Dissecting the shared genetic basis of migraine and mental disorders using novel statistical tools

Genome-wide analysis identifies novel shared loci between depression and white matter microstructure

Depression, a complex and heritable psychiatric disorder, is associated with alterations in white matter microstructure, yet their shared genetic basis remains largely unclear. Utilizing the largest available genome-wide association study (GWAS) datasets for depression (N = 674,452) and white matter microstructure (N = 33,224), assessed through diffusion tensor imaging metrics such as fractional anisotropy (FA) and mean diffusivity (MD), we employed linkage disequilibrium score regression method to estimate global genetic correlations, local analysis of [co]variant association approach to pinpoint genomic regions with local genetic correlations, and conjunctional false discovery rate analysis to identify shared variants. Our findings revealed that depression showed significant local genetic correlations with FA in 37 genomic regions and with MD in 59 regions, while global genetic correlations were weak. Variant-level analysis identified 78 distinct loci jointly associated with depression (25 novel loci) and FA (35 novel loci), and 41 distinct loci associated with depression (17 novel loci) and MD (25 novel loci). Further analyses showed that these shared loci exhibited both concordant and discordant effect directions between depression and white matter traits, as well as distinct yet overlapping hemispheric patterns in their genetic architecture. Enrichment analysis of these shared loci implicated biological processes related to metabolism and regulation. This study provides evidence of a mixed-direction shared genetic architecture between depression and white matter microstructure. The identification of specific loci and pathways offers potential insights for developing targeted interventions to improve white matter integrity and alleviate depressive symptoms.

Risks and familial coaggregation of suicide, accident mortality, and psychiatric disorders in first-degree relatives of individuals with migraine

Studies have revealed a comorbidity to be common between patients with migraine and those with psychiatric disorders, such as bipolar disorder (BD) and major depressive disorder (MDD). However, considering the substantial epidemiological differences in the prevalence of migraine between Western and Asian countries, further investigation was required to determine whether findings regarding familial coaggregation of migraine and psychiatric disorders can be generalized to Asian populations. Using the Taiwan National Health Insurance Database, we included 822,773 first-degree relatives (FDRs) of individuals with migraine and 3,291,092 FDRs of individuals without migraine. We investigated the prevalence of nine psychiatric disorders-schizophrenia, BD, MDD, autism, attention deficit hyperactivity disorder (ADHD), alcohol use disorder (AUD), substance use disorder (SUD), generalized anxiety disorder (GAD), and panic disorder (PD)-in addition to suicide and accidental death in these individuals. Poisson regression models with robust error variance revealed that the FDRs of individuals with migraine were at higher risks of suicide (relative risk: 1.11) as well as BD (1.09), MDD (1.16), autism (1.08), ADHD (1.23), AUD (1.20), SUD (1.14), GAD (1.23), and PD (1.26) than were those of individuals without migraine. The concurrent presence of psychiatric disorders, migraine, and suicide within families suggests a shared pathomechanism across these medical conditions. Thus, clinicians should closely monitor the mental health of FDRs of individuals with migraine. PERSPECTIVE: First-degree relatives of migraine probands had an increased risk of being diagnosed with psychiatric disorders, including schizophrenia, bipolar disorder, major depressive disorder, anxiety disorders, and alcohol or substance use disorders. They also died by suicide, regardless of the individual migraine and psychiatric comorbidities.

Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms

Many psychiatric disorders share genetic liabilities, but whether these shared liabilities can be utilized to classify and differentiate psychiatric disorders remains unclear. In this study, we use polygenic risk scores (PRSs) of 42 traits comorbid with schizophrenia (SCZ), bipolar disorder (BIP), and major depressive disorder (MDD) to evaluate their utilities. We found that combining target specific PRS with PRSs of comorbid traits can improve the classification of the target disorders. Importantly, without inclusion of PRSs from targeted disorders, we can still classify SCZ (accuracy 0.710 ± 0.008, AUC 0.789 ± 0.011), BIP (accuracy 0.782 ± 0.006, AUC 0.852 ± 0.004), and MDD (accuracy 0.753 ± 0.019, AUC 0.822 ± 0.010). Furthermore, PRSs from comorbid traits alone can effectively differentiate unaffected controls and patients with SCZ, BIP, and MDD (accuracy 0.861 ± 0.003, AUC 0.961 ± 0.041). Our results demonstrate that shared liabilities can be used effectively to improve the classification and differentiation of these disorders. The finding that PRSs from comorbid traits alone can classify and differentiate SCZ, BIP and MDD reasonably well implies that a majority of the risk variants composing target PRSs are shared with comorbid traits. Overall, our results suggest that a data-driven approach may be feasible to classify and differentiate these disorders.

Identification of novel genomic loci for anxiety symptoms and extensive genetic overlap with psychiatric disorders

AIMS

Anxiety disorders are prevalent and anxiety symptoms (ANX) co-occur with many psychiatric disorders. We aimed to identify genomic loci associated with ANX, characterize its genetic architecture, and genetic overlap with psychiatric disorders.

METHODS

We included a genome-wide association study of ANX (meta-analysis of UK Biobank and Million Veterans Program, n = 301,732), schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), and validated the findings in the Norwegian Mother, Father, and Child Cohort (n = 95,841). We employed the bivariate causal mixture model and local analysis of covariant association to characterize the genetic architecture including overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of loci associated with anxiety and shared with psychiatric disorders.

RESULTS

Anxiety was polygenic with 12.9k genetic variants and overlapped extensively with psychiatric disorders (4.1k-11.4k variants) with predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 119 novel loci for anxiety by conditioning on the psychiatric disorders, and loci shared between anxiety and MDn = 47 , BIPn = 33 , SCZn = 71 , ADHDn = 20 , and ASDn = 5 . Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways including cell adhesion and neurofibrillary tangle compared with genes annotated to the shared loci.

CONCLUSIONS

Anxiety is highly polygenic phenotype with extensive genetic overlap with psychiatric disorders, and we identified novel loci for anxiety implicating new molecular pathways. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified molecular underpinnings may lead to potential drug targets.

Nucleus accumbens myocyte enhancer factor 2C mediates the maintenance of peripheral nerve injury-induced physiological and behavioral maladaptations

ABSTRACT

Preclinical and clinical work has demonstrated altered plasticity and activity in the nucleus accumbens (NAc) under chronic pain states, highlighting critical therapeutic avenues for the management of chronic pain conditions. In this study, we demonstrate that myocyte enhancer factor 2C (MEF2C), a master regulator of neuronal activity and plasticity, is repressed in NAc neurons after prolonged spared nerve injury (SNI). Viral-mediated overexpression of Mef2c in NAc neurons partially ameliorated sensory hypersensitivity and emotional behaviors in mice with SNI, while also altering transcriptional pathways associated with synaptic signaling. Mef2c overexpression also reversed SNI-induced potentiation of phasic dopamine release and neuronal hyperexcitability in the NAc. Transcriptional changes induced by Mef2c overexpression were different than those observed after desipramine treatment, suggesting a mechanism of action different from antidepressants. Overall, we show that interventions in MEF2C-regulated mechanisms in the NAc are sufficient to disrupt the maintenance of chronic pain states, providing potential new treatment avenues for neuropathic pain.

Investigating the shared genetic architecture between depression and subcortical volumes

Depression, a widespread and highly heritable mental health condition, profoundly affects millions of individuals worldwide. Neuroimaging studies have consistently revealed volumetric abnormalities in subcortical structures associated with depression. However, the genetic underpinnings shared between depression and subcortical volumes remain inadequately understood. Here, we investigate the extent of polygenic overlap using the bivariate causal mixture model (MiXeR), leveraging summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 14 subcortical volumetric phenotypes (N = 33,224). Additionally, we identify shared genomic loci through conditional/conjunctional FDR analyses. MiXeR shows that subcortical volumetric traits share a substantial proportion of genetic variants with depression, with 44 distinct shared loci identified by subsequent conjunctional FDR analysis. These shared loci are predominantly located in intronic regions (58.7%) and non-coding RNA intronic regions (25.4%). The 269 protein-coding genes mapped by these shared loci exhibit specific developmental trajectories, with the expression level of 55 genes linked to both depression and subcortical volumes, and 30 genes linked to cognitive abilities and behavioral symptoms. These findings highlight a shared genetic architecture between depression and subcortical volumetric phenotypes, enriching our understanding of the neurobiological underpinnings of depression.

Identification of genetic susceptibility for Chinese migraine with depression using machine learning

Background

Migraine is a common primary headache that has a significant impact on patients' quality of life. The co-occurrence of migraine and depression is frequent, resulting in more complex symptoms and a poorer prognosis. The evidence suggests that depression and migraine comorbidity share a polygenic genetic background.

Objective

The aim of this study is to identify related genetic variants that contribute to genetic susceptibility to migraine with and without depression in a Chinese cohort.

Methods

In this case-control study, 263 individuals with migraines and 223 race-matched controls were included. Eight genetic polymorphism loci selected from the GWAS were genotyped using Sequenom's MALDI-TOF iPLEX platform.

Results

In univariate analysis, ANKDD1B rs904743 showed significant differences in genotype and allele distribution between migraineurs and controls. Furthermore, a machine learning approach was used to perform multivariate analysis. The results of the Random Forest algorithm indicated that ANKDD1B rs904743 was a significant risk factor for migraine susceptibility in China. Additionally, subgroup analysis by the Boruta algorithm showed a significant association between this SNP and migraine comorbid depression. Migraineurs with depression have been observed to have worse scores on the Beck Anxiety Inventory (BAI) and the Migraine Disability Assessment Scale (MIDAS).

Conclusion

The study indicates that there is an association between ANKDD1B rs904743 and susceptibility to migraine with and without depression in Chinese patients.

Epidemiologic association and shared genetic architecture between cataract and hearing difficulties among middle-aged and older adults

Age-related cataract and hearing difficulties are major sensory disorders that often co-exist in the global-wide elderly and have a tangible influence on the quality of life. However, the epidemiologic association between cataract and hearing difficulties remains unexplored, while little is known about whether the two share their genetic etiology. We first investigated the clinical association between cataract and hearing difficulties using the UK Biobank covering 502,543 individuals. Both unmatched analysis (adjusted for confounders) and a matched analysis (one control matched for each patient with cataract according to confounding factors) were undertaken and confirmed that cataract was associated with hearing difficulties (OR, 2.12; 95% CI, 1.98-2.27; OR, 2.03; 95% CI, 1.86-2.23, respectively). Furthermore, we explored and quantified the shared genetic architecture of these two complex sensory disorders at the common variant level using the bivariate causal mixture model (MiXeR) and conditional/conjunctional false discovery rate method based on the largest available genome-wide association studies of cataract (N = 585,243) and hearing difficulties (N = 323,978). Despite detecting only a negligible genetic correlation, we observe polygenic overlap between cataract and hearing difficulties and identify 6 shared loci with mixed directions of effects. Follow-up analysis of the shared loci implicates candidate genes QKI, STK17A, TYR, NSF, and TCF4 likely contribute to the pathophysiology of cataracts and hearing difficulties. In conclusion, this study demonstrates the presence of epidemiologic association between cataract and hearing difficulties and provides new insights into the shared genetic architecture of these two disorders at the common variant level.

Identification of Novel Genomic Loci for Anxiety and Extensive Genetic Overlap with Psychiatric Disorders

Background

Anxiety disorders are prevalent and anxiety symptoms co-occur with many psychiatric disorders. We aimed to identify genomic risk loci associated with anxiety, characterize its genetic architecture, and genetic overlap with psychiatric disorders.

Methods

We used the GWAS of anxiety symptoms, schizophrenia, bipolar disorder, major depression, and attention deficit hyperactivity disorder (ADHD). We employed MiXeR and LAVA to characterize the genetic architecture and genetic overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of genomic loci associated with anxiety and those shared with psychiatric disorders. Gene annotation and gene set analyses were conducted using OpenTargets and FUMA, respectively.

Results

Anxiety was polygenic with 12.9k estimated genetic risk variants and overlapped extensively with psychiatric disorders (4.1-11.4k variants). MiXeR and LAVA revealed predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 114 novel loci for anxiety by conditioning on the psychiatric disorders. We also identified loci shared between anxiety and major depression (n = 47), bipolar disorder (n = 33), schizophrenia (n = 71), and ADHD (n = 20). Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways and differential tissue expression in more diverse tissues than those annotated to the shared loci.

Conclusions

Anxiety is a highly polygenic phenotype with extensive genetic overlap with psychiatric disorders. These genetic overlaps enabled the identification of novel loci for anxiety. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified genetic loci implicate molecular pathways that may lead to potential drug targets.

Genetic causal relationship between immune diseases and migraine: a Mendelian randomization study

Background

Migraine has an increased prevalence in several immune disorders, but genetic cause-effect relationships remain unclear. Mendelian randomization (MR) was used in this study to explore whether immune diseases are causally associated with migraine and its subtypes.

Methods

We conducted a two-sample bidirectional multivariate Mendelian randomization study. Single-nucleotide polymorphisms (SNP) for six immune diseases, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes mellitus (T1D), allergic rhinitis (AR), asthma and psoriasis, were used as genetic instrumental variables. Summary statistics for migraine were obtained from 3 databases: the International Headache Genetics Consortium (IHGC), UK Biobank, and FinnGen study. MR analyses were performed per outcome database for each exposure and subsequently meta-analyzed. Reverse MR analysis was performed to determine whether migraine were risk factors for immune diseases. In addition, we conducted a genetic correlation to identify shared genetic variants for these two associations.

Results

No significant causal relationship was found between immune diseases and migraine and its subtypes. These results were robust with a series of sensitivity analyses. Using the linkage disequilibrium score regression method (LDSC), we detected no genetic correlation between migraine and immune diseases.

Conclusion

The evidence from our study does not support a causal relationship between immune diseases and migraine. The mechanisms underlying the frequent comorbidity of migraine and several immune diseases need to be further elucidated.

Genetics of migraine: complexity, implications, and potential clinical applications

Migraine is a common neurological disorder with large burden in terms of disability for individuals and costs for society. Accurate diagnosis and effective treatments remain priorities. Understanding the genetic factors that contribute to migraine risk and symptom manifestation could improve individual management. Migraine has a strong genetic basis that includes both monogenic and polygenic forms. Some distinct, rare, familial migraine subtypes are caused by pathogenic variants in genes involved in ion transport and neurotransmitter release, suggesting an underlying vulnerability of the excitatory-inhibitory balance in the brain, which might be exacerbated by disruption of homoeostasis and lead to migraine. For more prevalent migraine subtypes, genetic studies have identified many susceptibility loci, implicating genes involved in both neuronal and vascular pathways. Genetic factors can also reveal the nature of relationships between migraine and its associated biomarkers and comorbidities and could potentially be used to identify new therapeutic targets and predict treatment response.

Classification of Schizophrenia, Bipolar Disorder and Major Depressive Disorder with Comorbid Traits and Deep Learning Algorithms

Recent GWASs have demonstrated that comorbid disorders share genetic liabilities. But whether and how these shared liabilities can be used for the classification and differentiation of comorbid disorders remains unclear. In this study, we use polygenic risk scores (PRSs) estimated from 42 comorbid traits and the deep neural networks (DNN) architecture to classify and differentiate schizophrenia (SCZ), bipolar disorder (BIP) and major depressive disorder (MDD). Multiple PRSs were obtained for individuals from the schizophrenia (SCZ) (cases = 6,317, controls = 7,240), bipolar disorder (BIP) (cases = 2,634, controls 4,425) and major depressive disorder (MDD) (cases = 1,704, controls = 3,357) datasets, and classification models were constructed with and without the inclusion of PRSs of the target (SCZ, BIP or MDD). Models with the inclusion of target PRSs performed well as expected. Surprisingly, we found that SCZ could be classified with only the PRSs from 35 comorbid traits (not including the target SCZ and directly related traits) (accuracy 0.760 ± 0.007, AUC 0.843 ± 0.005). Similar results were obtained for BIP (33 traits, accuracy 0.768 ± 0.007, AUC 0.848 ± 0.009), and MDD (36 traits, accuracy 0.794 ± 0.010, AUC 0.869 ± 0.004). Furthermore, these PRSs from comorbid traits alone could effectively differentiate unaffected controls, SCZ, BIP, and MDD patients (average categorical accuracy 0.861 ± 0.003, average AUC 0.961 ± 0.041). These results suggest that the shared liabilities from comorbid traits alone may be sufficient to classify SCZ, BIP and MDD. More importantly, these results imply that a data-driven and objective diagnosis and differentiation of SCZ, BIP and MDD may be feasible.

Evaluation of the Observational Associations and Shared Genetics Between Glaucoma With Depression and Anxiety

Purpose

Glaucoma, a leading cause of blindness worldwide, is suspected to exhibit a notable association with psychological disturbances. This study aimed to investigate epidemiological associations and explore shared genetic architecture between glaucoma and mental traits, including depression and anxiety.

Methods

Multivariable logistic regression and Cox proportional hazards regression models were employed to investigate longitudinal associations based on UK Biobank. A stepwise approach was used to explore the shared genetic architecture. First, linkage disequilibrium score regression inferred global genetic correlations. Second, MiXeR analysis quantified the number of shared causal variants. Third, specific shared loci were detected through conditional/conjunctional false discovery rate (condFDR/conjFDR) analysis and characterized for biological insights. Finally, two-sample Mendelian randomization (MR) was conducted to investigate bidirectional causal associations.

Results

Glaucoma was significantly associated with elevated risks of hospitalized depression (hazard ratio [HR] = 1.54; 95% confidence interval [CI], 1.01-2.34) and anxiety (HR = 2.61; 95% CI, 1.70-4.01) compared to healthy controls. Despite the absence of global genetic correlations, MiXeR analysis revealed 300 variants shared between glaucoma and depression, and 500 variants shared between glaucoma and anxiety. Subsequent condFDR/conjFDR analysis discovered 906 single-nucleotide polymorphisms (SNPs) jointly associated with glaucoma and depression and two associated with glaucoma and anxiety. The MR analysis did not support robust causal associations but indicated the existence of pleiotropic genetic variants influencing both glaucoma and depression.

Conclusions

Our study enhances the existing epidemiological evidence and underscores the polygenic overlap between glaucoma and mental traits. This observation suggests a correlation shaped by pleiotropic genetic variants rather than being indicative of direct causal relationships.

Investigating the Shared Genetic Etiology Between Parkinson's Disease and Depression

Background

Depression is a common symptom in Parkinson's disease (PD), resulting from underlying neuropathological processes and psychological factors. However, the extent to which shared genetic risk factors contribute to the relationship between depression and PD is poorly understood.

Objective

To examine the effects of common genetic variants influencing the etiology of PD and depression risk at the genome-wide and local genomic regional level.

Methods

We comprehensively investigated the genetic relationship between PD and depression using genome-wide association studies data. First, we estimated the genetic correlation at the genome-wide level using linkage-disequilibrium score regression, followed by local genetic correlation analysis using the GWAS-pairwise method and functional annotation to identify genes that may jointly influence the risk for both traits. Also, we performed Latent Causal Variable, Latent Heritable Confounder Mendelian Randomization, and traditional Mendelian Randomization analyses to investigate the potential causal relationship.

Results

Although the genetic correlation between PD and depression was not statistically significant at the genome-wide level, GWAS-pairwise analyses identified 16 genomic segments associated with PD and depression, implicating nine genes. Further analyses revealed distinct patterns within individual genes, suggesting an intricate pattern. These genes involve various biological processes, including neurotransmitter regulation, senescence, and nucleo-cytoplasmic transport mechanisms. We did not observe genetic evidence of causality between PD and depression.

Conclusions

Our findings did not support a genome-wide genetic correlation or a causal association between both conditions. However, we identified genomic segments but identified genomic segments linked to distinct biological pathways influencing their etiology.Further research is needed to understand their functional consequences.

Mendelian randomization study supports the causal effects of air pollution on longevity via multiple age-related diseases

Growing evidence suggests that exposure to fine particulate matter (PM2.5) may reduce life expectancy; however, the causal pathways of PM2.5 exposure affecting life expectancy remain unknown. Here, we assess the causal effects of genetically predicted PM2.5 concentration on common chronic diseases and longevity using a Mendelian randomization (MR) statistical framework based on large-scale genome-wide association studies (GWAS) (>400,000 participants). After adjusting for other types of air pollution and smoking, we find significant causal relationships between PM2.5 concentration and angina pectoris, hypercholesterolaemia and hypothyroidism, but no causal relationship with longevity. Mediation analysis shows that although the association between PM2.5 concentration and longevity is not significant, PM2.5 exposure indirectly affects longevity via diastolic blood pressure (DBP), hypertension, angina pectoris, hypercholesterolaemia and Alzheimer's disease, with a mediated proportion of 31.5, 70.9, 2.5, 100, and 24.7%, respectively. Our findings indicate that public health policies to control air pollution may help improve life expectancy.

Characterizing the Shared Genetic Underpinnings of Schizophrenia and Cardiovascular Disease Risk Factors

OBJECTIVE

Schizophrenia is associated with increased risk of cardiovascular disease (CVD), although there is variation in risk among individuals. There are indications of shared genetic etiology between schizophrenia and CVD, but the nature of the overlap remains unclear. The aim of this study was to fill this gap in knowledge.

METHODS

Overlapping genetic architectures between schizophrenia and CVD risk factors were assessed by analyzing recent genome-wide association study (GWAS) results. The bivariate causal mixture model (MiXeR) was applied to estimate the number of shared variants and the conjunctional false discovery rate (conjFDR) approach was used to pinpoint specific shared loci.

RESULTS

Extensive genetic overlap was found between schizophrenia and CVD risk factors, particularly smoking initiation (N=8.6K variants) and body mass index (BMI) (N=8.1K variants). Several specific shared loci were detected between schizophrenia and BMI (N=304), waist-to-hip ratio (N=193), smoking initiation (N=293), systolic (N=294) and diastolic (N=259) blood pressure, type 2 diabetes (N=147), lipids (N=471), and coronary artery disease (N=35). The schizophrenia risk loci shared with smoking initiation had mainly concordant effect directions, and the risk loci shared with BMI had mainly opposite effect directions. The overlapping loci with lipids, blood pressure, waist-to-hip ratio, type 2 diabetes, and coronary artery disease had mixed effect directions. Functional analyses implicated mapped genes that are expressed in brain tissue and immune cells.

CONCLUSIONS

These findings indicate a genetic propensity to smoking and a reduced genetic risk of obesity among individuals with schizophrenia. The bidirectional effects of the shared loci with the other CVD risk factors may imply differences in genetic liability to CVD across schizophrenia subgroups, possibly underlying the variation in CVD comorbidity.

Migraine in bipolar disorder and schizophrenia: The hidden pain

OBJECTIVE

This study examined the prevalence of comorbid migraine in patients with bipolar disorder and those with schizophrenia and also examined the association between migraine comorbidity and disease characteristics in both disorders.

METHODS

In this cross-sectional study, 150 patients with bipolar disorder and 150 with schizophrenia were evaluated for migraine diagnosis using the International Classification of Headache Disorders (3rd ed). Patients were selected from psychiatry outpatient clinics at Kasr Al Ainy hospitals, Cairo University.  The Young Mania Rating Scale (YMRS) and the Hamilton Depression Rating Scale (HDRS) were administered to the bipolar group, whereas the Positive and Negative Syndrome Scale (PANSS) was administered to the schizophrenia group. Both groups were evaluated by the Clinical Global Impressions (CGI) scale.

RESULTS

The diagnosis of migraine was made in 34 (22.7%) of the bipolar group and 24 (16.0%) of the schizophrenia group. Patients with schizophrenia showed a significantly higher frequency and intensity of migraine attacks on the Migraine Disability Assessment scale than did the bipolar group (p < 0.001). In the bipolar group, there was no significant difference between patients with and without migraine on the YMRS, HDRS, and CGI. Among patients with schizophrenia, the duration of the migraine attacks was positively correlated with CGI scores (r = 0.40, p = 0.02).

CONCLUSION

Migraine was found to be a significant comorbidity in patients with bipolar disorder and schizophrenia. However, the intensity and frequency of migraine attacks were higher in the schizophrenia than in the bipolar group.

Multimodal Migraine Management and the Pursuit of Migraine Freedom: A Narrative Review

Migraine is a neurologic disease with a complex pathophysiology that can be controlled with current treatment options but not cured. Therefore, treatment expectations are highly variable. The concept of migraine freedom was recently introduced and can mean different things, with some, for example, expecting complete freedom from headache and associated symptoms and others accepting the occasional migraine attack if it does not impact functioning. Therefore, migraine management should be optimized so that patients can have the best opportunity to achieve their optimal treatment goals. With migraine freedom as a goal and, given the complex pathophysiology of migraine and the high incidence of comorbidities among individuals with migraine, treatment with a single modality may be insufficient, as it may not achieve migraine freedom in those with more frequent or disabling attacks. In this clinical perspective article, we have identified four key, partially overlapping principles of multimodal migraine treatment: (1) manage common comorbidities; (2) control modifiable risk factors for progression by addressing medication and caffeine overuse; (3) diagnose and treat secondary causes of headache, if present; and (4) individualize acute and preventive treatments to minimize pain, functional disability, and allodynia. There are many barriers to pursuing migraine freedom, and strategies to overcome them should be optimized. Migraine freedom should be an aspirational goal both at the individual attack level and for the disease overall. We believe that a comprehensive and multimodal approach that addresses all barriers people with migraine face could move patients closer to migraine freedom.

The shared genetic risk architecture of neurological and psychiatric disorders: a genome-wide analysis

While neurological and psychiatric disorders have historically been considered to reflect distinct pathogenic entities, recent findings suggest shared pathobiological mechanisms. However, the extent to which these heritable disorders share genetic influences remains unclear. Here, we performed a comprehensive analysis of GWAS data, involving nearly 1 million cases across ten neurological diseases and ten psychiatric disorders, to compare their common genetic risk and biological underpinnings. Using complementary statistical tools, we demonstrate widespread genetic overlap across the disorders, even in the absence of genetic correlations. This indicates that a large set of common variants impact risk of multiple neurological and psychiatric disorders, but with divergent effect sizes. Furthermore, biological interrogation revealed a range of biological processes associated with neurological diseases, while psychiatric disorders consistently implicated neuronal biology. Altogether, the study indicates that neurological and psychiatric disorders share key etiological aspects, which has important implications for disease classification, precision medicine, and clinical practice.

Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy

Personality and cognitive function are heritable mental traits whose genetic foundations may be distributed across interconnected brain functions. Previous studies have typically treated these complex mental traits as distinct constructs. We applied the 'pleiotropy-informed' multivariate omnibus statistical test to genome-wide association studies of 35 measures of neuroticism and cognitive function from the UK Biobank (n = 336,993). We identified 431 significantly associated genetic loci with evidence of abundant shared genetic associations, across personality and cognitive function domains. Functional characterization implicated genes with significant tissue-specific expression in all tested brain tissues and brain-specific gene sets. We conditioned independent genome-wide association studies of the Big 5 personality traits and cognitive function on our multivariate findings, boosting genetic discovery in other personality traits and improving polygenic prediction. These findings advance our understanding of the polygenic architecture of these complex mental traits, indicating a prominence of pleiotropic genetic effects across higher order domains of mental function such as personality and cognitive function.

Shared genetic loci between Alzheimer's disease and multiple sclerosis: Crossroads between neurodegeneration and immune system

BACKGROUND

Neuroinflammation is involved in the pathophysiology of Alzheimer's disease (AD), including immune-linked genetic variants and molecular pathways, microglia and astrocytes. Multiple Sclerosis (MS) is a chronic, immune-mediated disease with genetic and environmental risk factors and neuropathological features. There are clinical and pathobiological similarities between AD and MS. Here, we investigated shared genetic susceptibility between AD and MS to identify putative pathological mechanisms shared between neurodegeneration and the immune system.

METHODS

We analysed GWAS data for late-onset AD (N cases = 64,549, N controls = 634,442) and MS (N cases = 14,802, N controls = 26,703). Gaussian causal mixture modelling (MiXeR) was applied to characterise the genetic architecture and overlap between AD and MS. Local genetic correlation was investigated with Local Analysis of [co]Variant Association (LAVA). The conjunctional false discovery rate (conjFDR) framework was used to identify the specific shared genetic loci, for which functional annotation was conducted with FUMA and Open Targets.

RESULTS

MiXeR analysis showed comparable polygenicities for AD and MS (approximately 1800 trait-influencing variants) and genetic overlap with 20% of shared trait-influencing variants despite negligible genetic correlation (rg = 0.03), suggesting mixed directions of genetic effects across shared variants. conjFDR analysis identified 16 shared genetic loci, with 8 having concordant direction of effects in AD and MS. Annotated genes in shared loci were enriched in molecular signalling pathways involved in inflammation and the structural organisation of neurons.

CONCLUSIONS

Despite low global genetic correlation, the current results provide evidence for polygenic overlap between AD and MS. The shared loci between AD and MS were enriched in pathways involved in inflammation and neurodegeneration, highlighting new opportunities for future investigation.

Migraine and treatment-resistant depression

Migraine and major depressive disorders (MDD) or treatment resistant depression (TRD) represent a significant global burden and are often comorbid, further complicating diagnosis and treatment. Epidemiological studies have demonstrated a bidirectional relationship between migraine and MDD/TRD, with patients suffering from one disorder exhibiting a heightened risk of developing the other. This association is believed to result from shared genetic factors, neurotransmitter dysregulation, inflammation, hormonal alteration, and other conditions comorbid with both disorders. Emerging evidence suggests that therapeutics targeting common pathways in both disorders may be beneficial for comorbid patients. Novel therapeutics for migraine or MDD/TRD, such as calcitonin gene-related peptide (CGRP)-targeting therapy, onabotulinumtoxinA, ketamine/esketamine, vagus nerve stimulation or transcranial magnetic stimulation, may be helpful in selected patients with comorbid migraine-MDD/TRD. Nevertheless, continued efforts are needed to improve early detection and intervention, to better understand the complex interplay between genetic, environmental, and psychosocial factors contributing to this comorbidity, to identify novel therapeutic targets, and ultimately, to alleviate the disease burden caused by this comorbidity.

Genetics of migraine: where are we now?

Migraine is a complex brain disorder explained by the interaction of genetic and environmental factors. In monogenic migraines, including familial hemiplegic migraine and migraine with aura associated with hereditary small-vessel disorders, the identified genes code for proteins expressed in neurons, glial cells, or vessels, all of which increase susceptibility to cortical spreading depression. The study of monogenic migraines has shown that the neurovascular unit plays a prominent role in migraine. Genome-wide association studies have identified numerous susceptibility variants that each result in only a small increase in overall migraine risk. The more than 180 known variants belong to several complex networks of "pro-migraine" molecular abnormalities, which are mainly neuronal or vascular. Genetics has also highlighted the importance of shared genetic factors between migraine and its major co-morbidities, including depression and high blood pressure. Further studies are still needed to map all of the susceptibility loci for migraine and then to understand how these genomic variants lead to migraine cell phenotypes.

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.

New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications

Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.

Executive functions in migraine patients: a systematic review with meta-analysis

Introduction: Migraine, a common neurological disease, is known to impact the quality of life of individuals with this condition.Methods: We performed a systematic review with meta-analysis to investigate the abnormalities associated with executive functions of migraineurs as compared with healthy controls. In addition, we investigated the differences between patients with and without aura.Results: A total of 25 studies were included in the systematic review and 19 in the meta-analysis. Meta-analysis was conducted using random effects models, with the unit of analysis as the standardised mean difference (calculated as Hedges'g). Patients with migraine had worse performance in the trail making test A (g = 0.40; 95% confidence interval [CI] 0.05-0.74; p = 0.0271) and B (g = 0.40; 95% CI 0.16-0.64; p = 0.0026), and digit span backward test (g = -0.20; 95% CI - 0.31, - 0.09; p = 0.0105). Subgroup analysis revealed no difference between migraine with and without aura.Conclusion: These results suggest that migraine patients may present worse performance for specific executive functional domains, including attention, working memory, and mental flexibility.

Bidirectional genetic overlap between bipolar disorder and intelligence

BACKGROUND

Bipolar disorder (BD) is a highly heritable psychiatric illness exhibiting substantial correlation with intelligence.

METHODS

To investigate the shared genetic signatures between BD and intelligence, we utilized the summary statistics from genome-wide association studies (GWAS) to conduct the bivariate causal mixture model (MiXeR) and conjunctional false discovery rate (conjFDR) analyses. Subsequent expression quantitative trait loci (eQTL) mapping in human brain and enrichment analyses were also performed.

RESULTS

Analysis with MiXeR suggested that approximately 10.3K variants could influence intelligence, among which 7.6K variants were correlated with the risk of BD (Dice: 0.80), and 47% of these variants predicted BD risk and intelligence in consistent allelic directions. The conjFDR analysis identified 37 distinct genomic loci that were jointly associated with BD and intelligence with a conjFDR < 0.01, and 16 loci (43%) had the same directions of allelic effects in both phenotypes. Brain eQTL analyses found that genes affected by the "concordant loci" were distinct from those modulated by the "discordant loci". Enrichment analyses suggested that genes related to the "concordant loci" were significantly enriched in pathways/phenotypes related with synapses and sleep quality, whereas genes associated with the "discordant loci" were enriched in pathways related to cell adhesion, calcium ion binding, and abnormal emotional phenotypes.

CONCLUSIONS

We confirmed the polygenic overlap with mixed directions of allelic effects between BD and intelligence and identified multiple genomic loci and risk genes. This study provides hints for the mesoscopic phenotypes of BD and relevant biological mechanisms, promoting the knowledge of the genetic and phenotypic heterogeneity of BD. The essential value of leveraging intelligence in BD investigations is also highlighted.

Charting the Landscape of Genetic Overlap Between Mental Disorders and Related Traits Beyond Genetic Correlation

OBJECTIVE

Mental disorders are heritable and polygenic, and genome-wide genetic correlations (rg) have indicated widespread shared genetic risk across multiple disorders and related traits, mirroring their overlapping clinical characteristics. However, rg may underestimate the shared genetic underpinnings of mental disorders and related traits because it does not differentiate mixtures of concordant and discordant genetic effects from an absence of genetic overlap. Using novel statistical genetics tools, the authors aimed to evaluate the genetic overlap between mental disorders and related traits when accounting for mixed effect directions.

METHODS

The authors applied the bivariate causal mixture model (MiXeR) to summary statistics for four mental disorders, four related mental traits, and height from genome-wide association studies (Ns ranged from 53,293 to 766,345). MiXeR estimated the number of "causal" variants for a given trait ("polygenicity"), the number of variants shared between traits, and the genetic correlation of shared variants (rgs). Local rg was investigated using LAVA.

RESULTS

Among mental disorders, ADHD was the least polygenic (5.6K "causal" variants), followed by bipolar disorder (8.6K), schizophrenia (9.6K), and depression (14.5K). Most variants were shared across mental disorders (4.4K-9.3K) and between mental disorders and related traits (5.2K-12.8K), but with disorder-specific variations in rg and rgs. Overlap with height was small (0.7K-1.1K). MiXeR estimates correlated with LAVA local rg (r=0.88, p<0.001).

CONCLUSIONS

There is extensive genetic overlap across mental disorders and related traits, with mixed effect directions and few disorder-specific variants. This suggests that genetic risk for mental disorders is predominantly differentiated by divergent effect distributions of pleiotropic genetic variants rather than disorder-specific variants. This represents a conceptual advance in our understanding of the landscape of shared genetic architecture across mental disorders, which may inform genetic discovery, biological characterization, nosology, and genetic prediction.

Genetic Overlap Analysis Identifies a Shared Etiology between Migraine and Headache with Type 2 Diabetes

Migraine and headache frequently co-occur with type 2 diabetes (T2D), suggesting a shared aetiology between the two conditions. We used genome-wide association study (GWAS) data to investigate the genetic overlap and causal relationship between migraine and headache with T2D. Using linkage disequilibrium score regression (LDSC), we found a significant genetic correlation between migraine and T2D (rg = 0.06, p = 1.37 × 10−5) and between headache and T2D (rg = 0.07, p = 3.0 × 10−4). Using pairwise GWAS (GWAS-PW) analysis, we identified 11 pleiotropic regions between migraine and T2D and 5 pleiotropic regions between headache and T2D. Cross-trait SNP meta-analysis identified 23 novel SNP loci (Pmeta < 5 × 10−8) associated with migraine and T2D, and three novel SNP loci associated with headache and T2D. Cross-trait gene-based overlap analysis identified 33 genes significantly associated (Pgene-based < 3.85 × 10−6) with migraine and T2D, and 11 genes associated with headache and T2D, with 7 genes (EHMT2, SLC44A4, PLEKHA1, CFDP1, TMEM170A, CHST6, and BCAR1) common between them. There was also a significant overlap of genes nominally associated (Pgene-based < 0.05) with both migraine and T2D (Pbinomial-test = 2.83 × 10−46) and headache and T2D (Pbinomial-test = 4.08 × 10−29). Mendelian randomisation (MR) analyses did not provide consistent evidence for a causal relationship between migraine and T2D. However, we found headache was causally associated (inverse-variance weighted, ORIVW = 0.90, Pivw = 7 × 10−3) with T2D. Our findings robustly confirm the comorbidity of migraine and headache with T2D, with shared genetically controlled biological mechanisms contributing to their co-occurrence, and evidence for a causal relationship between headache and T2D.

The shared genetic basis of mood instability and psychiatric disorders: A cross-trait genome-wide association analysis

Recent genome-wide association studies of mood instability (MOOD) have found significant positive genetic correlation with major depression (DEP) and weak correlations with other psychiatric disorders. We investigated the polygenic overlap between MOOD and psychiatric disorders beyond genetic correlation to better characterize putative shared genetic determinants. GWAS summary statistics for schizophrenia (SCZ, n = 105,318), bipolar disorder (BIP, n = 413,466), DEP (n = 450,619), attention-deficit hyperactivity disorder (ADHD, n = 53,293), and MOOD (n = 363,705) were analyzed using the bivariate causal mixture model and conjunctional false discovery rate methods. MOOD correlated positively with all psychiatric disorders, but with wide variation in strength (rg  = 0.10-0.62). Of 10.4 K genomic variants influencing MOOD, 4 K-9.4 K influenced psychiatric disorders. Furthermore, MOOD was jointly associated with DEP at 163 loci, SCZ at 110, BIP at 60 and ADHD at 25. Fifty-three jointly associated loci were overlapping across two or more disorders, seven of which had discordant effect directions on psychiatric disorders. Genes mapped to loci associated with MOOD and all four disorders were enriched in a single gene-set, "synapse organization." The extensive polygenic overlap indicates shared molecular underpinnings across MOOD and psychiatric disorders. However, distinct patterns of genetic correlation and effect directions may relate to differences in the core clinical features of each disorder.

Characterizing the polygenic overlaps of bipolar disorder subtypes with schizophrenia and major depressive disorder

BACKGROUND

Large-scale studies have shown that bipolar I disorder (BD-I) and bipolar II disorder (BD-II) have differences in genetic association with schizophrenia (SCZ) and major depressive disorder (MDD). However, the underlying shared genetic architectures between BD subtypes and both SCZ and MDD remain largely unknown.

METHODS

We applied univariate and bivariate causal mixture models (MiXeR) to estimate the polygenicity and polygenic overlaps on large GWASs summary statistics of BD-I (n = 25,060), BD-II (n = 6781), SCZ (n = 69,369) and MDD (n = 170,756). Then, conjunctional false discovery rate approach was used to identify specific shared genetic loci between BD subtypes and both SCZ and MDD.

RESULTS

Univariate MiXeR revealed that BD-II was substantially more polygenic (22.37 K causal variants) as compared to BD-I, SCZ and MDD (7.87-12.43 K causal variants). Bivariate MiXeR revealed substantial polygenic overlaps between BD-I and SCZ (Dice-coefficient = 0.83) and between BD-I and MDD (Dice-coefficient = 0.76), which are beyond the genetic correlation (rg = 0.71 and 0.36). Conjunctional FDR analysis identified 236 distinct shared loci between BD-I and BD-II (2 loci), BD-I and SCZ (227 loci), BD-I and MDD (19 loci), BD-II and SCZ (1 locus), and BD-II and MDD (3 loci). Most of these shared loci have concordant effect directions among BD subtypes, SCZ and MDD.

LIMITATIONS

The bivariate MiXeR model was not applied for the BD-II because of insufficient power and inadequate model fit.

CONCLUSIONS

These findings provide evidence for extensive polygenic effects across BD subtypes, SCZ and MDD, which further our understanding of the potential genetic basis for the comorbid symptoms across these disorders.