451
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Cheng S, Han B, Ding M, Wen Y, Ma M, Zhang L, Qi X, Cheng B, Li P, Kafle OP, Liang X, Liu L, Du Y, Zhao Y, Zhang F. Identifying psychiatric disorder-associated gut microbiota using microbiota-related gene set enrichment analysis. Brief Bioinform 2019; 21:1016-1022. [DOI: 10.1093/bib/bbz034] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 02/20/2019] [Accepted: 03/01/2019] [Indexed: 12/21/2022] Open
Abstract
Abstract
Psychiatric disorders are a group of complex psychological syndromes with high prevalence. It has been reported that gut microbiota has a dominant influence on the risks of psychiatric disorders through gut microbiota–brain axis. We extended the classic gene set enrichment analysis (GSEA) approach to detect the association between gut microbiota and complex diseases using published genome-wide association study (GWAS) and GWAS of gut microbiota summary data. We applied our approach to real GWAS data sets of five psychiatric disorders, including attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (AUT), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD). To evaluate the performance of our approach, we also tested the genetic correlations of obesity and type 2 diabetes with gut microbiota. We identified several significant associations between psychiatric disorders and gut microbiota, such as ADHD and genus Desulfovibrio (P = 0.031), order Clostridiales (P = 0.034). For AUT, association signals were observed for genera Bacteroides (P = 0.012) and Desulfovibrio (P = 0.033). Genus Desulfovibrio (P = 0.005) appeared to be associated with BD. For MDD, association signals were observed for genus Desulfovibrio (P = 0.003), order Clostridiales (P = 0.004), family Lachnospiraceae (P = 0.007) and genus Bacteroides (P = 0.007). Genus Desulfovibrio (P = 0.012) and genus Bacteroides (P = 0.038) appeared to be associated with SCZ. Our study results provide novel clues for revealing the roles of gut microbiota in psychiatric disorders. This study also illustrated the good performance of GSEA approach for exploring the relationships between gut microbiota and complex diseases.
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Affiliation(s)
- Shiqiang Cheng
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Bei Han
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Miao Ding
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yan Wen
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Mei Ma
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Lu Zhang
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xin Qi
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Bolun Cheng
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Ping Li
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Om Prakash Kafle
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiao Liang
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Li Liu
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yanan Du
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yan Zhao
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Feng Zhang
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
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452
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Mistry S, Escott-Price V, Florio AD, Smith DJ, Zammit S. Genetic risk for bipolar disorder and psychopathology from childhood to early adulthood. J Affect Disord 2019; 246:633-639. [PMID: 30611060 DOI: 10.1016/j.jad.2018.12.091] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 11/29/2018] [Accepted: 12/24/2018] [Indexed: 01/10/2023]
Abstract
BACKGROUND Studying the phenotypic manifestations of increased genetic liability for Bipolar Disorder (BD) can increase understanding of this disorder. AIMS We assessed whether genetic risk for BD was associated with childhood psychopathology and features of hypomania in young adulthood within a large population-based birth cohort. METHODS We used data from the second Psychiatric Genetics Consortium Genome Wide Association Study (GWAS) for Bipolar Disorder to construct a polygenic risk score (PRS) for each individual in the Avon Longitudinal Study of Parents and Children (ALSPAC). Linear and logistic regression models were used to assess associations between the BD-PRS and emotional/behavioural difficulties, attention deficit hyperactivity disorder (ADHD) and borderline personality disorder (BPD) traits in childhood, as well as hypomania in early adulthood (sample sizes from 2654 to 6111). RESULTS The BD-PRS was not associated with total hypomania score, but was weakly associated with a binary measure of hypomania (OR = 1.13, 95%CI 0.98,1.32; p = 0.097), and particularly at higher hypomania symptom thresholds (strongest evidence OR = 1.33, 95%CI 1.07, 1.65; p = 0.01). The BD-PRS was also associated with ADHD (OR = 1.31, 95%CI 1.10, 1.57; p = 0.018), but not with other childhood psychopathology. LIMITATIONS The PRS only captures common genetic variation and currently explains a relatively small proportion of the variance for BD. CONCLUSIONS The BD-PRS was associated with ADHD in childhood, and weakly with adult hypomania, but not with other psychopathology examined. Our findings suggest that genetic risk for BD does not appear to manifest in childhood to the same extent as schizophrenia genetic risk has been reported to do.
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Affiliation(s)
- Sumit Mistry
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Maindy Road, CF24 4HQ, UK.
| | - Valentina Escott-Price
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Maindy Road, CF24 4HQ, UK
| | - Arianna D Florio
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Maindy Road, CF24 4HQ, UK
| | - Daniel J Smith
- Institute of Health and Wellbeing, 1 Lilybank Gardens, University of Glasgow, UK
| | - Stanley Zammit
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Maindy Road, CF24 4HQ, UK; Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, UK
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453
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Palk AC, Dalvie S, de Vries J, Martin AR, Stein DJ. Potential use of clinical polygenic risk scores in psychiatry - ethical implications and communicating high polygenic risk. Philos Ethics Humanit Med 2019; 14:4. [PMID: 30813945 PMCID: PMC6391805 DOI: 10.1186/s13010-019-0073-8] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 02/14/2019] [Indexed: 06/09/2023] Open
Abstract
Psychiatric disorders present distinct clinical challenges which are partly attributable to their multifactorial aetiology and the absence of laboratory tests that can be used to confirm diagnosis or predict risk. Psychiatric disorders are highly heritable, but also polygenic, with genetic risk conferred by interactions between thousands of variants of small effect that can be summarized in a polygenic risk score. We discuss four areas in which the use of polygenic risk scores in psychiatric research and clinical contexts could have ethical implications. First, there is concern that clinical use of polygenic risk scores may exacerbate existing health inequities. Second, research findings regarding polygenic risk could be misinterpreted in stigmatising or discriminatory ways. Third, there are concerns associated with testing minors as well as eugenics concerns elicited by prenatal polygenic risk testing. Fourth, potential challenges that could arise with the feedback and interpretation of high polygenic risk for a psychiatric disorder would require consideration. While there would be extensive overlap with the challenges of feeding back genetic findings in general, the potential clinical use of polygenic risk scoring warrants discussion in its own right, given the recency of this possibility. To this end, we discuss how lay interpretations of risk and genetic information could intersect. Consideration of these factors would be necessary for ensuring effective and constructive communication and interpretation of polygenic risk information which, in turn, could have implications for the uptake of any therapeutic recommendations. Recent advances in polygenic risk scoring have major implications for its clinical potential, however, care should be taken to ensure that communication of polygenic risk does not feed into problematic assumptions regarding mental disorders or support reductive interpretations.
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Affiliation(s)
- A. C. Palk
- Department of Psychiatry, University of Cape Town, Groote Schuur Hospital, Observatory, Cape Town, 7925 South Africa
| | - S. Dalvie
- Department of Psychiatry and SA MRC Unit on Risk and Resilience in Mental Disorders, University of Cape Town, Groote Schuur Hospital, Observatory, Cape Town, 7925 South Africa
| | - J. de Vries
- Department of Medicine, University of Cape Town, Groote Schuur Hospital, Observatory, Cape Town, 7925 South Africa
| | - A. R. Martin
- Analytic & Translational Genetics Unit, Massachusetts General Hospital, Boston, MA USA
- Stanley Center for Psychiatric Research & Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - D. J. Stein
- Department of Psychiatry and SA MRC Unit on Risk and Resilience in Mental Disorders, University of Cape Town, Groote Schuur Hospital, Observatory, Cape Town, 7925 South Africa
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454
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Nöthen MM, Degenhardt F, Forstner AJ. [Breakthrough in understanding the molecular causes of psychiatric disorders]. DER NERVENARZT 2019; 90:99-106. [PMID: 30758637 DOI: 10.1007/s00115-018-0670-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
A long-established hypothesis is that genetic factors contribute to the development of psychiatric diseases, including common illnesses such as schizophrenia and the affective disorders; however, reliable molecular identification of these factors represents a far more recent innovation. This has been rendered possible by technological advances in the individual characterization of the human genome and the combining of large genetic datasets at the international level. For the first time, the results of genome-wide analyses provide researchers with systematic insights into disease-relevant biological mechanisms. Here, the integrated analysis of different omics level data generates important insights into the functional interpretation of the genetic findings. The results of genetic studies also demonstrated the degree of etiological overlap between differing psychiatric disorders, with the greatest commonality having been observed to date between schizophrenia and bipolar affective disorder. Although the translation of genetic findings into routine clinical practice is being pursued at various levels, elaborate follow-up studies are typically necessary. The diagnostic investigation of rare genomic deletions/duplications (so-called copy number variants) in patients with schizophrenia is likely to represent one of the first examples of routine clinical application. The necessary prerequisites for this are currently being defined.
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Affiliation(s)
- Markus M Nöthen
- Institut für Humangenetik, Universitätsklinikum Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Deutschland.
| | - Franziska Degenhardt
- Institut für Humangenetik, Universitätsklinikum Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Deutschland
| | - Andreas J Forstner
- Institut für Humangenetik, Universitätsklinikum Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Deutschland.,Zentrum für Humangenetik, Philipps-Universität Marburg, Baldingerstraße, 35033, Marburg, Deutschland
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455
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Mineral Nutrition and the Risk of Chronic Diseases: A Mendelian Randomization Study. Nutrients 2019; 11:nu11020378. [PMID: 30759836 PMCID: PMC6412267 DOI: 10.3390/nu11020378] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 02/08/2019] [Accepted: 02/08/2019] [Indexed: 02/06/2023] Open
Abstract
We applied Mendelian randomization analyses to investigate the potential causality between blood minerals (calcium, magnesium, iron, copper, and zinc) and osteoporosis (OP), gout, rheumatoid arthritis (RA), type 2 diabetes (T2D), Alzheimer’s disease (AD), bipolar disorder (BD), schizophrenia, Parkinson’s disease and major depressive disorder. Single nucleotide polymorphisms (SNPs) that are independent (r2 < 0.01) and are strongly related to minerals (p < 5 × 10−8) are selected as instrumental variables. Each standard deviation increase in magnesium (0.16 mmol/L) is associated with an 8.94-fold increase in the risk of RA (p = 0.044) and an 8.78-fold increase in BD (p = 0.040) but a 0.10 g/cm2 increase in bone density related to OP (p = 0.014). Each per-unit increase in copper is associated with a 0.87-fold increase in the risk of AD (p = 0.050) and BD (p = 0.010). In addition, there is suggestive evidence that calcium is positively correlated (OR = 1.36, p = 0.030) and iron is negatively correlated with T2D risk (OR = 0.89, p = 0.010); both magnesium (OR = 0.26, p = 0.013) and iron (OR = 0.71, p = 0.047) are negatively correlated with gout risk. In the sensitivity analysis, causal estimation is not affected by pleiotropy. This study supports the long-standing hypothesis that magnesium supplementation can increase RA and BD risks and decrease OP risk and that copper intake can reduce AD and BD risks. This study will be helpful to address some controversial debates on the relationships between minerals and chronic diseases.
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456
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Genome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence. Mol Psychiatry 2019; 25:844-853. [PMID: 30610197 PMCID: PMC6609490 DOI: 10.1038/s41380-018-0332-x] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 10/18/2018] [Accepted: 11/29/2018] [Indexed: 02/08/2023]
Abstract
Schizophrenia (SCZ) and bipolar disorder (BD) are severe mental disorders associated with cognitive impairment, which is considered a major determinant of functional outcome. Despite this, the etiology of the cognitive impairment is poorly understood, and no satisfactory cognitive treatments exist. Increasing evidence indicates that genetic risk for SCZ may contribute to cognitive impairment, whereas the genetic relationship between BD and cognitive function remains unclear. Here, we combined large genome-wide association study data on SCZ (n = 82,315), BD (n = 51,710), and general intelligence (n = 269,867) to investigate overlap in common genetic variants using conditional false discovery rate (condFDR) analysis. We observed substantial genetic enrichment in both SCZ and BD conditional on associations with intelligence indicating polygenic overlap. Using condFDR analysis, we leveraged this enrichment to increase statistical power and identified 75 distinct genomic loci associated with both SCZ and intelligence, and 12 loci associated with both BD and intelligence at conjunctional FDR < 0.01. Among these loci, 20 are novel for SCZ, and four are novel for BD. Most SCZ risk alleles (61 of 75, 81%) were associated with poorer cognitive performance, whereas most BD risk alleles (9 of 12, 75%) were associated with better cognitive performance. A gene set analysis of the loci shared between SCZ and intelligence implicated biological processes related to neurodevelopment, synaptic integrity, and neurotransmission; the same analysis for BD was underpowered. Altogether, the study demonstrates that both SCZ and BD share genetic influences with intelligence, albeit in a different manner, providing new insights into their genetic architectures.
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457
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Wang D, Liu S, Warrell J, Won H, Shi X, Navarro FCP, Clarke D, Gu M, Emani P, Yang YT, Xu M, Gandal MJ, Lou S, Zhang J, Park JJ, Yan C, Rhie SK, Manakongtreecheep K, Zhou H, Nathan A, Peters M, Mattei E, Fitzgerald D, Brunetti T, Moore J, Jiang Y, Girdhar K, Hoffman GE, Kalayci S, Gümüş ZH, Crawford GE, Roussos P, Akbarian S, Jaffe AE, White KP, Weng Z, Sestan N, Geschwind DH, Knowles JA, Gerstein MB. Comprehensive functional genomic resource and integrative model for the human brain. Science 2018; 362:eaat8464. [PMID: 30545857 PMCID: PMC6413328 DOI: 10.1126/science.aat8464] [Citation(s) in RCA: 501] [Impact Index Per Article: 83.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 11/15/2018] [Indexed: 12/12/2022]
Abstract
Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with >88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.
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Affiliation(s)
- Daifeng Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Shuang Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Hyejung Won
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Xu Shi
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Fabio C. P. Navarro
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Mengting Gu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Prashant Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yucheng T. Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Min Xu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Michael J. Gandal
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California–Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jing Zhang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jonathan J. Park
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Chengfei Yan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Suhn Kyong Rhie
- Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90007, USA
| | - Kasidet Manakongtreecheep
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Holly Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Aparna Nathan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Eugenio Mattei
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Dominic Fitzgerald
- Institute for Genomics and Systems Biology, Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Tonya Brunetti
- Institute for Genomics and Systems Biology, Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Jill Moore
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Yan Jiang
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kiran Girdhar
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Gabriel E. Hoffman
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Selim Kalayci
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zeynep H. Gümüş
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Gregory E. Crawford
- Center for Genomic and Computational Biology, Department of Pediatrics, Duke University, Durham, NC 27708, USA
| | - Panos Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Schahram Akbarian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Andrew E. Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, and Departments of Mental Health and Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Kevin P. White
- Institute for Genomics and Systems Biology, Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
- Tempus Labs, Chicago, IL 60654, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Nenad Sestan
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520, USA
| | - Daniel H. Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California–Los Angeles, Los Angeles, CA 90095, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California–Los Angeles, Los Angeles, CA 90095, USA
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California–Los Angeles, Los Angeles, CA 90095, USA
| | - James A. Knowles
- SUNY Downstate Medical Center College of Medicine, Brooklyn, NY 11203, USA
| | - Mark B. Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
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458
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Gandal MJ, Zhang P, Hadjimichael E, Walker RL, Chen C, Liu S, Won H, van Bakel H, Varghese M, Wang Y, Shieh AW, Haney J, Parhami S, Belmont J, Kim M, Losada PM, Khan Z, Mleczko J, Xia Y, Dai R, Wang D, Yang YT, Xu M, Fish K, Hof PR, Warrell J, Fitzgerald D, White K, Jaffe AE, Peters MA, Gerstein M, Liu C, Iakoucheva LM, Pinto D, Geschwind DH. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 2018; 362:eaat8127. [PMID: 30545856 PMCID: PMC6443102 DOI: 10.1126/science.aat8127] [Citation(s) in RCA: 694] [Impact Index Per Article: 115.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 11/13/2018] [Indexed: 12/12/2022]
Abstract
Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic dysregulation of gene expression and splicing. However, comprehensive assessments of transcriptomic organization in diseased brains are limited. In this work, we integrated genotypes and RNA sequencing in brain samples from 1695 individuals with autism spectrum disorder (ASD), schizophrenia, and bipolar disorder, as well as controls. More than 25% of the transcriptome exhibits differential splicing or expression, with isoform-level changes capturing the largest disease effects and genetic enrichments. Coexpression networks isolate disease-specific neuronal alterations, as well as microglial, astrocyte, and interferon-response modules defining previously unidentified neural-immune mechanisms. We integrated genetic and genomic data to perform a transcriptome-wide association study, prioritizing disease loci likely mediated by cis effects on brain expression. This transcriptome-wide characterization of the molecular pathology across three major psychiatric disorders provides a comprehensive resource for mechanistic insight and therapeutic development.
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Affiliation(s)
- Michael J. Gandal
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Pan Zhang
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA
| | - Evi Hadjimichael
- Department of Psychiatry, and Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Rebecca L. Walker
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Chao Chen
- The School of Life Science, Central South University, Changsha, Hunan 410078, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, Hunan, China
| | - Shuang Liu
- Program in Computational Biology and Bioinformatics, Departments of Molecular Biophysics and Biochemistry and Computer Science, Yale University, New Haven, CT, USA
| | - Hyejung Won
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Harm van Bakel
- Department of Genetics and Genomic Sciences, and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Merina Varghese
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Fishberg Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yongjun Wang
- The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Annie W. Shieh
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Jillian Haney
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
| | - Sepideh Parhami
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
| | - Judson Belmont
- Department of Psychiatry, and Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Minsoo Kim
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Patricia Moran Losada
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA
| | - Zenab Khan
- Department of Genetics and Genomic Sciences, and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Justyna Mleczko
- Departments of Medicine and Cardiology, Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yan Xia
- The School of Life Science, Central South University, Changsha, Hunan 410078, China
| | - Rujia Dai
- The School of Life Science, Central South University, Changsha, Hunan 410078, China
| | - Daifeng Wang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Yucheng T. Yang
- Program in Computational Biology and Bioinformatics, Departments of Molecular Biophysics and Biochemistry and Computer Science, Yale University, New Haven, CT, USA
| | - Min Xu
- Program in Computational Biology and Bioinformatics, Departments of Molecular Biophysics and Biochemistry and Computer Science, Yale University, New Haven, CT, USA
| | - Kenneth Fish
- Departments of Medicine and Cardiology, Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Patrick R. Hof
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Fishberg Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Departments of Molecular Biophysics and Biochemistry and Computer Science, Yale University, New Haven, CT, USA
| | - Dominic Fitzgerald
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Kevin White
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
- Institute for Genomics and Systems Biology, University of Chicago, Chicago IL 60637
- Tempus Labs, Inc. Chicago IL 60654
| | - Andrew E. Jaffe
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Departments of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Mette A. Peters
- CNS Data Coordination group, Sage Bionetworks, Seattle, WA 98109, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Departments of Molecular Biophysics and Biochemistry and Computer Science, Yale University, New Haven, CT, USA
| | - Chunyu Liu
- The School of Life Science, Central South University, Changsha, Hunan 410078, China
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Lilia M. Iakoucheva
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA
| | - Dalila Pinto
- Department of Psychiatry, and Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Daniel H. Geschwind
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
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459
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Li M, Santpere G, Kawasawa YI, Evgrafov OV, Gulden FO, Pochareddy S, Sunkin SM, Li Z, Shin Y, Zhu Y, Sousa AMM, Werling DM, Kitchen RR, Kang HJ, Pletikos M, Choi J, Muchnik S, Xu X, Wang D, Lorente-Galdos B, Liu S, Giusti-Rodríguez P, Won H, de Leeuw CA, Pardiñas AF, Hu M, Jin F, Li Y, Owen MJ, O’Donovan MC, Walters JTR, Posthuma D, Reimers MA, Levitt P, Weinberger DR, Hyde TM, Kleinman JE, Geschwind DH, Hawrylycz MJ, State MW, Sanders SJ, Sullivan PF, Gerstein MB, Lein ES, Knowles JA, Sestan N. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 2018; 362:eaat7615. [PMID: 30545854 PMCID: PMC6413317 DOI: 10.1126/science.aat7615] [Citation(s) in RCA: 436] [Impact Index Per Article: 72.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 11/15/2018] [Indexed: 12/14/2022]
Abstract
To broaden our understanding of human neurodevelopment, we profiled transcriptomic and epigenomic landscapes across brain regions and/or cell types for the entire span of prenatal and postnatal development. Integrative analysis revealed temporal, regional, sex, and cell type-specific dynamics. We observed a global transcriptomic cup-shaped pattern, characterized by a late fetal transition associated with sharply decreased regional differences and changes in cellular composition and maturation, followed by a reversal in childhood-adolescence, and accompanied by epigenomic reorganizations. Analysis of gene coexpression modules revealed relationships with epigenomic regulation and neurodevelopmental processes. Genes with genetic associations to brain-based traits and neuropsychiatric disorders (including MEF2C, SATB2, SOX5, TCF4, and TSHZ3) converged in a small number of modules and distinct cell types, revealing insights into neurodevelopment and the genomic basis of neuropsychiatric risks.
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Affiliation(s)
- Mingfeng Li
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Gabriel Santpere
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Yuka Imamura Kawasawa
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
- Departments of Pharmacology and Biochemistry and Molecular Biology, Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Oleg V. Evgrafov
- Department of Cell Biology, SUNY Downstate Medical Center, Brooklyn NY, USA
| | - Forrest O. Gulden
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Sirisha Pochareddy
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | | | - Zhen Li
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Yurae Shin
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
- National Research Foundation of Korea, Daejeon, South Korea
| | - Ying Zhu
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - André M. M. Sousa
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Donna M. Werling
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
| | - Robert R. Kitchen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Hyo Jung Kang
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
- Department of Life Science, Chung-Ang University, Seoul, Korea
| | - Mihovil Pletikos
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
- Department of Anatomy & Neurobiology, Boston University School of Medicine, MA, USA
| | - Jinmyung Choi
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Sydney Muchnik
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Xuming Xu
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Daifeng Wang
- Department of Biomedical Informatics Stony Brook University, NY, USA
| | - Belen Lorente-Galdos
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Shuang Liu
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | | | - Hyejung Won
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Christiaan A. de Leeuw
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, Netherlands
| | - Antonio F. Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | | | | | | | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Fulai Jin
- Department of Genetics and Genome Science, Case Western Reserve University, Cleveland, OH, USA
| | - Yun Li
- Department of Genetics and Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Michael J. Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Michael C. O’Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - James T. R. Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, Netherlands
| | - Mark A. Reimers
- Neuroscience Program and Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA
| | - Pat Levitt
- Department of Pediatrics, Institute for the Developing Mind Keck School of Medicine of USC, Los Angeles, CA, USA
- Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Daniel H. Geschwind
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Matthew W. State
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
| | - Stephan J. Sanders
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
| | | | - Mark B. Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
- Department of Computer Science, Yale University, New Haven, CT, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, USA
| | - Ed S. Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | - James A. Knowles
- Department of Cell Biology, SUNY Downstate Medical Center, Brooklyn NY, USA
| | - Nenad Sestan
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Department of Comparative Medicine, Program in Integrative Cell Signaling and Neurobiology of Metabolism, Yale School of Medicine, New Haven, CT, USA
- Program in Cellular Neuroscience, Neurodegeneration, and Repair and Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
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460
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Smucny J. Cognitive Deficits, Structural Neuropathology, and Psychotic Illness: Is It All a Matter of Severity? BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:978-980. [PMID: 30526942 DOI: 10.1016/j.bpsc.2018.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 08/25/2018] [Accepted: 08/27/2018] [Indexed: 10/27/2022]
Affiliation(s)
- Jason Smucny
- Department of Psychiatry, University of California, Davis, Sacramento, California.
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461
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Zhao L, Chang H, Zhou DS, Cai J, Fan W, Tang W, Tang W, Li X, Liu W, Liu F, He Y, Bai Y, Sun Y, Dai J, Li L, Xiao X, Zhang C, Li M. Replicated associations of FADS1, MAD1L1, and a rare variant at 10q26.13 with bipolar disorder in Chinese population. Transl Psychiatry 2018; 8:270. [PMID: 30531795 PMCID: PMC6286364 DOI: 10.1038/s41398-018-0337-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 10/07/2018] [Accepted: 11/13/2018] [Indexed: 12/12/2022] Open
Abstract
Genetic analyses of psychiatric illnesses, such as bipolar disorder (BPD), have revealed essential information regarding the underlying pathological mechanisms. While such studies in populations of European ancestry have achieved prominent success, understanding the genetic risk factors of these illnesses (especially BPD) in Chinese population remains an urgent task. Given the lack of genome-wide association study (GWAS) of BPD in Chinese population from Mainland China, replicating the previously reported GWAS hits in distinct populations will provide valuable information for future GWAS analysis in Han Chinese. In the present study, we have recruited 1146 BPD cases and 1956 controls from Mainland China for genetic analyses, as well as 65 Han Chinese brain amygdala tissues for mRNA expression analyses. Using this clinical sample, one of the largest Han Chinese BPD samples till now, we have conducted replication analyses of 21 single nucleotide polymorphisms (SNPs) extracted from previous GWAS of distinct populations. Among the 21 tested SNPs, 16 showed the same direction of allelic effects in our samples compared with previous studies; 6 SNPs achieved nominal significance (p < 0.05) at one-tailed test, and 2 additional SNPs showed marginal significance (p < 0.10). Aside from replicating previously reported BPD risk SNPs, we herein also report several intriguing findings: (1) the SNP rs174576 was associated with BPD in our Chinese sample and in the overall global meta-analysis, and was significantly correlated with FADS1 mRNA in diverse public RNA-seq datasets as well as our in house collected Chinese amygdala samples; (2) two (partially) independent SNPs in MAD1L1 were both significantly associated with BPD in our Chinese sample, which was also supported by haplotype analysis; (3) a rare SNP rs78089757 in 10q26.13 region was a genome-wide significant variant for BPD in East Asians, and this SNP was near monomorphic in Europeans. In sum, these results confirmed several significant BPD risk genes. We hope this Chinese BPD case-control sample and the current brain amygdala tissues (with continuous increasing sample size in the near future) will provide helpful resources in elucidating the genetic and molecular basis of BPD in this major world population.
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Affiliation(s)
- Lijuan Zhao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Hong Chang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China
| | - Dong-Sheng Zhou
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| | - Jun Cai
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weixing Fan
- Jinhua Second Hospital, Jinhua, Zhejiang, China
| | - Wei Tang
- Wenzhou Kangning Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Wenxin Tang
- Hangzhou Seventh People's Hospital, Hangzhou, Zhejiang, China
| | - Xingxing Li
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| | - Weiqing Liu
- Department of Psychiatry, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Fang Liu
- Department of Psychiatry, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yuanfang He
- Department of Psychiatry, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yan Bai
- Department of Psychiatry, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yan Sun
- Wuhan Institute for Neuroscience and Neuroengineering, South-Central University for Nationalities, Wuhan, Hubei, China
- Chinese Brain Bank Center, Wuhan, Hubei, China
| | - Jiapei Dai
- Wuhan Institute for Neuroscience and Neuroengineering, South-Central University for Nationalities, Wuhan, Hubei, China
- Chinese Brain Bank Center, Wuhan, Hubei, China
| | - Lingyi Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China
| | - Xiao Xiao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China.
| | - Chen Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
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462
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Kas MJ, Serretti A, Marston H. Quantitative neurosymptomatics: Linking quantitative biology to neuropsychiatry. Neurosci Biobehav Rev 2018; 97:1-2. [PMID: 30476505 DOI: 10.1016/j.neubiorev.2018.11.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Martien J Kas
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, the Netherlands.
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Italy
| | - Hugh Marston
- Translational Neuroscience, Eli Lilly and Company, Windlesham, UK
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463
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Salagre E, Arango C, Artigas F, Ayuso-Mateos JL, Bernardo M, Castro-Fornieles J, Bobes J, Desco M, Fañanás L, González-Pinto A, Haro JM, Leza JC, Mckenna PJ, Meana JJ, Menchón JM, Micó JA, Palomo T, Pazos Á, Pérez V, Saiz-Ruiz J, Sanjuán J, Tabarés-Seisdedos R, Crespo-Facorro B, Casas M, Vilella E, Palao D, Olivares JM, Rodriguez-Jimenez R, Vieta E. CIBERSAM: Ten years of collaborative translational research in mental disorders. REVISTA DE PSIQUIATRIA Y SALUD MENTAL 2018; 12:1-8. [PMID: 30416047 DOI: 10.1016/j.rpsm.2018.10.001] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 10/15/2018] [Accepted: 10/15/2018] [Indexed: 01/08/2023]
Affiliation(s)
- Estela Salagre
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Servicio de Psiquiatría y Psicología, Hospital Clínic, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, España
| | - Celso Arango
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Servicio de Psiquiatría del Niño y del Adolescente, Hospital General Universitario Gregorio Marañón (IiSGM), Facultad de Medicina, Universidad Complutense, CIBERSAM, Madrid, España
| | - Francesc Artigas
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Departamento de Neuroquímica y Neurofarmacología, Institut d'Investigacions Biomèdiques de Barcelona IIBB-CSIC, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, España
| | - José Luis Ayuso-Mateos
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Departamento de Psiquiatría, Universidad Autónoma de Madrid, Madrid, España
| | - Miquel Bernardo
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Unidad Esquizofrenia Clínic, Institut Clínic de Neurociencias, Hospital Clínic, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, España
| | - Josefina Castro-Fornieles
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Servicio de Psiquiatría y Psicología Infantil y Juvenil, Institut Clínic de Neurociencias, IDIBAPS, Hospital Clínic de Barcelona, Barcelona, España
| | - Julio Bobes
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Área de Psiquiatría, Universidad de Oviedo, Servicio de Salud del Principado de Asturias, Instituto de Neurociencias del Principado de Asturias (INEUROPA), Oviedo, Asturias, España
| | - Manuel Desco
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Instituto de Investigación Sanitaria Gregorio Marañón, Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, España
| | - Lourdes Fañanás
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Secció Zoologia i Antropologia Biològica, Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, España
| | - Ana González-Pinto
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Departamento de Psiquiatría, Hospital Universitario Araba, Instituto de Investigación Sanitaria Bioaraba; Universidad del País Vasco, Vitoria, España
| | - Josep María Haro
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Parc Sanitari Sant Joan de Déu, Universitat de Barcelona, Sant Boi de Llobregat, Barcelona, España
| | - Juan Carlos Leza
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Departamento de Farmacología, Facultad de Medicina, Universidad Complutense de Madrid, Instituto Universitario de Investigación en Neuroquímica UCM, Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), Madrid, España
| | - Peter J Mckenna
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; FIDMAG Germanes Hospitalàries Research Foundation, Sant Boi de Llobregat, Barcelona, España
| | - José Javier Meana
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Departamento de Farmacología, Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Leioa, Bizkaia, Instituto de Investigación Sanitaria Biocruces Bizkaia, Barakaldo, Barakaldo, Bizkaia, España
| | - José Manuel Menchón
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Servicio de Psiquiatría, Hospital Universitari de Bellvitge, Instituto de Investigación Biomédica de Bellvitge (IDIBELL), L'Hospitalet de Llobregat; Departamento de Ciencias Clínicas, Universitat de Barcelona, Barcelona, España
| | - Juan Antonio Micó
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Grupo de Investigación en Neuropsicofarmacología y Psicobiología,, Departamento de Neurociencias, Universidad de Cádiz, Instituto de Investigación e Innovación en Ciencias Biomédicas de Cádiz, INiBICA, Hospital Universitario Puerta del Mar, Cádiz, España
| | - Tomás Palomo
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Departamento de Psiquiatría, Hospital 12 de Octubre, Madrid, España
| | - Ángel Pazos
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Departamento de Fisiología y Farmacología, Facultad de Medicina, Universidad de Cantabria, Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), Universidad de Cantabria-CSIC-SODERCAN, Santander, Cantabria, España
| | - Víctor Pérez
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Institut de Neuropsiquiatria i Addiccions, Hospital del Mar, Universitat Autònoma de Barcelona, Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, España
| | - Jerónimo Saiz-Ruiz
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Departamento de Psiquiatría, Hospital Ramon y Cajal, Universidad de Alcalá, IRYCIS, Madrid, España
| | - Julio Sanjuán
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; INCLIVA, Universidad de Valencia, Hospital Clínico Universitario de Valencia, Valencia, España
| | - Rafael Tabarés-Seisdedos
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Departamento de Medicina, INCLIVA, Universidad de Valencia, Valencia, España
| | - Benedicto Crespo-Facorro
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Departamento de Medicina y Psiquiatría, Universidad de Cantabria, Hospital Universitario Marqués de Valdecilla, IDIVAL, Santander, Cantabria, España
| | - Miquel Casas
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Servicio de Psiquiatría, Hospital Universitari Vall d'Hebron, Departamento de Psiquiatría y Medicina Legal, Universitat Autònoma de Barcelona, Barcelona, España
| | - Elisabet Vilella
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili, Reus, Tarragona, España
| | - Diego Palao
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Servei de Salut Mental, Parc Taulí Hospital Universitari, Institut de Recerca i Innovació Parc Taulí (I3PT), Universitat Autònoma de Barcelona, Sabadell, Barcelona, España
| | - Jose Manuel Olivares
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Unidad de Psiquiatría, Hospital Álvaro Cunqueiro, Complejo Hospitalario Universitario de Vigo, Instituto Biomédico Galicia Sur, Vigo, Pontevedra, España
| | - Roberto Rodriguez-Jimenez
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Departamento de Psiquiatría, Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), CogPsy-Group, Universidad Complutense de Madrid (UCM), Madrid, España
| | - Eduard Vieta
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, España; Servicio de Psiquiatría y Psicología, Hospital Clínic, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, España.
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464
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Association of schizophrenia polygenic risk score with manic and depressive psychosis in bipolar disorder. Transl Psychiatry 2018; 8:188. [PMID: 30201969 PMCID: PMC6131184 DOI: 10.1038/s41398-018-0242-3] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 07/13/2018] [Accepted: 08/05/2018] [Indexed: 01/10/2023] Open
Abstract
Bipolar disorder (BD) is highly heterogeneous in symptomatology. Narrowing the clinical phenotype may increase the power to identify risk genes that contribute to particular BD subtypes. This study was designed to test the hypothesis that genetic overlap between schizophrenia (SZ) and BD is higher for BD with a history of manic psychosis. Analyses were conducted using a Mayo Clinic Bipolar Biobank cohort of 957 bipolar cases (including 333 with history of psychosis during mania, 64 with history of psychosis only during depression, 547 with no history of psychosis, and 13 with unknown history of psychosis) and 778 controls. Polygenic risk score (PRS) analysis was performed by calculating a SZ-PRS for the BD cases and controls, and comparing the calculated SZ risk between different psychosis subgroups and bipolar types. The SZ-PRS was significantly higher for BD-I cases with manic psychosis than BD-I cases with depressive psychosis (Nagelkerke's R2 = 0.021; p = 0.045), BD-I cases without psychosis (R2 = 0.015; p = 0.007), BD-II cases without psychosis (R2 = 0.014; p = 0.017), and controls (R2 = 0.065; p = 2 × 10-13). No other significant differences were found. Our results show that BD-I with manic psychosis is genetically more similar to SZ than any other tested BD subgroup. Further investigations on genetics of distinct clinical phenotypes composing major psychoses may help refine the current diagnostic classification system.
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465
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Suvisaari J, Mantere O, Keinänen J, Mäntylä T, Rikandi E, Lindgren M, Kieseppä T, Raij TT. Is It Possible to Predict the Future in First-Episode Psychosis? Front Psychiatry 2018; 9:580. [PMID: 30483163 PMCID: PMC6243124 DOI: 10.3389/fpsyt.2018.00580] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 10/23/2018] [Indexed: 12/26/2022] Open
Abstract
The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk. Based on the review, it is currently possible to predict the future for FEP patients to some extent. Some clinical features-like the longer duration of untreated psychosis (DUP), poor premorbid adjustment, the insidious mode of onset, the greater severity of negative symptoms, comorbid substance use disorders (SUDs), a history of suicide attempts and suicidal ideation and having non-affective psychosis-are associated with a worse outcome. Of the social and demographic factors, male gender, social disadvantage, neighborhood deprivation, dysfunctional family environment, and ethnicity may be relevant. Treatment non-adherence is a substantial risk factor for relapse, but a small minority of patients with acute onset of FEP and early remission may benefit from antipsychotic discontinuation. Cognitive functioning is associated with functional outcomes. Brain imaging currently has limited utility as an outcome predictor, but this may change with methodological advancements. Polygenic risk scores (PRSs) might be useful as one component of a predictive tool, and pharmacogenetic testing is already available and valuable for patients who have problems in treatment response or with side effects. Most blood-based biomarkers need further validation. None of the currently available predictive markers has adequate sensitivity or specificity used alone. However, personalized treatment of FEP will need predictive tools. We discuss some methodologies, such as machine learning (ML), and tools that could lead to the improved prediction and clinical utility of different prognostic markers in FEP. Combination of different markers in ML models with a user friendly interface, or novel findings from e.g., molecular genetics or neuroimaging, may result in computer-assisted clinical applications in the near future.
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Affiliation(s)
- Jaana Suvisaari
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland
| | - Outi Mantere
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.,Department of Psychiatry, McGill University, Montreal, QC, Canada.,Bipolar Disorders Clinic, Douglas Mental Health University Institute, Montreal, QC, Canada.,Department of Psychiatry, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jaakko Keinänen
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.,Department of Psychiatry, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Teemu Mäntylä
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.,Department of Neuroscience and Biomedical Engineering, and Advanced Magnetic Imaging Center, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland.,Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Eva Rikandi
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.,Department of Neuroscience and Biomedical Engineering, and Advanced Magnetic Imaging Center, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland.,Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Maija Lindgren
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland
| | - Tuula Kieseppä
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.,Department of Psychiatry, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Tuukka T Raij
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.,Department of Neuroscience and Biomedical Engineering, and Advanced Magnetic Imaging Center, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland
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466
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Salagre E, Dodd S, Aedo A, Rosa A, Amoretti S, Pinzon J, Reinares M, Berk M, Kapczinski FP, Vieta E, Grande I. Toward Precision Psychiatry in Bipolar Disorder: Staging 2.0. Front Psychiatry 2018; 9:641. [PMID: 30555363 PMCID: PMC6282906 DOI: 10.3389/fpsyt.2018.00641] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 11/13/2018] [Indexed: 12/23/2022] Open
Abstract
Personalized treatment is defined as choosing the "right treatment for the right person at the right time." Although psychiatry has not yet reached this level of precision, we are on the way thanks to recent technological developments that may aid to detect plausible molecular and genetic markers. At the moment there are some models that are contributing to precision psychiatry through the concept of staging. While staging was initially presented as a way to categorize patients according to clinical presentation, course, and illness severity, current staging models integrate multiple levels of information that can help to define each patient's characteristics, severity, and prognosis in a more precise and individualized way. Moreover, staging might serve as the foundation to create a clinical decision-making algorithm on the basis of the patient's stage. In this review we will summarize the evolution of the bipolar disorder staging model in relation to the new discoveries on the neurobiology of bipolar disorder. Furthermore, we will discuss how the latest and future progress in psychiatry might transform current staging models into precision staging models.
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Affiliation(s)
- Estela Salagre
- Barcelona Bipolar Disorders Program, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
| | - Seetal Dodd
- IMPACT Strategic Research Centre, Barwon Health, Deakin University, Geelong, VIC, Australia.,Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, VIC, Australia
| | - Alberto Aedo
- Barcelona Bipolar Disorders Program, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain.,Bipolar Disorders Unit, Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Adriane Rosa
- Laboratory of Molecular Psychiatry, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.,Postgraduate Program: Psychiatry and Behavioral Science, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.,Department of Pharmacology and Postgraduate Program: Pharmacology and Therapeutics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Silvia Amoretti
- Barcelona Clínic Schizophrenia Unit, Hospital Clinic de Barcelona, CIBERSAM, Barcelona, Spain
| | - Justo Pinzon
- Barcelona Bipolar Disorders Program, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
| | - Maria Reinares
- Barcelona Bipolar Disorders Program, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
| | - Michael Berk
- IMPACT Strategic Research Centre, Barwon Health, Deakin University, Geelong, VIC, Australia.,Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, VIC, Australia.,Florey Institute for Neuroscience and Mental Health, Parkville, VIC, Australia
| | | | - Eduard Vieta
- Barcelona Bipolar Disorders Program, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
| | - Iria Grande
- Barcelona Bipolar Disorders Program, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
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