Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.

Bipolar disorder (BD) is a mental illness characterized by alternating episodes of elevated mood and depression, while major depressive disorder (MDD) is a debilitating condition that ranks second globally in terms of disease burden. Pharmacotherapy plays a crucial role in managing both BD and MDD. We investigated the genetic differences in populations of individuals with MDD and BD, and from a genetic perspective, we offered new insights into potential drug targets. This will provide clues to potential drug targets. This study employed genome-wide association studies (GWAS) and summary-data-based Mendelian randomization (SMR) methods to investigate the genetic underpinnings of patients with bipolar disorder (BD) and major depressive disorder (MDD) and to predict potential drug target genes. Genetic variants associated with BD and MDD were identified through large-scale GWAS datasets. For BD, the study utilized a comprehensive meta-analysis comprising 57 BD cohorts from Europe, North America, and Australia, including 41,917 BD cases and 371,549 controls of European ancestry. This dataset included both type 1 and type 2 BD cases diagnosed based on DSM-IV, ICD-9, or ICD-10 criteria through standardized assessments. For MDD, we used data from a meta-analysis by Howard DM etal., which integrated the largest GWAS studies of MDD, totaling 246,363 cases and 561,190 controls. The SMR approach, combined with expression quantitative trait loci (eQTL) data, was then applied to assess causal associations between these genetic variants and gene expression, aiming to identify genetic markers and potential drug targets associated with BD and MDD. Furthermore, two-sample Mendelian randomization (TSMR) analyses were performed to explore causal links between protein quantitative trait loci (pQTL) and these disorders. The SMR analysis revealed 41 druggable genes associated with BD, of which five genes appeared in both brain tissue and blood eQTL datasets and were significantly associated with BD risk. Furthermore, 45 druggable genes were found to be associated with MDD by SMR analysis, of which three genes appeared simultaneously in both datasets and were significantly associated with MDD risk. NEK4, a common drug candidate gene for BD and MDD, was also significantly associated with a high risk of both diseases and may help differentiate between type 1 and type 2 BD. Specifically, NEK4 showed a strong association with BD (β brain=0.126, P FDR=0.001; βblood=1.158, P FDR=0.003) and MDD (β brain=0.0316, P FDR=0.022; βblood=0.254, P FDR=0.045). Additionally, NEK4 was notably linked to BD type 1 (βbrain=0.123, P FDR=2.97E-05; βblood=1.018, P FDR=0.002), but showed no significant association with BD type 2.Moreover, TSMR analysis identified four proteins (BMP1, F9, ITIH3, and SIGIRR) affecting the risk of BD, and PSMB4 affecting the risk of MDD. Our study identified NEK4 as a key gene linked to both bipolar disorder (BD) and major depressive disorder (MDD), suggesting its potential as a drug target and a biomarker for differentiating BD subtypes. Using GWAS, SMR, and TSMR approaches, we revealed multiple druggable genes and protein associations with BD and MDD risk, providing new insights into the genetic basis of these disorders. These findings offer promising directions for precision medicine and novel therapeutic strategies in mental health treatment.

can diagnose florid mania, as he speeds by and looks out the window at a patient” (Melvin G. McInnis, M.D., personal communication).Despitethe bitofhyperbole,thereistruthtothe idea that classic mania, and thus bipolar I disorder, often can be straightforward to recognize when it is directly encountered, or even when inquired about after the fact (1). Other forms of bipolar disorder, such as bipolar II, are more subtle, though careful examination can yield high-reliability diagnoses here as well (2). As reported in this issue of the Journal, Castro et al. (3) asked whether a man or woman on a fast computer could diagnose bipolar disorder. They took advantage of the power of the electronic health record (EHR) to identify more than 50,000 potential bipolar disorder cases. Manual review of a subset of these showed that 63% of the individuals could be classified as having bipolar disorder. The researchers then used text features and coded data from the EHR to generate automatedalgorithmsthatclassifiedpatientsaslikelytohave bipolar disorder. It is important to note that they next conducted a validation study on a selected subset of cases, for which they compared the EHR- and algorithm-derived diagnoses to those made on the basis of direct diagnostic interview by clinicians using the Structured Clinical Interview for DSM-IV. A quite respectable 79%285% of the patients electronically classified as having bipolar disorder also had the diagnosis on direct interview, while none of those classified as control subjects did. The authors are ultimately interested in using their method to identify samples for genetics studies of bipolar disorder. Their result represents an important step forward in the application of the big data approach to pinpointing genetic susceptibility variants in bipolar disorder. A little context: we have hadevidence sincethe 1920s that bipolar disorder has a major genetic component, as it runs in families (4) and is more likely to be shared by identical than fraternal twins. This evidence was solidified in the 1970s and 1980s by further studies, such as the Iowa 500, which confirmed the familial aggregation of the illness (5). Eventually psychiatric researchers began collecting blood from patients with the idea that DNA from these samples could be examined to finger the genetic culprits that set bipolar disorder in motion. People such as Raymond DePaulo at Johns Hopkins University led groups that carefully assessed family members to determine their clinical picture, or phenotype, with the idea that imprecision in the determination of who did and did not have bipolar disorder would undermine the gene-hunting process, just as being one digit off for a telephonenumberwouldrenderitimpossibletoconnectwiththe rightpersonontheotherendoftheline.Afterawhileitbegan to seem likely that bipolar disorder is the result of an accumulation of many small genetic effects and that to detect any oneofthem,alargesamplewouldbeneeded.Initially“large” was thought to be hundreds of patients, but then it became thousands, and now it appears to be tens of thousands. Where,exactly,doesone findtensofthousandsofpatients? And how do you secure the clinician time to assess them all evenifyou findthem?Oneapproachistohavemanyindividual research groups band together and pool their resources. This concept formed the basis

We conducted a combined genome-wide association study (GWAS) of 7,481 individuals with bipolar disorder (cases) and 9,250 controls as part of the Psychiatric GWAS Consortium. Our replication study tested 34 SNPs in 4,496 independent cases with bipolar disorder and 42,422 independent controls and found that 18 of 34 SNPs had P < 0.05, with 31 of 34 SNPs having signals with the same direction of effect (P = 3.8 × 10(-7)). An analysis of all 11,974 bipolar disorder cases and 51,792 controls confirmed genome-wide significant evidence of association for CACNA1C and identified a new intronic variant in ODZ4. We identified a pathway comprised of subunits of calcium channels enriched in bipolar disorder association intervals. Finally, a combined GWAS analysis of schizophrenia and bipolar disorder yielded strong association evidence for SNPs in CACNA1C and in the region of NEK4-ITIH1-ITIH3-ITIH4. Our replication results imply that increasing sample sizes in bipolar disorder will confirm many additional loci.

Low glial numbers in the amygdala in major depressive disorder

P01-246-3Q29 case-control association study of co-morbid migraine in bipolar affective disorder

BackgroundBipolar disorder patients often display abnormalities in circadian rhythm, and they are sensitive to irregular diurnal rhythms. CRY2 participates in the core clock that generates circadian rhythms. CRY2 mRNA expression in blood mononuclear cells was recently shown to display a marked diurnal variation and to respond to total sleep deprivation in healthy human volunteers. It was also shown that bipolar patients in a depressive state had lower CRY2 mRNA levels, nonresponsive to total sleep deprivation, compared to healthy controls, and that CRY2 gene variation was associated with winter depression in both Swedish and Finnish cohorts.Principal FindingsFour CRY2 SNPs spanning from intron 2 to downstream 3′UTR were analyzed for association to bipolar disorder type 1 (n = 497), bipolar disorder type 2 (n = 60) and bipolar disorder with the feature rapid cycling (n = 155) versus blood donors (n = 1044) in Sweden. Also, the rapid cycling cases were compared with bipolar disorder cases without rapid cycling (n = 422). The haplotype GGAC was underrepresented among rapid cycling cases versus controls and versus bipolar disorder cases without rapid cycling (OR = 0.7, P = 0.006−0.02), whereas overrepresentation among rapid cycling cases was seen for AAAC (OR = 1.3−1.4, P = 0.03−0.04) and AGGA (OR = 1.5, P = 0.05). The risk and protective CRY2 haplotypes and their effect sizes were similar to those recently suggested to be associated with winter depression in Swedes.ConclusionsWe propose that the circadian gene CRY2 is associated with rapid cycling in bipolar disorder. This is the first time a clock gene is implicated in rapid cycling, and one of few findings showing a molecular discrimination between rapid cycling and other forms of bipolar disorder.

Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. Am J Psychiatry 172:363–372, 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3330 BD cases and 3952 controls of European ancestry were used to estimate SNP-based heritability (h2g) and genetic correlation (rg) between EHR-based phenotype definitions and traditionally ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and three rule-based algorithms using codified EHR with decreasing levels of stringency—“coded-strict”, “coded-broad”, and “coded-broad based on a single clinical encounter” (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1968 coded-strict, 2581 coded-broad, 408 coded-broad-SV BD cases, and 3 952 controls. The estimated h2g were 0.24 (p = 0.015), 0.09 (p = 0.064), 0.13 (p = 0.003), 0.00 (p = 0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h2g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h2g) was 0.12 (p = 0.004). These h2g were lower or similar to the h2g observed by the ICCBD + PGCBD (0.23, p = 3.17E−80, total N = 33,181). However, the rg between ICCBD + PGCBD and the EHR-based cases were high for 95-NLP (0.66, p = 3.69 × 10–5), coded-strict (1.00, p = 2.40 × 10−4), and coded-broad (0.74, p = 8.11 × 10–7). The rg between EHR-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.

Insomnia and substance use disorders (SUD) are common comorbidities of bipolar disorder (BD). Genome-wide association studies (GWAS) have uncovered shared genetic contributions to insomnia and BD as well as SUDs and BD. Electronic health record (EHR) derived phenotypes (phecodes) and questionnaire data were used to examine the relationship between insomnia genetic liability and SUDs in BD. 40,839 participants from the Mayo Clinic Bipolar Disorder Biobank (BD Biobank; n = 774) and Mayo Clinic Biobank (n = 485 BD cases, n = 39,580 controls) were included in the analyses of diagnosis (phecode) outcomes (insomnia, SUD, alcohol use disorder [AUD] and tobacco use disorder [TUD]). Analyses of specific SUD outcomes obtained through the BD Biobank questionnaire included 1789 cases and considered BD subtype. Logistic regression was used to test for associations between insomnia polygenic risk scores (PRS) and insomnia and SUD outcomes in BD cases and controls. Insomnia PRS was associated with having an insomnia diagnosis (phecode) in the EHR in controls (OR = 1.19, p = 9.64e-33) but not in BD cases (OR = 1, p = 0.95). Associations between insomnia PRS and SUD diagnoses were significant in BD cases and controls, with the association being stronger in BD cases (interaction p = 0.024). In the BD Biobank data, the insomnia PRS was associated with increased odds of AUD (OR = 1.19, p = 4.26e-04), TUD (OR = 1.21, p = 1.25e-05) and cannabis use disorder (OR = 1.16, p = 4.19e-03). The effect of genetic predisposition to insomnia on SUD risk may be stronger in BD cases than in controls, which could have clinical care implications for individuals with BD and comorbid SUD.

BackgroundBipolar disorder is a highly heritable psychiatric condition for which specific genetic factors remain largely unknown. In the present study, we used combined whole-exome sequencing and linkage analysis to identify risk loci and dissect the contribution of common and rare variants in families with a high density of illness.MethodsOverall, 117 participants from 15 Australian extended families with bipolar disorder (72 with affective disorder, including 50 with bipolar disorder type I or II, 13 with schizoaffective disorder–manic type and 9 with recurrent unipolar disorder) underwent whole-exome sequencing. We performed genome-wide linkage analysis using MERLIN and conditional linkage analysis using LAMP. We assessed the contribution of potentially functional rare variants using a gene-based segregation test.ResultsWe identified a significant linkage peak on chromosome 10q11-q21 (maximal single nucleotide polymorphism = rs10761725; exponential logarithm of the odds [LODexp] = 3.03; empirical p = 0.046). The linkage interval spanned 36 protein-coding genes, including a gene associated with bipolar disorder, ankyrin 3 (ANK3). Conditional linkage analysis showed that common ANK3 risk variants previously identified in genome-wide association studies — or variants in linkage disequilibrium with those variants — did not explain the linkage signal (rs10994397 LOD = 0.63; rs9804190 LOD = 0.04). A family-based segregation test with 34 rare variants from 14 genes under the linkage interval suggested rare variant contributions of 3 brain-expressed genes: NRBF2 (p = 0.005), PCDH15 (p = 0.002) and ANK3 (p = 0.014).LimitationsWe did not examine non-coding variants, but they may explain the remaining linkage signal.ConclusionCombining family-based linkage analysis with next-generation sequencing data is effective for identifying putative disease genes and specific risk variants in complex disorders. We identified rare missense variants in ANK3, PCDH15 and NRBF2 that could confer disease risk, providing valuable targets for functional characterization.

Bipolar disorder (BD) is associated with binge eating behavior (BE), and both conditions are heritable. Previously, using data from the Genetic Association Information Network (GAIN) study of BD, we performed genome-wide association (GWA) analyses of BD with BE comorbidity. Here, utilizing data from the Mayo Clinic BD Biobank (969 BD cases, 777 controls), we performed a GWA analysis of a BD subtype defined by BE, and case-only analysis comparing BD subjects with and without BE. We then performed a meta-analysis of the Mayo and GAIN results. The meta-analysis provided genome-wide significant evidence of association between single nucleotide polymorphisms (SNPs) in PRR5-ARHGAP8 and BE in BD cases (rs726170 OR = 1.91, P = 3.05E-08). In the meta-analysis comparing cases with BD with comorbid BE vs. non-BD controls, a genome-wide significant association was observed at SNP rs111940429 in an intergenic region near PPP1R2P5 (p = 1.21E-08). PRR5-ARHGAP8 is a read-through transcript resulting in a fusion protein of PRR5 and ARHGAP8. PRR5 encodes a subunit of mTORC2, a serine/threonine kinase that participates in food intake regulation, while ARHGAP8 encodes a member of the RhoGAP family of proteins that mediate cross-talk between Rho GTPases and other signaling pathways. Without BE information in controls, it is not possible to determine whether the observed association reflects a risk factor for BE in general, risk for BE in individuals with BD, or risk of a subtype of BD with BE. The effect of PRR5-ARHGAP8 on BE risk thus warrants further investigation.

Brain-derived neurotrophic factor (BDNF) Val66Met (rs6265) functional polymorphism has been implicated in early-onset bipolar disorder. However, results of studies are inconsistent. We aimed to further explore this association. DNA samples from the Treatment of Early Age Mania (TEAM) and Mayo Clinic Bipolar Disorder Biobank were investigated for association of rs6265 with early-onset bipolar disorder. Bipolar cases were classified as early onset if the first manic or depressive episode occurred at age ≤19 years (versus adult-onset cases at age >19 years). After quality control, 69 TEAM early-onset bipolar disorder cases, 725 Mayo Clinic bipolar disorder cases (including 189 early-onset cases), and 764 controls were included in the analysis of association, assessed with logistic regression assuming log-additive allele effects. Comparison of TEAM cases with controls suggested association of early-onset bipolar disorder with the rs6265 minor allele [odds ratio (OR) = 1.55, p = 0.04]. Although comparison of early-onset adult bipolar disorder cases from the Mayo Clinic versus controls was not statistically significant, the OR estimate indicated the same direction of effect (OR = 1.21, p = 0.19). When the early-onset TEAM and Mayo Clinic early-onset adult groups were combined and compared with the control group, the association of the minor allele rs6265 was statistically significant (OR = 1.30, p = 0.04). These preliminary analyses of a relatively small sample with early-onset bipolar disorder are suggestive that functional variation in BDNF is implicated in bipolar disorder risk and may have a more significant role in early-onset expression of the disorder.

Gene expression dysregulation in the brain has been associated with bipolar disorder, but little is known about the role of non-coding RNAs. Circular RNAs are a novel class of long noncoding RNAs that have recently been shown to be important in brain development and function. However, their potential role in psychiatric disorders, including bipolar disorder, has not been well investigated. In this study, we profiled circular RNAs in the brain tissue of individuals with bipolar disorder. Total RNA sequencing was initially performed in samples from the anterior cingulate cortex of a cohort comprised of individuals with bipolar disorder (N = 13) and neurotypical controls (N = 13) and circular RNAs were identified and analyzed using “circtools”. Significant circular RNAs were validated by RT-qPCR and replicated in the anterior cingulate cortex in an independent cohort (24 bipolar disorder cases and 27 controls). In addition, we conducted in vitro studies using B-lymphoblastoid cells collected from bipolar cases (N = 19) and healthy controls (N = 12) to investigate how circular RNAs respond following lithium treatment. In the discovery RNA sequencing analysis, 26 circular RNAs were significantly differentially expressed between bipolar disorder cases and controls (FDR < 0.1). Of these, circCCNT2 was RT-qPCR validated showing significant upregulation in bipolar disorder (p = 0.03). This upregulation in bipolar disorder was replicated in an independent post-mortem human anterior cingulate cortex cohort and in B-lymphoblastoid cell culture. Furthermore, circCCNT2 expression was reduced in response to lithium treatment in vitro. Together, our study is the first to associate circCCNT2 to bipolar disorder and lithium treatment.

Back to table of contents Previous article Next article DepartmentsFull AccessThe Effectiveness of High-Dosage Amisulpride Combined With Moderate-Dosage Sodium Valproate Treatment for an Overweight Patient With Psychotic Bipolar DisorderYi-Cheng Hou, M.Sc., and Chien-Han Lai, M.D.Yi-Cheng HouSearch for more papers by this author, M.Sc., and Chien-Han LaiSearch for more papers by this author, M.D.Published Online:1 Apr 2014https://doi.org/10.1176/appi.neuropsych.13040091AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To the Editor: Several atypical antipsychotics were approved for the treatment of bipolar affective disorder.1 However, the data of amisulpiride in this field is still limited. I want to share a case of psychotic bipolar disorder with successful control of symptoms under the combined treatment of high-dosage amisulpiride and moderate-dosage sodium valproate.Case ReportMiss Y is an overweight case of psychotic bipolar disorder with unstable symptom control under several atypical antipsychotics, such as olanzapine, risperidone, quetiapine, or aripiprazole in recent 2–3 years. She also received several mood stabilizers, such as lithium or sodium valproate, but with undesirable body weight gain side effects. The bipolar symptoms (mostly mania) and psychotic symptoms (delusion of reference) still did not respond to the above medications [Young Mania Rating Scales (YMRS) scores: 36; Brief Psychiatric Rating Scale-18 (BPRS-18) scores: 38]. The medications were abruptly switched to amisulpiride 800 mg/day with sodium valproate 1000 mg/day to avoid previous body weight gain side effects under high dose of sodium valproate [body mass index (BMI): 31.21]. Her symptoms improved with residual delusion of reference and irritability after 3 weeks of the above-mentioned therapy (YMRS scores: 15; BPRS-18 scores: 20). She requested for more amisulpiride to control residual symptoms; the dose of amislpiride was increased to 1000 mg/day with significant improvement since the use of amisulpiride and sodium valproate for 6 weeks (YMRS scores: 9; BPRS scores: 12). No intolerable side effects were mentioned under high-dosage amisulpiride combined with moderate-dosage sodium valproate (BMI: 29.98). There was no relapse of symptoms under this regimen at the next 3-month follow-up.DiscussionThe role of atypical antipsychotics in manic phase is discussed and compared with typical antipsychotics in recent years.1,2 Even though there is some controversy about the intolerable side effects and the efficacy of atypical antipsychotics,2 the lower relapse rate of mania during maintenance therapy of atypical antipsychotics is still an advantage for bipolar disorder treatment.1 Yatham mentioned that atypical antipsychotics, such as olanzapine, quetiapine, ziprasidone, aripiprazole, and risperidone, can be an alternative to or combined with the mood stabilizers.3,4 However, among the atypical antipsychotics, there is no well-controlled study of amisulpiride for bipolar disorder. In this case, the effectiveness of 1000 mg/day amisulpiride in the control of psychotic and manic symptoms has been shown with combination of 1000 mg/day sodium benzoate. The dopamine-related pathophysiology of bipolar disorder are dopamine D2 and D3 receptors.5,6 As we know, amisulpiride is a compound with actions over dopamine D2 and D3 receptors,7 the treatment effect might be associated with dopamine mechanisms. Amisulpiride has a favorable profile for metabolic syndrome or body weight gain side effects, which is an unfavorable consequence of previous antipsychotics in this patient. To my knowledge, this is the first case report about high-dosage amisulpiride combined with medium-dosage sodium valproate for bipolar disorder. It can be an option for our patients while facing such dilemma of treatment.Department of Nutrition, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation New Taipei City, Taiwan, ROCDepartment of Psychiatry, Cheng Hsin General Hospital, Taipei City, Taiwan, ROCSend correspondence to Dr. Lai; e-mail: [email protected]comThe authors report no financial relationships with commercial interests.References1 Derry S, Moore RA: Atypical antipsychotics in bipolar disorder: systematic review of randomised trials. BMC Psychiatry 2007; 7:40Crossref, Medline, Google Scholar2 Gentile S: Atypical antipsychotics for the treatment of bipolar disorder: more shadows than lights. CNS Drugs 2007; 21:367–387Crossref, Medline, Google Scholar3 Yatham LN: Acute and maintenance treatment of bipolar mania: the role of atypical antipsychotics. Bipolar Disord 2003; 5(Suppl 2):7–19Crossref, Medline, Google Scholar4 Yatham LN: Atypical antipsychotics for bipolar disorder. Psychiatr Clin North Am 2005; 28:325–347Crossref, Medline, Google Scholar5 Massat I, Souery D, Del-Favero J, et al.: Positive association of dopamine D2 receptor polymorphism with bipolar affective disorder in a European Multicenter Association Study of affective disorders. Am J Med Genet 2002; 114:177–185Crossref, Medline, Google Scholar6 Chiaroni P, Azorin JM, Dassa D, et al.: Possible involvement of the dopamine D3 receptor locus in subtypes of bipolar affective disorder. Psychiatr Genet 2000; 10:43–49Crossref, Medline, Google Scholar7 Chivers JK, Gommeren W, Leysen JE, et al.: Comparison of the in-vitro receptor selectivity of substituted benzamide drugs for brain neurotransmitter receptors. J Pharm Pharmacol 1988; 40:415–421Crossref, Medline, Google Scholar FiguresReferencesCited byDetailsCited ByNone Volume 26Issue 2 Spring 2014Pages E34-E35 Metrics PDF download History Published online 1 April 2014 Published in print 1 April 2014

The genetic basis for bipolar disorder (BPD) is complex with the involvement of multiple genes. As it is well established that cyclic adenosine monophosphate (cAMP) signaling regulates behavior, we tested variants in 29 genes that encode components of this signaling pathway for associations with BPD type I (BPD I) and BPD type II (BPD II). A total of 1172 individuals with BPD I, 516 individuals with BPD II and 1728 controls were analyzed. Single SNP (single-nucleotide polymorphism), haplotype and SNP × SNP interactions were examined for association with BPD. Several statistically significant single-SNP associations were observed between BPD I and variants in the PDE10A gene and between BPD II and variants in the DISC1 and GNAS genes. Haplotype analysis supported the conclusion that variation in these genes is associated with BPD. We followed-up PDE10A's association with BPD I by sequencing a 23-kb region in 30 subjects homozygous for seven minor allele risk SNPs and discovered eight additional rare variants (minor allele frequency <1%). These single-nucleotide variants were genotyped in 999 BPD cases and 801 controls. We obtained a significant association for these variants in the combined sample using multiple methods for rare variant analysis. After using newly developed methods to account for potential bias from sequencing BPD cases only, the results remained significant. In addition, SNP × SNP interaction studies suggested that variants in several cAMP signaling pathway genes interact to increase the risk of BPD. This report is among the first to use multiple rare variant analysis methods following common tagSNPs associations with BPD.

VII. Bipolar Disorder Genome‐Scans and Overlap With Schizophrenia O55 DIFFERENT INHERITANCE MODELS BY AGE OF ONSET IN BIPOLAR I DISORDER Grigoroiu‐Serbanescu M 1 , Martinez M 2 , Nöthen MM 3 , Grinberg M 4 , Sima D 4 , and Propping P 5 1 Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Psychiatric Hospital, Sos. Berceni, 10, O.P. 8 R‐75622, Bucharest, Romania, Phone: 40‐1‐332.39.29; 40‐1‐683.57.62; Fax: 40‐1‐334.71.64; E‐mail: mserban@dnt.ro 2 I.N.S.E.R.M., Unité 358, EPI 06, Paris, France 3 Department of Medical Genetics, University of Antwerp, Belgium 4 Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Psychiatric Hospital, Bucharest, Romania 5 Institute of Human Genetics, University of Bonn, Germany In bipolar affective disorder, where the majority of linkage studies have produced conflicting results, studies reporting clinical characteristics and familial occurrence of disease have suggested that age of onset might serve as an indicator for identifying more homogenous subgroups of disease. Our study was the first to examine this hypothesis by the means of segregation analysis. We investigated a sample of 177 bipolar I probands recruited from consecutive admissions and their first‐ and second‐degree relatives (2,407 subjects). Probands were subdivided into an early‐onset (N=107) and a late‐onset group (N=70) using an age of onset of 25 as a cut‐off point. This age was chosen because the observed age of onset distribution was bimodal with a cut‐off of 25 years. Morbid risks for affective disorder were found significantly higher ( P =.01) in relatives of probands with an early‐onset than in probands with late‐onset of disease. The segregation analysis showed that the disease is transmitted differently in early‐ and late‐onset groups. In the early‐onset group a non‐Mendelian major gene with a polygenic component was favored while the data in the late‐onset group were compatible with a multifactorial model. This result may have important implications for molecular studies. O56 THE RISK FOR SCHIZOPHRENIA AND BIPOLAR DISORDER IN SIBLINGS TO PROBANDS WITH SCHIZOPHRENIA AND BIPOLAR DISORDER Ösby U, Brandt L, and Terenius L Department of Clinical Neuroscience Karolinska Institutet 171 75 Stockholm, Sweden, Phone: 46 70 772 70 93; Fax: 46 8 27 70 76; E‐mail: urban.osby@nvso.sll.se All patients in Sweden with an inpatient diagnosis of schizophrenia or bipolar disorder from 1973 to 1995 were identified from the Swedish patient register. All siblings were identified by the second‐generation register and their inpatient diagnoses were determined from the patient register. Standardized incidence ratios (SIR) for full and half siblings were calculated in 5‐year age and calendar time classes. There were 13,870 schizophrenia probands with 23,223 full and 8,369 half siblings, and 5,400 bipolar disorder probands with 8,846 full and 2,758 half siblings. In siblings to schizophrenia probands, SIR for schizophrenia was 7.4 for full and 4.4 for half siblings, and 3.6 for full and 2.8 for half siblings for bipolar disorder. In siblings to bipolar probands, SIR for bipolar disorder was 12.8 for full and 8.1 for half siblings, and 4.4 for full and 2.2 for half siblings for schizophrenia. If both parents were affected, the risk increased for full siblings in both schizophrenia and bipolar disorder. One affected parent increased the risk in bipolar disorder only. When the first admission for the proband was before age 25, the risk increased for schizophrenia in full siblings to schizophrenia probands but not for bipolar disorder in full siblings to bipolar probands. O57 A SEARCH FOR SPECIFIC AND COMMON SUSCEPTIBILITY LOCI FOR SCHIZOPHRENIA AND BIPOLAR DISORDER Mérette C, Phaneuf D, Fournier A, Roy MA, Cliche D, Dion C, and Maziade M Centre de recherche Université Laval Robert‐Giffard, 2601, de la Canardière Beauport, PQ G1J 2G3 Canada, Phone: 418‐663‐5741; Fax: 418‐663‐9540; E‐mail: chantal.merette@psa.ulaval.ca Schizophrenia (SZ) and bipolar disorder (BP) are prevalent major psychoses underlain by complex genetic components. To identify the susceptibility loci contributing to these disorders, we have undertaken a two‐stage genome wide scan on 480 individuals from 21 multigenerational pedigrees of Eastern Québec. Here we report the second stage based on 220 microsatellite markers. In addition to testing susceptibility loci specific to each disorder, we also tested the hypothesis that some susceptibility loci might be common to both SZ and BP using an affection status that included both disorders. Two‐point and multipoint model‐based linkage analyses were performed and the resulting mod scores will be reported. In the first stage of the genome scan targetting 13 candidate chromosomes, the strongest linkage signals were detected at D18S1145 (in 18q12; Lod=4.03) for BP, and at D6S334 (net Lod=3.47; theta=0.66) for SZ. The 18q12 result met the Lander &amp; Krugliak (1995) criterion for a genome wide significant linkage and, moreover, provided support for a susceptibility region that may overlap SZ and BP. Three other chromosomal areas (3q, 10p, and 21q) yielded positive linkage signals. Chromosomes 4p, 5q, 6q, 8p, 11q, and 22q showed no evidence of linkage. O58 ASSOCIATION OF CAG REPEAT LOCI ON CHROMOSOME 22 WITH SCHIZOPHRENIA AND BIPOLAR DISORDER Jain S, Saleem QP, Dash D, Gandhi C, Benegal V, Mukherjee O, and Brahmachari SK Department of Psychiatry, Molecular Genetics Laboratory, National Institute of Mental Health and Neuro‐Sciences, Hosur Road, Bangalore, Karnataka 560029 India, Centre for Biochemical Technology, Delhi University Campus, Mall Road, Delhi 110007 Chromosome 22 has been implicated in schizophrenia and bipolar disorder in a number of studies. CAG repeat expansion may also be involved in these diseases. To explore the involvement of CAG repeats on Chr.22, we created an integrated map of all CAG repeats &gt;5 on this chromosome together with microsatellite markers associated with these diseases. Of the 52 CAG repeat loci identified, four repeat stretches in regions previously implicated by linkage analyses were chosen for further study. Three of the four repeat containing loci were found in the coding region with the CAG repeats coding for glutamine, and were expressed in the brain. All the loci studied showed varying degrees of polymorphism, and one locus had two alleles of 7 and 8 CAG repeats. The 8 repeat allele was significantly over represented in patient groups when compared to ethnically matched controls, while alleles at the other three loci did not show any difference. The repeat lies within a gene that shows homology to an androgen receptor related apoptosis protein in rat. We also identified other candidate genes in the vicinity of this locus. Our results suggest that the repeats within this gene or other genes in the vicinity of this locus are likely to be implicated in bipolar disorder and schizophrenia. O59 LINKAGE ANALYSIS USING QUANTITATIVE PHENOTYPES IN BIPOLAR DISORDER: A GENOME SCAN OF A SIB‐PAIR SAMPLE O'Mahony E, Corvin A, Craddock N, and Gill M Dept of Psychiatry, Trinity Centre for Health Sciences, St James Hospital Dublin 8, Ireland, Phone: 353 1 608 2465; Fax: 353 1 608 3405; E‐mail: omahonep@tcd.ie In a previous sibling‐pair study of bipolar illness the authors investigated the degree of familial aggregation of a number of demographic and clinical features: age at onset; frequency of manic and depressive episodes; proportion of manic to depressive episodes; dimension scores for mania, depression, psychosis and incongruence of psychotic symptoms with mood. Of these, intra‐pair Spearman correlations were most significant for dimension scores for psychosis (r=0.332, P &lt;0.001) and age at onset (r=0.293, P &lt;0.001). On the basis of the hypothesis that different aspects of the bipolar phenotype may be primarily influenced by different genes we have sought to apply a quantitative scale to phenotype assignment in our study of familial bipolar illness. We used 398 highly polymorphic microsatellite markers with an average inter‐marker distance of 9.6cM to genotype all individuals and GENEHUNTER 2.0 was used for non‐parametric analysis of the quantitative phenotype data. We identified 8 regions, suggestive of linkage for the ‘age at onset’ phenotype; These were on chromosomes 1q, 2p, 3p, 4q, 7p, 10p, 16p and 20p. With regard to the ‘psychosis dimension’ phenotype, we identified 6 regions suggestive of linkage; on 1p, 2p, 5p, 10p, 13q and 18p. O60 GENOME‐WIDE GENETIC LINKAGE STUDIES IN BIPOLAR DISORDER: A REVIEW Segurado R and Gill M Trinity College, University of Dublin, Department of Genetics, Dublin, IE Dublin, 2 Ireland, Phone: 353 1 608 2444; Fax: 353 1 679 8558; E‐mail: seguradr@tcd.ie Genetic linkage studies are prone to publication bias, as are genome scans which have been published in incomplete form, sometimes before the completion of genotyping and analysis across the entire genome. In order to overview genetic link