Psychiatric GWAS Consortium Research Articles

The one and the many: effects of the cell adhesion molecule pathway on neuropsychological function in psychosis.

Genetic studies of single gene variants have been criticized as providing a simplistic characterization of the genetic basis of illness risk that ignores the effects of other variants within the same biological pathways. Of candidate biological pathways for schizophrenia (SZ), the cell adhesion molecule (CAM) pathway has repeatedly been linked to both psychosis and neurocognitive dysfunction. Here we tested, using risk allele scores derived from the Schizophrenia Psychiatric Genome-Wide Association Study Consortium (PGC-SCZ), whether alleles within the CAM pathway were correlated with poorer neuropsychological function in patients. In total, 424 patients with psychosis were assessed in areas of cognitive ability typically found to be impaired in SZ: intelligence quotient, memory, working memory and attentional control. CAM pathway genes were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Alleles within these genes identified as significantly associated with SZ risk in the PGC-SCZ were then used to calculate a CAM pathway-based polygenic risk allele score for each patient and these scores were tested for association with cognitive ability. Increased CAM pathway polygenic risk scores were significantly associated with poorer performance on measures of memory and attention, explaining 1-3% of variation on these measures. Notably, the most strongly associated single nucleotide polymorphism (SNP) in the CAM pathway (rs9272105 within HLA-DQA1) explained a similar amount of variance in attentional control, but not memory, as the polygenic risk analysis. These data support a role for the CAM pathway in cognitive function, both at the level of individual SNPs and the wider pathway. In so doing these data highlight the value of pathway-based polygenic risk score studies as well as single gene studies for understanding SZ-associated deficits in cognition.

Metamoodics: meta-analysis and bioinformatics resource for mood disorders

Mood disorders, including Major Depression (MD) and Bipolar Disorder (BP), are among the most common psychiatric disorders1. Although genetic factors play an important role in their etiology2, 3, the heritability of these disorders remains largely unexplained. Due to advances in high-throughput genomic technologies, it is becoming increasingly feasible to investigate the genetic architecture of these disorders at an unprecedented resolution. The challenge is how to make sense of the enormous flood of data generated by the diverse genomic studies that use these new technologies. It is essential to integrate the findings from such studies in order to develop a more comprehensive understanding of the genetic contribution to these disorders that can be readily translated into new and more rational therapeutic interventions. Towards this end, we have developed a bioinformatics resource called Metamoodics (www.metamoodics.org), which integrate results from various genomic studies of mood disorders and enhance their interpretation by placing them within an enriched genomic context. Metamoodics was created as a resource for the research community to explore what is currently known about the genetic contribution to mood disorders. Metamoodics synthesizes data from existing genome-wide linkage, expression and association studies for both MD and BP. It brings together results from up-to-date meta- or mega-analyses of these studies, and presents them in both tabular and graphical modes. The genome-wide linkage data comes from among the largest and most comprehensive of such studies with MD and BP. Data for BP comes from a mega-analysis carried out with 972 informative pedigrees of European ancestry pooled from eight research groups in North America and the United Kingdom3. Linkage statistics for twelve different phenotype-by-genotype models were calculated for a genome-wide panel of 6090 SNPs. For MD, data from the Genetics of Recurrent Early-Onset Major Depression (GenRED) collaborative are provided4. This included data on 418 microsatellite markers genotyped in 656 families consisting of 1,494 all possible informative affective relative pairs. Data for genome-wide association studies (GWAS) were obtained from the psychiatric GWAS consortium (PGC). For BP, the sample included 7,481 cases and 9,250 controls, all of European ancestry, with data on ~2.5M genotyped and/or imputed SNPs5. Data on MD consisted of more than 1.2 million SNPs in 9,240 cases and 9,519 controls of recent European ancestry6. Gene expression data was obtained through a systematic review of studies on BP (n=13) and MD (n=7) using post-mortem brain tissue. Mega-analyses of the most comprehensive and rigorous of these studies were carried out using samples from all regions of the brain, the pre-frontal cortex, and the hippocampus, where sufficient data was available7. Finally, Metamoodics includes a comprehensive meta-analysis of published candidate gene association studies of BP. Data on 33 polymorphisms in 18 genes reported on by three or more case-control studies were included in the meta-analysis8. A similar meta-analysis for MD is forthcoming. Metamoodics includes a variety of online applications and tools for visualization and further analysis of the genomics data. Data visualization makes use of NCBI SeqView API and a local mirror of the UCSC genome browser. The results can be queried by gene or chromosomal position to obtain either a gene level view via chromosomal or regional plots, or a more global view via the genome browser to investigate the genomic context of the findings. User-defined data can be uploaded to the genome browser using the custom track option to visualize alongside the Metamoodics tracks. Analysis tools include SVAw9 for surrogate variable analysis, METAw for meta-analysis and a gene expression package for individual and mega analysis of expression data. Metamoodics presents the latest results from genomic studies of mood disorders in a user-friendly web interface that enables users to rapidly compare findings from different experimental platforms across the genome, as well as drill down to specific findings to visualize how these relate to underlying genomic structure and function. Metamoodics provides the entire set of results of a given study rather than just significant ones, thus providing a complete and unbiased report of the findings. We will continue to update Metamoodics with findings from the latest genomic experiments of mood disorders, such as whole exome or genome sequencing and epigenetic studies. Metamoodics is a vital bioinformatics tool for investigating the genetic etiology of mood disorders that will only grow more powerful as new data are added.

Molecular Validation of the Schizophrenia Spectrum

Early descriptive work and controlled family and adoption studies support the hypothesis that a range of personality and nonschizophrenic psychotic disorders aggregate in families of schizophrenic probands. Can we validate, using molecular polygene scores from genome-wide association studies (GWAS), this schizophrenia spectrum? The predictive value of polygenic findings reported by the Psychiatric GWAS Consortium (PGC) was applied to 4 groups of relatives from the Irish Study of High-Density Schizophrenia Families (ISHDSF; N = 836) differing on their assignment within the schizophrenia spectrum. Genome-wide single nucleotide polymorphism data for affected and unaffected relatives were used to construct per-individual polygene risk scores based on the PGC stage-I results. We compared mean polygene scores in the ISHDSF with mean scores in ethnically matched population controls (N = 929). The schizophrenia polygene score differed significantly across diagnostic categories and was highest in those with narrow schizophrenia spectrum, lowest in those with no psychiatric illness, and in-between in those classified in the intermediate, broad, and very broad schizophrenia spectrum. Relatives of all of these groups of affected subjects, including those with no diagnosis, had schizophrenia polygene scores significantly higher than the control sample. In the relatives of high-density families, the observed pattern of enrichment of molecular indices of schizophrenia risk suggests an underlying, continuous liability distribution and validates, using aggregate common risk alleles, a genetic basis for the schizophrenia spectrum disorders. In addition, as predicted by genetic theory, nonpsychotic members of multiply-affected schizophrenia families are significantly enriched for replicated, polygenic risk variants compared with the general population.

Bipolar Disorder is associated with the rs6971 polymorphism in the gene encoding 18kDa Translocator Protein (TSPO)

SummaryTSPO mediated transport of cholesterol into the mitochondrion is a necessary step in steroid synthesis. The rs6971 polymorphism in the TSPO gene causes an amino acid substitution (Ala147Thr) within the transmembrane domain where the cholesterol-binding pocket is located, and has been shown to affect the steroidogenic pathway. We report a nominal association between this TSPO polymorphism and the diagnosis of Bipolar Disorder in both the genome-wide dataset of the Wellcome Trust Case–Control Consortium and the Psychiatric Genome-Wide Association Study Consortium Bipolar Disorder group (OR = 1.11, p = 0.007; OR = 1.10, p = 0.011, respectively). We propose that the amino acid substitution affects hypothalamic–pituitary–adrenal (HPA) regulation, and hence may predispose to Bipolar Disorder. This supports the hypothesis that HPA dysregulation has a causal role in Bipolar Disorder, and is not just a consequence of the disease.

A Genome-wide Association Analysis of a Broad Psychosis Phenotype Identifies Three Loci for Further Investigation

BackgroundGenome-wide association studies (GWAS) have identified several loci associated with schizophrenia and/or bipolar disorder. We performed a GWAS of psychosis as a broad syndrome rather than within specific diagnostic categories.Methods1239 cases with schizophrenia, schizoaffective disorder, or psychotic bipolar disorder; 857 of their unaffected relatives, and 2739 healthy controls were genotyped with the Affymetrix 6.0 single nucleotide polymorphism (SNP) array. Analyses of 695,193 SNPs were conducted using UNPHASED, which combines information across families and unrelated individuals. We attempted to replicate signals found in 23 genomic regions using existing data on nonoverlapping samples from the Psychiatric GWAS Consortium and Schizophrenia-GENE-plus cohorts (10,352 schizophrenia patients and 24,474 controls).ResultsNo individual SNP showed compelling evidence for association with psychosis in our data. However, we observed a trend for association with same risk alleles at loci previously associated with schizophrenia (one-sided p = .003). A polygenic score analysis found that the Psychiatric GWAS Consortium’s panel of SNPs associated with schizophrenia significantly predicted disease status in our sample (p = 5 × 10–14) and explained approximately 2% of the phenotypic variance.ConclusionsAlthough narrowly defined phenotypes have their advantages, we believe new loci may also be discovered through meta-analysis across broad phenotypes. The novel statistical methodology we introduced to model effect size heterogeneity between studies should help future GWAS that combine association evidence from related phenotypes. Applying these approaches, we highlight three loci that warrant further investigation. We found that SNPs conveying risk for schizophrenia are also predictive of disease status in our data.

Sequence Kernel Association Tests for the Combined Effect of Rare and Common Variants

Recent developments in sequencing technologies have made it possible to uncover both rare and common genetic variants. Genome-wide association studies (GWASs) can test for the effect of common variants, whereas sequence-based association studies can evaluate the cumulative effect of both rare and common variants on disease risk. Many groupwise association tests, including burden tests and variance-component tests, have been proposed for this purpose. Although such tests do not exclude common variants from their evaluation, they focus mostly on testing the effect of rare variants by upweighting rare-variant effects and downweighting common-variant effects and can therefore lose substantial power when both rare and common genetic variants in a region influence trait susceptibility. There is increasing evidence that the allelic spectrum of risk variants at a given locus might include novel, rare, low-frequency, and common genetic variants. Here, we introduce several sequence kernel association tests to evaluate the cumulative effect of rare and common variants. The proposed tests are computationally efficient and are applicable to both binary and continuous traits. Furthermore, they can readily combine GWAS and whole-exome-sequencing data on the same individuals, when available, and are also applicable to deep-resequencing data of GWAS loci. We evaluate these tests on data simulated under comprehensive scenarios and show that compared with the most commonly used tests, including the burden and variance-component tests, they can achieve substantial increases in power. We next show applications to sequencing studies for Crohn disease and autism spectrum disorders. The proposed tests have been incorporated into the software package SKAT.

Up-Regulation of NOTCH4 Gene Expression in Bipolar Disorder: Future Studies

Back to table of contents Previous article Next article Communications and UpdatesFull AccessUp-Regulation of NOTCH4 Gene Expression in Bipolar Disorder: Future StudiesMing Li, Ph.D., and Bing Su, Ph.D.Ming LiSearch for more papers by this author, Ph.D., and Bing SuSearch for more papers by this author, Ph.D.Published Online:1 May 2013https://doi.org/10.1176/appi.ajp.2013.12121529AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To the Editor: In the December 2012 issue, Dieset et al. (1) reported significant up-regulation of NOTCH4 gene expression in whole blood in patients with bipolar disorder relative to healthy comparison subjects, and they identified several single-nucleotide polymorphisms (SNPs) that were significantly associated with NOTCH4 expression. This is a nice piece of research, and their findings have encouraged future research on the molecular mechanisms of NOTCH4 in bipolar disorder. However, several lines of their study data await further validation.First, we are curious about whether the altered NOTCH4 expression that was observed in patients was caused by changes in the genetic background in bipolar disorder (related to the pathogenesis of the illness) or was just an outcome of the patients' physiological conditions. This is an important issue, but was inconclusive in the article, although the authors conducted the analyses adjusting for a range of confounders. A plausible solution to this problem is the use of an intermediate group: the healthy siblings of patients. These populations shared numerous genetic risk factors with the clinical patients but did not have any psychiatric illnesses. If the NOTCH4 expression was also elevated in the healthy siblings of bipolar patients relative to healthy comparison subjects, we could conclude that up-regulation of NOTCH4 expression may be related to the genetic mechanism in bipolar disorder and is likely involved in the pathophysiology of bipolar illness. However, because such data were absent in the Dieset et al. study, we are cautious about drawing any conclusions before further investigations.Another issue we are concerned with is the analyses of SNPs in or around NOTCH4 and their associations with gene expression. Dieset et al. observed significant effects of the SNPs on NOTCH4 expression only in healthy individuals but not in bipolar patients, although the effect went in the same direction. Are these significantly associated SNPs authentic genetic risk factors for bipolar disorder? We examined their associations with bipolar disorder in the case-control samples of European ancestries published by the Psychiatric GWAS Consortium (2), which are the largest samples so far. However, none of these SNPs were significant (Table 1), suggesting that they are not risk variants for bipolar disorder even if they show strong associations with NOTCH4 expression in healthy individuals. On the other hand, if the increased NOTCH4 expression in patients was related to genetic mechanisms in bipolar disorder, the gene may harbor unidentified SNPs that are significantly associated with up-regulated NOTCH4 expression in patients. We think these SNPs may be the authentic risk genetic variants for bipolar disorder, and we call for future studies.TABLE 1. Association of the NOTCH4 Single-Nucleotide Polymorphisms (SNPs) With Bipolar Disorder in Samples Reported by the Psychiatric GWAS Consortium (7,481 Case Subjects and 8,250 Comparison Subjects)AnalysisSNPPositionAlleleFrequencypOdds RatioSErs51032132302370A0.18850.0771.0550.030rs38970332307217C0.81150.0830.9540.027rs36505332303966G0.19670.0661.0520.028rs40489032306845A0.29510.9530.9990.024rs313492632308125C0.78690.0500.9500.026rs41592932297010C0.28690.7500.9920.026rs926787332307330C0.57380.1960.9690.025TABLE 1. Association of the NOTCH4 Single-Nucleotide Polymorphisms (SNPs) With Bipolar Disorder in Samples Reported by the Psychiatric GWAS Consortium (7,481 Case Subjects and 8,250 Comparison Subjects)Enlarge tableFrom the State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.The authors report no financial relationships with commercial interests.References1 Dieset I, Djurovic S, Tesli M, Hope S, Mattingsdal M, Michelsen A, Joa I, Larsen TK, Agartz I, Melle I, Røssberg JI, Aukrust P, Andreassen OA, Ueland T: Up-regulation of NOTCH4 gene expression in bipolar disorder. Am J Psychiatry 2012; 169:1292–1300Link, Google Scholar2 Psychiatric GWAS Consortium Bipolar Disorder Working Group: Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet 2011; 43:977–983Crossref, Medline, Google Scholar FiguresReferencesCited byDetailsCited ByNone Volume 170Issue 5 May 2013Pages 560a-561 Metrics PDF download History Accepted 1 January 2013 Published online 1 May 2013 Published in print 1 May 2013

Polygenic Risk for Schizophrenia Is Associated with Cognitive Change Between Childhood and Old Age

Genome-wide association studies (GWAS) have shown a polygenic component to the risk of schizophrenia. The disorder is associated with impairments in general cognitive ability that also have a substantial genetic contribution. No study has determined whether cognitive impairments can be attributed to schizophrenia's polygenic architecture using data from GWAS. Members of the Lothian Birth Cohort 1936 (LBC1936, n = 937) were assessed using the Moray House Test at age 11 and with the Moray House Test and a further cognitive battery at age 70. To create polygenic risk scores for schizophrenia, we obtained data from the latest GWAS of the Psychiatric GWAS Consortium on Schizophrenia. Schizophrenia polygenic risk profile scores were calculated using information from the Psychiatric GWAS Consortium on Schizophrenia GWAS. In LBC1936, polygenic risk for schizophrenia was negatively associated with IQ at age 70 but not at age 11. Greater polygenic risk for schizophrenia was associated with more relative decline in IQ between these ages. These findings were maintained when the results of LBC1936 were combined with that of the independent Lothian Birth Cohort 1921 (n = 517) in a meta-analysis. Increased polygenic risk of schizophrenia is associated with lower cognitive ability at age 70 and greater relative decline in general cognitive ability between the ages of 11 and 70. Common genetic variants may underlie both cognitive aging and risk of schizophrenia.

Genome-wide association study of clinical dimensions of schizophrenia: polygenic effect on disorganized symptoms.

Multiple sources of evidence suggest that genetic factors influence variation in clinical features of schizophrenia. The authors present the first genome-wide association study (GWAS) of dimensional symptom scores among individuals with schizophrenia. Based on the Lifetime Dimensions of Psychosis Scale ratings of 2,454 case subjects of European ancestry from the Molecular Genetics of Schizophrenia (MGS) sample, three symptom factors (positive, negative/disorganized, and mood) were identified with exploratory factor analysis. Quantitative scores for each factor from a confirmatory factor analysis were analyzed for association with 696,491 single-nucleotide polymorphisms (SNPs) using linear regression, with correction for age, sex, clinical site, and ancestry. Polygenic score analysis was carried out to determine whether case and comparison subjects in 16 Psychiatric GWAS Consortium (PGC) schizophrenia samples (excluding MGS samples) differed in scores computed by weighting their genotypes by MGS association test results for each symptom factor. No genome-wide significant associations were observed between SNPs and factor scores. Most of the SNPs producing the strongest evidence for association were in or near genes involved in neurodevelopment, neuroprotection, or neurotransmission, including genes playing a role in Mendelian CNS diseases, but no statistically significant effect was observed for any defined gene pathway. Finally, polygenic scores based on MGS GWAS results for the negative/disorganized factor were significantly different between case and comparison subjects in the PGC data set; for MGS subjects, negative/disorganized factor scores were correlated with polygenic scores generated using case-control GWAS results from the other PGC samples. The polygenic signal that has been observed in cross-sample analyses of schizophrenia GWAS data sets could be in part related to genetic effects on negative and disorganized symptoms (i.e., core features of chronic schizophrenia).

Replication of bipolar disorder susceptibility alleles and identification of two novel genome-wide significant associations in a new bipolar disorder case–control sample

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.

Genetic Schizophrenia Risk Variants Jointly Modulate Total Brain and White Matter Volume

Thousands of common single nucleotide polymorphisms (SNPs) are weakly associated with schizophrenia. It is likely that subsets of disease-associated SNPs are associated with distinct heritable disease-associated phenotypes. Therefore, we examined the shared genetic susceptibility modulating schizophrenia and brain volume. Odds ratios for genome-wide SNP data were calculated in the sample collected by the Psychiatric Genome-wide Association Study Consortium (8690 schizophrenia patients and 11,831 control subjects, excluding subjects from the present study). These were used to calculate individual polygenic schizophrenia (risk) scores in an independent sample of 152 schizophrenia patients and 142 healthy control subjects with available structural magnetic resonance imaging scans. In the entire group, the polygenic schizophrenia score was significantly associated with total brain volume (R2 = .048, p = 1.6 × 10(-4)) and white matter volume (R2 = .051, p = 8.6 × 10(-5)) equally in patients and control subjects. The number of (independent) SNPs that substantially influenced both disease risk and white matter (n = 2020) was much smaller than the entire set of SNPs that modulated disease status (n = 14,751). From the set of 2020 SNPs, a group of 186 SNPs showed most evidence for association with white matter volume and an explorative functional analysis showed that these SNPs were located in genes with neuronal functions. These results indicate that a relatively small subset of schizophrenia genetic risk variants is related to the (normal) development of white matter. This, in turn, suggests that disruptions in white matter growth increase the susceptibility to develop schizophrenia.

Genome-wide association of mood-incongruent psychotic bipolar disorder.

Mood-incongruent psychotic features (MICP) are familial symptoms of bipolar disorder (BP) that also occur in schizophrenia (SZ), and may represent manifestations of shared etiology between the major psychoses. In this study we have analyzed three large samples of BP with imputed genome-wide association data and have performed a meta-analysis of 2196 cases with MICP and 8148 controls. We found several regions with suggestive evidence of association (P<10–6), although no marker met genome-wide significance criteria. The top associations were on chromosomes: 6q14.2 within the PRSS35/SNAP91 gene complex (rs1171113, P=9.67 × 10–8); 3p22.2 downstream of TRANK/LBA1 (rs9834970, P=9.71 × 10–8); and 14q24.2 in an intron of NUMB (rs2333194, P=7.03 × 10–7). These associations were present in all three samples, and both rs1171113 and rs2333194 were found to be overrepresented in an analysis of MICP cases compared with all other BP cases. To test the relationship of MICP with SZ, we performed polygenic analysis using the Psychiatric GWAS Consortium SZ results and found evidence of association between SZ polygenes and the presence of MICP in BP cases (meta-analysis P=0.003). In summary, our analysis of the MICP phenotype in BP has provided suggestive evidence for association of common variants in several genes expressed in the nervous system. The results of our polygenic analysis provides support for a modest degree of genetic overlap between BP with MICP and SZ, highlighting that phenotypic correlations across syndromes may be due to the influence of polygenic risk factors.

Genome-wide association study of multiplex schizophrenia pedigrees.

The authors used a genome-wide association study (GWAS) of multiply affected families to investigate the association of schizophrenia to common single-nucleotide polymorphisms (SNPs) and rare copy number variants (CNVs). The family sample included 2,461 individuals from 631 pedigrees (581 in the primary European-ancestry analyses). Association was tested for single SNPs and genetic pathways. Polygenic scores based on family study results were used to predict case-control status in the Schizophrenia Psychiatric GWAS Consortium (PGC) data set, and consistency of direction of effect with the family study was determined for top SNPs in the PGC GWAS analysis. Within-family segregation was examined for schizophrenia-associated rare CNVs. No genome-wide significant associations were observed for single SNPs or for pathways. PGC case and control subjects had significantly different genome-wide polygenic scores (computed by weighting their genotypes by log-odds ratios from the family study) (best p=10(-17), explaining 0.4% of the variance). Family study and PGC analyses had consistent directions for 37 of the 58 independent best PGC SNPs (p=0.024). The overall frequency of CNVs in regions with reported associations with schizophrenia (chromosomes 1q21.1, 15q13.3, 16p11.2, and 22q11.2 and the neurexin-1 gene [NRXN1]) was similar to previous case-control studies. NRXN1 deletions and 16p11.2 duplications (both of which were transmitted from parents) and 22q11.2 deletions (de novo in four cases) did not segregate with schizophrenia in families. Many common SNPs are likely to contribute to schizophrenia risk, with substantial overlap in genetic risk factors between multiply affected families and cases in large case-control studies. Our findings are consistent with a role for specific CNVs in disease pathogenesis, but the partial segregation of some CNVs with schizophrenia suggests that researchers should exercise caution in using them for predictive genetic testing until their effects in diverse populations have been fully studied.

Bipolar Disorder Expert Laments Lithium’s Fading Popularity

Back to table of contents Previous article Next article Clinical & Research NewsFull AccessBipolar Disorder Expert Laments Lithium’s Fading PopularityLeslie SinclairLeslie SinclairSearch for more papers by this authorPublished Online:6 Jul 2012https://doi.org/10.1176/pn.47.13.psychnews_47_13_11-aAbstractPharmacogenetics may soon diminish the trial-and-error process of determining which medication will work best for a patient with bipolar disorder.John Kelsoe, M.D.That’s the hope of John Kelsoe, M.D., a professor of psychiatry with the Laboratory of Psychiatric Genomics in the Department of Psychiatry at the University of California, San Diego. Kelsoe directs the Bipolar Genome Study (BiGS), a 13-site consortium focused on identifying genes for bipolar disorder and their relationship to clinical symptoms. He also co-directs the Psychiatric GWAS Consortium for Bipolar Disorder (PGC-BD), an international collaborative effort designed to identify genes for bipolar disorder in a sample of over 10,000 patients.Kelsoe spoke at APA’s 2012 annual meeting in Philadelphia in May as part of the “Research Advances in Psychiatric Pharmacogenomics” symposium, discussing his work on advances in the pharmacogenetics of lithium response in the search for a personalized treatment of bipolar disorder.“It’s long been an observation that there’s a subset of individuals who have a fantastic response to lithium,” he said. “They are essentially cured … as long as they stay on their medication. So it’s been disturbing to me to see lithium fall to the wayside as other medications have come along. I’m concerned there are lots of patients who will miss the opportunity to find out if they are one of these excellent lithium responders, because they’ll never get tried on it. The clinical pitch I make is that every bipolar patient deserves a trial of lithium somewhere early in their course of illness to find out if they are in this category.”Kelsoe said the individuals who are good lithium responders tend to have distinct clinical features: euphoric manias, positive family history, few comorbidities, and symptom-free intervals between episodes. “I’ll make the argument that that’s because they may have a somewhat different illness, with a different underlying mechanism,” he said.Kelsoe credited the hypothesis to Paul Grof, M.D., Ph.D., a professor of psychiatry at the University of Toronto and director of the Mood Disorders Centre of Ottawa. “Grof asked the question almost a decade ago: If lithium response is familial … is lithium-responsive bipolar disorder a unique, distinct form of illness?” Grof and his colleagues, in the Journal of Clinical Psychiatry in 2002, compared the response to long-term lithium treatment in bipolar relatives of bipolar lithium responders with those of bipolar controls and found that the response to lithium prophylaxis clusters in families.In a study published in Pharmacogenomics in 2010, Kelsoe and his colleagues detailed possible scenarios that may describe the relationship of lithium response to the pathophysiology of bipolar disease. Movement toward a simple test for lithium response is hampered by a lack of knowledge about the drug’s actual mechanism of action in the disorder.“Studies of lithium pharmacogenetics are largely in the initial trial stages,” they said in the 2010 article, “but they offer great promise in improving clinical care.” The group offered direction for future research, recommending approaches that use large, prospectively assessed clinical samples, genomewide measurements of genetic variation, and gene expression coupled by powerful computational approaches. “Studying pathways identified in this manner may prove more sensitive and reliable than studying isolated candidate genes,” they wrote.So, for now at least, determining which patients with bipolar disorder might respond best to lithium must be done the old-fashioned way, through trial and error, but Kelsoe remains hopeful that pharmacogenomics testing will personalize the selection of mood stabilizers for bipolar disorder in the near future: “Anything that would reduce the number of medication trials, doctors, and diagnoses for these patients would be welcomed by patient and doctor alike.” An abstract of “Pharmacogenetics of Lithium Response in Bipolar Disorder” is posted at www.ncbi.nlm.nih.gov/pubmed/21047205. An abstract of “Is Response to Prophylactic Lithium a Familial Trait?” is posted at www.ncbi.nlm.nih.gov/pubmed/12416605. ISSUES NewArchived

The influence of polygenic risk for bipolar disorder on neural activation assessed using fMRI

Genome-wide association studies (GWAS) have demonstrated a significant polygenic contribution to bipolar disorder (BD) where disease risk is determined by the summation of many alleles of small individual magnitude. Modelling polygenic risk scores may be a powerful way of identifying disrupted brain regions whose genetic architecture is related to that of BD. We determined the extent to which common genetic variation underlying risk to BD affected neural activation during an executive processing/language task in individuals at familial risk of BD and healthy controls. Polygenic risk scores were calculated for each individual based on GWAS data from the Psychiatric GWAS Consortium Bipolar Disorder Working Group (PGC-BD) of over 16 000 subjects. The familial group had a significantly higher polygene score than the control group (P=0.04). There were no significant group by polygene interaction effects in terms of association with brain activation. However, we did find that an increasing polygenic risk allele load for BD was associated with increased activation in limbic regions previously implicated in BD, including the anterior cingulate cortex and amygdala, across both groups. The findings suggest that this novel polygenic approach to examine brain-imaging data may be a useful means of identifying genetically mediated traits mechanistically linked to the aetiology of BD.

Investigation of the Genetic Association between Quantitative Measures of Psychosis and Schizophrenia: A Polygenic Risk Score Analysis

The presence of subclinical levels of psychosis in the general population may imply that schizophrenia is the extreme expression of more or less continuously distributed traits in the population. In a previous study, we identified five quantitative measures of schizophrenia (positive, negative, disorganisation, mania, and depression scores). The aim of this study is to examine the association between a direct measure of genetic risk of schizophrenia and the five quantitative measures of psychosis. Estimates of the log of the odds ratios of case/control allelic association tests were obtained from the Psychiatric GWAS Consortium (PGC) (minus our sample) which included genome-wide genotype data of 8,690 schizophrenia cases and 11,831 controls. These data were used to calculate genetic risk scores in 314 schizophrenia cases and 148 controls from the Netherlands for whom genotype data and quantitative symptom scores were available. The genetic risk score of schizophrenia was significantly associated with case-control status (p<0.0001). In the case-control sample, the five psychosis dimensions were found to be significantly associated with genetic risk scores; the correlations ranged between.15 and.27 (all p<.001). However, these correlations were not significant in schizophrenia cases or controls separately. While this study confirms the presence of a genetic risk for schizophrenia as categorical diagnostic trait, we did not find evidence for the genetic risk underlying quantitative schizophrenia symptom dimensions. This does not necessarily imply that a genetic basis is nonexistent, but does suggest that it is distinct from the polygenic risk score for schizophrenia.

Genome-wide significant associations in schizophrenia to ITIH3/4, CACNA1C and SDCCAG8, and extensive replication of associations reported by the Schizophrenia PGC.

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.

Meta-analysis of genetic association studies on bipolar disorder.

Numerous candidate gene association studies of bipolar disorder (BP) have been carried out, but the results have been inconsistent. Individual studies are typically underpowered to detect associations with genes of small effect sizes. We conducted a meta-analysis of published candidate gene studies to evaluate the cumulative evidence. We systematically searched for all published candidate gene association studies of BP. We then carried out a random-effects meta-analysis on all polymorphisms that were reported on by three or more case-control studies. The results from meta-analyses of these genes were compared with the findings from a recent mega-analysis of eleven genome-wide association studies (GWAS) in BP performed by the Psychiatric GWAS Consortium (PGC). A total of 487 articles were included in our review. Among these, 33 polymorphisms in 18 genes were reported on by three or more case-control studies and included in the random-effects meta-analysis. Polymorphisms in BDNF, DRD4, DAOA, and TPH1, were found to be nominally significant with a P-value < 0.05. However, none of the findings were significant after correction for multiple testing. Moreover, none of these polymorphisms were nominally significant in the PGC-BP GWAS. A number of plausible candidate genes have been previously associated with BP. However, the lack of robust findings in our review of these candidate genes highlights the need for more atheoretical approaches to study the genetics of BP afforded by GWAS. The results of this meta-analysis and from other on-going genomic experiments in BP are available online at Metamoodics (http://metamoodics.igm.jhmi.edu).

Association between schizophrenia and common variation in neurocan (NCAN), a genetic risk factor for bipolar disorder

A recent study found genome-wide significant association between common variation in the gene neurocan (NCAN, rs1064395) and bipolar disorder (BD). In view of accumulating evidence that BD and schizophrenia partly share genetic risk factors, we tested this single-nucleotide polymorphism for association with schizophrenia in three independent patient–control samples of European ancestry, totaling 5061 patients and 9655 controls. The rs1064395 A-allele, which confers risk for BD, was significantly over-represented in schizophrenia patients compared to controls (p=2.28×10−3; odds ratio=1.11). Follow-up in non-overlapping samples from the Schizophrenia Psychiatric GWAS Consortium (5537 patients, 8043 controls) provided further support for our finding (p=0.0239, odds ratio=1.07). Our data suggest that genetic variation in NCAN is a common risk factor for BD and schizophrenia.

A mega-analysis of genome-wide association studies for major depressive disorder

Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P<0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance (P<5 × 10(-8)), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083-53 822 102, minimum P=5.9 × 10(-9) at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status.