Association Study Of Bipolar Disorder Research Articles

M62 - TWO SIDES OF THE SAME COIN: SHARED POLYGENIC RISK OF PERSONALITY TRAITS AND SUICIDE ATTEMPT IMPLICATES COMMON PATHOMECHANISMS IN IMPAIRED EXECUTIVE FUNCTIONING AND EXTERNALIZING DISORDERS

Background Suicide undoubtedly is the most devastating complications of psychiatric disorders. The link between suicide and mental health problems is strong, particularly for mood disorders and personality disorders. Growing evidence suggest large extent of shared etiology and psychopathology between psychiatric and personality disorders, even indicating the theory of a unified, general psychopathological dimension behind all mental disorder. However, little is known about the common genetic risk factors of personality traits and suicidal behavior. The aim of our study was to assess the extent of the shared genetic etiology between suicidal behavior and the NEO Big Five personality traits (NEO-FFI) by conducting a Polygenic Risk Score prediction (PRS) analysis. Methods Target sample of the PRS analysis included subject with BPD from the GAIN Whole Genome Association Study of Bipolar Disorder (accession: phs000017.v3.p1, total n=999, suicidal n=358, control n=616, missing n=25) Suicidal attempt was assessed with the Diagnostic Interview for Genetic Studies versions 2,3 and 4. To increase the genomic overlap between the training and target datasets, GAIN BPD samples were imputed by using SHAPEIT and IMPUTE2 with 1000 Genomes Project's Phase I v3 reference genome. SNP-based associations were tested with PLINK, whereas PRS analyses were carried out by using PRSice. PRS scores for each NEO-FFI personality traits were computed from the publicly available summary result files of the first phase of the Genetics of Personality Consortium GWAS meta-analysis (GPC1, 10 cohorts, n=17,375 subjects). All PRS models were adjusted for sex, age and potential population stratification. Results Nominally significant PRS prediction of suicidal attempt in the target sample were only shown for Conscientiousness at pt Discussion Polygenic profile score analysis suggest shared genetic etiology between suicidal attempt and certain personality traits. Since Conscientiousness kinks both externalizing and internalizing behaviors, and low Conscientiousness scores and high Extraversion scores correlate with impaired executive functioning, our findings confirm the involvement of executive functions in the pathomechanism of suicide and support the theory of a general psychopathologic dimension. However, due to the relatively small size of the target sample, and the modest strength of PRS predictions, our findings should be interpreted cautiously.

Trauma exposure interacts with the genetic risk of bipolar disorder in alcohol misuse of US soldiers.

To investigate whether trauma exposure moderates the genetic correlation between substance use disorders and psychiatric disorders, we tested whether trauma exposure modifies the association of genetic risks for mental disorders with alcohol misuse and nicotine dependence (ND) symptoms. High-resolution polygenic risk scores (PRSs) were calculated for 10 732 US Army soldiers (8346 trauma-exposed and 2386 trauma-unexposed) based on genome-wide association studies of bipolar disorder (BD), major depressive disorder, and schizophrenia. The main finding was a significant BD PRS-by-trauma interaction with respect to alcohol misuse (P = 6.07 × 10-3 ). We observed a positive correlation between BD PRS and alcohol misuse in trauma-exposed soldiers (r = 0.029, P = 7.5 × 10-3 ) and a negative correlation in trauma-unexposed soldiers (r = -0.071, P = 5.61 × 10-4 ). Consistent (nominally significant) result with concordant effect, directions were observed in the schizophrenia PRS-by-trauma interaction analysis. The variants included in the BD PRS-by-trauma interaction showed significant enrichments for gene ontologies related to high voltage-gated calcium channel activity (GO:0008331, P = 1.51 × 10-5 ; GO:1990454, P = 4.49 × 10-6 ; GO:0030315, P = 2.07 × 10-6 ) and for Beta1/Beta2 adrenergic receptor signaling pathways (P = 2.61 × 10-4 ). These results indicate that the genetic overlap between alcohol misuse and BD is significantly moderated by trauma exposure. This provides molecular insight into the complex mechanisms that link substance abuse, psychiatric disorders, and trauma exposure.

Genome-wide association studies of bipolar disorder: A systematic review of recent findings and their clinical implications.

Recent advances in molecular genetics have enabled assessments of the associations among genetic variants (e.g., single-nucleotide polymorphisms) and susceptibility for complex diseases, including psychiatric disorders. Specifically, genome-wide association studies (GWAS), meta-analyses of the GWAS summary statistics, and mega-analyses (which use raw data, not summary statistics) of GWAS have provided revolutionary results and have identified numerous susceptibility genes or single-nucleotide polymorphisms. By using several tens of thousands of subjects, >40 genes have been identified as being associated with susceptibility for bipolar disorder so far. The purpose of this systematic review was to summarize the recent findings of bipolar disorder GWAS and discuss their clinical implications.

Evaluation of potential novel variations and their interactions related to bipolar disorders: analysis of genome-wide association study data.

BackgroundMultifactor dimensionality reduction (MDR) is a nonparametric approach that can be used to detect relevant interactions between single-nucleotide polymorphisms (SNPs). The aim of this study was to build the best genomic model based on SNP associations and to identify candidate polymorphisms that are the underlying molecular basis of the bipolar disorders.MethodsThis study was performed on Whole-Genome Association Study of Bipolar Disorder (dbGaP [database of Genotypes and Phenotypes] study accession number: phs000017.v3.p1) data. After preprocessing of the genotyping data, three classification-based data mining methods (ie, random forest, naïve Bayes, and k-nearest neighbor) were performed. Additionally, as a nonparametric, model-free approach, the MDR method was used to evaluate the SNP profiles. The validity of these methods was evaluated using true classification rate, recall (sensitivity), precision (positive predictive value), and F-measure.ResultsRandom forests, naïve Bayes, and k-nearest neighbors identified 16, 13, and ten candidate SNPs, respectively. Surprisingly, the top six SNPs were reported by all three methods. Random forests and k-nearest neighbors were more successful than naïve Bayes, with recall values >0.95. On the other hand, MDR generated a model with comparable predictive performance based on five SNPs. Although different SNP profiles were identified in MDR compared to the classification-based models, all models mapped SNPs to the DOCK10 gene.ConclusionThree classification-based data mining approaches, random forests, naïve Bayes, and k-nearest neighbors, have prioritized similar SNP profiles as predictors of bipolar disorders, in contrast to MDR, which has found different SNPs through analysis of two-way and three-way interactions. The reduced number of associated SNPs discovered by MDR, without loss in the classification performance, would facilitate validation studies and decision support models, and would reduce the cost to develop predictive and diagnostic tests. Nevertheless, we need to emphasize that translation of genomic models to the clinical setting requires models with higher classification performance.

A genome-wide association study of bipolar disorder with comorbid eating disorder replicates the SOX2-OT region

BackgroundBipolar disorder is a heterogeneous mood disorder associated with several important clinical comorbidities, such as eating disorders. This clinical heterogeneity complicates the identification of genetic variants contributing to bipolar susceptibility. Here we investigate comorbidity of eating disorders as a subphenotype of bipolar disorder to identify genetic variation that is common and unique to both disorders. MethodsWe performed a genome-wide association analysis contrasting 184 bipolar subjects with eating disorder comorbidity against both 1370 controls and 2006 subjects with bipolar disorder only from the Bipolar Genome Study (BiGS). ResultsThe most significant genome-wide finding was observed bipolar with comorbid eating disorder vs. controls within SOX2-OT (p=8.9×10−8 for rs4854912) with a secondary peak in the adjacent FXR1 gene (p=1.2×10−6 for rs1805576) on chromosome 3q26.33. This region was also the most prominent finding in the case-only analysis (p=3.5×10−7 and 4.3×10−6, respectively). Several regions of interest containing genes involved in neurodevelopment and neuroprotection processes were also identified. LimitationsWhile our primary finding did not quite reach genome-wide significance, likely due to the relatively limited sample size, these results can be viewed as a replication of a recent study of eating disorders in a large cohort. ConclusionsThese findings replicate the prior association of SOX2-OT with eating disorders and broadly support the involvement of neurodevelopmental/neuroprotective mechanisms in the pathophysiology of both disorders. They further suggest that different clinical manifestations of bipolar disorder may reflect differential genetic contributions and argue for the utility of clinical subphenotypes in identifying additional molecular pathways leading to illness.

Evaluation of Whole Genome Association Study Data in Bipolar Disorders: Potential Novel SNPs and Genes

ABS TRACT: Evaluation of whole genome association study data in bipolar disorders: potential novel SNPs and genes Objective: As a result of studies of multifactorial conditions, genetic, physiological and environmental factors, the overall heritability of bipolar disorders has been estimated to be up to 70%. In this study, an analysis of genome-wide association study data using data mining algorithms has revealed single-nucleotide polymorphisms that may be the basis for the molecular etiology of bipolar disorders. Methods: The study was conducted as a case-control study, and data from the Whole Genome Association Study of Bipolar Disorder (dbGaP Study Accession: phs000017.v3.p1) were used. The goal of the project was to identify genes that make individuals more susceptible to bipolar disorders. The data set included 1767 controls and 653 bipolar disorder only cases. Genotyping data were generated by Affymetrix Affy 6.0. A total of 934,940 oligos were scanned. Results: Various data mining approaches have identified 6 common SNPs which also have a statistically higher importance than others (rs10415145, rs10857580, rs11023096, rs4654814, rs4792189, rs7569781). rs10415145 is located on chromosome 19 at 19q13.11 and is related to ZNF507 (Zinc Finger Protein 507). While there are no publications reporting ZNF507 in bipolar disorders, a few publications exist studying other zinc finger protein genes. In addition, because it had the highest regulome score, DOCK10 was found to be a potentially related gene. Conclusion: Zinc finger protein genes may play a role in the etiology of bipolar disorders. More detailed studies would help clarify this relation and describe its pathway.

CDH13 and HCRTR2 May Be Associated with Hypersomnia Symptom of Bipolar Depression: A Genome-Wide Functional Enrichment Pathway Analysis.

Although bipolar disorder is highly heritable, the identification of specific genetic variations is limited because of the complex traits underlying the disorder. We performed a genome-wide association study of bipolar disorder using a subphenotype that shows hypersomnia symptom during a major depressive episode. We investigated a total of 2,191 cases, 1,434 controls, and 703,012 single nucleotide polymorphisms (SNPs) in the merged samples obtained from the Translational Genomics Institute and the Genetic Association Information Network. The gene emerging as the most significant by statistical analysis was rs1553441 (odds ratio=0.4093; p=1.20×10-5; Permuted p=6.0×10-6). However, the 5×0-8 threshold for statistical significance required in a genome-wide association study was not achieved. The functional enrichment pathway analysis showed significant enrichments in the adhesion, development-related, synaptic transmission-related, and cell recognition-related pathways. For further evaluation, each gene of the enriched pathways was reviewed and matched with genes that were suggested to be associated with psychiatric disorders by previous genetic studies. We found that the cadherin 13 and hypocretin (orexin) receptor 2 genes may be involved in the hypersomnia symptom during a major depressive episode of bipolar disorder.

Genome-wide association study of bipolar disorder in Canadian and UK populations corroborates disease loci including SYNE1 and CSMD1.

BackgroundRecently, genome-wide association studies (GWAS) for cases versus controls using single nucleotide polymorphism microarray data have shown promising findings for complex neuropsychiatric disorders, including bipolar disorder (BD).MethodsHere we describe a comprehensive genome-wide study of bipolar disorder (BD), cross-referencing analysis from a family-based study of 229 small families with association analysis from over 950 cases and 950 ethnicity-matched controls from the UK and Canada. Further, loci identified in these analyses were supported by pathways identified through pathway analysis on the samples.ResultsAlthough no genome-wide significant markers were identified, the combined GWAS findings have pointed to several genes of interest that support GWAS findings for BD from other groups or consortia, such as at SYNE1 on 6q25, PPP2R2C on 4p16.1, ZNF659 on 3p24.3, CNTNAP5 (2q14.3), and CDH13 (16q23.3). This apparent corroboration across multiple sites gives much confidence to the likelihood of genetic involvement in BD at these loci. In particular, our two-stage strategy found association in both our combined case/control analysis and the family-based analysis on 1q21.2 (closest gene: sphingosine-1-phosphate receptor 1 gene, S1PR1) and on 1q24.1 near the gene TMCO1, and at CSMD1 on 8p23.2, supporting several previous GWAS reports for BD and for schizophrenia. Pathway analysis suggests association of pathways involved in calcium signalling, neuropathic pain signalling, CREB signalling in neurons, glutamate receptor signalling and axonal guidance signalling.ConclusionsThe findings presented here show support for a number of genes previously implicated genes in the etiology of BD, including CSMD1 and SYNE1, as well as evidence for previously unreported genes such as the brain-expressed genes ADCY2, NCALD, WDR60, SCN7A and SPAG16.

Genome-wide association study of bipolar disorder accounting for effect of body mass index identifies a new risk allele in TCF7L2

Bipolar disorder (BD) is associated with higher body mass index (BMI) and increased metabolic comorbidity. Considering the associated phenotypic traits in genetic studies of complex diseases, either by adjusting for covariates or by investigating interactions between genetic variants and covariates, may help to uncover the missing heritability. However, obesity-related traits have not been incorporated in prior genome-wide analyses of BD as covariates or potential interacting factors. To investigate the genetic factors underlying BD while considering BMI, we conducted genome-wide analyses using data from the Genetic Association Information Network BD study. We analyzed 729,454 genotyped single-nucleotide polymorphism (SNP) markers on 388 European-American BD cases and 1020 healthy controls with available data for maximum BMI. We performed genome-wide association analyses of the genetic effects while accounting for the effect of maximum BMI, and also evaluated SNP-BMI interactions. A joint test of main and interaction effects demonstrated significant evidence of association at the genome-wide level with rs12772424 in an intron of TCF7L2 (P=2.85E-8). This SNP exhibited interaction effects, indicating that the bipolar susceptibility risk of this SNP is dependent on BMI. TCF7L2 codes for the transcription factor TCF/LF, part of the Wnt canonical pathway, and is one of the strongest genetic risk variants for type 2 diabetes (T2D). This is consistent with BD pathophysiology, as the Wnt pathway has crucial implications in neurodevelopment, neurogenesis and neuroplasticity, and is involved in the mechanisms of action of BD and depression treatments. We hypothesize that genetic risk for BD is BMI dependent, possibly related to common genetic risk with T2D.

Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments

Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein–protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.

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).

Incorporating Information of microRNAs into Pathway Analysis in a Genome-Wide Association Study of Bipolar Disorder.

MicroRNAs (miRNAs) are known to be important post-transcriptional regulators that are involved in the etiology of complex psychiatric traits. The present study aimed to incorporate miRNAs information into pathway analysis using a genome-wide association dataset to identify relevant biological pathways for bipolar disorder (BPD). We selected psychiatric- and neurological-associated miRNAs (N = 157) from PhenomiR database. The miRNA target genes (miTG) predictions were obtained from microRNA.org. Canonical pathways (N = 4,051) were downloaded from the Molecule Signature Database. We employed a novel weighting scheme for miTGs in pathway analysis using methods of gene set enrichment analysis and sum-statistic. Under four statistical scenarios, 38 significantly enriched pathways (P-value < 0.01 after multiple testing correction) were identified for the risk of developing BPD, including pathways of ion channels associated (e.g., gated channel activity, ion transmembrane transporter activity, and ion channel activity) and nervous related biological processes (e.g., nervous system development, cytoskeleton, and neuroactive ligand receptor interaction). Among them, 19 were identified only when the weighting scheme was applied. Many miRNA-targeted genes were functionally related to ion channels, collagen, and axonal growth and guidance that have been suggested to be associated with BPD previously. Some of these genes are linked to the regulation of miRNA machinery in the literature. Our findings provide support for the potential involvement of miRNAs in the psychopathology of BPD. Further investigations to elucidate the functions and mechanisms of identified candidate pathways are needed.

Broadening bipolar diagnostic criteria: why not start with hypomania?

Broadening bipolar diagnostic criteria: why not start with hypomania?

Genome-Wide Searches for Bipolar Disorder Genes

Whole-genome linkage and association studies of bipolar disorder are beginning to provide some compelling evidence for the involvement of several chromosomal regions and susceptibility genes in the pathogenesis of bipolar disorder. Developments in genotyping technology and efforts to combine data from different studies have helped in identifying chromosomes 6q16-q25, 13q, and 16p12 as probable susceptibility loci for bipolar disorder and confirmed CACNA1C and ANK3 as susceptibility genes for bipolar disorder. However, a lack of replication is still apparent in the literature. New studies focusing on copy number variants as well as new analytical approaches utilizing pathway analysis offer a new direction in the study of the genetics of bipolar disorder.

FP07-6 A Genome-wide Association Study of Bipolar Disorder Using DNA Pooling

FP07-6 A Genome-wide Association Study of Bipolar Disorder Using DNA Pooling

Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS

Bioinformatics researchers have a variety of programming languages and architectures at their disposal, and recent advances in graphics processing unit (GPU) computing have added a promising new option. However, many performance comparisons inflate the actual advantages of GPU technology. In this study, we carry out a realistic performance evaluation of SNPrank, a network centrality algorithm that ranks single nucleotide polymorhisms (SNPs) based on their importance in the context of a phenotype-specific interaction network. Our goal is to identify the best computational engine for the SNPrank web application and to provide a variety of well-tested implementations of SNPrank for Bioinformaticists to integrate into their research. Using SNP data from the Wellcome Trust Case Control Consortium genome-wide association study of Bipolar Disorder, we compare multiple SNPrank implementations, including Python, Matlab and Java as well as CPU versus GPU implementations. When compared with naïve, single-threaded CPU implementations, the GPU yields a large improvement in the execution time. However, with comparable effort, multi-threaded CPU implementations negate the apparent advantage of GPU implementations. The SNPrank code is open source and available at http://insilico.utulsa.edu/snprank.

A genome‐wide association study of bipolar disorder and comorbid migraine

Both migraine and bipolar affective disorder (BPAD) are complex phenotypes with significant genetic and nongenetic components. Epidemiological and clinical studies have showed a high degree of comorbidity between migraine and BPAD, and overlapping regions of linkage have been shown in numerous genome-wide linkage studies. To identify susceptibility factors for the BPAD/migraine phenotype, we conducted a genome-wide association study (GWAS) in 1001 cases with bipolar disorder collected through the NIMH Genetics Initiative for Bipolar Disorder and genotyped at 1 m single-nucleotide polymorphisms (SNPs) as part of the Genetic Association Information Network (GAIN). We compared BPAD patients without any headache (n = 699) with BPAD patients with doctor diagnosed migraine (n = 56). The strongest evidence for association was found for several SNPs in a 317-kb region encompassing the uncharacterized geneKIAA0564 {e.g. rs9566845 [OR = 4.98 (95% CI: 2.6-9.48), P = 7.7 × 10(-8)] and rs9566867 (P = 8.2 × 10(-8))}. Although the level of significance was significantly reduced when using the Fisher's exact test (as a result of the low count of cases with migraine), rs9566845 P = 1.4 × 10(-5) and rs9566867 P = 1.5 × 10(-5), this region remained the most prominent finding. Furthermore, marker rs9566845 was genotyped and found associated with migraine in an independent Norwegian sample of adult attention deficit hyperactivity disorder (ADHD) patients with and without comorbid migraine (n = 131 and n = 324, respectively), OR = 2.42 (1.18-4.97), P = 0.013. This is the first GWAS examining patients with bipolar disorder and comorbid migraine. These data suggest that genetic variants in the KIAA0564 gene region may predispose to migraine headaches in subgroups of patients with both BPAD and ADHD.

A genome-wide association study of bipolar disorder in Norwegian individuals, followed by replication in Icelandic sample

Background In the present study we investigated genetic variants associated with bipolar disorder in a homogenous Norwegian sample, and potential genetic overlap with schizophrenia, using the Affymetrix 6.0 array. Methods We carried out a genome-wide association study (GWAS) by genotyping 620 390 single-nucleotide polymorphisms (SNPs) in a case-control sample of Norwegian origin (the TOP study) including bipolar disorder ( n = 194), healthy controls ( n = 336) and schizophrenia ( n = 230), followed by replication and combined analysis in a genetically concordant Icelandic sample of bipolar disorder ( n = 435), and healthy controls ( n = 10,258). Results We selected 1000 markers with the lowest P values in the TOP discovery GWAS and tested these (or their surrogates) for association in the Icelandic replication sample. Polymorphisms on 35 loci were confirmed associated with bipolar disorder (nominal P value < 0.05; not corrected for multiple testing) in the replication sample. The most significant markers were located in DLEU2, GUCY1B2, PKIA, CCL2, CNTNAP5, DPP10, and FBN1. The combined group of schizophrenia and bipolar disorder compared to controls did not provide additional significant findings. Limitations Relatively small number of samples. Conclusions We detected weak but reproducible association with markers in several genes, in proximity to susceptibility loci found in previous GWAS studies of bipolar disorder. Further work is required to study their localization, expression, and regulation and international meta-analytic efforts will help to further elucidate their role.

Protein Kinase C Inhibitors

Bipolar disorder is one of the most severely debilitating of all medical illnesses. For a large number of patients, outcomes are quite poor. The illness results in tremendous suffering for patients and their families and commonly impairs functioning and workplace productivity. Risks of increased morbidity and mortality, unfortunately, are frequent occurrences as well. Until recently, little has been known about the specific molecular and cellular underpinnings of bipolar disorder. Such knowledge is crucial for the prospect of developing specific targeted therapies that are more effective and that have a more rapid onset of action than currently available treatments. Exciting recent data suggest that regulation of certain signalling pathways may be involved in the aetiology of bipolar disorder and that these pathways may be profitably targeted to treat the disorder. In particular, mania is associated with overactive protein kinase C (PKC) intracellular signalling, and recent genome-wide association studies of bipolar disorder have implicated an enzyme that reduces the activation of PKC. Importantly, the current mainstays in the treatment of mania, lithium (a monovalent cation) and valproate (a small fatty acid) indirectly inhibit PKC. In addition, recent clinical studies with the relatively selective PKC inhibitor tamoxifen add support to the relevance of the PKC target in bipolar disorder. Overall, a growing body of work both on a preclinical and clinical level indicates that PKC signalling may play an important role in the pathophysiology and treatment of bipolar disorder. The development of CNS-penetrant PKC inhibitors may have considerable benefit for this devastating illness.

Genome-wide association study of bipolar disorder in European American and African American individuals

To identify bipolar disorder (BD) genetic susceptibility factors, we conducted two genome-wide association (GWA) studies: one involving a sample of individuals of European ancestry (EA; n=1001 cases; n=1033 controls), and one involving a sample of individuals of African ancestry (AA; n=345 cases; n=670 controls). For the EA sample, single-nucleotide polymorphisms (SNPs) with the strongest statistical evidence for association included rs5907577 in an intergenic region at Xq27.1 (P=1.6 x 10(-6)) and rs10193871 in NAP5 at 2q21.2 (P=9.8 x 10(-6)). For the AA sample, SNPs with the strongest statistical evidence for association included rs2111504 in DPY19L3 at 19q13.11 (P=1.5 x 10(-6)) and rs2769605 in NTRK2 at 9q21.33 (P=4.5 x 10(-5)). We also investigated whether we could provide support for three regions previously associated with BD, and we showed that the ANK3 region replicates in our sample, along with some support for C15Orf53; other evidence implicates BD candidate genes such as SLITRK2. We also tested the hypothesis that BD susceptibility variants exhibit genetic background-dependent effects. SNPs with the strongest statistical evidence for genetic background effects included rs11208285 in ROR1 at 1p31.3 (P=1.4 x 10(-6)), rs4657247 in RGS5 at 1q23.3 (P=4.1 x 10(-6)), and rs7078071 in BTBD16 at 10q26.13 (P=4.5 x 10(-6)). This study is the first to conduct GWA of BD in individuals of AA and suggests that genetic variations that contribute to BD may vary as a function of ancestry.