scholarly journals Developmental profile of psychiatric risk associated with voltage-gated cation channel activity

2020 ◽  
Author(s):  
Nicholas E Clifton ◽  
Leonardo Collado-Torres ◽  
Emily E Burke ◽  
Antonio F Pardiñas ◽  
Janet C Harwood ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Psychiatric Disorders ◽  
Association Studies ◽  
Early Adulthood ◽  
Biological Pathways ◽  
Cation Channel ◽  
Genome Wide Association Studies ◽  
Channel Activity ◽  
Voltage Gated ◽  
The Relationship

AbstractBackgroundRecent breakthroughs in psychiatric genetics have implicated biological pathways onto which genetic risk for psychiatric disorders converges. However, these studies do not reveal the developmental time point(s) at which these pathways are relevant.MethodsWe aimed to determine the relationship between psychiatric risk and developmental gene expression relating to discrete biological pathways. We used post-mortem RNA sequencing data (BrainSeq and BrainSpan) from brain tissue at multiple pre- and post-natal timepoints and summary statistics from recent genome-wide association studies of schizophrenia, bipolar disorder and major depressive disorder. We prioritised gene sets for overall enrichment of association with each disorder, and then tested the relationship between the association of each of their constituent genes with their relative expression at each developmental stage.ResultsWe observed relationships between the expression of genes involved in voltage-gated cation channel activity during Early Midfetal, Adolescence and Early Adulthood timepoints and association with schizophrenia and bipolar disorder, such that genes more strongly associated with these disorders had relatively low expression during Early Midfetal development and higher expression during Adolescence and Early Adulthood. The relationship with schizophrenia was strongest for the subset of genes related to calcium channel activity, whilst for bipolar disorder the relationship was distributed between calcium and potassium channel activity genes.ConclusionsOur results indicate periods during development when biological pathways related to the activity of calcium and potassium channels may be most vulnerable to the effects of genetic variants conferring risk to psychiatric disorders. Furthermore, they indicate key time points and potential targets for disorder-specific therapeutic interventions.

scholarly journals Prediction of response to drug therapy in psychiatric disorders

Open Biology ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 180031 ◽  
Author(s):  
Shani Stern ◽  
Sara Linker ◽  
Krishna C. Vadodaria ◽  
Maria C. Marchetto ◽  
Fred H. Gage
Keyword(s):  
Autism Spectrum Disorder ◽  
Bipolar Disorder ◽  
Major Depression ◽  
Personalized Medicine ◽  
Psychiatric Disorders ◽  
Association Studies ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Genome Wide Association Studies ◽  
Patient Reported

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

scholarly journals Characterising the shared genetic determinants of bipolar disorder, schizophrenia and risk-taking

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Guy Hindley ◽  
Shahram Bahrami ◽  
Nils Eiel Steen ◽  
Kevin S. O’Connell ◽  
Oleksandr Frei ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Psychiatric Disorders ◽  
Risk Taking ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Risky Behaviours ◽  
Shared Risk ◽  
Shared Genetic

AbstractIncreased risk-taking is a central component of bipolar disorder (BIP) and is implicated in schizophrenia (SCZ). Risky behaviours, including smoking and alcohol use, are overrepresented in both disorders and associated with poor health outcomes. Positive genetic correlations are reported but an improved understanding of the shared genetic architecture between risk phenotypes and psychiatric disorders may provide insights into underlying neurobiological mechanisms. We aimed to characterise the genetic overlap between risk phenotypes and SCZ, and BIP by estimating the total number of shared variants using the bivariate causal mixture model and identifying shared genomic loci using the conjunctional false discovery rate method. Summary statistics from genome wide association studies of SCZ, BIP, risk-taking and risky behaviours were acquired (n = 82,315–466,751). Genomic loci were functionally annotated using FUMA. Of 8.6–8.7 K variants predicted to influence BIP, 6.6 K and 7.4 K were predicted to influence risk-taking and risky behaviours, respectively. Similarly, of 10.2–10.3 K variants influencing SCZ, 9.6 and 8.8 K were predicted to influence risk-taking and risky behaviours, respectively. We identified 192 loci jointly associated with SCZ and risk phenotypes and 206 associated with BIP and risk phenotypes, of which 68 were common to both risk-taking and risky behaviours and 124 were novel to SCZ or BIP. Functional annotation implicated differential expression in multiple cortical and sub-cortical regions. In conclusion, we report extensive polygenic overlap between risk phenotypes and BIP and SCZ, identify specific loci contributing to this shared risk and highlight biologically plausible mechanisms that may underlie risk-taking in severe psychiatric disorders.

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jurjen J. Luykx ◽  
Bochao D. Lin
Keyword(s):  
Bipolar Disorder ◽  
Psychiatric Disorders ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Neuropsychiatric Disorders ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Genetic Liability ◽  
Bidirectional Associations

AbstractObservational 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.

Migraine: Genetic Variants and Clinical Phenotypes

2019 ◽  
Vol 26 (34) ◽  
pp. 6207-6221 ◽  
Author(s):  
Innocenzo Rainero ◽  
Alessandro Vacca ◽  
Flora Govone ◽  
Annalisa Gai ◽  
Lorenzo Pinessi ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Mthfr Gene ◽  
Genome Wide Association Studies ◽  
Clinical Phenotypes ◽  
Network Analyses ◽  
Genome Wide ◽  
Genomic Studies ◽  
The Common ◽  
The Relationship

Migraine is a common, chronic neurovascular disorder caused by a complex interaction between genetic and environmental risk factors. In the last two decades, molecular genetics of migraine have been intensively investigated. In a few cases, migraine is transmitted as a monogenic disorder, and the disease phenotype cosegregates with mutations in different genes like CACNA1A, ATP1A2, SCN1A, KCNK18, and NOTCH3. In the common forms of migraine, candidate genes as well as genome-wide association studies have shown that a large number of genetic variants may increase the risk of developing migraine. At present, few studies investigated the genotype-phenotype correlation in patients with migraine. The purpose of this review was to discuss recent studies investigating the relationship between different genetic variants and the clinical characteristics of migraine. Analysis of genotype-phenotype correlations in migraineurs is complicated by several confounding factors and, to date, only polymorphisms of the MTHFR gene have been shown to have an effect on migraine phenotype. Additional genomic studies and network analyses are needed to clarify the complex pathways underlying migraine and its clinical phenotypes.

scholarly journals Pharmacogenomics of Lithium Response in Bipolar Disorder

2021 ◽  
Vol 14 (4) ◽  
pp. 287
Author(s):  
Courtney M. Vecera ◽  
Gabriel R. Fries ◽  
Lokesh R. Shahani ◽  
Jair C. Soares ◽  
Rodrigo Machado-Vieira
Keyword(s):  
Bipolar Disorder ◽  
Association Studies ◽  
Mood Stabilizer ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Non Coding Rna ◽  
Genome Wide ◽  
Lithium Response ◽  
Genetic Loading ◽  
Long Non Coding Rna

Despite being the most widely studied mood stabilizer, researchers have not confirmed a mechanism for lithium’s therapeutic efficacy in Bipolar Disorder (BD). Pharmacogenomic applications may be clinically useful in the future for identifying lithium-responsive patients and facilitating personalized treatment. Six genome-wide association studies (GWAS) reviewed here present evidence of genetic variations related to lithium responsivity and side effect expression. Variants were found on genes regulating the glutamate system, including GAD-like gene 1 (GADL1) and GRIA2 gene, a mutually-regulated target of lithium. In addition, single nucleotide polymorphisms (SNPs) discovered on SESTD1 may account for lithium’s exceptional ability to permeate cell membranes and mediate autoimmune and renal effects. Studies also corroborated the importance of epigenetics and stress regulation on lithium response, finding variants on long, non-coding RNA genes and associations between response and genetic loading for psychiatric comorbidities. Overall, the precision medicine model of stratifying patients based on phenotype seems to derive genotypic support of a separate clinical subtype of lithium-responsive BD. Results have yet to be expounded upon and should therefore be interpreted with caution.

Insights into the genomics of affective disorders

2020 ◽  
Vol 32 (1) ◽  
pp. 9-18
Author(s):  
Andreas J. Forstner ◽  
Per Hoffmann ◽  
Markus M. Nöthen ◽  
Sven Cichon
Keyword(s):  
Bipolar Disorder ◽  
Major Depression ◽  
Affective Disorders ◽  
Association Studies ◽  
Phenotypic Variability ◽  
Genetic Research ◽  
Autism Spectrum ◽  
Risk Scores ◽  
Depression Symptoms ◽  
Genome Wide Association Studies

Abstract Affective disorders, or mood disorders, are a group of neuropsychiatric illnesses that are characterized by a disturbance of mood or affect. Most genetic research in this field to date has focused on bipolar disorder and major depression. Symptoms of major depression include a depressed mood, reduced energy, and a loss of interest and enjoyment. Bipolar disorder is characterized by the occurrence of (hypo)manic episodes, which generally alternate with periods of depression. Formal and molecular genetic studies have demonstrated that affective disorders are multifactorial diseases, in which both genetic and environmental factors contribute to disease development. Twin and family studies have generated heritability estimates of 58–85 % for bipolar disorder and 40 % for major depression. Large genome-wide association studies have provided important insights into the genetics of affective disorders via the identification of a number of common genetic risk factors. Based on these studies, the estimated overall contribution of common variants to the phenotypic variability (single-nucleotide polymorphism [SNP]-based heritability) is 17–23 % for bipolar disorder and 9 % for major depression. Bioinformatic analyses suggest that the associated loci and implicated genes converge into specific pathways, including calcium signaling. Research suggests that rare copy number variants make a lower contribution to the development of affective disorders than to other psychiatric diseases, such as schizophrenia or the autism spectrum disorders, which would be compatible with their less pronounced negative impact on reproduction. However, the identification of rare sequence variants remains in its infancy, as available next-generation sequencing studies have been conducted in limited samples. Future research strategies will include the enlargement of genomic data sets via innovative recruitment strategies; functional analyses of known associated loci; and the development of new, etiologically based disease models. Researchers hope that deeper insights into the biological causes of affective disorders will eventually lead to improved diagnostics and disease prediction, as well as to the development of new preventative, diagnostic, and therapeutic strategies. Pharmacogenetics and the application of polygenic risk scores represent promising initial approaches to the future translation of genomic findings into psychiatric clinical practice.

A systematic analysis of genetically regulated differences in gene expression and the role of co-expression networks across 16 psychiatric disorders and substance use phenotypes

2021 ◽  
Author(s):  
Zachary F Gerring ◽  
Jackson G Thorp ◽  
Eric R Gamazon ◽  
Eske M Derks
Keyword(s):  
Gene Expression ◽  
Mental Health ◽  
Substance Use ◽  
Prefrontal Cortex ◽  
Psychiatric Disorders ◽  
Developmental Disorders ◽  
Association Studies ◽  
Genetic Correlations ◽  
Autism Spectrum ◽  
Genome Wide Association Studies

ABSTRACTGenome-wide association studies (GWASs) have identified thousands of risk loci for many psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (collectively termed “mental health phenotypes”) using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation due to predicted genetically regulated expression between pairs of mental health phenotypes, and compared the results with the genetic correlations. We identified 1,645 genes with at least one significant trait association, comprising 2,176 significant associations across the 16 mental health phenotypes of which 572 (26%) are novel. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, showed a much higher transcriptomic correlation (r=0.84) than genetic correlation (r=0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from prefrontal cortex. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.

Distinct Genetic Profiles in Postpartum Depression With Different Trajectory of Illness

2020 ◽  
Author(s):  
Nagahide Takahashi ◽  
Hanae Tainaka ◽  
Tomoko Nishimura ◽  
Taeko Harada ◽  
Akemi Okumura ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Postpartum Depression ◽  
Risk Score ◽  
Association Studies ◽  
Depression Scale ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Gene Set Enrichment ◽  
Polygenic Risk ◽  
Gene Set

Abstract BackgroundPostpartum depression (PPD) is a common and highly heritabledisorder in the postnatal period of new mothers. The development of PPD is shown to affectneurodevelopment in children and recent evidence suggests thatthe trajectory of PPDisalso associated with children’s neurodevelopment and mental conditions. Thus, early identification and intervention for individuals at high risk of PPD are urgently needed.Additionally, it is not clear whether genetic factors affect thetrajectory of PPD. Therefore, using a polygenic risk score (PRS) approach, we investigated if PRS for depression (Depression-PRS) and bipolar disorder (Bipolar-PRS) are associated with the development and clinical course of PPD.Methods Usingrecent large genome-wide association studies(GWAS) of depression and bipolar disorder as discovery cohorts, we calculatedDepression-PRS and Bipolar-PRS in each individual. Then, we investigated the possible association between Depression-PRS and Bipolar-PRS with the development andtrajectory of PPD insubjects from the Hamamatsu Birth Cohort for mothers and children (n = 136). Depressive symptoms were assessed using the Edinburgh Postpartum Depression Scale. Gene-set enrichment analyses were used to identify pathways underlying these conditions. ResultsDepression-PRS was significantly higher in subjects with PPD than in those without PPD(t = -3.283, P = 0.002)and logistic analysis showed that Depression-PRS significantly increases therisk of developing PPD(OR [SE] = 2.274 [0.585], P = 0.002). Furthermore, Depression-PRS was positively associated with continuity of PPD (β [SE]=1.621 [0.672]; P = 0.032).Gene-set enrichment analyses revealed that pathways such as“response to hormone”(β[SE] -2.285[1.002], P < 0.001) and “epigenetic regulation”(β[SE] 2.831 [1.317], P < 0.001) were involved in the continuity of PPD. ConclusionThese preliminary findings indicate that the genetic component plays an important role not only in the development but also inthe continuity of PPD. A polygenic risk score approach could be useful to identify subjects at risk for PPD, especially for persistent PPD,who needcareful monitoring and intervention after delivery.

scholarly journals Assessing the Causal Effects of Human Serum Metabolites on 5 Major Psychiatric Disorders

2020 ◽  
Vol 46 (4) ◽  
pp. 804-813 ◽  
Author(s):  
Jian Yang ◽  
Bin Yan ◽  
Binbin Zhao ◽  
Yajuan Fan ◽  
Xiaoyan He ◽  
...  
Keyword(s):  
Human Serum ◽  
Psychiatric Disorders ◽  
Genetic Variants ◽  
Association Studies ◽  
Autism Spectrum ◽  
Causal Effects ◽  
Human Diseases ◽  
Genome Wide Association Studies ◽  
Serum Metabolites ◽  
Intermediate Phenotypes

Abstract Psychiatric disorders are the leading cause of disability worldwide while the pathogenesis remains unclear. Genome-wide association studies (GWASs) have made great achievements in detecting disease-related genetic variants. However, functional information on the underlying biological processes is often lacking. Current reports propose the use of metabolic traits as functional intermediate phenotypes (the so-called genetically determined metabotypes or GDMs) to reveal the biological mechanisms of genetics in human diseases. Here we conducted a two-sample Mendelian randomization analysis that uses GDMs to assess the causal effects of 486 human serum metabolites on 5 major psychiatric disorders, which respectively were schizophrenia (SCZ), major depression (MDD), bipolar disorder (BIP), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD). Using genetic variants as proxies, our study has identified 137 metabolites linked to the risk of psychiatric disorders, including 2-methoxyacetaminophen sulfate, which affects SCZ (P = 1.7 × 10–5) and 1-docosahexaenoylglycerophosphocholine, which affects ADHD (P = 5.6 × 10–5). Fourteen significant metabolic pathways involved in the 5 psychiatric disorders assessed were also detected, such as glycine, serine, and threonine metabolism for SCZ (P = .0238), Aminoacyl-tRNA biosynthesis for both MDD (P = .0144) and ADHD (P = .0029). Our study provided novel insights into integrating metabolomics with genomics in order to understand the mechanisms underlying the pathogenesis of human diseases.

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