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

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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Prediction of response to drug therapy in psychiatric disorders

Open Biology ◽

10.1098/rsob.180031 ◽

2018 ◽

Vol 8 (5) ◽

pp. 180031 ◽

Cited By ~ 23

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.

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Investigating genetic correlations and causal effects between caffeine consumption and sleep behaviours

10.1101/199828 ◽

2017 ◽

Author(s):

Jorien L. Treur ◽

Mark Gibson ◽

Amy E Taylor ◽

Peter J Rogers ◽

Marcus R Munafò

Keyword(s):

Genetic Correlation ◽

Sleep Duration ◽

Genetic Variants ◽

Mendelian Randomization ◽

Association Studies ◽

Genetic Correlations ◽

Causal Effects ◽

Genome Wide Association Studies ◽

Caffeine Consumption ◽

Insomnia Complaints

AbstractStudy Objectives:Higher caffeine consumption has been linked to poorer sleep and insomnia complaints. We investigated whether these observational associations are the result of genetic risk factors influencing both caffeine consumption and poorer sleep, and/or whether they reflect (possibly bidirectional) causal effects.Methods:Summary-level data were available from genome-wide association studies (GWAS) on caffeine consumption (n=91,462), sleep duration, and chronotype (i.e., being a ‘morning’ versus an ‘evening’ person) (both n=128,266), and insomnia complaints (n=113,006). Linkage disequilibrium (LD) score regression was used to calculate genetic correlations, reflecting the extent to which genetic variants influencing caffeine consumption and sleep behaviours overlap. Causal effects were tested with bidirectional, two-sample Mendelian randomization (MR), an instrumental variable approach that utilizes genetic variants robustly associated with an exposure variable as an instrument to test causal effects. Estimates from individual genetic variants were combined using inverse-variance weighted meta-analysis, weighted median regression and MR Egger regression methods.Results:There was no clear evidence for genetic correlation between caffeine consumption and sleep duration (rg=0.000,p=0.998), chronotype (rg=0.086,p=0.192) or insomnia (rg=-0.034,p=0.700). Two-sample Mendelian randomization analyses did not support causal effects from caffeine consumption to sleep behaviours, or the other way around.Conclusions:We found no evidence in support of genetic correlation or causal effects between caffeine consumption and sleep. While caffeine may have acute effects on sleep when taken shortly before habitual bedtime, our findings suggest that a more sustained pattern of high caffeine consumption is likely associated with poorer sleep through shared environmental factors.

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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

10.1101/2021.01.28.428688 ◽

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.

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Mapping the genetics of neuropsychological traits to the molecular network of the human brain using a data integrative approach

10.1101/336776 ◽

2018 ◽

Cited By ~ 2

Author(s):

Afsheen Yousaf ◽

Eftichia Duketis ◽

Tomas Jarczok ◽

Michael Sachse ◽

Monica Biscaldi ◽

...

Keyword(s):

Genetic Variants ◽

Genetic Factors ◽

Association Studies ◽

Brain Regions ◽

Autism Spectrum ◽

Integrative Approach ◽

Phenotypic Trait ◽

Genome Wide Association Studies ◽

Transcriptome Data ◽

Brain Areas

AbstractMotivationComplex neuropsychiatric conditions including autism spectrum disorders are among the most heritable neurodevelopmental disorders with distinct profiles of neuropsychological traits. A variety of genetic factors modulate these traits (phenotypes) underlying clinical diagnoses. To explore the associations between genetic factors and phenotypes, genome-wide association studies are broadly applied. Stringent quality checks and thorough downstream analyses for in-depth interpretation of the associations are an indispensable prerequisite. However, in the area of neuropsychology there is no framework existing, which besides performing association studies also affiliates genetic variants at the brain and gene network level within a single framework.ResultsWe present a novel bioinformatics approach in the field of neuropsychology that integrates current state-of-the-art tools, algorithms and brain transcriptome data to elaborate the association of phenotype and genotype data. The integration of transcriptome data gives an advantage over the existing pipelines by directly translating genetic associations to brain regions and developmental patterns. Based on our data integrative approach, we identify genetic variants associated with Intelligence Quotient (IQ) in an autism cohort and found their respective genes to be expressed in specific brain areas.ConclusionOur data integrative approach revealed that IQ is related to early down-regulated and late up-regulated gene modules implicated in frontal cortex and striatum, respectively. Besides identifying new gene associations with IQ we also provide a proof of concept, as several of the identified genes in our analysis are candidate genes related to intelligence in autism, intellectual disability, and Alzheimer’s disease. The framework provides a complete extensive analysis starting from a phenotypic trait data to its association at specific brain areas at vulnerable time points within a timespan of four days.Availability and ImplementationOur framework is implemented in R and Python. It is available as an in-house script, which can be provided on [email protected]

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Using three-dimensional regulatory chromatin interactions from adult and fetal cortex to interpret genetic results for psychiatric disorders and cognitive traits

10.1101/406330 ◽

2018 ◽

Cited By ~ 14

Author(s):

Paola MD Giusti-Rodriguez ◽

Patrick F Sullivan

Keyword(s):

Psychiatric Disorders ◽

Association Studies ◽

Three Dimensional ◽

Fetal Brain ◽

Autism Spectrum ◽

Open Chromatin ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

3D Genome ◽

Cognitive Traits

Genome-wide association studies have identified hundreds of genetic associations for complex psychiatric disorders and cognitive traits. However, interpretation of most of these findings is complicated by the presence of many significant and highly correlated genetic variants located in non-coding regions. Here, we address this issue by creating a high-resolution map of the three-dimensional (3D) genome organization by applying Hi-C to adult and fetal brain cortex with concomitant RNA-seq, open chromatin (ATAC-seq), and ChIP-seq data (H3K27ac, H3K4me3, and CTCF). Extensive analyses established the quality, information content, and salience of these new Hi-C data. We used these data to connect 938 significant genetic loci for schizophrenia, intelligence, ADHD, alcohol dependence, Alzheimer's disease, anorexia nervosa, autism spectrum disorder, bipolar disorder, major depression, and educational attainment to 8,595 genes (with 42.1% of these genes implicated more than once). We show that assigning genes to traits based on proximity provides a limited view of the complexity of GWAS findings and that gene set analyses based on functional genomic data provide an expanded view of the biological processes involved in the etiology of schizophrenia and other complex brain traits.

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Genetic correlations between subcortical brain volumes and psychiatric disorders

The British Journal of Psychiatry ◽

10.1192/bjp.2019.277 ◽

2020 ◽

Vol 216 (5) ◽

pp. 280-283

Author(s):

Kazutaka Ohi ◽

Takamitsu Shimada ◽

Yuzuru Kataoka ◽

Toshiki Yasuyama ◽

Yasuhiro Kawasaki ◽

...

Keyword(s):

Psychiatric Disorders ◽

Genetic Variants ◽

Large Scale ◽

Association Studies ◽

Genetic Correlations ◽

Intracranial Volume ◽

Genome Wide Association Studies ◽

Brain Volumes ◽

Subcortical Brain ◽

Genome Wide

SummaryPsychiatric disorders as well as subcortical brain volumes are highly heritable. Large-scale genome-wide association studies (GWASs) for these traits have been performed. We investigated the genetic correlations between five psychiatric disorders and the seven subcortical brain volumes and the intracranial volume from large-scale GWASs by linkage disequilibrium score regression. We revealed weak overlaps between the genetic variants associated with psychiatric disorders and subcortical brain and intracranial volumes, such as in schizophrenia and the hippocampus and bipolar disorder and the accumbens. We confirmed shared aetiology and polygenic architecture across the psychiatric disorders and the specific subcortical brain and intracranial volume.

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Dissecting the phenotype in genome-wide association studies of psychiatric illness

The British Journal of Psychiatry ◽

10.1192/bjp.bp.108.063156 ◽

2009 ◽

Vol 195 (2) ◽

pp. 97-99 ◽

Cited By ~ 37

Keyword(s):

Psychiatric Disorders ◽

Psychiatric Illness ◽

Genetic Architecture ◽

Association Studies ◽

Human Diseases ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

The Past ◽

Genome Wide

SummaryOver the past 2 years genome-wide association studies have made major contributions to understanding the genetic architecture of many common human diseases. This editorial outlines the development of such studies in psychiatry and highlights the opportunities for advancing understanding of the biological underpinnings and nosological structure of psychiatric disorders.

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Pairwise common variant meta-analyses of schizophrenia with other psychiatric disorders reveals shared and distinct gene and gene-set associations

10.1101/725614 ◽

2019 ◽

Cited By ~ 1

Author(s):

William R. Reay ◽

Murray J. Cairns

Keyword(s):

Psychiatric Disorders ◽

Association Studies ◽

Meta Analysis ◽

Autism Spectrum ◽

Common Variant ◽

Joint Analysis ◽

Genome Wide Association Studies ◽

Gene Set ◽

Common Variation ◽

Meta Analyses

ABSTRACTThe complex aetiology of schizophrenia is postulated to share factors with other psychiatric disorders. Recently, this has been supported by genome-wide association studies, with several psychiatric phenotypes displaying high genomic correlation with schizophrenia. We sought to investigate pleiotropy amongst the common variant genomics of schizophrenia and seven other psychiatric disorders using a multimarker test of association. Gene-based analysis of common variation revealed over 50 schizophrenia-associated genes shared with other psychiatric phenotypes; including bipolar disorder, major depressive disorder, ADHD, and autism spectrum disorder. In addition, we uncovered 78 genes significantly enriched with common variant associations for schizophrenia that were not linked to any of these seven disorders (P > 0.05). Transcriptomic imputation was then leveraged to investigate the functional significance of variation mapped to these genes, prioritising several interesting functional candidates. Pairwise meta-analysis of schizophrenia and each psychiatric phenotype further revealed 330 significantly associated genes (PMeta < 2.7 × 10−6) that were only nominally associated with each disorder individually (P < 0.05). Multivariable gene-set association suggested that common variation enrichment within biologically constrained genes observed for schizophrenia also occurs across several psychiatric phenotypes. These analyses consolidate the overlap between the genomic architecture of schizophrenia and other psychiatric disorders and uncovered several pleiotropic genes which warrant further investigation.AUTHOR SUMMARYSchizophrenia and other psychiatric disorders have many similarities, and this includes features of their overall genetic risk. Here, we investigate genes which may play a role in schizophrenia as well one or more of seven other psychiatric phenotypes and demonstrate that a number of them are pleiotropic and influence at least one other disorder. We also identify genes amongst the psychiatric disorders studied here which only show association with schizophrenia. Furthermore, we find a number of genes which were only significant when combining genetic association data from schizophrenia and one of the other seven disorders, suggesting there are shared genetic influences that are revealed through the power of joint analysis. This study identifies interesting novel shared (pleiotropic) genes in psychiatry which warrant future study.

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Mendelian randomization studies of biomarkers and type 2 diabetes

Endocrine Connections ◽

10.1530/ec-15-0087 ◽

2015 ◽

Vol 4 (4) ◽

pp. 249-260 ◽

Cited By ~ 12

Author(s):

Ali Abbasi

Keyword(s):

Type 2 Diabetes ◽

Genetic Variants ◽

Large Scale ◽

Mendelian Randomization ◽

Association Studies ◽

Causal Effects ◽

Current Evidence ◽

Genome Wide Association Studies ◽

Glycaemic Traits

Many biomarkers are associated with type 2 diabetes (T2D) risk in epidemiological observations. The aim of this study was to identify and summarize current evidence for causal effects of biomarkers on T2D. A systematic literature search in PubMed and EMBASE (until April 2015) was done to identify Mendelian randomization studies that examined potential causal effects of biomarkers on T2D. To replicate the findings of identified studies, data from two large-scale, genome-wide association studies (GWAS) were used: DIAbetes Genetics Replication And Meta-analysis (DIAGRAMv3) for T2D and the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for glycaemic traits. GWAS summary statistics were extracted for the same genetic variants (or proxy variants), which were used in the original Mendelian randomization studies. Of the 21 biomarkers (from 28 studies), ten have been reported to be causally associated with T2D in Mendelian randomization. Most biomarkers were investigated in a single cohort study or population. Of the ten biomarkers that were identified, nominally significant associations with T2D or glycaemic traits were reached for those genetic variants related to bilirubin, pro-B-type natriuretic peptide, delta-6 desaturase and dimethylglycine based on the summary data from DIAGRAMv3 or MAGIC. Several Mendelian randomization studies investigated the nature of associations of biomarkers with T2D. However, there were only a few biomarkers that may have causal effects on T2D. Further research is needed to broadly evaluate the causal effects of multiple biomarkers on T2D and glycaemic traits using data from large-scale cohorts or GWAS including many different genetic variants.

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Genome-wide association studies: a primer

Psychological Medicine ◽

10.1017/s0033291709991723 ◽

2009 ◽

Vol 40 (7) ◽

pp. 1063-1077 ◽

Cited By ~ 64

Author(s):

A. Corvin ◽

N. Craddock ◽

P. F. Sullivan

Keyword(s):

Genetic Variants ◽

Association Studies ◽

Human Diseases ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Psychiatric Genetics ◽

Genome Wide ◽

Common Genetic Variants ◽

Gwas Approach

There have been nearly 400 genome-wide association studies (GWAS) published since 2005. The GWAS approach has been exceptionally successful in identifying common genetic variants that predispose to a variety of complex human diseases and biochemical and anthropometric traits. Although this approach is relatively new, there are many excellent reviews of different aspects of the GWAS method. Here, we provide a primer, an annotated overview of the GWAS method with particular reference to psychiatric genetics. We dissect the GWAS methodology into its components and provide a brief description with citations and links to reviews that cover the topic in detail.

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