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.

BackgroundPsychiatric 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.AimsTo 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.MethodWe 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.ResultsThe 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.ConclusionsOur 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.

Genome-wide association studies (GWAS) have identified a number of loci that have strong support for their association with bipolar disorder (BD). The Psychiatric Genome-Wide Association Study (GWAS) Consortium Bipolar Disorder Working Group (PGC-BD) meta-analysis of BD GWAS data sets and replication samples identified evidence (P=6.7 × 10⁻⁷, odds ratio (OR)=1.147) of association with the risk of BD at the polymorphism rs9371601 within SYNE1, a gene which encodes nesprin-1. Here we have tested this polymorphism in an independent BD case (n=1527) and control (n=1579) samples, and find evidence for association (P=0.0095) with similar effect sizes to those previously observed in BD (allelic OR=1.148). In a combined (meta) analysis of PGC-BD data (both primary and replication data) and our independent BD samples, we found genome-wide significant evidence for association (P=2.9 × 10⁻⁸, OR=1.104). We have also examined the polymorphism in our recurrent unipolar depression cases (n=1159) and control (n=2592) sample, and found that the risk allele was associated with risk for recurrent major depression (P=0.032, OR=1.118). Our findings add to the evidence that association at this locus influences susceptibility to bipolar and unipolar mood disorders.

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