not late onset, group (OR=2.28). A 2012 study by Rezaei, Saadat, and Saadat looked for associations with polymorphisms for glutathione S-transferase Z1 among 228 individuals with BD. They found that early-onset BD (≤19 years) was associated with polymorphisms at Glu32Lys and G-1002A in GSTZ1, along with the haplotype “−1002G, 32Glu, 42Gly”.

4.6. Circadian rhythm and clock genes

There has been consistent evidence linking disruptions in circadian and social rhythms to the onset of BD (Murray and Harvey, 2010). A handful of studies have investigated polymorphisms in genes related to biological clocks and circadian rhythms in adult BD (Milhiet et al., 2011, 2014; see Murray and Harvey, 2010) and in the early-onset subtype, but the findings are conflicting and unconvincing.

One oft-studied gene is that of glycogen synthase kinase 3-β (GSK3-β), which has a diverse range of functions including the regulation of transcription factors and involvement in the biological clock (Grimes and Jope, 2001). GSK3-β is also inhibited by lithium and valproate (Gould and Manji, 2005). Two studies found that the −50T/T (rs334558) genotype in the effective promoter region for the gene encoding GSK3-β was associated with age-of-onset in 185 Italian patients with BD-I, but not with BD in general (Benedetti et al., 2004a, 2004b). Most studies published after this initial finding have not replicated it (Lee and Kim, 2011; Lin et al., 2012; Szczepankiewicz et al., 2006), including a meta-analysis of six studies involving 649 BD-I patients (Chen et al., 2014). In 118 patients with BD, Lee and Kim (2011) found that the −172A/T polymorphism was associated with age-of-onset. Lin et al. (2012) investigated a sample of 138 Taiwanese BD-I patients and found that −157C/T (rs6438552) was associated with older onset BD in females. Both of these studies did not find an association with the polymorphisms and BD in general.

In regards to other circadian-rhythm genes, Benedetti et al. (2008) found that age-of-onset was associated with a variable-number tandem repeat (VNTR) polymorphism in the Period 3 (Per3) gene, which is involved in the biological clock. Artioli et al. (2007) found that two exonic polymorphisms in Per3 were associated with age-of-onset (T/G in exon 15 and T/C in exon 18). Severino et al. (2009) investigated SNPs in the REV-ERBα gene (NR1D1) in 300 bipolar patients of Sardinian ancestry. REV-ERBα interacts with GSK3-β to synchronize the biological clock (Yin et al., 2006). One haplotype was associated with late-onset BD (>38 years) and one SNP (rs12941497) was associated with the early-onset group, but no associations were found for BD in general. McGrath et al. (2009) found associations between RORB and BD, but not for early-onset BD. Lee et al. (2010) found an association between the −3111T/C SNP (rs1801260) in the CLOCK gene and BD, but not with age-of-onset in BD.

4.7. Linkage-analysis driven candidate gene-association studies

With the evidence that the vast majority of candidate gene-association studies have returned null or conflicting findings, a number of papers have pointed out that the candidate gene approach necessarily relies on current (and inherently speculative) theories of the neurobiology of mental disorders (Barnett and Smoller, 2009; Craddock and Sklar, 2013; Seifuddin et al., 2012). In recent years, research groups have attempted to avoid the limitations of the theory-driven selection of candidate genes by using the results of linkage studies to locate candidate genes. Three studies have used this technique to study early-onset BD, which has led to the implication of three genes: ADRB2 (encoding the beta-2-adrenergic receptor OMIM 109690; Dizier et al., 2012); SNAP25 (encoding the synaptosomal-associated protein of 25 kDa; Etain et al., 2010); and the SNP rs1156026 (perhaps related to Diacylglycerol kinase eta [DGKH] or the serotonin receptor 2A [HTR2A]; Bureau et al., 2013). Given that none of these studies has been replicated, these results can only be interpreted cautiously. Still, the use of atheoretical methods to identify candidate genes in small populations is a positive step for the field.

5. Genome-wide association studies

Genome-wide association studies (GWAS) enable the detection of common SNPs (typically in >5% of the population) across the vast majority of the genome. Despite the substantial evidence that BD is highly heritable, GWAS have implicated only a very small number of polymorphisms, including ones in genes for calcium ion channel subunits (e.g. CACNA1C) and cell-surface or extracellular matrix proteins involved in inter-cellular communication (e.g., ODZ4, ANK3, NEK4-ITIH1,3,4, and NCAN; see Shinozaki and Potash, 2014; Sklar et al., 2011). The paucity of findings is almost certainly due to the fact that the common SNPs involved in complex mental illnesses have very small effects, meaning that extremely large samples are required to identify any causal polymorphisms (Gratten et al., 2014). Nonetheless, there is compelling evidence that common SNPs constitute a large fraction of susceptibility to BD (roughly 25%) and can be identified in GWAS with extremely large samples (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014).

We identified only two GWAS that have examined age-of-onset in BD (Jamain et al., 2014; Mahon et al., 2011). Both investigated the early-onset subtype primarily to reduce the heterogeneity of the sample rather than to identify differences between early- and late-onset BD. Neither found any SNPs that achieved the stringent genome-wide statistical significance but both were underpowered: Jamain and colleagues included a total of 370 early-onset cases and over 2400 controls while Mahon et al. included 2744 controls and 2836 BD cases with information about age-of-onset and psychotic symptoms. With such small sample sizes, it was very unlikely that either study could have detected a causal variant in early-onset BD. Nevertheless, these negative results suggest that the genetic architecture of early-onset BD is likely composed of many common variants with small effects, similar to adult BD. Moreover, these studies cast doubt on the idea that the early-onset subtype has enough genetic homogeneity to require significantly fewer participants than conventional GWAS on adult BD.

6. Copy number variants (CNVs)

Since the completion of the Human Genome Project, researchers have primarily investigated the role of common SNPs in the etiology of psychiatric disorders. Recently, there has been growing interest in the role of structural variations in mental illness, primarily the rare but large (>1000 bp) duplications and deletions called copy number variants (CNVs). Although CNVs are far less common than SNPs, each CNV tends to be much more pathogenic, given its grater size and functional impact on the genome. Gratten et al. (2014) reported that common SNPs will typically have odds-ratios of ~1.05–1.2 while rare CNVs will have odds-ratios of ~2 to >20. Interest in CNVs has also stemmed from evidence that common SNPs are responsible for roughly 17–29% of variance in liability for common mental illnesses (24–26% in BD; Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013; Lee and Chow, 2014; Yang et al., 2011), meaning that a substantial proportion of liability must not come from common SNPs. Rare SNPs and rare CNVs could represent two major sources of this liability. A number of studies have investigated the role of CNVs in early-onset BD, and can be grouped into two categories: the total number (burden) of CNVs in the genome and the frequency of de novo CNVs.

6.1. Total number (burden) of CNVs

There is compelling evidence that the burden of CNVs in the genome (total number of both de novo and inherited CNVs) is elevated in a number of psychiatric disorders, particularly autism (Pinto et al., 2010; Sanders et al., 2011), schizophrenia (Rees et al., 2014; Sebat et al., 2009; Walsh et al., 2008), intellectual disability/developmental delay (Coe et al., 2014; Girirajan et al., 2012), and ADHD (Williams et al., 2010).

A small number of studies have investigated the burden of total CNVs in BD. The majority have not found an elevated number of CNVs in BD compared to controls (Grozeva et al., 2010, 2013; McQuillin et al., 2011; Noor et al., 2014; Priebe et al., 2012). In fact, at least three studies have found lower rates of CNVs in BD than in controls, especially for very large (>1 Mb) CNVs (Georgieva et al., 2014; Grozeva et al., 2013; McQuillin et al., 2011). Two studies have found an increased burden of CNVs in cases of early-onset BD, one of which focused on singleton deletions (Priebe et al., 2012; Zhang et al., 2009). However, two studies with comparable sample sizes failed to replicate this result (Grozeva et al., 2013; Noor et al., 2014). Even in the studies that associated CNVs with early-onset BD, the effect size was fairly small (OR around 1.5), suggesting that these CNVs are not the major source of liability that some rare variant models predicted.

6.2. De novo CNVs

There is inconsistent evidence that the frequency of de novo CNVs (variants in a child’s DNA that are not in either parent’s) is increased in BD or early-onset BD. The earliest report of increased de novo CNVs in BD came from Malhotra et al. (2011) who found de novo CNVs in 4.3% of 185 BD probands (OR=4.8), 4.5% of schizophrenia probands (OR=5.0), and 0.9% of 426 healthy controls. The rate of de novo CNVs was 5.6% (OR=6.3) among individuals with an onset before 18. A recent meta-analysis of 768 parent–child trios, however, only found a significant difference in CNVs between BD patients and controls for the less stringent CNV threshold (>10 kb) and not for the more stringent one (>100 kb) (Georgieva et al., 2014). Furthermore, no study has found an association between the frequency of de novo CNVs and age of onset (Georgieva et al., 2014; Noor et al., 2014), although most of these studies are likely underpowered given the rarity of de novo CNVs.

7. Discussion

The findings from this review highlight the nascent status of genetics research on early-onset BD. Across four major molecular genetics methods, there is little convincing evidence of differences in the genetic architecture of early- and late-onset subtypes of BD. In one sense, the lack of findings from this research is not surprising. Thus far, most genetics research on complex mental illnesses (including adult BD) has had only limited success in identifying causal genetic variants, particularly when using linkage analysis and candidate gene-association studies. There is a growing consensus among researchers that heightened vulnerability for BD is associated with thousands of common SNPs with small effects and by a small number of rare variants with larger effects (Craddock and Sklar, 2013; Gratten et al., 2014; Shinozaki and Potash, 2014; for disagreement see Cirulli and Goldstein, 2010). Currently, most common variants can only be detected by using GWAS with extremely large sample sizes that are relatively homogenous. Rare variants are most easily detected by using high-resolution techniques like whole-exome or whole-genome sequencing (or with GWAS, in the case of CNVs). Genetics research on early-onset BD has typically been vastly underpowered and has employed techniques that are ill-equipped to detect common and rare variants, like low-resolution linkage studies. As this review indicates, they have primarily offered negative, conflicting, and irreproducible findings.

Candidate gene-association studies of early-onset BD have also been plagued by inconsistent and irreproducible results. While dozens of studies have been conducted on a wide-range of polymorphisms, these studies have generally been underpowered and have employed varying methodologies (e.g. defining early-onset differently, using mixed BD-MDD populations, using mixed ethnicities, etc.) which have hindered efforts at generalizing or replicating findings. In general, there have been too few attempts to replicate polymorphisms that have previously been implicated, especially in much larger populations. As a result, there are now a large (and growing) number of genes that have been tentatively associated with BD but few that have been robustly implicated or excluded. Before testing new theories of candidate genes, a concerted effort should be made to identify a very large population of early-onset BD patients who can be enrolled in GWAS or gene-association studies that test all of the currently-implicated polymorphisms. The largest GWAS on schizophrenia, for instance, has uncovered 128 polymorphisms at 108 loci after using over 36,000 cases and 113,000 controls (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). Assembling sample sizes this large will require coordinated effort of many research groups at many institutions or nation-wide initiatives. Although this undoubtedly will be an difficult a task, it is increasingly apparent that a small number of large-scale studies are far more valuable than a large number of underpowered studies that neither prove nor disprove an hypothesis (as the null and conflicting findings reviewed in this paper serve to demonstrate).

Research on CNVs is another place where larger sample sizes are needed to determine the role of CNVs in early onset BD. Despite the discrepancies between some studies, current evidence indicates that there does not seem to be a major difference in the burden of total CNVs or de novo CNVs in early-onset cases. It seems possible that the very rare variants with large effect-sizes are not CNVs, but very rare SNPs that are not detectable on current microarrays. These would require detection by whole exome or whole genome sequencing. Despite being a relatively new field, exome sequencing has already been applied to adult BD to search for highly penetrant rare variants (in <1% of population) with some success (e.g. Kato, 2015).

In future studies on early-onset BD, efforts should be made to standardize methodology. There is a pressing need for genetics studies to use a consistent definition of “early-onset” and “late-onset.” It is difficult to compare studies or to conduct meta-analyses when the definition of “late-onset” or “early-onset” varies by as much as 10 or 15 years between studies. Participants who are classified as “early-onset” in one study are often defined as “late-onset” by a different study looking at the same polymorphisms. While age is only a rough estimate of development, a greater consensus in the field about these issues would be optimal.

Similarly, efforts to standardized methodology will require a closer look at diagnostic considerations. It may be advisable to restrict the sample to a single type of BD (e.g. BD-I). Although there is still considerable debate regarding the relationship between DSM classifications and genetic homogeneity, it is likely that homogeneity gained by studying the early-onset phenotype will be negated by including the full range of BDs, especially when there is evidence of substantial genetic heterogeneity among the bipolar disorders (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013). This discussion about diagnostic inclusion criteria has been especially salient when the focus is on a population who is continuing to undergo development, leading to ongoing, lively debates in the field about what features of BD are represented early in development, how these characteristics change across development, and methods of altering the probabilistic risk trajectories (Klimes-Dougan et al., in press).

It is also possible that phenotypic and endophenotic features may be better markers of genetic homogeneity, especially when one considers the significant variation in the symptom profile of those with early-onset BD (e.g. Coryell et al., 2013). Such phenotypic features include measures of affective temperament (Greenwood et al., 2012), dimensional analysis of symptoms (Labbe et al., 2012), analysis of multi-dimensional phenotypes like brain structure/function relationships (Schumann, 2014), and endophenotypes like cognitive deficits and resting state brain activity (Gottesman and Gould, 2003; Hall and Smoller, 2010; Hasler et al., 2006; Hasler and Northoff, 2011). One study combined GWAS with fMRI of amygdala activation in a small sample of youth with BD and identified a SNP in the gene DOK5 that accounted for 33% of the variation in amygdala activation in the BD sample compared to 12% of the variance in the controls (Liu et al., 2010). On the other hand, a large study conducted at the University of Minnesota cast doubt on the idea that endophenotypes can simplify genetics research by showing that endopheno-types are “massively polygenic” and are “not sufficiently simpler genetically to aide in gene discovery” in a sample of nearly 5000 participants (Iacono et al., 2014).

Although it is clear that the heterogeneity and genetic complexity of BD has hindered the elucidation of the genetic etiology of BD, molecular genetics research has not offered compelling evidence that early-onset BD has sufficient genetic homogeneity to be advantageous in research. Currently, none of the papers reviewed convincingly shows that the use of the early-onset subtype allowed for the detection of associations that would not be present in a mixed-age-of-onset population. Neither is there evidence that the use of an early-onset group enabled the detection of polymorphisms with smaller sample sizes than in a conventional study. The null findings even raise the question of whether early-onset bipolar disorder is qualitatively different from the adult-onset disorder. If nothing else, the negative findings indicate that the early-onset subtype has sufficiently genetic overlap with adult BD that their differences cannot be detected with low-resolution probes and small samples. When we recognize that environmental and developmental factors could help account for the age-of-onset clusters (Alloy et al., 2006), it is very possible that early-onset BD is a (quantitatively) more severe form of BD, and that both disorders can be best situated along the bipolar spectrum. If there are major genetic differences between early-onset and late-onset BD, it seems that the use of GWAS and high-resolution techniques will be needed to identify them. At a minimum, GWAS could definitively calculate the genetic heterogeneity between patients with different ages-of-onset and the proportion of common SNPs that comprise each subtype, much as it has for adult BD (see Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013).

In total, the idea that early-onset BD has a large degree of homogeneity and a unique genetic etiology is an interesting theory but one that has not yet been successful in practice. Small sample sizes and varying methodologies have hindered the progress of this research. Nonetheless, the attempt to use narrow phenotypic features to study an illness is a positive direction for the field to take and there have already been very interesting preliminary studies conducted on candidate genes involved in a wide range of biological pathways, including oxidative stress, immune function, and inflammation. For assurance that these results are true-positives, however, these studies should be replicated in GWAS with large sample sizes. Large-scale collaborative efforts employing atheoretical genetics research (e.g. GWAS, whole exome sequencing, and whole genome sequencing) are the most promising way forward.

8. Limitations

The central limitation of this review is the small sample sizes of the studies included, many of which evaluated early-onset BD through subset analysis. In some cases, this led to very small samples (N<30) which have difficulty implicating many causal polymorphisms. These studies were included to show where preliminary genetics research has been conducted on early-onset BD. A second limitation is that meaningful meta-analysis could not be conducted on this research, given the varying methods used by the studies. Thus, for some genetics methods, this review explores the state of the field more than it can definitively implicate genetic variants in early-onset BD.

Acknowledgments

The authors express their deep gratitude to Irving Gottesman for offering feedback on the paper. They also thank Brian Apland for help compiling references and Kristen Doucette for proof-reading the paper. There were no funding sources for this review.

Footnotes

Conflict of Interest

Disclosures: The authors have no conflicts of interests to disclose. All authors have approved the final article.

Role of funding source

There was no funding source for this review.

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