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Strom NI, Verhulst B, Bacanu SA, Cheesman R, Purves KL, Gedik H, Mitchell BL, Kwong AS, Faucon AB, Singh K, Medland S, Colodro-Conde L, Krebs K, Hoffmann P, Herms S, Gehlen J, Ripke S, Awasthi S, Palviainen T, Tasanko EM, Peterson RE, Adkins DE, Shabalin AA, Adams MJ, Iveson MH, Campbell A, Thomas LF, Winsvold BS, Drange OK, Børte S, Ter Kuile AR, Nguyen TH, Meier SM, Corfield EC, Hannigan L, Levey DF, Czamara D, Weber H, Choi KW, Pistis G, Couvy-Duchesne B, Van der Auwera S, Teumer A, Karlsson R, Garcia-Argibay M, Lee D, Wang R, Bjerkeset O, Stordal E, Bäckmann J, Salum GA, Zai CC, Kennedy JL, Zai G, Tiwari AK, Heilmann-Heimbach S, Schmidt B, Kaprio J, Kennedy MM, Boden J, Havdahl A, Middeldorp CM, Lopes FL, Akula N, McMahon FJ, Binder EB, Fehm L, Ströhle A, Castelao E, Tiemeier H, Stein DJ, Whiteman D, Olsen C, Fuller Z, Wang X, Wray NR, Byrne EM, Lewis G, Timpson NJ, Davis LK, Hickie IB, Gillespie NA, Milani L, Schumacher J, Woldbye DP, Forstner AJ, Nöthen MM, Hovatta I, Horwood J, Copeland WE, Maes HH, McIntosh AM, Andreassen OA, Zwart JA, Mors O, Børglum AD, Mortensen PB, Ask H, Reichborn-Kjennerud T, Najman JM, Stein MB, Gelernter J, Milaneschi Y, Penninx BW, Boomsma DI, Maron E, Erhardt-Lehmann A, Rück C, Kircher TT, Melzig CA, Alpers GW, Arolt V, Domschke K, Smoller JW, Preisig M, Martin NG, Lupton MK, Luik AI, Reif A, Grabe HJ, Larsson H, Magnusson PK, Oldehinkel AJ, Hartman CA, Breen G, Docherty AR, Coon H, Conrad R, Lehto K, Deckert J, Eley TC, Mattheisen M, Hettema JM. Genome-wide association study of major anxiety disorders in 122,341 European-ancestry cases identifies 58 loci and highlights GABAergic signaling. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.03.24309466. [PMID: 39006447 PMCID: PMC11245051 DOI: 10.1101/2024.07.03.24309466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
The major anxiety disorders (ANX; including generalized anxiety disorder, panic disorder, and phobias ) are highly prevalent, often onset early, persist throughout life, and cause substantial global disability. Although distinct in their clinical presentations, they likely represent differential expressions of a dysregulated threat-response system. Here we present a genome-wide association meta-analysis comprising 122,341 European ancestry ANX cases and 729,881 controls. We identified 58 independent genome-wide significant ANX risk variants and 66 genes with robust biological support. In an independent sample of 1,175,012 self-report ANX cases and 1,956,379 controls, 51 of the 58 associated variants were replicated. As predicted by twin studies, we found substantial genetic correlation between ANX and depression, neuroticism, and other internalizing phenotypes. Follow-up analyses demonstrated enrichment in all major brain regions and highlighted GABAergic signaling as one potential mechanism underlying ANX genetic risk. These results advance our understanding of the genetic architecture of ANX and prioritize genes for functional follow-up studies.
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Adams MJ. Genome-wide study of half a million individuals with major depression identifies 697 independent associations, infers causal neuronal subtypes and biological targets for novel pharmacotherapies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.29.24306535. [PMID: 38746223 PMCID: PMC11092713 DOI: 10.1101/2024.04.29.24306535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
In a genome-wide association study (GWAS) of 685,808 individuals with major depression (MD) and 4,364,225 controls from 29 countries and across diverse and admixed ancestries, we identify 697 independent associations at 636 genetic loci, 293 of which are novel. Using fine-mapping and functional genomic datasets, we find 308 high-confidence gene associations and enrichment of postsynaptic density and receptor clustering. Leveraging new single-cell gene expression data, we conducted a causal neural cell type enrichment analysis that implicated excitatory and inhibitory midbrain and forebrain neurons, peptidergic neurons, and medium spiny neurons in MD. Critically, our findings are enriched for the targets of antidepressants and provide potential antidepressant repurposing opportunities (e.g., pregabalin and modafinil). Polygenic scores (PGS) from European ancestries explained up to 5.7% of the variance in liability to MD in European samples and PGS trained using either European or multi-ancestry data significantly predicted case control status across all four diverse ancestries. These findings represent a major advance in our understanding of MD across global populations. We provide evidence that MD GWAS reveals known and novel biological targets that may be used to target and develop pharmacotherapies addressing the considerable unmet need for effective treatment.
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Affiliation(s)
- Mark J Adams
- Psychiatric Genomics Consortium Major Depressive Disorder Working Group
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024:S0168-9525(24)00095-7. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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Pinakhina D, Loboda A, Sergushichev A, Artomov M. Gene, cell type, and drug prioritization analysis suggest genetic basis for the utility of diuretics in treating Alzheimer disease. HGG ADVANCES 2023; 4:100203. [PMID: 37250495 PMCID: PMC10209737 DOI: 10.1016/j.xhgg.2023.100203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 04/25/2023] [Indexed: 05/31/2023] Open
Abstract
We introduce a user-friendly tool for risk gene, cell type, and drug prioritization for complex traits: GCDPipe. It uses gene-level GWAS-derived data and gene expression data to train a model for the identification of disease risk genes and relevant cell types. Gene prioritization information is then coupled with known drug target data to search for applicable drug agents based on their estimated functional effects on the identified risk genes. We illustrate the utility of our approach in different settings: identification of the cell types, implicated in disease pathogenesis, was tested in inflammatory bowel disease (IBD) and Alzheimer disease (AD); gene target and drug prioritization was tested in IBD and schizophrenia. The analysis of phenotypes with known disease-affected cell types and/or existing drug candidates shows that GCDPipe is an effective tool to unify genetic risk factors with cellular context and known drug targets. Next, analysis of the AD data with GCDPipe suggested that gene targets of diuretics, as an Anatomical Therapeutic Chemical drug subgroup, are significantly enriched among the genes prioritized by GCDPipe, indicating their possible effect on the course of the disease.
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Affiliation(s)
- Daria Pinakhina
- ITMO University, 197101 Saint Petersburg, Russia
- Bekhterev National Medical Research Center, 192019 Saint Petersburg, Russia
| | - Alexander Loboda
- ITMO University, 197101 Saint Petersburg, Russia
- Almazov National Medical Research Center, 191014 Saint Petersburg, Russia
| | | | - Mykyta Artomov
- ITMO University, 197101 Saint Petersburg, Russia
- Broad Institute, Cambridge, MA 02142, USA
- Massachusetts General Hospital, Boston, MA 02114, USA
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA
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5
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Silveira PP, Pokhvisneva I, Howard DM, Meaney MJ. A sex-specific genome-wide association study of depression phenotypes in UK Biobank. Mol Psychiatry 2023; 28:2469-2479. [PMID: 36750733 PMCID: PMC10611579 DOI: 10.1038/s41380-023-01960-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 12/07/2022] [Accepted: 01/11/2023] [Indexed: 02/09/2023]
Abstract
There are marked sex differences in the prevalence, phenotypic presentation and treatment response for major depression. While genome-wide association studies (GWAS) adjust for sex differences, to date, no studies seek to identify sex-specific markers and pathways. In this study, we performed a sex-stratified genome-wide association analysis for broad depression with the UK Biobank total participants (N = 274,141), including only non-related participants, as well as with males (N = 127,867) and females (N = 146,274) separately. Bioinformatics analyses were performed to characterize common and sex-specific markers and associated processes/pathways. We identified 11 loci passing genome-level significance (P < 5 × 10-8) in females and one in males. In both males and females, genetic correlations were significant between the broad depression GWA and other psychopathologies; however, correlations with educational attainment and metabolic features including body fat, waist circumference, waist-to-hip ratio and triglycerides were significant only in females. Gene-based analysis showed 147 genes significantly associated with broad depression in the total sample, 64 in the females and 53 in the males. Gene-based analysis revealed "Regulation of Gene Expression" as a common biological process, but suggested sex-specific molecular mechanisms. Finally, sex-specific polygenic risk scores (PRSs) for broad depression outperformed total and the opposite sex PRSs in the prediction of broad major depressive disorder. These findings provide evidence for sex-dependent genetic pathways for clinical depression as well as for health conditions comorbid with depression.
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Affiliation(s)
- Patrícia Pelufo Silveira
- Ludmer Centre for Neuroinformatics and Mental Health, Department of Psychiatry, Faculty of Medicine & Douglas Research Centre, McGill University, Montreal, QC, Canada
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Irina Pokhvisneva
- Ludmer Centre for Neuroinformatics and Mental Health, Department of Psychiatry, Faculty of Medicine & Douglas Research Centre, McGill University, Montreal, QC, Canada
| | - David M Howard
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Michael J Meaney
- Ludmer Centre for Neuroinformatics and Mental Health, Department of Psychiatry, Faculty of Medicine & Douglas Research Centre, McGill University, Montreal, QC, Canada.
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Translational Neuroscience Program, Singapore Institute for Clinical Sciences and Brain - Body Initiative, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
- Brain-Body Initiative, Institute for Cell & Molecular Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
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Silveira PP, Meaney MJ. Examining the biological mechanisms of human mental disorders resulting from gene-environment interdependence using novel functional genomic approaches. Neurobiol Dis 2023; 178:106008. [PMID: 36690304 DOI: 10.1016/j.nbd.2023.106008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/30/2022] [Accepted: 01/18/2023] [Indexed: 01/21/2023] Open
Abstract
We explore how functional genomics approaches that integrate datasets from human and non-human model systems can improve our understanding of the effect of gene-environment interplay on the risk for mental disorders. We start by briefly defining the G-E paradigm and its challenges and then discuss the different levels of regulation of gene expression and the corresponding data existing in humans (genome wide genotyping, transcriptomics, DNA methylation, chromatin modifications, chromosome conformational changes, non-coding RNAs, proteomics and metabolomics), discussing novel approaches to the application of these data in the study of the origins of mental health. Finally, we discuss the multilevel integration of diverse types of data. Advance in the use of functional genomics in the context of a G-E perspective improves the detection of vulnerabilities, informing the development of preventive and therapeutic interventions.
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Affiliation(s)
- Patrícia Pelufo Silveira
- Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada; Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.
| | - Michael J Meaney
- Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada; Translational Neuroscience Program, Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore; Brain - Body Initiative, Agency for Science, Technology and Research (ASTAR), Singapore.
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Liu J, Li X, Luo XJ. Proteome-wide Association Study Provides Insights Into the Genetic Component of Protein Abundance in Psychiatric Disorders. Biol Psychiatry 2021; 90:781-789. [PMID: 34454697 DOI: 10.1016/j.biopsych.2021.06.022] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 05/29/2021] [Accepted: 06/29/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND Genome-wide association studies have identified multiple risk variants for psychiatric disorders. Nevertheless, how the risk variants confer risk of psychiatric disorders remains largely unknown. METHODS We performed proteome-wide association studies to identify genes whose cis-regulated protein abundance change in the human brain were associated with psychiatric disorders. RESULTS By integrating genome-wide associations of four common psychiatric disorders and two independent brain proteomes (n = 376 and n = 152, respectively) from the dorsolateral prefrontal cortex, we identified 61 genes (including 48 genes for schizophrenia, 12 genes for bipolar disorder, 5 genes for depression, and 2 genes for attention-deficit/hyperactivity disorder) whose genetically regulated protein abundance levels were associated with risk of psychiatric disorders. Comparison with transcriptome-wide association studies identified 18 overlapping genes that showed significant associations with psychiatric disorders at both proteome-wide and transcriptome-wide levels, suggesting that genetic risk variants likely confer risk of psychiatric disorders by regulating messenger RNA expression and protein abundance of these genes. CONCLUSIONS Our study not only provides new insights into the genetic component of protein abundance in psychiatric disorders but also highlights several high-confidence risk proteins (including CNNM2 and CTNND1) for schizophrenia and depression. These high-confidence risk proteins represent promising therapeutic targets for future drug development.
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Affiliation(s)
- Jiewei Liu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Xiaoyan Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China; Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China
| | - Xiong-Jian Luo
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China; KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan, China.
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8
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Dafniet B, Cerisier N, Boezio B, Clary A, Ducrot P, Dorval T, Gohier A, Brown D, Audouze K, Taboureau O. Development of a chemogenomics library for phenotypic screening. J Cheminform 2021; 13:91. [PMID: 34819133 PMCID: PMC8611952 DOI: 10.1186/s13321-021-00569-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 11/06/2021] [Indexed: 12/03/2022] Open
Abstract
With the development of advanced technologies in cell-based phenotypic screening, phenotypic drug discovery (PDD) strategies have re-emerged as promising approaches in the identification and development of novel and safe drugs. However, phenotypic screening does not rely on knowledge of specific drug targets and needs to be combined with chemical biology approaches to identify therapeutic targets and mechanisms of actions induced by drugs and associated with an observable phenotype. In this study, we developed a system pharmacology network integrating drug-target-pathway-disease relationships as well as morphological profile from an existing high content imaging-based high-throughput phenotypic profiling assay known as “Cell Painting”. Furthermore, from this network, a chemogenomic library of 5000 small molecules that represent a large and diverse panel of drug targets involved in diverse biological effects and diseases has been developed. Such a platform and a chemogenomic library could assist in the target identification and mechanism deconvolution of some phenotypic assays. The usefulness of the platform is illustrated through examples.
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Affiliation(s)
- Bryan Dafniet
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Natacha Cerisier
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Batiste Boezio
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Anaelle Clary
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Pierre Ducrot
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Thierry Dorval
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Arnaud Gohier
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - David Brown
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Karine Audouze
- Université de Paris, INSERM UMR S-1124, 75006, Paris, France
| | - Olivier Taboureau
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France.
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Reay WR, Cairns MJ. Advancing the use of genome-wide association studies for drug repurposing. Nat Rev Genet 2021; 22:658-671. [PMID: 34302145 DOI: 10.1038/s41576-021-00387-z] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2021] [Indexed: 02/07/2023]
Abstract
Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring.
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Affiliation(s)
- William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, New South Wales, Australia.,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, New South Wales, Australia. .,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, New South Wales, Australia.
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10
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Gao H, Ni Y, Mo X, Li D, Teng S, Huang Q, Huang S, Liu G, Zhang S, Tang Y, Lu L, Liang H. Drug repositioning based on network-specific core genes identifies potential drugs for the treatment of autism spectrum disorder in children. Comput Struct Biotechnol J 2021; 19:3908-3921. [PMID: 34306572 PMCID: PMC8280514 DOI: 10.1016/j.csbj.2021.06.046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 06/28/2021] [Accepted: 06/29/2021] [Indexed: 12/13/2022] Open
Abstract
Identification of exact causative genes is important for in silico drug repositioning based on drug-gene-disease relationships. However, the complex polygenic etiology of the autism spectrum disorder (ASD) is a challenge in the identification of etiological genes. The network-based core gene identification method can effectively use the interactions between genes and accurately identify the pathogenic genes of ASD. We developed a novel network-based drug repositioning framework that contains three steps: network-specific core gene (NCG) identification, potential therapeutic drug repositioning, and candidate drug validation. First, through the analysis of transcriptome data for 178 brain tissues, gene network analysis identified 365 NCGs in 18 coexpression modules that were significantly correlated with ASD. Second, we evaluated two proposed drug repositioning methods. In one novel approach (dtGSEA), we used the NCGs to probe drug-gene interaction data and identified 35 candidate drugs. In another approach, we compared NCG expression patterns with drug-induced transcriptome data from the Connectivity Map database and found 46 candidate drugs. Third, we validated the candidate drugs using an in-house mental diseases and compounds knowledge graph (MCKG) that contained 7509 compounds, 505 mental diseases, and 123,890 edges. We found a total of 42 candidate drugs that were associated with mental illness, among which 10 drugs (baclofen, sulpiride, estradiol, entinostat, everolimus, fluvoxamine, curcumin, calcitriol, metronidazole, and zinc) were postulated to be associated with ASD. This study proposes a powerful network-based drug repositioning framework and also provides candidate drugs as well as potential drug targets for the subsequent development of ASD therapeutic drugs.
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Affiliation(s)
- Huan Gao
- Clinical Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou 510080, Guangdong, China
| | - Yuan Ni
- Ping An Technology, No. 20 Keji South 12 Road, Shen Zhen 518063, Guangdong, China
| | - Xueying Mo
- School of Information Management, Wuhan University, Wuhan 430072, Hubei, China
| | - Dantong Li
- Clinical Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou 510080, Guangdong, China
| | - Shan Teng
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou,510515, China
| | - Qingsheng Huang
- Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, No. 9 Jinsui Road, Guangzhou 510623, Guangdong, China
| | - Shuai Huang
- Clinical Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou 510080, Guangdong, China
| | - Guangjian Liu
- Clinical Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou 510080, Guangdong, China
| | - Sheng Zhang
- Ping An Technology, No. 20 Keji South 12 Road, Shen Zhen 518063, Guangdong, China
| | - Yaping Tang
- Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, No. 9 Jinsui Road, Guangzhou 510623, Guangdong, China
| | - Long Lu
- School of Information Management, Wuhan University, Wuhan 430072, Hubei, China
- Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, No. 9 Jinsui Road, Guangzhou 510623, Guangdong, China
| | - Huiying Liang
- Clinical Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou 510080, Guangdong, China
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11
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Buch AM, Liston C. Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics. Neuropsychopharmacology 2021; 46:156-175. [PMID: 32781460 PMCID: PMC7688954 DOI: 10.1038/s41386-020-00789-3] [Citation(s) in RCA: 98] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/07/2020] [Accepted: 07/16/2020] [Indexed: 12/12/2022]
Abstract
Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from distinct pathophysiological mechanisms. Motivated by a need to advance our understanding of these mechanisms and develop new treatment strategies, there is a renewed interest in investigating the neurobiological basis of heterogeneity in depression and rethinking our approach to diagnosis for research purposes. Large-scale genome-wide association studies have now identified multiple genetic risk variants implicating excitatory neurotransmission and synapse function and underscoring a highly polygenic inheritance pattern that may be another important contributor to heterogeneity in depression. Here, we review various sources of phenotypic heterogeneity and approaches to defining and studying depression subtypes, including symptom-based subtypes and biology-based approaches to decomposing the depression syndrome. We review "dimensional," "categorical," and "hybrid" approaches to parsing phenotypic heterogeneity in depression and defining subtypes using functional neuroimaging. Next, we review recent progress in neuroimaging genetics (correlating neuroimaging patterns of brain function with genetic data) and its potential utility for generating testable hypotheses concerning molecular and circuit-level mechanisms. We discuss how genetic variants and transcriptomic profiles may confer risk for depression by modulating brain structure and function. We conclude by highlighting several promising areas for future research into the neurobiological underpinnings of heterogeneity, including efforts to understand sexually dimorphic mechanisms, the longitudinal dynamics of depressive episodes, and strategies for developing personalized treatments and facilitating clinical decision-making.
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Affiliation(s)
- Amanda M Buch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY, 10021, USA
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY, 10021, USA.
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12
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Pathak GA, Polimanti R, Silzer TK, Wendt FR, Chakraborty R, Phillips NR. Genetically-regulated transcriptomics & copy number variation of proctitis points to altered mitochondrial and DNA repair mechanisms in individuals of European ancestry. BMC Cancer 2020; 20:954. [PMID: 33008348 PMCID: PMC7530964 DOI: 10.1186/s12885-020-07457-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 09/23/2020] [Indexed: 02/08/2023] Open
Abstract
Background Proctitis is an inflammation of the rectum and may be induced by radiation treatment for cancer. The genetic heritability of developing radiotoxicity and prior role of genetic variants as being associated with side-effects of radiotherapy necessitates further investigation for underlying molecular mechanisms. In this study, we investigated gene expression regulated by genetic variants, and copy number variation in prostate cancer survivors with radiotoxicity. Methods We investigated proctitis as a radiotoxic endpoint in prostate cancer patients who received radiotherapy (n = 222). We analyzed the copy number variation and genetically regulated gene expression profiles of whole-blood and prostate tissue associated with proctitis. The SNP and copy number data were genotyped on Affymetrix® Genome-wide Human SNP Array 6.0. Following QC measures, the genotypes were used to obtain gene expression by leveraging GTEx, a reference dataset for gene expression association based on genotype and RNA-seq information for prostate (n = 132) and whole-blood tissue (n = 369). Results In prostate tissue, 62 genes were significantly associated with proctitis, and 98 genes in whole-blood tissue. Six genes - CABLES2, ATP6AP1L, IFIT5, ATRIP, TELO2, and PARD6G were common to both tissues. The copy number analysis identified seven regions associated with proctitis, one of which (ALG1L2) was also associated with proctitis based on transcriptomic profiles in the whole-blood tissue. The genes identified via transcriptomics and copy number variation association were further investigated for enriched pathways and gene ontology. Some of the enriched processes were DNA repair, mitochondrial apoptosis regulation, cell-to-cell signaling interaction processes for renal and urological system, and organismal injury. Conclusions We report gene expression changes based on genetic polymorphisms. Integrating gene-network information identified these genes to relate to canonical DNA repair genes and processes. This investigation highlights genes involved in DNA repair processes and mitochondrial malfunction possibly via inflammation. Therefore, it is suggested that larger studies will provide more power to infer the extent of underlying genetic contribution for an individual’s susceptibility to developing radiotoxicity.
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Affiliation(s)
- Gita A Pathak
- Department of Microbiology, Immunology & Genetics, Graduate School of Biomedical Sciences, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, USA.,Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Talisa K Silzer
- Department of Microbiology, Immunology & Genetics, Graduate School of Biomedical Sciences, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Frank R Wendt
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, USA.,Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Ranajit Chakraborty
- Department of Microbiology, Immunology & Genetics, Graduate School of Biomedical Sciences, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Nicole R Phillips
- Department of Microbiology, Immunology & Genetics, Graduate School of Biomedical Sciences, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA.
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Johnson EC, Chang Y, Agrawal A. An update on the role of common genetic variation underlying substance use disorders. CURRENT GENETIC MEDICINE REPORTS 2020; 8:35-46. [PMID: 33457110 PMCID: PMC7810203 DOI: 10.1007/s40142-020-00184-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
PURPOSE OF THE REVIEW Sample size increases have resulted in novel and replicable loci for substance use disorders (SUDs). We summarize some of the latest insights into SUD genetics and discuss some next steps in addiction genetics. RECENT FINDINGS Genome-wide association studies have substantiated the role of previously known variants (e.g., rs1229984 in ADH1B for alcohol) and identified several novel loci for alcohol, tobacco, cannabis, opioid and cocaine use disorders. SUDs are genetically correlated with psychiatric outcomes, while liability to substance use is inconsistently associated with these outcomes and more closely associated with lifestyle factors. Specific variant associations appear to differ somewhat across populations, although similar genes and systems are implicated. SUMMARY The next decade of human genetic studies of addiction should focus on expanding to non-European populations, consider pleiotropy across SUD and with other psychiatric disorders, and leverage human and cross-species functional data to elucidate the biological mechanisms underlying SUDs.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO
| | - Yoonhoo Chang
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, Saint Louis, MO
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO
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14
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Gaspar HA, Gerring Z, Hübel C, Middeldorp CM, Derks EM, Breen G. Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder. Transl Psychiatry 2019; 9:117. [PMID: 30877270 PMCID: PMC6420656 DOI: 10.1038/s41398-019-0451-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 01/28/2019] [Accepted: 02/12/2019] [Indexed: 12/25/2022] Open
Abstract
The major depressive disorder (MDD) working group of the Psychiatric Genomics Consortium (PGC) has published a genome-wide association study (GWAS) for MDD in 130,664 cases, identifying 44 risk variants. We used these results to investigate potential drug targets and repurposing opportunities. We built easily interpretable bipartite drug-target networks integrating interactions between drugs and their targets, genome-wide association statistics, and genetically predicted expression levels in different tissues, using the online tool Drug Targetor ( drugtargetor.com ). We also investigated drug-target relationships that could be impacting MDD. MAGMA was used to perform pathway analyses and S-PrediXcan to investigate the directionality of tissue-specific expression levels in patients vs. controls. Outside the major histocompatibility complex (MHC) region, 153 protein-coding genes are significantly associated with MDD in MAGMA after multiple testing correction; among these, five are predicted to be down or upregulated in brain regions and 24 are known druggable genes. Several drug classes were significantly enriched, including monoamine reuptake inhibitors, sex hormones, antipsychotics, and antihistamines, indicating an effect on MDD and potential repurposing opportunities. These findings not only require validation in model systems and clinical examination, but also show that GWAS may become a rich source of new therapeutic hypotheses for MDD and other psychiatric disorders that need new-and better-treatment options.
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Affiliation(s)
- Héléna A Gaspar
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, SE5 8AF, UK.
- National Institute for Health Research Biomedical Research Centre, South London and Maudsley National Health Service Trust, London, EC1V 2PD, UK.
| | - Zachary Gerring
- Translational Neurogenomics Laboratory, QIMR Berghofer Institute of Medical Research, Brisbane City, QLD 4006, Australia
| | - Christopher Hübel
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, SE5 8AF, UK
- National Institute for Health Research Biomedical Research Centre, South London and Maudsley National Health Service Trust, London, EC1V 2PD, UK
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Christel M Middeldorp
- Child Health Research Centre, University of Queensland, South Brisbane, QLD 4072, Australia
- Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Service, South Brisbane, QLD 4101, Australia
- Biological Psychology, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, Netherlands
| | - Eske M Derks
- Translational Neurogenomics Laboratory, QIMR Berghofer Institute of Medical Research, Brisbane City, QLD 4006, Australia
| | - Gerome Breen
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, SE5 8AF, UK
- National Institute for Health Research Biomedical Research Centre, South London and Maudsley National Health Service Trust, London, EC1V 2PD, UK
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