Bipolar Disorder Genome-wide Association Studies Research Articles

Polygenic prediction of bipolar disorder in a Latin American sample.

To date, bipolar disorder (BD) genetic studies and polygenic risk scores (PRSs) for BD are based primarily on populations of European descent (EUR) and lack representation from other ancestries including Latin American (LAT). Here, we describe a new LAT cohort from the Mayo Clinic Bipolar Biobank (MCBB), a multisite collaboration with recruitment sites in the United States (EUR; 1,443 cases and 777 controls) and Mexico and Chile (LAT; 211 cases and 161 controls) and use the sample to explore the performance of a BD-PRS in a LAT population. Using results from the largest genome-wide association study of BD in EUR individuals, PRSice2 and LDpred2 were used to compute BD-PRSs in the LAT and EUR samples from the MCBB. PRSs explained up to 1.4% (PRSice) and 4% (LDpred2) of the phenotypic variance on the liability scale in the LAT sample compared to 3.8% (PRSice2) and 3.4% (LDpred2) in the EUR samples. Future larger studies should further explore the differential performance of different PRS approaches across ancestries. International multisite studies, such as this one, have the potential to address diversity-related limitations of prior genomic studies and ultimately contribute to the reduction of health disparities.

Genetic associations between bipolar disorder and brain structural phenotypes.

Patients with bipolar disorder (BD) and their first-degree relatives exhibit alterations in brain volume and cortical structure, whereas the underlying genetic mechanisms remain unclear. In this study, based on the published genome-wide association studies (GWAS), the extent of polygenic overlap between BD and 15 brain structural phenotypes was investigated using linkage disequilibrium score regression and MiXeR tool, and the shared genomic loci were discovered by conjunctional false discovery rate (conjFDR) and expression quantitative trait loci (eQTL) analyses. MiXeR estimated the overall measure of polygenic overlap between BD and brain structural phenotypes as 4-53% on a 0-100% scale (as quantified by the Dice coefficient). Subsequent conjFDR analyses identified 54 independent loci (71 risk single-nucleotide polymorphisms) jointly associated with BD and brain structural phenotypes with a conjFDR < 0.05, among which 33 were novel that had not been reported in the previous BD GWAS. Follow-up eQTL analyses in respective brain regions both confirmed well-known risk genes (e.g. CACNA1C, NEK4, GNL3, MAPK3) and discovered novel risk genes (e.g. LIMK2 and CAMK2N2). This study indicates a substantial shared genetic basis between BD and brain structural phenotypes, and provides novel insights into the developmental origin of BD and related biological mechanisms.

Genetic liability to bipolar disorder and body mass index: A bidirectional two-sample Mendelian randomization study.

Bipolar disorder is associated with increased body mass index (BMI), but it remains undetermined if this association is causal and, if so, in which direction it goes. Here, we sought to answer these questions using bidirectional two-sample Mendelian randomization, a method from genetic epidemiology that uses data from genome-wide association studies (GWAS) to examine whether a risk factor is causal for an outcome METHODS: We used summary statistics from GWAS of bipolar disorder and BMI conducted using data collected by the Psychiatric Genomics Consortium and the UK Biobank, respectively. The genetic instrument for bipolar disorder contained 53 SNPs and explained 0.5% of phenotypic variance, while the genetic instrument for BMI contained 517 SNPs and explained 7.1% of phenotypic variance RESULTS: Our findings suggest that genetic liability to bipolar disorder reduces BMI (slope from Egger regression=-0.195, p=0.004). It follows that a twofold increase in the genetic liability to bipolar disorder leads to a 0.6 (kg/m2 ) reduction in BMI, predominantly driven by reduced fat mass. Conversely, we found no evidence that BMI causes changes in the risk of developing bipolar disorder CONCLUSION: The results of this study suggest that the increased BMI observed among individuals with bipolar disorder is not a direct consequence of genetic liability to bipolar disorder, but may more likely represent the sum of downstream correlates of manifest bipolar disorder, such as side effects of pharmacological treatment, poor diet, and sedentary lifestyle. As these factors are all modifiable, they can be targeted as part of clinical management.

Genetic and functional analyses implicate microRNA 499A in bipolar disorder development

Bipolar disorder (BD) is a complex mood disorder with a strong genetic component. Recent studies suggest that microRNAs contribute to psychiatric disorder development. In BD, specific candidate microRNAs have been implicated, in particular miR-137, miR-499a, miR-708, miR-1908 and miR-2113. The aim of the present study was to determine the contribution of these five microRNAs to BD development. For this purpose, we performed: (i) gene-based tests of the five microRNA coding genes, using data from a large genome-wide association study of BD; (ii) gene-set analyses of predicted, brain-expressed target genes of the five microRNAs; (iii) resequencing of the five microRNA coding genes in 960 BD patients and 960 controls and (iv) in silico and functional studies for selected variants. Gene-based tests revealed a significant association with BD for MIR499A, MIR708, MIR1908 and MIR2113. Gene-set analyses revealed a significant enrichment of BD associations in the brain-expressed target genes of miR-137 and miR-499a-5p. Resequencing identified 32 distinct rare variants (minor allele frequency < 1%), all of which showed a non-significant numerical overrepresentation in BD patients compared to controls (p = 0.214). Seven rare variants were identified in the predicted stem-loop sequences of MIR499A and MIR2113. These included rs142927919 in MIR2113 (pnom = 0.331) and rs140486571 in MIR499A (pnom = 0.297). In silico analyses predicted that rs140486571 might alter the miR-499a secondary structure. Functional analyses showed that rs140486571 significantly affects miR-499a processing and expression. Our results suggest that MIR499A dysregulation might contribute to BD development. Further research is warranted to elucidate the contribution of the MIR499A regulated network to BD susceptibility.

Epigenetic signatures relating to disease-associated genotypic burden in familial risk of bipolar disorder

Environmental factors contribute to risk of bipolar disorder (BD), but how environmental factors impact the development of psychopathology within the context of elevated genetic risk is unknown. We herein sought to identify epigenetic signatures operating in the context of polygenic risk for BD in young people at high familial risk (HR) of BD. Peripheral blood-derived DNA was assayed using Illumina PsychArray, and Methylation-450K or -EPIC BeadChips. Polygenic risk scores (PRS) were calculated using summary statistics from recent genome-wide association studies for BD, major depressive disorder (MDD) and cross-disorder (meta-analysis of eight psychiatric disorders). Unrelated HR participants of European ancestry (n = 103) were stratified based on their BD-PRS score within the HR-population distribution, and the top two quintiles (High-BD-PRS; n = 41) compared against the bottom two quintiles (Low-BD-PRS; n = 41). The High-BD-PRS stratum also had higher mean cross-disorder-PRS and MDD-PRS (ANCOVA p = 0.035 and p = 0.024, respectively). We evaluated DNA methylation differences between High-BD-PRS and Low-BD-PRS strata using linear models. One differentially methylated probe (DMP) (cg00933603; p = 3.54 × 10−7) in VARS2, a mitochondrial aminoacyl-tRNA synthetase, remained significantly hypomethylated after multiple-testing correction. Overall, BD-PRS appeared to broadly impact epigenetic processes, with 1,183 genes mapped to nominal DMPs (p < 0.05); these displayed convergence with genes previously associated with BD, schizophrenia, chronotype, and risk taking. We tested poly-methylomic epigenetic profiles derived from nominal DMPs in two independent samples (n = 54 and n = 82, respectively), and conducted an exploratory evaluation of the effects of family environment, indexing cohesion and flexibility. This study highlights an important interplay between heritable risk and epigenetic factors, which warrant further exploration.

Phenotypes, mechanisms and therapeutics: insights from bipolar disorder GWAS findings

Genome-wide association studies (GWAS) have reported substantial genomic loci significantly associated with clinical risk of bipolar disorder (BD), and studies combining techniques of genetics, neuroscience, neuroimaging, and pharmacology are believed to help tackle clinical problems (e.g., identifying novel therapeutic targets). However, translating findings of psychiatric genetics into biological mechanisms underlying BD pathogenesis remains less successful. Biological impacts of majority of BD GWAS risk loci are obscure, and the involvement of many GWAS risk genes in this illness is yet to be investigated. It is thus necessary to review the progress of applying BD GWAS risk genes in the research and intervention of the disorder. A comprehensive literature search found that a number of such risk genes had been investigated in cellular or animal models, even before they were highlighted in BD GWAS. Intriguingly, manipulation of many BD risk genes (e.g., ANK3, CACNA1C, CACNA1B, HOMER1, KCNB1, MCHR1, NCAN, SHANK2 etc.) resulted in altered murine behaviors largely restoring BD clinical manifestations, including mania-like symptoms such as hyperactivity, anxiolytic-like behavior, as well as antidepressant-like behavior, and these abnormalities could be attenuated by mood stabilizers. In addition to recapitulating phenotypic characteristics of BD, some GWAS risk genes further provided clues for the neurobiology of this illness, such as aberrant activation and functional connectivity of brain areas in the limbic system, and modulated dendritic spine morphogenesis as well as synaptic plasticity and transmission. Therefore, BD GWAS risk genes are undoubtedly pivotal resources for modeling this illness, and might be translational therapeutic targets in the future clinical management of BD. We discuss both promising prospects and cautions in utilizing the bulk of useful resources generated by GWAS studies. Systematic integrations of findings from genetic and neuroscience studies are called for to promote our understanding and intervention of BD.

Polygenic risk scores across the extended psychosis spectrum

As early detection of symptoms in the subclinical to clinical psychosis spectrum may improve health outcomes, knowing the probabilistic susceptibility of developing a disorder could guide mitigation measures and clinical intervention. In this context, polygenic risk scores (PRSs) quantifying the additive effects of multiple common genetic variants hold the potential to predict complex diseases and index severity gradients. PRSs for schizophrenia (SZ) and bipolar disorder (BD) were computed using Bayesian regression and continuous shrinkage priors based on the latest SZ and BD genome-wide association studies (Psychiatric Genomics Consortium, third release). Eight well-phenotyped groups (n = 1580; 56% males) were assessed: control (n = 305), lower (n = 117) and higher (n = 113) schizotypy (both groups of healthy individuals), at-risk for psychosis (n = 120), BD type-I (n = 359), BD type-II (n = 96), schizoaffective disorder (n = 86), and SZ groups (n = 384). PRS differences were investigated for binary traits and the quantitative Positive and Negative Syndrome Scale. Both BD-PRS and SZ-PRS significantly differentiated controls from at-risk and clinical groups (Nagelkerke’s pseudo-R2: 1.3–7.7%), except for BD type-II for SZ-PRS. Out of 28 pairwise comparisons for SZ-PRS and BD-PRS, 9 and 12, respectively, reached the Bonferroni-corrected significance. BD-PRS differed between control and at-risk groups, but not between at-risk and BD type-I groups. There was no difference between controls and schizotypy. SZ-PRSs, but not BD-PRSs, were positively associated with transdiagnostic symptomology. Overall, PRSs support the continuum model across the psychosis spectrum at the genomic level with possible irregularities for schizotypy. The at-risk state demands heightened clinical attention and research addressing symptom course specifiers. Continued efforts are needed to refine the diagnostic and prognostic accuracy of PRSs in mental healthcare.

Decreased DNA methylation at promoters and gene-specific neuronal hypermethylation in the prefrontal cortex of patients with bipolar disorder

Bipolar disorder (BD) is a severe mental disorder characterized by repeated mood swings. Although genetic factors are collectively associated with the etiology of BD, the underlying molecular mechanisms, particularly how environmental factors affect the brain, remain largely unknown. We performed promoter-wide DNA methylation analysis of neuronal and nonneuronal nuclei in the prefrontal cortex of patients with BD (N = 34) and controls (N = 35). We found decreased DNA methylation at promoters in both cell types in the BD patients. Gene Ontology (GO) analysis of differentially methylated region (DMR)-associated genes revealed enrichment of molecular motor-related genes in neurons, chemokines in both cell types, and ion channel- and transporter-related genes in nonneurons. Detailed GO analysis further revealed that growth cone- and dendrite-related genes, including NTRK2 and GRIN1, were hypermethylated in neurons of BD patients. To assess the effect of medication, neuroblastoma cells were cultured under therapeutic concentrations of three mood stabilizers. We observed that up to 37.9% of DMRs detected in BD overlapped with mood stabilizer-induced DMRs. Interestingly, mood stabilizer-induced DMRs showed the opposite direction of changes in DMRs, suggesting the therapeutic effects of mood stabilizers. Among the DMRs, 12 overlapped with loci identified in a genome-wide association study (GWAS) of BD. We also found significant enrichment of neuronal DMRs in the loci reported in another GWAS of BD. Finally, we performed qPCR of DNA methylation-related genes and found that DNMT3B was overexpressed in BD. The cell-type-specific DMRs identified in this study will be useful for understanding the pathophysiology of BD.

Are psychiatric disorders risk factors for COVID-19 susceptibility and severity? a two-sample, bidirectional, univariable, and multivariable Mendelian Randomization study

Observational studies have suggested bidirectional associations between psychiatric disorders and COVID-19 phenotypes, but results of such studies are inconsistent. Mendelian Randomization (MR) may overcome the limitations of observational studies, e.g., unmeasured confounding and uncertainties about cause and effect. We aimed to elucidate associations between neuropsychiatric disorders and COVID-19 susceptibility and severity. To that end, we applied a two-sample, bidirectional, univariable, and multivariable MR design to genetic data from genome-wide association studies (GWASs) of neuropsychiatric disorders and COVID-19 phenotypes (released in January 2021). In single-variable Generalized Summary MR analysis, the most significant and only Bonferroni-corrected significant result was found for genetic liability to BIP-SCZ (a combined GWAS of bipolar disorder and schizophrenia as cases vs. controls) increasing risk of COVID-19 (OR = 1.17, 95% CI, 1.06–1.28). However, we found a significant, positive genetic correlation between BIP-SCZ and COVID-19 of 0.295 and could not confirm causal or horizontally pleiotropic effects using another method. No genetic liabilities to COVID-19 phenotypes increased the risk of (neuro)psychiatric disorders. In multivariable MR using both neuropsychiatric and a range of other phenotypes, only genetic instruments of BMI remained causally associated with COVID-19. All sensitivity analyses confirmed the results. In conclusion, while genetic liability to bipolar disorder and schizophrenia combined slightly increased COVID-19 susceptibility in one univariable analysis, other MR and multivariable analyses could only confirm genetic underpinnings of BMI to be causally implicated in COVID-19 susceptibility. Thus, using MR we found no consistent proof of genetic liabilities to (neuro)psychiatric disorders contributing to COVID-19 liability or vice versa, which is in line with at least two observational studies. Previously reported positive associations between psychiatric disorders and COVID-19 by others may have resulted from statistical models incompletely capturing BMI as a continuous covariate.

Risk Stratification for Bipolar Disorder Using Polygenic Risk Scores Among Young High-Risk Adults.

Objective: Identifying high-risk groups with an increased genetic liability for bipolar disorder (BD) will provide insights into the etiology of BD and contribute to early detection of BD. We used the BD polygenic risk score (PRS) derived from BD genome-wide association studies (GWAS) to explore how such genetic risk manifests in young, high-risk adults. We postulated that BD-PRS would be associated with risk factors for BD.Methods: A final sample of 185 young, high-risk German adults (aged 18–35 years) were grouped into three risk groups and compared to a healthy control group (n = 1,100). The risk groups comprised 117 cases with attention deficit hyperactivity disorder (ADHD), 45 with major depressive disorder (MDD), and 23 help-seeking adults with early recognition symptoms [ER: positive family history for BD, (sub)threshold affective symptomatology and/or mood swings, sleeping disorder]. BD-PRS was computed for each participant. Logistic regression models (controlling for sex, age, and the first five ancestry principal components) were used to assess associations of BD-PRS and the high-risk phenotypes.Results: We observed an association between BD-PRS and combined risk group status (OR = 1.48, p < 0.001), ADHD diagnosis (OR = 1.32, p = 0.009), MDD diagnosis (OR = 1.96, p < 0.001), and ER group status (OR = 1.7, p = 0.025; not significant after correction for multiple testing) compared to healthy controls.Conclusions: In the present study, increased genetic risk for BD was a significant predictor for MDD and ADHD status, but not for ER. These findings support an underlying shared risk for both MDD and BD as well as ADHD and BD. Improving our understanding of the underlying genetic architecture of these phenotypes may aid in early identification and risk stratification.

Integrative analyses prioritize GNL3 as a risk gene for bipolar disorder.

Genome-wide association studies (GWASs) have identified numerous single nucleotide polymorphisms (SNPs) associated with bipolar disorder (BD), but what the causal variants are and how they contribute to BD is largely unknown. In this study, we used FUMA, a GWAS annotation tool, to pinpoint potential causal variants and genes from the latest BD GWAS findings, and performed integrative analyses, including brain expression quantitative trait loci (eQTL), gene coexpression network, differential gene expression, protein-protein interaction, and brain intermediate phenotype association analysis to identify the functions of a prioritized gene and its connection to BD. Convergent lines of evidence prioritized protein-coding gene G Protein Nucleolar 3 (GNL3) as a BD risk gene, with integrative analyses revealing GNL3's roles in cell proliferation, neuronal functions, and brain phenotypes. We experimentally revealed that BD-related eQTL SNPs rs10865973, rs12635140, and rs4687644 regulate GNL3 expression using dual luciferase reporter assay and CRISPR interference experiment in human neural progenitor cells. We further identified that GNL3 knockdown and overexpression led to aberrant neuronal proliferation and differentiation, using two-dimensional human neural cell cultures and three-dimensional forebrain organoid model. This study gathers evidence that BD-related genetic variants regulate GNL3 expression which subsequently affects neuronal proliferation and differentiation.

Translational genomics and beyond in bipolar disorder.

Genome-wide association studies (GWAS) have revealed multiple genomic loci conferring risk of bipolar disorder (BD), providing hints for its underlying pathobiology. However, there are still remaining questions to answer. For example, discordance exists between BD heritability estimated with earlier epidemiological evidence and that calculated based on common GWAS variations. Where is the "missing heritability"? How can we explain the biology of the disease based on genetic findings? In this review, we summarize the accomplishments and limitations of current BD GWAS, and discuss potential reasons for the "missing heritability." In addition, progresses of research for the biological mechanisms underlying BD genetic risk using brain tissues, reprogrammed cells, and model animals are reviewed. While our knowledge of BD genetic basis is significantly promoted by these efforts, the complexities of gene regulation in the genome, the spatial-temporal heterogeneity during brain development, and the limitations of different experimental models should always be considered. Notably, several genes have been widely studied given their relatively well-characterized involvement in BD (e.g., CACAN1C and ANK3), and findings of these genes are summarized to both outline possible biological mechanisms of BD and describe examples of translating GWAS discoveries into the pathophysiology.

Missing a beat: assessment of circadian rhythm abnormalities in bipolar disorder in the genomic era.

Circadian rhythm abnormalities have been recognized as a central feature of bipolar disorder (BD) but a coherent biological explanation for them remains lacking. Using genetic mutation of 'clock genes', robust animal models of mania and depression have been developed that elucidate key aspects of circadian rhythms and the circadian clock-mood connection. However, translation of this knowledge into humans remains incomplete. In recent years, very large genome-wide association studies (GWAS) have been conducted and the genetic underpinnings of BD are beginning to emerge. However, these genetic studies in BD do not match well with the evidence from animal studies that implicate the circadian clock in mood regulation. Even larger GWAS have been conducted for circadian phenotypes including chronotype, rhythm amplitude, sleep duration, and insomnia. These studies have identified a diverse set of associated genes, including a minority with previously well-characterized functions in the circadian clock. Taken together, the data from recent GWAS of BD and circadian phenotypes indicate that the genetic organization of the circadian clock, both in health and in BD is complex. The findings from GWAS elucidate potentially novel circadian mechanism that may be partly distinct from those identified in animal models. Pleiotropy, epistasis and nongenetic factors may play important roles in regulating circadian rhythms, some of which may underlie circadian rhythm disturbances in BD.

Systematic genetic analyses of genome-wide association study data reveal an association between the key nucleosome remodeling and deacetylase complex and bipolar disorder development.

Genome-wide association studies (GWASs) are used to identify genetic variants for association with bipolar disorder (BD) risk; however, each GWAS can only reveal a small fraction of this association. This study systematically analyzed multiple GWAS data sets to provide further insights into potential causal BD processes by integrating the results of Psychiatric Genomics Consortium Phase I (PGC-I) for BD with core human pathways and functional networks. The i-Gsea4GwasV2 program was used to analyze data from the PGC-I GWAS for BD (the pathways came from Reactome), as well as the nominally significant pathways. We established a gene network of the significant pathways and performed a gene set analysis for each gene cluster of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) GWAS data for the volumes of the intracranial region and seven subcortical regions. A total of 30 of 1816 Reactome pathways were identified and showed associations with BD risk. We further revealed 22 interconnected functional and topologically interacting clusters (Clusters 0-21) that were associated with BD risk. Moreover, we obtained brain transcriptome data from BrainSpan and found significant associations between common variants of the genes in Cluster 1 with the hippocampus (HIP; P=.026; family-wise error [FWE] correction) and amygdala (AMY; P=.016; FEW correction) in Cluster 8 with HIP (P=.022; FWE correction). The genes in Cluster 1 were enriched for the transcriptional co-expression profile in the prenatal AMY, and core genes (CDH4, MTA2, RBBP4, and HDAC2) were identified to be involved in regulating early brain development. This study demonstrated that the HIP and AMY play a central role in neurodevelopment and BD risk.

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.

COMBAT: A Combined Association Test for Genes Using Summary Statistics.

Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Traditional analysis of GWAS typically examines one marker at a time, usually single nucleotide polymorphisms (SNPs), to identify individual variants associated with a disease. However, due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. As a complementary approach to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal. Here we propose a combined association test (COMBAT) for genes, which incorporates strengths from existing gene-based tests and shows higher overall performance than any individual test. Our method does not require raw genotype or phenotype data, but needs only SNP-level P-values and correlations between SNPs from ancestry-matched samples. Extensive simulations showed that COMBAT has an appropriate type I error rate, maintains higher power across a wide range of genetic models, and is more robust than any individual gene-based test. We further demonstrated the superior performance of COMBAT over several other gene-based tests through reanalysis of the meta-analytic results of GWAS for bipolar disorder. Our method allows for the more powerful application of gene-based analysis to complex diseases, which will have broad use given that GWAS summary results are increasingly publicly available.

Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder.

Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behaviour. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ∼2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the X-chromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, P = 5.87 × 10 - 9; odds ratio (OR) = 1.12) and markers within ERBB2 (rs2517959, P = 4.53 × 10 - 9; OR = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.

The ANK3 gene and facial affect processing: An ERP study.

ANK3 is one of the most promising candidate genes for bipolar disorder (BD). A polymorphism (rs10994336) within the ANK3 gene has been associated with BD in at least three genome-wide association studies of BD [McGuffin et al., 2003; Kieseppä, 2004; Edvardsen et al., 2008]. Because facial affect processing is disrupted in patients with BD, the current study aimed to explore whether the BD risk alleles are associated with the N170, an early event-related potential (ERP) component related to facial affect processing. We collected data from two independent samples of healthy individuals (Ns = 83 and 82, respectively) to test the association between rs10994336 and an early event-related potential (ERP) component (N170) that is sensitive to facial affect processing. Repeated-measures analysis of covariance in both samples consistently revealed significant main effects of rs10994336 genotype (Sample I: F (1, 72) = 7.24, P = 0.009; Sample II: F (1, 69) = 11.81, P = 0.001), but no significant interaction of genotype × electrodes (Ps > 0.05) or genotype × emotional conditions (Ps > 0.05). These results suggested that rs10994336 was linked to early ERP component reflecting facial structural encoding during facial affect processing. These results shed new light on the brain mechanism of this risk SNP and associated disorders such as BD. © 2016 Wiley Periodicals, Inc.

Impact of a cis-associated gene expression SNP on chromosome 20q11.22 on bipolar disorder susceptibility, hippocampal structure and cognitive performance.

Bipolar disorder is a highly heritable polygenic disorder. Recent enrichment analyses suggest that there may be true risk variants for bipolar disorder in the expression quantitative trait loci (eQTL) in the brain. We sought to assess the impact of eQTL variants on bipolar disorder risk by combining data from both bipolar disorder genome-wide association studies (GWAS) and brain eQTL. To detect single nucleotide polymorphisms (SNPs) that influence expression levels of genes associated with bipolar disorder, we jointly analysed data from a bipolar disorder GWAS (7481 cases and 9250 controls) and a genome-wide brain (cortical) eQTL (193 healthy controls) using a Bayesian statistical method, with independent follow-up replications. The identified risk SNP was then further tested for association with hippocampal volume (n = 5775) and cognitive performance (n = 342) among healthy individuals. Integrative analysis revealed a significant association between a brain eQTL rs6088662 on chromosome 20q11.22 and bipolar disorder (log Bayes factor = 5.48; bipolar disorder P = 5.85 × 10(-5)). Follow-up studies across multiple independent samples confirmed the association of the risk SNP (rs6088662) with gene expression and bipolar disorder susceptibility (P = 3.54 × 10(-8)). Further exploratory analysis revealed that rs6088662 is also associated with hippocampal volume and cognitive performance in healthy individuals. Our findings suggest that 20q11.22 is likely a risk region for bipolar disorder; they also highlight the informative value of integrating functional annotation of genetic variants for gene expression in advancing our understanding of the biological basis underlying complex disorders, such as bipolar disorder.

FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics.

Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Despite remarkable success in uncovering many risk variants and providing novel insights into disease biology, genetic variants identified to date fail to explain the vast majority of the heritability for most complex diseases. One explanation is that there are still a large number of common variants that remain to be discovered, but their effect sizes are generally too small to be detected individually. Accordingly, gene set analysis of GWAS, which examines a group of functionally related genes, has been proposed as a complementary approach to single-marker analysis. Here, we propose a FL: exible and A: daptive test for G: ene S: ets (FLAGS), using summary statistics. Extensive simulations showed that this method has an appropriate type I error rate and outperforms existing methods with increased power. As a proof of principle, through real data analyses of Crohn's disease GWAS data and bipolar disorder GWAS meta-analysis results, we demonstrated the superior performance of FLAGS over several state-of-the-art association tests for gene sets. Our method allows for the more powerful application of gene set analysis to complex diseases, which will have broad use given that GWAS summary results are increasingly publicly available.