Sian Caesar

Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder

To identify susceptibility loci for bipolar disorder, we tested 1.8 million variants in 4,387 cases and 6,209 controls and identified a region of strong association (rs10994336, P = 9.1 × 10−9) in ANK3 (ankyrin G). We also found further support for the previously reported CACNA1C (alpha 1C subunit of the L-type voltage-gated calcium channel; combined P = 7.0 × 10−8, rs1006737). Our results suggest that ion channelopathies may be involved in the pathogenesis of bipolar disorder.

The bipolar disorder risk allele at CACNA1C also confers risk of recurrent major depression and of schizophrenia

Molecular genetic analysis offers opportunities to advance our understanding of the nosological relationship between psychiatric diagnostic categories in general, and the mood and psychotic disorders in particular. Strong evidence (P=7.0 × 10−7) of association at the polymorphism rs1006737 (within CACNA1C, the gene encoding the α-1C subunit of the L-type voltage-gated calcium channel) with the risk of bipolar disorder (BD) has recently been reported in a meta-analysis of three genome-wide association studies of BD, including our BD sample (N=1868) studied within the Wellcome Trust Case Control Consortium. Here, we have used our UK case samples of recurrent major depression (N=1196) and schizophrenia (N=479) and UK non-psychiatric comparison groups (N=15316) to examine the spectrum of phenotypic effect of the bipolar risk allele at rs1006737. We found that the risk allele conferred increased risk for schizophrenia (P=0.034) and recurrent major depression (P=0.013) with similar effect sizes to those previously observed in BD (allelic odds ratio ∼1.15). Our findings are evidence of some degree of overlap in the biological underpinnings of susceptibility to mental illness across the clinical spectrum of mood and psychotic disorders, and show that at least some loci can have a relatively general effect on susceptibility to diagnostic categories, as currently defined. Our findings will contribute to a better understanding of the pathogenesis of major psychiatric illness, and such knowledge should be useful in providing an etiological rationale for shaping psychiatric nosology, which is currently reliant entirely on descriptive clinical data.

Meta-Analysis of Genome-Wide Association Data of Bipolar Disorder and Major Depressive Disorder

Meta-analysis of genome-wide association data of bipolar disorder and major depressive disorder

Strong genetic evidence for a selective influence of GABAA receptors on a component of the bipolar disorder phenotype.

Despite compelling evidence for a major genetic contribution to risk of bipolar mood disorder, conclusive evidence implicating specific genes or pathophysiological systems has proved elusive. In part this is likely to be related to the unknown validity of current phenotype definitions and consequent aetiological heterogeneity of samples. In the recent Wellcome Trust Case Control Consortium genome-wide association analysis of bipolar disorder (1868 cases, 2938 controls) one of the most strongly associated polymorphisms lay within the gene encoding the GABAA receptor β1 subunit, GABRB1. Aiming to increase biological homogeneity, we sought the diagnostic subset that showed the strongest signal at this polymorphism and used this to test for independent evidence of association with other members of the GABAA receptor gene family. The index signal was significantly enriched in the 279 cases meeting Research Diagnostic Criteria for schizoaffective disorder, bipolar type (P=3.8 × 10−6). Independently, these cases showed strong evidence that variation in GABAA receptor genes influences risk for this phenotype (independent system-wide P=6.6 × 10−5) with association signals also at GABRA4, GABRB3, GABRA5 and GABRR1. Our findings have the potential to inform understanding of presentation, pathogenesis and nosology of bipolar disorders. Our method of phenotype refinement may be useful in studies of other complex psychiatric and non-psychiatric disorders.

Bipolar disorder and polymorphisms in the dysbindin gene (DTNBP1)

BACKGROUND Several studies support the dysbindin (dystrobrevin binding protein 1) gene (DTNBP1) as a susceptibility gene for schizophrenia. We previously reported that variation at a specific 3-locus haplotype influences susceptibility to schizophrenia in a large United Kingdom (UK) Caucasian case-control sample. METHODS Using similar methodology to our schizophrenia study, we have investigated this same 3-locus haplotype in a large, well-characterized bipolar sample (726 Caucasian UK DSM-IV bipolar I patients; 1407 ethnically matched controls). RESULTS No significant differences were found in the distribution of the 3-locus haplotype in the full sample. Within the subset of bipolar I cases with predominantly psychotic episodes of mood disturbance (n = 133) we found nominally significant support for association at this haploptype (p < .042) and at SNP rs2619538 (p = .003), with a pattern of findings similar to that in our schizophrenia sample. This finding was not significant after correction for multiple testing. CONCLUSIONS Our data suggest that variation at the polymorphisms examined does not make a major contribution to susceptibility to bipolar disorder in general. They are consistent with the possibility that DTNBP1 influences susceptibility to a subset of bipolar disorder cases with psychosis. However, our subset sample is small and the hypothesis requires testing in independent, adequately powered samples.

Clinical differences between bipolar and unipolar depression

It is commonly -- but wrongly -- assumed that there are no important differences between the clinical presentations of major depressive disorder and bipolar depression. Here we compare clinical course variables and depressive symptom profiles in a large sample of individuals with major depressive disorder (n=593) and bipolar disorder (n=443). Clinical characteristics associated with a bipolar course included the presence of psychosis, diurnal mood variation and hypersomnia during depressive episodes, and a greater number of shorter depressive episodes. Such features should alert a clinician to a possible bipolar course. This is important because optimal management is not the same for bipolar and unipolar depression.

Genetic utility of broadly defined bipolar schizoaffective disorder as a diagnostic concept

Background Psychiatric phenotypes are currently defined according to sets of descriptive criteria. Although many of these phenotypes are heritable, it would be useful to know whether any of the various diagnostic categories in current use identify cases that are particularly helpful for biological–genetic research. Aims To use genome-wide genetic association data to explore the relative genetic utility of seven different descriptive operational diagnostic categories relevant to bipolar illness within a large UK case–control bipolar disorder sample. Method We analysed our previously published Wellcome Trust Case Control Consortium (WTCCC) bipolar disorder genome-wide association data-set, comprising 1868 individuals with bipolar disorder and 2938 controls genotyped for 276 122 single nucleotide polymorphisms (SNPs) that met stringent criteria for genotype quality. For each SNP we performed a test of association (bipolar disorder group v. control group) and used the number of associated independent SNPs statistically significant at P<0.00001 as a metric for the overall genetic signal in the sample. We next compared this metric with that obtained using each of seven diagnostic subsets of the group with bipolar disorder: Research Diagnostic Criteria (RDC): bipolar I disorder; manic disorder; bipolar II disorder; schizoaffective disorder, bipolar type; DSM–IV: bipolar I disorder; bipolar II disorder; schizoaffective disorder, bipolar type. Results The RDC schizoaffective disorder, bipolar type (v. controls) stood out from the other diagnostic subsets as having a significant excess of independent association signals (P<0.003) compared with that expected in samples of the same size selected randomly from the total bipolar disorder group data-set. The strongest association in this subset of participants with bipolar disorder was at rs4818065 (P = 2.42×10–7). Biological systems implicated included gamma amniobutyric acid (GABA)A receptors. Genes having at least one associated polymorphism at P<10–4 included B3GALTS, A2BP1, GABRB1, AUTS2, BSN, PTPRG, GIRK2 and CDH12. Conclusions Our findings show that individuals with broadly defined bipolar schizoaffective features have either a particularly strong genetic contribution or that, as a group, are genetically more homogeneous than the other phenotypes tested. The results point to the importance of using diagnostic approaches that recognise this group of individuals. Our approach can be applied to similar data-sets for other psychiatric and non-psychiatric phenotypes.

Age-at-onset in bipolar-I disorder: Mixture analysis of 1369 cases identifies three distinct clinical sub-groups

BACKGROUND To assess whether bipolar disorder type I segregates into three clinically distinct sub-groups defined by age-at-onset. METHODS Clinical data were available on 1369 individuals with DSM-IV bipolar I disorder. Mixture analysis was performed on the age-at-onset (AAO) data to determine whether they were composed of more than one normal distribution. Individuals were allocated to groups according to the results of the mixture analysis. Categorical logistic regression was then used to investigate relationships between AAO and nine clinical characteristics. RESULTS The distribution of AAOs in our sample comprised a mixture of three normal distributions with means of 18.7 (SD=3.7), 28.3 (SD=5.5) and 43.3 (SD=9.1) years, with relative proportions of 0.47, 0.39 and 0.14 respectively. Individuals were allocated into three groups dependent on their AAO: < or = 22; 25-37; and > or = 40 years, producing a sample of 1225 individuals (144 with borderline values were excluded). Eight out of the nine clinical characteristics showed evidence for a statistical association with AAO group. LIMITATIONS Systematic and non-systematic recruitment of participants. Some data relied on retrospective recall. CONCLUSIONS Our results provide further robust evidence to suggest that the AAO distribution of individuals affected with bipolar disorder is composed of three normal distributions. Substantial clinical heterogeneity between the three AAO groups may reflect genetic heterogeneity within bipolar I disorder. Future genetic studies should consider AAO grouping as potential sub-phenotypes.

Identifying hypomanic features in major depressive disorder using the hypomania checklist (HCL-32)

BACKGROUND Recent studies have challenged the traditional unipolar/bipolar divide with increasing support for a more dimensional view of affective disorders. We here examine the occurrence of hypomanic symptoms in individuals with a history of major depression selected to exclude indicators of underlying bipolarity. METHODS The presence of hypomanic symptoms was assessed by the Hypomania Checklist (HCL-32) self-report questionnaire in a sample of almost 600 patients meeting DSM-IV criteria for Bipolar I disorder (BPI N=260) or Major Recurrent Depressive disorder (MDDR N=322). Subjects were recruited and assessed using consistent, robust methodology. RESULTS We found that a score of 20 or more on the HCL-32 yielded the best combination of sensitivity (68%) and specificity (83%) to distinguish between BPI and MDDR. Within our highly selected and well defined MDDR sample (for which exclusion criteria included personal or family histories of bipolar or psychotic illness), 17% of MDDR subjects scored over the threshold of 20 on the HCL-32. CONCLUSIONS The HCL-32 identified a substantial number of patients meeting DSM-IV criteria for recurrent major depression (even when selected to exclude personal and family histories of bipolar illness) who reported bipolar symptoms at a level similar to that reported by patients meeting diagnostic criteria for bipolar disorder. This demonstrates the limitations of using DSM-IV criteria to distinguish those with and without bipolar features of illness.

Polarity at illness onset in bipolar I disorder and clinical course of illness

OBJECTIVES Studies have suggested that episode polarity at illness onset in bipolar disorder may be predictive of some aspects of lifetime clinical characteristics. We here examine this possibility in a large, well-characterized sample of patients with bipolar I disorder. METHODS We assessed polarity at onset in patients with bipolar I disorder (N = 553) recruited as part of our ongoing studies of affective disorders. Lifetime clinical characteristics of illness were compared in patients who had a depressive episode at first illness onset (n = 343) and patients who had a manic episode at first illness onset (n = 210). RESULTS Several lifetime clinical features differed between patients according to the polarity of their onset episode of illness. A logistic regression analysis showed that the lifetime clinical features significantly associated with a depressive episode at illness onset in our sample were: an earlier age at illness onset; a predominantly depressive polarity during the lifetime; more frequent and more severe depressive episodes; and less prominent lifetime psychotic features. CONCLUSIONS Knowledge of pole of onset may help the clinician in providing prognostic information and management advice to an individual with bipolar disorder.