Identification of pleiotropy at the gene level between psychiatric disorders and related traits

AbstractMajor mental disorders are highly prevalent and make a substantial contribution to the global disease burden. It is known that mental disorders share clinical characteristics, and genome-wide association studies (GWASs) have recently provided evidence for shared genetic factors as well. Genetic overlaps are usually identified at the single-marker level. Here, we aimed to identify genetic overlaps at the gene level between 7 mental disorders (schizophrenia, autism spectrum disorder, major depressive disorder, anorexia nervosa, ADHD, bipolar disorder and anxiety), 8 brain morphometric traits, 2 cognitive traits (educational attainment and general cognitive function) and 9 personality traits (subjective well-being, depressive symptoms, neuroticism, extraversion, openness to experience, agreeableness and conscientiousness, children’s aggressive behaviour, loneliness) based on publicly available GWASs. We performed systematic conditional regression analyses to identify independent signals and select loci associated with more than one trait. We identified 48 genes containing independent markers associated with several traits (pleiotropy at the gene level). We also report 9 genes with different markers that show independent associations with single traits (allelic heterogeneity). This study demonstrates that mental disorders and related traits do show pleiotropy at the gene level as well as the single-marker level. The identification of these genes might be important for prioritizing further deep genotyping, functional studies, or drug targeting.

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Investigation of gene–environment interactions in relation to tic severity

Journal of Neural Transmission ◽

10.1007/s00702-021-02396-y ◽

2021 ◽

Author(s):

Mohamed Abdulkadir ◽

Dongmei Yu ◽

Lisa Osiecki ◽

Robert A. King ◽

Thomas V. Fernandez ◽

...

Keyword(s):

Tourette Syndrome ◽

Association Studies ◽

Autism Spectrum ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Linear Regression Models ◽

Compulsive Disorder ◽

Gene Environment ◽

Tic Severity

AbstractTourette syndrome (TS) is a neuropsychiatric disorder with involvement of genetic and environmental factors. We investigated genetic loci previously implicated in Tourette syndrome and associated disorders in interaction with pre- and perinatal adversity in relation to tic severity using a case-only (N = 518) design. We assessed 98 single-nucleotide polymorphisms (SNPs) selected from (I) top SNPs from genome-wide association studies (GWASs) of TS; (II) top SNPs from GWASs of obsessive–compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD); (III) SNPs previously implicated in candidate-gene studies of TS; (IV) SNPs previously implicated in OCD or ASD; and (V) tagging SNPs in neurotransmitter-related candidate genes. Linear regression models were used to examine the main effects of the SNPs on tic severity, and the interaction effect of these SNPs with a cumulative pre- and perinatal adversity score. Replication was sought for SNPs that met the threshold of significance (after correcting for multiple testing) in a replication sample (N = 678). One SNP (rs7123010), previously implicated in a TS meta-analysis, was significantly related to higher tic severity. We found a gene–environment interaction for rs6539267, another top TS GWAS SNP. These findings were not independently replicated. Our study highlights the future potential of TS GWAS top hits in gene–environment studies.

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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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An approach to gene-based testing accounting for dependence of tests among nearby genes

10.1101/2021.05.24.445494 ◽

2021 ◽

Author(s):

Ronald J Yurko ◽

Kathryn Roeder ◽

Bernie Devlin ◽

Max G'Sell

Keyword(s):

Multiple Testing ◽

Association Studies ◽

Autism Spectrum ◽

P Value ◽

Genome Wide Association Studies ◽

Strongly Correlated ◽

Test Statistics ◽

Test Statistic ◽

Genome Wide ◽

Insight Into

In genome-wide association studies (GWAS), it has become commonplace to test millions of SNPs for phenotypic association. Gene-based testing can improve power to detect weak signal by reducing multiple testing and pooling signal strength. While such tests account for linkage disequilibrium (LD) structure of SNP alleles within each gene, current approaches do not capture LD of SNPs falling in different nearby genes, which can induce correlation of gene-based test statistics. We introduce an algorithm to account for this correlation. When a gene's test statistic is independent of others, it is assessed separately; when test statistics for nearby genes are strongly correlated, their SNPs are agglomerated and tested as a locus. To provide insight into SNPs and genes driving association within loci, we develop an interactive visualization tool to explore localized signal. We demonstrate our approach in the context of weakly powered GWAS for autism spectrum disorder, which is contrasted to more highly powered GWAS for schizophrenia and educational attainment. To increase power for these analyses, especially those for autism, we use adaptive p-value thresholding (AdaPT), guided by high-dimensional metadata modeled with gradient boosted trees, highlighting when and how it can be most useful. Notably our workflow is based on summary statistics.

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Insights into the genomics of affective disorders

Medizinische Genetik ◽

10.1515/medgen-2020-2003 ◽

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.

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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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Making the Case

Stress and Mental Disorders: Insights from Animal Models ◽

10.1093/med-psych/9780190697266.003.0002 ◽

2020 ◽

pp. 29-56

Author(s):

Richard McCarty

Keyword(s):

Mental Disorders ◽

Stressful Life Events ◽

Association Studies ◽

Genome Wide Association Studies ◽

Psychiatric Genetics ◽

Nucleotide Polymorphisms ◽

Individual Genome ◽

Diagnostic Categories ◽

Genome Wide ◽

Research Findings

Genome-wide association studies (GWAS) have revolutionized the field of psychiatric genetics by examining genetic variation at millions of single-nucleotide polymorphisms (SNPs) in many thousands of individual genome using microarrays. The sample sizes for these studies range from tens of thousands on up. Results to date from GWAS have called into question the validity of current diagnostic categories in psychiatry. For example, there may be some level of genetic risk that is shared across many psychiatric disorders, with the final symptom clusters of a given disorder being shaped by other genetic, epigenetic, and environmental variables. Research findings on three mental disorders are evaluated to make the case that stressful life events play a crucial role in the etiology of mental disorders. The mental disorders discussed include schizophrenia, bipolar disorder, and depression. These findings set the stage for the remainder of the book.

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

Schizophrenia Bulletin ◽

10.1093/schbul/sbz138 ◽

2020 ◽

Vol 46 (4) ◽

pp. 804-813 ◽

Cited By ~ 2

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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Using Genetics to Examine a General Liability to Childhood Psychopathology

Behavior Genetics ◽

10.1007/s10519-019-09985-4 ◽

2019 ◽

Vol 50 (4) ◽

pp. 213-220 ◽

Cited By ~ 7

Author(s):

Lucy Riglin ◽

Ajay K. Thapar ◽

Beate Leppert ◽

Joanna Martin ◽

Alexander Richards ◽

...

Keyword(s):

Association Studies ◽

Full Range ◽

Population Based ◽

Autism Spectrum ◽

Developmental Changes ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Childhood Psychopathology ◽

Specific Factors ◽

P Factor

AbstractPsychiatric disorders show phenotypic as well as genetic overlaps. There are however also marked developmental changes throughout childhood. We investigated the extent to which, for a full range of early childhood psychopathology, a general “p” factor was explained by genetic liability, as indexed by multiple different psychiatric polygenic risk scores (PRS) and whether these relationships altered with age. The sample was a UK, prospective, population-based cohort with psychopathology data at age 7 (N = 8161) and age 13 (N = 7017). PRS were generated from large published genome-wide association studies. At both ages, we found evidence for a childhood “p” factor as well as for specific factors. Schizophrenia and attention-deficit/hyperactivity disorder (ADHD) PRS were associated with this general “p” factor at both ages but depression and autism spectrum disorder (ASD) PRS were not. We also found some evidence of associations between schizophrenia, ADHD and depression PRS with specific factors, but these were less robust and there was evidence for developmental changes.

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Linking Autism Risk Genes to Disruption of Cortical Development

Cells ◽

10.3390/cells9112500 ◽

2020 ◽

Vol 9 (11) ◽

pp. 2500

Author(s):

Marta Garcia-Forn ◽

Andrea Boitnott ◽

Zeynep Akpinar ◽

Silvia De Rubeis

Keyword(s):

Large Scale ◽

Association Studies ◽

Neurodevelopmental Disorder ◽

Cortical Development ◽

Autism Spectrum ◽

Repetitive Behaviors ◽

Genome Wide Association Studies ◽

Restricted Interests ◽

Risk Genes ◽

Whole Exome

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder characterized by impairments in social communication and social interaction, and the presence of repetitive behaviors and/or restricted interests. In the past few years, large-scale whole-exome sequencing and genome-wide association studies have made enormous progress in our understanding of the genetic risk architecture of ASD. While showing a complex and heterogeneous landscape, these studies have led to the identification of genetic loci associated with ASD risk. The intersection of genetic and transcriptomic analyses have also begun to shed light on functional convergences between risk genes, with the mid-fetal development of the cerebral cortex emerging as a critical nexus for ASD. In this review, we provide a concise summary of the latest genetic discoveries on ASD. We then discuss the studies in postmortem tissues, stem cell models, and rodent models that implicate recently identified ASD risk genes in cortical development.

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Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down

Current Directions in Psychological Science ◽

10.1177/0963721418807729 ◽

2019 ◽

Vol 28 (1) ◽

pp. 82-90 ◽

Cited By ~ 18

Author(s):

Daniel W. Belsky ◽

K. Paige Harden

Keyword(s):

Educational Attainment ◽

Association Studies ◽

Science Research ◽

Well Being ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Research Activities ◽

Genome Wide ◽

Polygenic Scores ◽

Methodological Considerations

Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential “outside-the-skin” environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being. We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development. Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.

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