Comprehensive exploration of the genetic contribution of the dopaminergic and serotonergic pathways to psychiatric disorders

ABSTRACTPsychiatric disorders are highly prevalent and display considerable clinical and genetic overlap. Dopaminergic and serotonergic neurotransmission have been shown to have an important role in many psychiatric disorders. Here we aim to assess the genetic contribution of these systems to eight psychiatric disorders (ADHD, ANO, ASD, BIP, MD, OCD, SCZ and TS) using publicly available GWAS analyses performed by the Psychiatric Genomics Consortium. To do so, we elaborated four different gene sets using the Gene Ontology and KEGG pathways tools: two ‘wide’ selections for dopamine (DA) and for serotonin (SERT), and two ‘core’ selections for the same systems. At the gene level, we found 67 genes from the DA and/or SERT gene sets significantly associated with one of the studied disorders, and 12 of them were associated with two different disorders. Gene-set analysis revealed significant associations for ADHD and ASD with the wide DA gene set, for BIP with the wide SERT gene set, and for MD with both the core DA set and the core SERT set. Interestingly, interrogation of the cross-disorder GWAS meta-analysis displayed association with the wide DA gene set. To our knowledge, this is the first time that these two neurotransmitter systems have systematically been inspected in these disorders. Our results support a cross-disorder contribution of dopaminergic and serotonergic systems in several psychiatric conditions.

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Comprehensive exploration of the genetic contribution of the dopaminergic and serotonergic pathways to psychiatric disorders

Translational Psychiatry ◽

10.1038/s41398-021-01771-3 ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Judit Cabana-Domínguez ◽

Bàrbara Torrico ◽

Andreas Reif ◽

Noèlia Fernàndez-Castillo ◽

Bru Cormand

Keyword(s):

Psychiatric Disorders ◽

Meta Analysis ◽

Autism Spectrum ◽

Genetic Contribution ◽

Obsessive Compulsive ◽

Gene Set ◽

Compulsive Disorder ◽

Kegg Pathways ◽

Gene Sets ◽

Psychiatric Conditions

AbstractPsychiatric disorders are highly prevalent and display considerable clinical and genetic overlap. Dopaminergic and serotonergic neurotransmission have been shown to play an important role in many psychiatric disorders. Here we aim to assess the genetic contribution of these systems to eight psychiatric disorders (attention-deficit hyperactivity disorder (ADHD), anorexia nervosa (ANO), autism spectrum disorder (ASD), bipolar disorder (BIP), major depression (MD), obsessive-compulsive disorder (OCD), schizophrenia (SCZ) and Tourette’s syndrome (TS)) using publicly available GWAS analyses performed by the Psychiatric Genomics Consortium that include more than 160,000 cases and 275,000 controls. To do so, we elaborated four different gene sets: two ‘wide’ selections for dopamine (DA) and for serotonin (SERT) using the Gene Ontology and KEGG pathways tools, and two’core’ selections for the same systems, manually curated. At the gene level, we found 67 genes from the DA and/or SERT gene sets significantly associated with one of the studied disorders, and 12 of them were associated with two different disorders. Gene-set analysis revealed significant associations for ADHD and ASD with the wide DA gene set, for BIP with the wide SERT gene set, and for MD with the core SERT set. Interestingly, interrogation of a cross-disorder GWAS meta-analysis of the eight psychiatric conditions displayed association with the wide DA gene set. To our knowledge, this is the first systematic examination of genes encoding proteins essential to the function of these two neurotransmitter systems in these disorders. Our results support a pleiotropic contribution of the dopaminergic and serotonergic systems in several psychiatric conditions.

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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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A Meta-Analysis of the 16S-rRNA Gut Microbiome Data in Honeybees (Apis Mellifera)

10.1101/2021.12.18.473299 ◽

2021 ◽

Author(s):

Alexis Gkantiragas ◽

Jacopo Gabrielli

Keyword(s):

Apis Mellifera ◽

16S Rrna ◽

Meta Analysis ◽

Core Microbiome ◽

Future Studies ◽

The Core ◽

Study Evaluation ◽

Form Part ◽

Microbiome Data ◽

First Time

Honeybees (Apis Mellifera) perform an essential role in the ecosystem and economy through pollination of insect-pollinated plants, but their population is declining. Many causes of honeybees' decline are likely to be influenced by the microbiome which is thought to play an important role in bees and is particularly susceptible to infection and pesticides. However, there has been no systematic review or meta-analysis on honeybee microbiome data. Therefore, we conducted the first systematic meta-analysis of 16S-rRNA data to address this gap in the literature. Four studies were in a usable format - accounting for 336 honeybee's worth of data - the largest such dataset to the best of our knowledge. We analysed these datasets in QIIME2 and visualised the results in R-studio. For the first time, we conducted a multi-study evaluation of the core and rare bee microbiome and confirmed previous compositional microbiome data. We established that Snodgrassella, Lactobacillus, Bifidobacterium, Fructobacillus and Saccaribacter form part of the core microbiome and identify 251 rare bacterial genera. Additional components of the core microbiome were likely obscured by incomplete classification. Future studies should refine and add to our existing dataset to produce a more conclusive and high-resolution portrait of the honeybee microbiome. Furthermore, we emphasise the need for an actively curated dataset and enforcement of data sharing standards.

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A Genomewide Integrative Analysis of GWAS and eQTLs Data Identifies Multiple Genes and Gene Sets Associated with Obesity

BioMed Research International ◽

10.1155/2018/3848560 ◽

2018 ◽

Vol 2018 ◽

pp. 1-5 ◽

Cited By ~ 5

Author(s):

Li Liu ◽

Qianrui Fan ◽

Feng Zhang ◽

Xiong Guo ◽

Xiao Liang ◽

...

Keyword(s):

Meta Analysis ◽

Single Gene ◽

Susceptibility Gene ◽

Enrichment Analysis ◽

Integrative Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Analysis ◽

Gene Set ◽

Gene Sets ◽

Summary Data

To identify novel susceptibility genes and gene sets for obesity, we conducted a genomewide expression association analysis of obesity via integrating genomewide association study (GWAS) and expression quantitative trait loci (eQTLs) data. GWAS summary data of body mass index (BMI) and waist-to-hip ratio (WHR) was driven from a published study, totally involving 339,224 individuals. The eQTLs dataset (containing 927,753 eQTLs) was obtained from eQTLs meta-analysis of 5,311 subjects. Integrative analysis of GWAS and eQTLs data was conducted by SMR software. The SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA) for identifying obesity associated gene sets. A total of 13,311 annotated gene sets were analyzed in this study. SMR single gene analysis identified 20 BMI associated genes (TUFM, SPI1, APOB48R, etc.). Also 3 WHR associated genes were detected (CPEB4, WARS2, and L3MBTL3). The significant association between Chr16p11 and BMI was observed by GSEA (FDR adjusted p value = 0.040). The TGCTGCT, MIR-15A, MIR-16, MIR-15B, MIR-195, MIR-424, and MIR-497 (FDR adjusted p value = 0.049) gene set appeared to be linked with WHR. Our results provide novel clues for the genetic mechanism studies of obesity. This study also illustrated the good performance of SMR for susceptibility gene mapping.

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GSA-Genie: a web application for gene set analysis

10.1101/125443 ◽

2017 ◽

Author(s):

Zhe Zhang ◽

Deanne Taylor

Keyword(s):

Statistical Methods ◽

Web Application ◽

Gene List ◽

Gene Set Analysis ◽

Gene Set ◽

Gene Sets ◽

Level Statistics ◽

Gene Level ◽

Amazon Web Services ◽

Selection Of

AbstractGene set analysis is often used to interpret results from upstream analysis through predefined gene sets that are linked to biological features such as cell cycle or tumorgenesis. Gene sets have been defined in the literature via various criteria and are archived by numerous databases. We compiled over 2.3 million gene sets from 17 sources, and made them accessible through a web application, GSA-Genie. Selected gene sets can be analyzed online using one of 16 statistical methods. These methods can be grouped into two strategies: test of gene set over-representation within a gene list, or comparison of a gene-level statistics between gene set and background. GSA-Genie operates on a Shiny web server, hosted in a cloud instance within Amazon Web Services. GSA-Genie offers a broad selection of gene sets and statistical methods comparing to existing tools. GSA-Genie is freely available at http://gsagenie.awsomics.org.

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Gene Sets Analysis using Network Patterns

10.1101/629816 ◽

2019 ◽

Cited By ~ 2

Author(s):

Gregory Linkowski ◽

Charles Blatti ◽

Krishna Kalari ◽

Saurabh Sinha ◽

Shobha Vasudevan

Keyword(s):

Learning Algorithm ◽

Gene Families ◽

Response To Treatment ◽

Experimental Conditions ◽

Functional Interpretation ◽

Gene Set ◽

Gene Sets ◽

Breast Cancer Study ◽

Network Patterns ◽

First Time

ABSTRACTHigh throughput assays allow researchers to identify sets of genes related to experimental conditions or phenotypes of interest. These gene sets are frequently subjected to functional interpretation using databases of gene annotations. Recent approaches have extended this approach to also consider networks of gene-gene relationships and interactions when attempting to characterize properties of a gene set. We present here a supervised learning algorithm for gene set analysis, called ‘GeneSet MAPR’, that for the first time explicitly considers the patterns of direct as well as indirect relationships present in the network to quantify gene-gene similarities and then report shared properties of the gene set. Our extensive evaluations show that GeneSet MAPR performs better than other network-based methods for the task of identifying genes related to a given gene set, enabling more reliable functional characterizations of the gene set. When applied to the set of response-associated genes from a triple negative breast cancer study, GeneSet MAPR uncovers gene families such as claudins, kallikreins, and collagen type alpha chains related to patient’s response to treatment, and which are not uncovered with traditional analysis.

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An Epistemological Criticism of Social Entrepreneurship: Is Social Entrepreneurship a Sound Scientific Field of Research

10.31124/advance.7091927.v1 ◽

2018 ◽

Author(s):

Amir Forouharfar

Keyword(s):

Social Entrepreneurship ◽

Social Constructivism ◽

Social Epistemology ◽

American English ◽

Meta Analysis ◽

Research Question ◽

Knowledge Structure ◽

Scientific Field ◽

The Other ◽

First Time

The paper was shaped around the pivotal question: Is SE a sound and scientific field of research? The question has given a critical tone to the paper and has also helped to bring out some of the controversial debates in the realm of SE. The paper was organized under five main discussions to be able to provide a scientific answer to the research question: (1) is “social entrepreneurship” an oxymoron?, (2) the characteristics of SE knowledge, (3) sources of social entrepreneurship knowledge, (4) SE knowledge: structure and limitations and (5) contributing epistemology-making concepts for SE. Based on the sections, the study relied on the relevant philosophical schools of thought in Epistemology (e.g. Empiricism, Rationalism, Skepticism, Internalism vs. Externalism, Essentialism, Social Constructivism, Social Epistemology, etc.) to discuss these controversies around SE and proposes some solutions by reviewing SE literature. Also, to determine the governing linguistic discourse in the realm of SE, which was necessary for our discussion, Corpus of Contemporary American English (COCA) for the first time in SE studies was used. Further, through the study, SE buzzwords which constitute SE terminology were derived and introduced to help us narrowing down and converging the thoughts in this field and demarking the epistemological boundaries of SE. The originality of the paper on one hand lies in its pioneering discussions on SE epistemology and on the other hand in paving the way for a construction of sound epistemology for SE; therefore in many cases after preparing the philosophical ground for the discussions, it went beyond the prevalent SE literature through meta-analysis to discuss the cases which were raised. The results of the study verified previously claimed embryonic pre-paradigmatic phase in SE which was far from a sound and scientific knowledge, although the scholarly endeavors are the harbingers of such a possibility in the future which calls for further mature academic discussion and development of SE knowledge by the SE academia.

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Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1574888x13666180105125347 ◽

2018 ◽

Vol 21 (2) ◽

pp. 74-83

Author(s):

Tzu-Hung Hsiao ◽

Yu-Chiao Chiu ◽

Yu-Heng Chen ◽

Yu-Ching Hsu ◽

Hung-I Harry Chen ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Therapy ◽

Cancer Treatment ◽

Cancer Survival ◽

Expression Profiles ◽

Functional Gene ◽

Gene Set Enrichment Analysis ◽

Gene Set ◽

Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

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Gene Set Correlation Analysis and Visualization Using Gene Expression Data

Current Bioinformatics ◽

10.2174/1574893615999200629124444 ◽

2020 ◽

Vol 15 ◽

Author(s):

Chen-An Tsai ◽

James J. Chen

Keyword(s):

Gene Expression ◽

Correlation Analysis ◽

Gene Expression Data ◽

Differentially Expressed Gene ◽

Differentially Expressed ◽

Superior Performance ◽

Expression Data ◽

Gene Set ◽

Gene Sets ◽

Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

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Sequencing of Organellar Genomes of Nowellia curvifolia (Cephaloziaceae Jungermanniales) Revealed the Smallest Plastome with Complete Gene Set and High Intraspecific Variation Suggesting Cryptic Speciation

Diversity ◽

10.3390/d13020081 ◽

2021 ◽

Vol 13 (2) ◽

pp. 81

Author(s):

Jakub Sawicki ◽

Katarzyna Krawczyk ◽

Monika Ślipiko ◽

Monika Szczecińska

Keyword(s):

Cryptic Speciation ◽

Editing Event ◽

Protein Coding ◽

Gene Set ◽

Protein Coding Genes ◽

Organellar Genomes ◽

The Family ◽

Leafy Liverwort ◽

Aneura Mirabilis ◽

First Time

The leafy liverwort Nowellia curvifolia is a widespread Holarctic species belonging to the family Cephaloziaceae. It is made up of a newly sequenced, assembled and annotated organellar genomes of two European specimens, which revealed the structure typical for liverworts, but also provided new insights into its microevolution. The plastome of N. curvifolia is the second smallest among photosynthetic liverworts, with the shortest known inverted repeats. Moreover, it is the smallest liverwort genome with a complete gene set, since two smaller genomes of Aneura mirabilis and Cololejeunea lanciloba are missing six and four protein-coding genes respectively. The reduction of plastome size in leafy liverworts seems to be mainly impacted by deletion within specific region between psbA and psbD genes. The comparative intraspecific analysis revealed single SNPs difference among European individuals and a low number of 35 mutations differentiating European and North American specimens. However, the genetic resources of Asian specimen enabled to identify 1335 SNPs in plastic protein-coding genes suggesting an advanced cryptic speciation within N. curvifolia or the presence of undescribed morphospecies in Asia. Newly sequenced mitogenomes from European specimens revealed identical gene content and structure to previously published and low intercontinental differentiation limited to one substitution and three indels. The RNA-seq based RNA editing analysis revealed 17 and 127 edited sites in plastome and mitogenome respectively including one non-canonical editing event in plastid chiL gene. The U to C editing is common in non-seed plants, but in liverwort plastome is reported for the first time.

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