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Prior to the genome-wide association era, candidate gene studies were a major approach in schizophrenia genetics. In this invited review, we consider the current status of 25 historical candidate genes for schizophrenia (for example, COMT , DISC1 , DTNBP1 and NRG1 ). The initial study for 24 of these genes explicitly evaluated common variant hypotheses about schizophrenia. Our evaluation included a meta-analysis of the candidate gene literature, incorporation of the results of the largest genomic study yet published for schizophrenia, ratings from informed researchers who have published on these genes, and ratings from 24 schizophrenia geneticists. On the basis of current empirical evidence and mostly consensual assessments of informed opinion, it appears that the historical candidate gene literature did not yield clear insights into the genetic basis of schizophrenia. A likely reason why historical candidate gene studies did not achieve their primary aims is inadequate statistical power. However, the considerable efforts embodied in these early studies unquestionably set the stage for current successes in genomic approaches to schizophrenia.

A small number of rare, recurrent genomic copy number variants (CNVs) are known to substantially increase susceptibility to schizophrenia. As a consequence of the low fecundity in people with schizophrenia and other neurodevelopmental phenotypes to which these CNVs contribute, CNVs with large effects on risk are likely to be rapidly removed from the population by natural selection. Accordingly, such CNVs must frequently occur as recurrent de novo mutations. In a sample of 662 schizophrenia proband–parent trios, we found that rare de novo CNV mutations were significantly more frequent in cases (5.1% all cases, 5.5% family history negative) compared with 2.2% among 2623 controls, confirming the involvement of de novo CNVs in the pathogenesis of schizophrenia. Eight de novo CNVs occurred at four known schizophrenia loci (3q29, 15q11.2, 15q13.3 and 16p11.2). De novo CNVs of known pathogenic significance in other genomic disorders were also observed, including deletion at the TAR (thrombocytopenia absent radius) region on 1q21.1 and duplication at the WBS (Williams–Beuren syndrome) region at 7q11.23. Multiple de novos spanned genes encoding members of the DLG (discs large) family of membrane-associated guanylate kinases (MAGUKs) that are components of the postsynaptic density (PSD). Two de novos also affected EHMT1 , a histone methyl transferase known to directly regulate DLG family members. Using a systems biology approach and merging novel CNV and proteomics data sets, systematic analysis of synaptic protein complexes showed that, compared with control CNVs, case de novos were significantly enriched for the PSD proteome ( P =1.72 × 10 −6 ). This was largely explained by enrichment for members of the N -methyl- D -aspartate receptor (NMDAR) ( P =4.24 × 10 −6 ) and neuronal activity-regulated cytoskeleton-associated protein (ARC) ( P =3.78 × 10 −8 ) postsynaptic signalling complexes. In an analysis of 18 492 subjects (7907 cases and 10 585 controls), case CNVs were enriched for members of the NMDAR complex ( P =0.0015) but not ARC ( P =0.14). Our data indicate that defects in NMDAR postsynaptic signalling and, possibly, ARC complexes, which are known to be important in synaptic plasticity and cognition, play a significant role in the pathogenesis of schizophrenia.

Genome-wide association (GWAS) analyses have identified susceptibility loci for many diseases, but most risk for any complex disorder remains unattributed. There is therefore scope for complementary approaches to these data sets. Gene-wide approaches potentially offer additional insights. They might identify association to genes through multiple signals. Also, by providing support for genes rather than single nucleotide polymorphisms (SNPs), they offer an additional opportunity to compare the results across data sets. We have undertaken gene-wide analysis of two GWAS data sets: schizophrenia and bipolar disorder. We performed two forms of analysis, one based on the smallest P -value per gene, the other on a truncated product of P method. For each data set and at a range of statistical thresholds, we observed significantly more SNPs within genes ( P min for excess<0.001) showing evidence for association than expected whereas this was not true for extragenic SNPs ( P min for excess>0.1). At a range of thresholds of significance, we also observed substantially more associated genes than expected ( P min for excess in schizophrenia=1.8 × 10 −8 , in bipolar=2.4 × 10 −6 ). Moreover, an excess of genes showed evidence for association across disorders. Among those genes surpassing thresholds highly enriched for true association, we observed evidence for association to genes reported in other GWAS data sets ( CACNA1C ) or to closely related family members of those genes including CSF2RB , CACNA1B and DGKI . Our analyses show that association signals are enriched in and around genes, large numbers of genes contribute to both disorders and gene-wide analyses offer useful complementary approaches to more standard methods.

We have conducted a genotyping study using a custom Illumina Infinium HD genotyping array, the ImmunoChip, in a new UK sample of 1218 bipolar disorder (BD) cases and 2913 controls that have not been used in any studies previously reported independently or in meta-analyses. The ImmunoChip was designed before the publication of the Psychiatric Genome-Wide Association Study Consortium Bipolar Disorder Working Group (PGC-BD) meta-analysis data. As such 3106 single-nucleotide polymorphisms (SNPs) with a P -value <1 × 10 −3 from the BD meta-analysis by Ferreira et al. were genotyped. We report support for two of the three most strongly associated chromosomal regions in the Ferreira study, CACNA1C (rs1006737, P =4.09 × 10 −4 ) and 15q14 (rs2172835, P =0.043) but not ANK3 (rs10994336, P =0.912). We have combined our ImmunoChip data (569 quasi-independent SNPs from the 3016 SNPs genotyped) with the recently published PGC-BD meta-analysis data, using either the PGC-BD combined discovery and replication data where available or just the discovery data where the SNP was not typed in a replication sample in PGC-BD. Our data provide support for two regions, at ODZ4 and CACNA1C, with prior evidence for genome-wide significant (GWS) association in PGC-BD meta-analysis. In addition, the combined analysis shows two novel GWS associations. First, rs7296288 ( P =8.97 × 10 −9 , odds ratio (OR)=0.9), an intergenic polymorphism on chromosome 12 located between RHEBL1 and DHH . Second, rs3818253 ( P =3.88 × 10 −8 , OR=1.16), an intronic SNP on chromosome 20q11.2 in the gene TRPC4AP , which lies in a high linkage disequilibrium region along with the genes GSS and MYH7B .

The Schizophrenia Psychiatric Genome-Wide Association Study Consortium (PGC) highlighted 81 single-nucleotide polymorphisms (SNPs) with moderate evidence for association to schizophrenia. After follow-up in independent samples, seven loci attained genome-wide significance (GWS), but multi-locus tests suggested some SNPs that did not do so represented true associations. We tested 78 of the 81 SNPs in 2640 individuals with a clinical diagnosis of schizophrenia attending a clozapine clinic (CLOZUK), 2504 cases with a research diagnosis of bipolar disorder, and 2878 controls. In CLOZUK, we obtained significant replication to the PGC-associated allele for no fewer than 37 (47%) of the SNPs, including many prior GWS major histocompatibility complex (MHC) SNPs as well as 3/6 non-MHC SNPs for which we had data that were reported as GWS by the PGC. After combining the new schizophrenia data with those of the PGC, variants at three loci ( ITIH3/4 , CACNA1C and SDCCAG8 ) that had not previously been GWS in schizophrenia attained that level of support. In bipolar disorder, we also obtained significant evidence for association for 21% of the alleles that had been associated with schizophrenia in the PGC. Our study independently confirms association to three loci previously reported to be GWS in schizophrenia, and identifies the first GWS evidence in schizophrenia for a further three loci. Given the number of independent replications and the power of our sample, we estimate 98% (confidence interval (CI) 78–100%) of the original set of 78 SNPs represent true associations. We also provide strong evidence for overlap in genetic risk between schizophrenia and bipolar disorder.

The genetic architecture of schizophrenia is complex, involving risk alleles ranging from common alleles of weak effect to rare alleles of large effect, the best exemplar of the latter being large copy number variants (CNVs). It is currently unknown whether pathophysiology in those with defined rare mutations overlaps with that in other individuals with the disorder who do not share the same rare mutation. Under an extreme heterogeneity model, carriers of specific high-penetrance mutations form distinct subgroups. In contrast, under a polygenic threshold model, high-penetrance rare allele carriers possess many risk factors, of which the rare allele is the only one, albeit an important, factor. Under the latter model, cases with rare mutations can be expected to share some common risk alleles, and therefore pathophysiological mechanisms, with cases without the same mutation. Here we show that, compared with controls, individuals with schizophrenia who have known pathogenic CNVs carry an excess burden of common risk alleles ( P =2.25 × 10 −17 ) defined from a genome-wide association study largely based on individuals without known CNVs. Our finding is not consistent with an extreme heterogeneity model for CNV carriers, but does offer support for the polygenic threshold model of schizophrenia. That this is so provides support for the notion that studies aiming to model the effects of rare variation may uncover pathophysiological mechanisms of relevance to those with the disorder more widely.

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.

The enzyme catechol- O -methyl transferase (COMT), identified in the 1950s, is involved in catabolism of monoamines that are influenced by psychotropic medications, including neuroleptics and antidepressants. The COMT gene lies in a chromosomal region of interest for psychosis and bipolar spectrum disorder and a common polymorphism within the gene alters the activity of the enzyme. As a consequence, COMT has been one of the most studied genes for psychosis. On the basis of prior probabilities it would seem surprising if functional variation at COMT did not have some influence either on susceptibility to psychiatric phenotypes, modification of the course of illness or moderation of response to treatment. There is now robust evidence that variation at COMT influences frontal lobe function. However, despite considerable research effort, it has not proved straightforward to demonstrate and characterise a clear relationship between genetic variation at COMT and psychiatric phenotypes. It is of course, possible that COMT will turn out to be an unusually intractable case but it seems more likely that the experiences with this gene will provide a foretaste of the complexity of genotype–phenotype relationships that will be found for psychiatric traits. In this review, we consider the current state of evidence and the implications both for further studies of COMT and more generally for studies of other genes.

The high heritability of schizophrenia has stimulated much work aimed at identifying susceptibility genes using positional genetics. However, difficulties in obtaining clear replicated linkages have led to the scepticism that such approaches would ever be successful. Fortunately, there are now signs of real progress. Several strong and well-established linkages have emerged. Three of the best-supported regions are 6p24–22, 1q21–22 and 13q32–34. In these cases, single studies achieved genome-wide significance at P <0.05 and suggestive positive findings have also been reported in other samples. The other promising regions include 8p21–22, 6q21–25, 22q11–12, 5q21–q33, 10p15–p11 and 1q42. The study of chromosomal abnormalities in schizophrenia has also added to the evidence for susceptibility loci at 22q11 and 1q42. Recently, evidence implicating individual genes within some of the linked regions has been reported and more importantly replicated. The weight of evidence now favours NRG1 and DTNBP1 as susceptibility loci, though work remains before we understand precisely how genetic variation at each locus confers susceptibility and protection. The evidence for catechol- O -methyl transferase, RGS4 and G72 is promising but not yet persuasive. While further replications remain the top priority, the respective contributions of each gene, relationships with aspects of the phenotype, the possibility of epistatic interactions between genes and functional interactions between the gene products will all need investigation. The ability of positional genetics to implicate novel genes and pathways will open up new vistas for neurobiological research, and all the signs are that it is now poised to deliver crucial insights into the nature of schizophrenia.

Major depressive disorder (MDD) is a common and disabling condition with well-established heritability and environmental risk factors. Gene-environment interaction studies in MDD have typically investigated candidate genes, though the disorder is known to be highly polygenic. This study aims to test for interaction between polygenic risk and stressful life events (SLEs) or childhood trauma (CT) in the aetiology of MDD. The RADIANT UK sample consists of 1605 MDD cases and 1064 controls with SLE data, and a subset of 240 cases and 272 controls with CT data. Polygenic risk scores (PRS) were constructed using results from a mega-analysis on MDD by the Psychiatric Genomics Consortium. PRS and environmental factors were tested for association with case/control status and for interaction between them. PRS significantly predicted depression, explaining 1.1% of variance in phenotype (p = 1.9 × 10(-6)). SLEs and CT were also associated with MDD status (p = 2.19 × 10(-4) and p = 5.12 × 10(-20), respectively). No interactions were found between PRS and SLEs. Significant PRSxCT interactions were found (p = 0.002), but showed an inverse association with MDD status, as cases who experienced more severe CT tended to have a lower PRS than other cases or controls. This relationship between PRS and CT was not observed in independent replication samples. CT is a strong risk factor for MDD but may have greater effect in individuals with lower genetic liability for the disorder. Including environmental risk along with genetics is important in studying the aetiology of MDD and PRS provide a useful approach to investigating gene-environment interactions in complex traits.