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Borgogna NC, Owen T, Aita SL. The absurdity of the latent disease model in mental health: 10,130,814 ways to have a DSM-5-TR psychological disorder. J Ment Health 2024; 33:451-459. [PMID: 37947129 DOI: 10.1080/09638237.2023.2278107] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/21/2023] [Accepted: 10/11/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Latent disease classification is currently the accepted approach to mental illness diagnosis. In the United States, this takes the form of the Diagnostic and Statistical Manual of Mental Disorders-5-Text Revision (DSM-5-TR). Latent disease classification has been criticized for reliability and validity problems, particularly regarding diagnostic heterogeneity. No authors have calculated the scope of the heterogeneity problem of the entire DSM-5-TR. AIMS We addressed this issue by calculating the unique diagnostic profiles that exist for every DSM-5-TR diagnosis. METHODS We did this by applying formulas previously used in smaller heterogeneity analyses to all diagnoses within the DSM-5-TR. RESULTS We found that there are 10,130,814 ways to be diagnosed with a mental illness using DSM-5-TR criteria. When specifiers are considered, this number balloons to over 161 septillion unique diagnostic presentations (driven mainly by bipolar II disorder). Additionally, there are 1,951,065 ways to present with psychiatric symptoms, yet not meet diagnostic criteria. CONCLUSIONS Latent disease classification leads to considerable heterogeneity in possible presentations. We provide examples of how latent disease classification harms research and treatment programs. We echo recommendations for the dismissal of latent disease classification as a mental illness diagnostic program.
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Affiliation(s)
- Nicholas C Borgogna
- Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA
| | - Tyler Owen
- Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA
| | - Stephen L Aita
- Department of Psychology, University of Maine, Orono, ME, USA
- Department of Mental Health, VA Maine Healthcare System, Augusta, ME, USA
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2
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Scott J, Crouse JJ, Medland S, Byrne E, Iorfino F, Mitchell B, Gillespie NA, Martin N, Wray N, Hickie IB. Polygenic risk scores and the prediction of onset of mood and psychotic disorders in adolescents and young adults. Early Interv Psychiatry 2024; 18:397-405. [PMID: 37787636 PMCID: PMC11100301 DOI: 10.1111/eip.13472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 09/02/2023] [Accepted: 09/25/2023] [Indexed: 10/04/2023]
Abstract
AIM To examine whether polygenic risk scores (PRS) for neuroticism, depression, bipolar disorder and schizophrenia are higher in individuals manifesting trans-diagnostic risk factors for the development of major mental disorders and whether PRS enhance prediction of early onset full-threshold disorders. METHODS Using data from the Brisbane Longitudinal Twin Study, we examined individual PRS for neuroticism, depression, bipolar disorder and schizophrenia, recorded evidence of subthreshold syndromes and family history of mood and/or psychotic disorders and noted progression to trans-diagnostic clinical caseness (onset of major mental disorders) at follow-up. We undertook multivariate, receiver operating curve and logistic regression analyses that were adjusted for known variables of influence (age, twin status, and so on). RESULTS Of 1473 eligible participants (female = 866, 59%; mean age 26.3 years), 28% (n = 409) met caseness criteria for a mood and/or psychotic disorder. All PRS were higher in cases versus non-cases but associations with different levels of risk were inconsistent. The prediction of caseness (reported as area under the curve with 95% confidence intervals [CI]) improved from 0.68 (95% CI: 0.65, 0.71) when estimated using clinical risk factors alone up to 0.71 (95% CI: 0.69, 0.73) when PRS were added to the model. Logistic regression identified five variables that optimally classified individuals according to caseness: age, sex, individual risk characteristics, PRS for depression and mental health case status of cotwins or siblings. CONCLUSIONS The findings need replication. However, this exploratory study suggests that combining PRS with other risk factors has the potential to improve outcome prediction in youth.
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Affiliation(s)
- Jan Scott
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
- Institute of Neuroscience, Newcastle University, Newcastle, United Kingdom
| | - Jacob J Crouse
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Sarah Medland
- QIMR Berghofer Institute of Medical Research, Brisbane, Australia
- Institute of Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Enda Byrne
- QIMR Berghofer Institute of Medical Research, Brisbane, Australia
| | - Frank Iorfino
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | | | - Nathan A. Gillespie
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond VA, USA
| | - Nicholas Martin
- QIMR Berghofer Institute of Medical Research, Brisbane, Australia
| | - Naomi Wray
- Institute of Molecular Bioscience, The University of Queensland, Brisbane, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland,Australia
| | - Ian B. Hickie
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
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3
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Bi XA, Wang Y, Luo S, Chen K, Xing Z, Xu L. Hypergraph Structural Information Aggregation Generative Adversarial Networks for Diagnosis and Pathogenetic Factors Identification of Alzheimer's Disease With Imaging Genetic Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7420-7434. [PMID: 36264725 DOI: 10.1109/tnnls.2022.3212700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease with profound pathogenetic causes. Imaging genetic data analysis can provide comprehensive insights into its causes. To fully utilize the multi-level information in the data, this article proposes a hypergraph structural information aggregation model, and constructs a novel deep learning method named hypergraph structural information aggregation generative adversarial networks (HSIA-GANs) for the automatic sample classification and accurate feature extraction. Specifically, HSIA-GAN is composed of generator and discriminator. The generator has three main functions. First, vertex graph and edge graph are constructed based on the input hypergraph to present the low-order relations. Second, the low-order structural information of hypergraph is extracted by the designed vertex convolution layers and edge convolution layers. Finally, the synthetic hypergraph is generated as the input of the discriminator. The discriminator can extract the high-order structural information directly from hypergraph through vertex-edge convolution, fuse the high and low-order structural information, and finalize the results through the full connection (FC) layers. Based on the data acquired from AD neuroimaging initiative, HSIA-GAN shows significant advantages in three classification tasks, and extracts discriminant features conducive to better disease classification.
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4
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Hong Y, Sourander C, Hackl B, Patton JS, John J, Paatero I, Coffey E. Jnk1 and downstream signalling hubs regulate anxiety-like behaviours in a zebrafish larvae phenotypic screen. Sci Rep 2024; 14:11174. [PMID: 38750129 PMCID: PMC11096340 DOI: 10.1038/s41598-024-61337-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 05/04/2024] [Indexed: 05/18/2024] Open
Abstract
Current treatments for anxiety and depression show limited efficacy in many patients, indicating the need for further research into the underlying mechanisms. JNK1 has been shown to regulate anxiety- and depressive-like behaviours in mice, however the effectors downstream of JNK1 are not known. Here we compare the phosphoproteomes from wild-type and Jnk1-/- mouse brains and identify JNK1-regulated signalling hubs. We next employ a zebrafish (Danio rerio) larvae behavioural assay to identify an antidepressant- and anxiolytic-like (AA) phenotype based on 2759 measured stereotypic responses to clinically proven antidepressant and anxiolytic (AA) drugs. Employing machine learning, we classify an AA phenotype from extracted features measured during and after a startle battery in fish exposed to AA drugs. Using this classifier, we demonstrate that structurally independent JNK inhibitors replicate the AA phenotype with high accuracy, consistent with findings in mice. Furthermore, pharmacological targeting of JNK1-regulated signalling hubs identifies AKT, GSK-3, 14-3-3 ζ/ε and PKCε as downstream hubs that phenocopy clinically proven AA drugs. This study identifies AKT and related signalling molecules as mediators of JNK1-regulated antidepressant- and anxiolytic-like behaviours. Moreover, the assay shows promise for early phase screening of compounds with anti-stress-axis properties and for mode of action analysis.
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Affiliation(s)
- Ye Hong
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
| | - Christel Sourander
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
| | - Benjamin Hackl
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
| | - Jedidiah S Patton
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
| | - Jismi John
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
| | - Ilkka Paatero
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
| | - Eleanor Coffey
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland.
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5
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Yin B, Cai Y, Teng T, Wang X, Liu X, Li X, Wang J, Wu H, He Y, Ren F, Kou T, Zhu ZJ, Zhou X. Identifying plasma metabolic characteristics of major depressive disorder, bipolar disorder, and schizophrenia in adolescents. Transl Psychiatry 2024; 14:163. [PMID: 38531835 DOI: 10.1038/s41398-024-02886-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 03/14/2024] [Accepted: 03/19/2024] [Indexed: 03/28/2024] Open
Abstract
Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.
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Affiliation(s)
- Bangmin Yin
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuping Cai
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Teng Teng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaolin Wang
- Health Management Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xueer Liu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Wang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongyan Wu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuqian He
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fandong Ren
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Tianzhang Kou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, China.
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Fang J, Lv Y, Xie Y, Tang X, Zhang X, Wang X, Yu M, Zhou C, Qin W, Zhang X. Polygenic effects on brain functional endophenotype for deficit and non-deficit schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:18. [PMID: 38365896 PMCID: PMC10873412 DOI: 10.1038/s41537-024-00432-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 01/08/2024] [Indexed: 02/18/2024]
Abstract
Deficit schizophrenia (DS) is a subtype of schizophrenia (SCZ). The polygenic effects on the neuroimaging alterations in DS still remain unknown. This study aims to calculate the polygenic risk scores for schizophrenia (PRS-SCZ) in DS, and further explores the potential associations with functional features of brain. PRS-SCZ was calculated according to the Whole Exome sequencing and Genome-wide association studies (GWAS). Resting-state fMRI, as well as biochemical features and neurocognitive data were obtained from 33 DS, 47 NDS and 41 HCs, and association studies of genetic risk with neuroimaging were performed in this sample. The analyses of amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were performed to detect the functional alterations between DS and NDS. In addition, correlation analysis was used to investigate the relationships between functional features (ALFF, ReHo, FC) and PRS-SCZ. The PRS-SCZ of DS was significantly lower than that in NDS and HC. Compared to NDS, there was a significant increase in the ALFF of left inferior temporal gyrus (ITG.L) and left inferior frontal gyrus (IFG.L) and a significant decrease in the ALFF of right precuneus (PCUN.R) and ReHo of right middle frontal gyrus (MFG.R) in DS. FCs were widely changed between DS and NDS, mainly concentrated in default mode network, including ITG, PCUN and angular gyrus (ANG). Correlation analysis revealed that the ALFF of left ITG, the ReHo of right middle frontal gyrus, the FC value between insula and ANG, left ITG and right corpus callosum, left ITG and right PCUN, as well as the scores of Trail Making Test-B, were associated with PRS-SCZ in DS. The present study demonstrated the differential polygenic effects on functional changes of brain in DS and NDS, providing a potential neuroimaging-genetic perspective for the pathogenesis of schizophrenia.
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Affiliation(s)
- Jin Fang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Yiding Lv
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xiaowei Tang
- Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou, Jiangsu, 225003, China
| | - Xiaobin Zhang
- Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou, Jiangsu, 225003, China
| | - Xiang Wang
- Medical Psychological Institute of the Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Miao Yu
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Chao Zhou
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China.
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China.
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7
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Jiang X, Zai CC, Sultan AA, Dimick MK, Nikolova YS, Felsky D, Young LT, MacIntosh BJ, Goldstein BI. Association of polygenic risk for bipolar disorder with resting-state network functional connectivity in youth with and without bipolar disorder. Eur Neuropsychopharmacol 2023; 77:38-52. [PMID: 37717349 DOI: 10.1016/j.euroneuro.2023.08.503] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 09/19/2023]
Abstract
Little is known regarding the polygenic underpinnings of anomalous resting-state functional connectivity (rsFC) in youth bipolar disorder (BD). The current study examined the association of polygenic risk for BD (BD-PRS) with whole-brain rsFC at the large-scale network level in youth with and without BD. 99 youth of European ancestry (56 BD, 43 healthy controls [HC]), ages 13-20 years, completed resting-state fMRI scans. BD-PRS was calculated using summary statistics from the latest adult BD genome-wide association study. Data-driven independent component analyses of the resting-state fMRI data were implemented to examine the association of BD-PRS with rsFC in the overall sample, and separately in BD and HC. In the overall sample, higher BD-PRS was associated with lower rsFC of the salience network and higher rsFC of the frontoparietal network with frontal and parietal regions. Within the BD group, higher BD-PRS was associated with higher rsFC of the default mode network with orbitofrontal cortex, and altered rsFC of the visual network with frontal and occipital regions. Within the HC group, higher BD-PRS was associated with altered rsFC of the frontoparietal network with frontal, temporal and occipital regions. In conclusion, the current study found that BD-PRS generated based on adult genetic data was associated with altered rsFC patterns of brain networks in youth. Our findings support the usefulness of BD-PRS to investigate genetically influenced neuroimaging markers of vulnerability to BD, which can be observed in youth with BD early in their course of illness as well as in healthy youth.
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Affiliation(s)
- Xinyue Jiang
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada
| | - Clement C Zai
- Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Alysha A Sultan
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Mikaela K Dimick
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Yuliya S Nikolova
- Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Daniel Felsky
- Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Totonto, ON, Canada
| | - L Trevor Young
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Bradley J MacIntosh
- Sandra E Black Centre for Brain Resilience and Recovery, Sunnybrook Research Institute, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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8
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Nasir Hashmi A, Sabina Raja M, Taj R, Ahmed Dharejo R, Agha Z, Qamar R, Azam M. Association of 11 variants of the dopaminergic and cognitive pathways genes with major depression, schizophrenia and bipolar disorder in the Pakistani population. Int J Neurosci 2023:1-13. [PMID: 37642370 DOI: 10.1080/00207454.2023.2251661] [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: 06/01/2023] [Revised: 07/20/2023] [Accepted: 08/19/2023] [Indexed: 08/31/2023]
Abstract
Background: The dopaminergic pathways control neural signals that modulate mood and behaviour along and have a vital role in the aetiology of major depression (MDD), schizophrenia (SHZ) and bipolar disorder (BD). Genome-wide association studies (GWAS) have reported several dopaminergic and cognitive pathway genes association with these disorders however, no such comprehensive data was available regarding the Pakistani population.Objective: The present study was conducted to analyse the 11 genetic variants of dopaminergic and cognitive system genes in MDD, SHZ, and BD in the Pakistani population.Methods: A total of 1237 subjects [MDD n = 479; BD n = 222; SHZ n = 146; and controls n = 390], were screened for 11 genetic variants through polymerase chain reaction (PCR) techniques. Univariant followed by multivariant logistic regression analysis was applied to determine the genetic association.Results: Significant risk associations were observed for rs4532 and rs1799732 with MDD; and rs1006737 and rs2238056 with BD. However, after applying multiple test corrections rs4532 and rs1799732 association did not remain significant for MDD. Moreover, a protective association was found for three variants; DRD4-120bp, rs10033951 and rs2388334 in the current cohort.Conclusions: The present study revealed the risk association of single nucleotide polymorphisms (SNPs) rs1006737 and rs2238056 with BD and the protective effect of the DRD4-120bp variant in MDD and BD, of rs2388334 in BD and of rs10033951 in MDD, BD, and SHZ in the current Pakistani cohort. Thus, the study is valuable in understanding the genetic basis of MDD, BD and SHZ in the Pakistani population, which may pave the way for future functional studies.
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Affiliation(s)
- Aisha Nasir Hashmi
- Translational Genomics Laboratory, COMSATS University Islamabad, Islamabad, Pakistan
| | - Merlyn Sabina Raja
- Translational Genomics Laboratory, COMSATS University Islamabad, Islamabad, Pakistan
| | - Rizwan Taj
- Department of Psychiatry, Pakistan Institute of Medical Sciences, Islamabad, Pakistan
| | - Raees Ahmed Dharejo
- Department of Psychiatry, Pakistan Institute of Medical Sciences, Islamabad, Pakistan
| | - Zehra Agha
- Translational Genomics Laboratory, COMSATS University Islamabad, Islamabad, Pakistan
| | - Raheel Qamar
- Science and Technology Sector, ICESCO, Rabat, Morocco
- Pakistan Academy of Science, Islamabad, Pakistan
| | - Maleeha Azam
- Translational Genomics Laboratory, COMSATS University Islamabad, Islamabad, Pakistan
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9
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Luna LP, Sousa MB, Passinho JS, Nardi AE, Oertel V, Veras AB, Alves GS. Resting-state fMRI functional connectivity and clinical correlates in Afro-descendants with schizophrenia and bipolar disorder. Psychiatry Res Neuroimaging 2023; 331:111628. [PMID: 36924740 DOI: 10.1016/j.pscychresns.2023.111628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 02/12/2023] [Accepted: 03/07/2023] [Indexed: 03/18/2023]
Abstract
Schizophrenia (SCZ) and bipolar disorder (BD) exhibited altered activation in several brain areas, including the prefrontal and temporal cortex; however, a less explored topic is how brain connectivity and functional disturbances occur in non-Caucasian samples of SCZ and BD. Individuals with SCZ (n=20), BD (n=21), and healthy controls (HC, n=21) from indigenous and African ethnicity were submitted to clinical screening and functional assessments. Mood, compulsive and psychotic symptoms were also correlated to network dysfunction in each group. Two distinct networks' subcomponents demonstrated significant lower global efficiency (GE) in SCZ versus HC, corresponding to left posterior dorsal attention and medial left ventral attention (VA) networks. Lower GE was found in BD versus controls in four subcomponents, including the left medial and right VA. Higher compulsion scores correlated in BD with lower GE in the left VA, whereas increased report of alcohol abuse was associated with higher GE in left default mode network. Although preliminary, differences in the activation of specific networks, notably the left hemisphere, in SCZ versus controls, and lower activation in VA areas, in BD versus controls. Results highlight default mode and salient network as relevant for the emotional processing of SCZ and BD of indigenous and black ethnicity. Abstract: schizophrenia, bipolar disorder, functional neuroimaging, ethnicity, default network.
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Affiliation(s)
- Licia P Luna
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | | | - Jhule S Passinho
- Neuropsychology Laboratory, CEUMA University, São Luís, Maranhão, Brazil
| | - Antônio E Nardi
- Post-Graduation in Psychiatry and Mental Health (PROPSAM), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Viola Oertel
- Department of Psychiatry, Psychosomatics, and Psychotherapy, Frankfurt Goethe University, Germany
| | - André Barciela Veras
- Post-Graduation in Psychiatry and Mental Health (PROPSAM), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil; Translational Research Group on Mental Health (GPTranSMe), Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil
| | - Gilberto Sousa Alves
- Post-Graduation in Psychiatry and Mental Health (PROPSAM), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil; Translational Psychiatry Research Group, Federal University of Maranhão, São Luís, Maranhão, Brazil.
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10
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Liu S, Smit DJA, Abdellaoui A, van Wingen GA, Verweij KJH. Brain Structure and Function Show Distinct Relations With Genetic Predispositions to Mental Health and Cognition. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:300-310. [PMID: 35961582 DOI: 10.1016/j.bpsc.2022.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 07/09/2022] [Accepted: 08/01/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND Mental health and cognitive achievement are partly heritable, highly polygenic, and associated with brain variations in structure and function. However, the underlying neural mechanisms remain unclear. METHODS We investigated the association between genetic predispositions to various mental health and cognitive traits and a large set of structural and functional brain measures from the UK Biobank (N = 36,799). We also applied linkage disequilibrium score regression to estimate the genetic correlations between various traits and brain measures based on genome-wide data. To decompose the complex association patterns, we performed a multivariate partial least squares model of the genetic and imaging modalities. RESULTS The univariate analyses showed that certain traits were related to brain structure (significant genetic correlations with total cortical surface area from rg = -0.101 for smoking initiation to rg = 0.230 for cognitive ability), while other traits were related to brain function (significant genetic correlations with functional connectivity from rg = -0.161 for educational attainment to rg = 0.318 for schizophrenia). The multivariate analysis showed that genetic predispositions to attention-deficit/hyperactivity disorder, smoking initiation, and cognitive traits had stronger associations with brain structure than with brain function, whereas genetic predispositions to most other psychiatric disorders had stronger associations with brain function than with brain structure. CONCLUSIONS These results reveal that genetic predispositions to mental health and cognitive traits have distinct brain profiles.
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Affiliation(s)
- Shu Liu
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Dirk J A Smit
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Abdel Abdellaoui
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Guido A van Wingen
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Karin J H Verweij
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
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11
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Wang Z, Liu C, Dong Q, Xue G, Chen C. Polygenic risk score for five major psychiatric disorders associated with volume of distinct brain regions in the general population. Biol Psychol 2023; 178:108530. [PMID: 36858107 DOI: 10.1016/j.biopsycho.2023.108530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 02/23/2023] [Accepted: 02/25/2023] [Indexed: 03/03/2023]
Abstract
Risk genes and abnormal brain structural indices of psychiatric disorders have been extensively studied. However, whether genetic risk influences brain structure in the general population has been rarely studied. The current study enrolled 483 young Chinese adults, calculated their polygenic risk scores (PRS) for psychiatric disorders based on Psychiatric Genomics Consortium GWAS results, and examined the association between PRSs and brain volume. We found that PRSs were associated with the volume of many brain regions, with differences between PRS for different disorder, calculated at different threshold, and calculated using European or East Asian ancestry. Of them, the PRS for Major Depressive Disorder based on European ancestry was positively associated with right temporal gyrus; the PRS for schizophrenia based on East Asian ancestry was negatively associated with right precentral and postcentral gyrus; the PRS for schizophrenia based on European ancestry was positively associated with right superior temporal gyrus. All these brain regions are critical for corresponding disorders. However, no significant associations were found between PRS for Autism Spectrum Disorder / Bipolar Disorder and brain volume; and the association between PRS for Attention Deficit Hyperactivity Disorder at different thresholds and brain volume was inconsistent. These findings suggest distinct brain mechanisms underlying different psychiatric disorders.
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Affiliation(s)
- Ziyi Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Experimental School Attached to Haidian Teachers' Training College, Xiangshan Branch, Beijing, China
| | - Chang Liu
- Department of Psychology, Washington State University, Pullman, WA, USA
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Gui Xue
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chunhui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
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12
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Shao J, Zhang Y, Xue L, Wang X, Wang H, Zhu R, Yao Z, Lu Q. Shared and disease-sensitive dysfunction across bipolar and unipolar disorder during depressive episodes: a transdiagnostic study. Neuropsychopharmacology 2022; 47:1922-1930. [PMID: 35177806 PMCID: PMC9485137 DOI: 10.1038/s41386-022-01290-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 01/21/2022] [Accepted: 01/28/2022] [Indexed: 02/05/2023]
Abstract
Patients with depressive episodes (PDE), such as unipolar disorder (UD) and bipolar disorder (BD), are often defined as distinct diagnostic categories, but increasing converging evidence indicated shared etiologies and pathophysiological characteristics across different clinical diagnoses. We explored whether these transdiagnostic deficits are caused by the common neural substrates across diseases or disease-sensitive mechanisms, or a combination of both. In this study, we utilized a Bayesian model to decompose the resting-state brain activity into multiple hyper- and hypo-activity patterns (refer to as "factors"), so as to explore the shared and disease-sensitive alteration patterns in PDE. The model was constructed over a total of 259 patients (131 UD and 128 BD) with 100 healthy controls as the reference. The other 32 initial depressive episode BD (IDE-BD) patients who had symptoms of mania or hypomania during follow-up were taken as an independent set to estimate the factor composition using the established model for further analysis. We revealed three transdiagnostic alteration factors in PDE. Based on the distribution of factors and the tendency of factor composition at the group level, these factors were defined as BD sensitive factor, UD sensitive factor and shared basic alteration factor. We further found that the factor composition and the ROIs-based alteration degree (mainly involving in orbitofrontal gyrus and part of parietal lobe) were associated with the bipolar index in IDE-BD patients. Our findings contributed to understanding the core transdiagnostic shared and disease-sensitive alterations in PDE and to predicting the risk of emotional state transition in IDE-BD patients.
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Affiliation(s)
- Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
| | - Yujie Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
| | - Li Xue
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
| | - Xinyi Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
| | - Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
| | - Rongxin Zhu
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China.
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China.
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13
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Yang Y, Li X, Cui Y, Liu K, Qu H, Lu Y, Li W, Zhang L, Zhang Y, Song J, Lv L. Reduced Gray Matter Volume in Orbitofrontal Cortex Across Schizophrenia, Major Depressive Disorder, and Bipolar Disorder: A Comparative Imaging Study. Front Neurosci 2022; 16:919272. [PMID: 35757556 PMCID: PMC9226907 DOI: 10.3389/fnins.2022.919272] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 05/02/2022] [Indexed: 11/29/2022] Open
Abstract
Schizophrenia (SZ), major depressive disorder (MDD), and bipolar disorder (BD) are severe psychiatric disorders and share common characteristics not only in clinical symptoms but also in neuroimaging. The purpose of this study was to examine common and specific neuroanatomical features in individuals with these three psychiatric conditions. In this study, 70 patients with SZ, 85 patients with MDD, 42 patients with BD, and 95 healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) analysis was used to explore brain imaging characteristics. Psychopathology was assessed using the Beck Depression Inventory (BDI), the Beck Anxiety Inventory (BAI), the Young Mania Rating Scale (YMRS), and the Positive and Negative Syndrome Scale (PANSS). Cognition was assessed using the digit symbol substitution test (DSST), forward-digital span (DS), backward-DS, and semantic fluency. Common reduced gray matter volume (GMV) in the orbitofrontal cortex (OFC) region was found across the SZ, MDD, and BD. Specific reduced GMV of brain regions was also found. For patients with SZ, we found reduced GMV in the frontal lobe, temporal pole, occipital lobe, thalamus, hippocampus, and cerebellum. For patients with MDD, we found reduced GMV in the frontal and temporal lobes, insular cortex, and occipital regions. Patients with BD had reduced GMV in the medial OFC, inferior temporal and fusiform regions, insular cortex, hippocampus, and cerebellum. Furthermore, the OFC GMV was correlated with processing speed as assessed with the DSST across four groups (r = 0.17, p = 0.004) and correlated with the PANSS positive symptoms sub-score in patients with SZ (r = − 0.27, p = 0.026). In conclusion, common OFC alterations in SZ, MDD, and BD provided evidence that this region dysregulation may play a critical role in the pathophysiology of these three psychiatric disorders.
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Affiliation(s)
- Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Xue Li
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Yue Cui
- Brainnetome Center and Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Kang Liu
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Haoyang Qu
- Department of Psychiatry, The Second Clinic College of Xinxiang Medical University, Xinxiang, China
| | - Yanli Lu
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Wenqiang Li
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Luwen Zhang
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Yan Zhang
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Jinggui Song
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
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14
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Wang Y, Fu Y, Luo X. Identification of Pathogenetic Brain Regions via Neuroimaging Data for Diagnosis of Autism Spectrum Disorders. Front Neurosci 2022; 16:900330. [PMID: 35655751 PMCID: PMC9152096 DOI: 10.3389/fnins.2022.900330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is a kind of neurodevelopmental disorder that often occurs in children and has a hidden onset. Patients usually have lagged development of communication ability and social behavior and thus suffer an unhealthy physical and mental state. Evidence has indicated that diseases related to ASD have commonalities in brain imaging characteristics. This study aims to study the pathogenesis of ASD based on brain imaging data to locate the ASD-related brain regions. Specifically, we collected the functional magnetic resonance image data of 479 patients with ASD and 478 normal subjects matched in age and gender and used a machine-learning framework named random support vector machine cluster to extract distinctive brain regions from the preprocessed data. According to the experimental results, compared with other existing approaches, the method used in this study can more accurately distinguish patients from normal individuals based on brain imaging data. At the same time, this study found that the development of ASD was highly correlated with certain brain regions, e.g., lingual gyrus, superior frontal gyrus, medial gyrus, insular lobe, and olfactory cortex. This study explores the effectiveness of a novel machine-learning approach in the study of ASD brain imaging and provides a reference brain area for the medical research and clinical treatment of ASD.
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Affiliation(s)
- Yu Wang
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
- Hunan Xiangjiang Artificial Intelligence Academy, Changsha, China
| | - Yu Fu
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
- Hunan Xiangjiang Artificial Intelligence Academy, Changsha, China
- *Correspondence: Yu Fu
| | - Xun Luo
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
- Hunan Xiangjiang Artificial Intelligence Academy, Changsha, China
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15
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Gutman BA, van Erp TG, Alpert K, Ching CRK, Isaev D, Ragothaman A, Jahanshad N, Saremi A, Zavaliangos‐Petropulu A, Glahn DC, Shen L, Cong S, Alnæs D, Andreassen OA, Doan NT, Westlye LT, Kochunov P, Satterthwaite TD, Wolf DH, Huang AJ, Kessler C, Weideman A, Nguyen D, Mueller BA, Faziola L, Potkin SG, Preda A, Mathalon DH, Bustillo J, Calhoun V, Ford JM, Walton E, Ehrlich S, Ducci G, Banaj N, Piras F, Piras F, Spalletta G, Canales‐Rodríguez EJ, Fuentes‐Claramonte P, Pomarol‐Clotet E, Radua J, Salvador R, Sarró S, Dickie EW, Voineskos A, Tordesillas‐Gutiérrez D, Crespo‐Facorro B, Setién‐Suero E, van Son JM, Borgwardt S, Schönborn‐Harrisberger F, Morris D, Donohoe G, Holleran L, Cannon D, McDonald C, Corvin A, Gill M, Filho GB, Rosa PGP, Serpa MH, Zanetti MV, Lebedeva I, Kaleda V, Tomyshev A, Crow T, James A, Cervenka S, Sellgren CM, Fatouros‐Bergman H, Agartz I, Howells F, Stein DJ, Temmingh H, Uhlmann A, de Zubicaray GI, McMahon KL, Wright M, Cobia D, Csernansky JG, Thompson PM, Turner JA, Wang L. A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium. Hum Brain Mapp 2022; 43:352-372. [PMID: 34498337 PMCID: PMC8675416 DOI: 10.1002/hbm.25625] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 01/06/2023] Open
Abstract
Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.
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Affiliation(s)
- Boris A. Gutman
- Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoIllinoisUSA
- Institute for Information Transmission Problems (Kharkevich Institute)MoscowRussia
| | - Theo G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA
| | - Kathryn Alpert
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Dmitry Isaev
- Department of Biomedical EngineeringDuke UniversityDurhamNorth CarolinaUSA
| | - Anjani Ragothaman
- Department of biomedical engineeringOregon Health and Science universityPortlandOregonUSA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Arvin Saremi
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Artemis Zavaliangos‐Petropulu
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - David C. Glahn
- Department of PsychiatryBoston Children's Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Li Shen
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Shan Cong
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Dag Alnæs
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Ole Andreas Andreassen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Nhat Trung Doan
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Peter Kochunov
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Theodore D. Satterthwaite
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Daniel H. Wolf
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Alexander J. Huang
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Charles Kessler
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Andrea Weideman
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Dana Nguyen
- Department of PediatricsUniversity of California IrvineIrvineCaliforniaUSA
| | - Bryon A. Mueller
- Department of Psychiatry and Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Lawrence Faziola
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Steven G. Potkin
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Adrian Preda
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Daniel H. Mathalon
- Department of Psychiatry and Weill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Judith Ford Mental HealthVA San Francisco Healthcare SystemSan FranciscoCaliforniaUSA
| | - Juan Bustillo
- Departments of Psychiatry & NeuroscienceUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Vince Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology]Emory UniversityAtlantaGeorgiaUSA
- Department of Electrical and Computer EngineeringThe University of New MexicoAlbuquerqueNew MexicoUSA
| | - Judith M. Ford
- Judith Ford Mental HealthVA San Francisco Healthcare SystemSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | | | - Stefan Ehrlich
- Division of Psychological & Social Medicine and Developmental NeurosciencesFaculty of Medicine, TU‐DresdenDresdenGermany
| | | | - Nerisa Banaj
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Fabrizio Piras
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Federica Piras
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Gianfranco Spalletta
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | | | | | | | - Joaquim Radua
- FIDMAG Germanes Hospitalàries Research FoundationCIBERSAMBarcelonaSpain
- Institut d'Investigacions Biomdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research FoundationCIBERSAMBarcelonaSpain
| | - Salvador Sarró
- FIDMAG Germanes Hospitalàries Research FoundationCIBERSAMBarcelonaSpain
| | - Erin W. Dickie
- Centre for Addiction and Mental Health (CAMH)TorontoCanada
| | | | | | | | | | | | - Stefan Borgwardt
- Department of PsychiatryUniversity of BaselBaselSwitzerland
- Department of Psychiatry and PsychotherapyUniversity of LübeckLübeckGermany
| | | | - Derek Morris
- Centre for Neuroimaging and Cognitive Genomics, Discipline of BiochemistryNational University of Ireland GalwayGalwayIreland
| | - Gary Donohoe
- Centre for Neuroimaging and Cognitive Genomics, School of PsychologyNational University of Ireland GalwayGalwayIreland
| | - Laurena Holleran
- Centre for Neuroimaging and Cognitive Genomics, School of PsychologyNational University of Ireland GalwayGalwayIreland
| | - Dara Cannon
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive GenomicsNational University of Ireland GalwayGalwayIreland
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive GenomicsNational University of Ireland GalwayGalwayIreland
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of PsychiatryTrinity College DublinDublinIreland
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
| | - Michael Gill
- Neuropsychiatric Genetics Research Group, Department of PsychiatryTrinity College DublinDublinIreland
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
| | - Geraldo Busatto Filho
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
| | - Pedro G. P. Rosa
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
| | - Mauricio H. Serpa
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
| | - Marcus V. Zanetti
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
- Hospital Sirio‐LibanesSao PauloSPBrazil
| | - Irina Lebedeva
- Laboratory of Neuroimaging and Multimodal AnalysisMental Health Research CenterMoscowRussia
| | - Vasily Kaleda
- Department of Endogenous Mental DisordersMental Health Research CenterMoscowRussia
| | - Alexander Tomyshev
- Laboratory of Neuroimaging and Multimodal AnalysisMental Health Research CenterMoscowRussia
| | - Tim Crow
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Anthony James
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Simon Cervenka
- Centre for Psychiatry Reserach, Department of Clinical NeuroscienceKarolinska Institutet, & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - Carl M Sellgren
- Department of Physiology and PharmacologyKarolinska InstitutetStockholmSweden
| | - Helena Fatouros‐Bergman
- Centre for Psychiatry Reserach, Department of Clinical NeuroscienceKarolinska Institutet, & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - Ingrid Agartz
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Fleur Howells
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
- Neuroscience InstituteUniversity of Cape Town, Cape TownWCSouth Africa
| | - Dan J. Stein
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
- Neuroscience InstituteUniversity of Cape Town, Cape TownWCSouth Africa
- SA MRC Unit on Risk & Resilience in Mental DisordersUniversity of Cape TownCape TownWCSouth Africa
| | - Henk Temmingh
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
| | - Anne Uhlmann
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
- Department of Child and Adolescent PsychiatryTU DresdenGermany
| | - Greig I. de Zubicaray
- School of Psychology, Faculty of HealthQueensland University of Technology (QUT)BrisbaneQLDAustralia
| | - Katie L. McMahon
- School of Clinical SciencesQueensland University of Technology (QUT)BrisbaneQLDAustralia
| | - Margie Wright
- Queensland Brain InstituteUniversity of QueenslandBrisbaneQLDAustralia
| | - Derin Cobia
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of Psychology and Neuroscience CenterBrigham Young UniversityProvoUtahUSA
| | - John G. Csernansky
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | | | - Lei Wang
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of Psychiatry and Behavioral HealthOhio State University Wexner Medical CenterColumbusOhioUSA
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16
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Takahashi E, Allan N, Peres R, Ortug A, van der Kouwe AJW, Valli B, Ethier E, Levman J, Baumer N, Tsujimura K, Vargas-Maya NI, McCracken TA, Lee R, Maunakea AK. Integration of structural MRI and epigenetic analyses hint at linked cellular defects of the subventricular zone and insular cortex in autism: Findings from a case study. Front Neurosci 2022; 16:1023665. [PMID: 36817099 PMCID: PMC9935943 DOI: 10.3389/fnins.2022.1023665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 12/20/2022] [Indexed: 02/05/2023] Open
Abstract
Introduction Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction, communication and repetitive, restrictive behaviors, features supported by cortical activity. Given the importance of the subventricular zone (SVZ) of the lateral ventrical to cortical development, we compared molecular, cellular, and structural differences in the SVZ and linked cortical regions in specimens of ASD cases and sex and age-matched unaffected brain. Methods We used magnetic resonance imaging (MRI) and diffusion tractography on ex vivo postmortem brain samples, which we further analyzed by Whole Genome Bisulfite Sequencing (WGBS), Flow Cytometry, and RT qPCR. Results Through MRI, we observed decreased tractography pathways from the dorsal SVZ, increased pathways from the posterior ventral SVZ to the insular cortex, and variable cortical thickness within the insular cortex in ASD diagnosed case relative to unaffected controls. Long-range tractography pathways from and to the insula were also reduced in the ASD case. FACS-based cell sorting revealed an increased population of proliferating cells in the SVZ of ASD case relative to the unaffected control. Targeted qPCR assays of SVZ tissue demonstrated significantly reduced expression levels of genes involved in differentiation and migration of neurons in ASD relative to the control counterpart. Finally, using genome-wide DNA methylation analyses, we identified 19 genes relevant to neurological development, function, and disease, 7 of which have not previously been described in ASD, that were significantly differentially methylated in autistic SVZ and insula specimens. Conclusion These findings suggest a hypothesis that epigenetic changes during neurodevelopment alter the trajectory of proliferation, migration, and differentiation in the SVZ, impacting cortical structure and function and resulting in ASD phenotypes.
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Affiliation(s)
- Emi Takahashi
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Research, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States.,Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Nina Allan
- Epigenomics Research Program, Department of Anatomy, Institute for Biogenesis Research, Biochemistry and Physiology, John A. Burns School of Medicine, University of Hawai'i at Mānoa, Honolulu, HI, United States
| | - Rafael Peres
- Epigenomics Research Program, Department of Anatomy, Institute for Biogenesis Research, Biochemistry and Physiology, John A. Burns School of Medicine, University of Hawai'i at Mānoa, Honolulu, HI, United States
| | - Alpen Ortug
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Research, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States.,Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Andre J W van der Kouwe
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Research, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States.,Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Briana Valli
- Department of Behavioral Neuroscience, Northeastern University, Boston, MA, United States
| | - Elizabeth Ethier
- Department of Behavioral Neuroscience, Northeastern University, Boston, MA, United States
| | - Jacob Levman
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Research, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States.,Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.,Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Nicole Baumer
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Keita Tsujimura
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Research, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States.,Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Nauru Idalia Vargas-Maya
- Epigenomics Research Program, Department of Anatomy, Institute for Biogenesis Research, Biochemistry and Physiology, John A. Burns School of Medicine, University of Hawai'i at Mānoa, Honolulu, HI, United States
| | - Trevor A McCracken
- Epigenomics Research Program, Department of Anatomy, Institute for Biogenesis Research, Biochemistry and Physiology, John A. Burns School of Medicine, University of Hawai'i at Mānoa, Honolulu, HI, United States
| | - Rosa Lee
- Epigenomics Research Program, Department of Anatomy, Institute for Biogenesis Research, Biochemistry and Physiology, John A. Burns School of Medicine, University of Hawai'i at Mānoa, Honolulu, HI, United States
| | - Alika K Maunakea
- Epigenomics Research Program, Department of Anatomy, Institute for Biogenesis Research, Biochemistry and Physiology, John A. Burns School of Medicine, University of Hawai'i at Mānoa, Honolulu, HI, United States
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17
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Sha Z, Schijven D, Francks C. Patterns of brain asymmetry associated with polygenic risks for autism and schizophrenia implicate language and executive functions but not brain masculinization. Mol Psychiatry 2021; 26:7652-7660. [PMID: 34211121 PMCID: PMC8872997 DOI: 10.1038/s41380-021-01204-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/11/2021] [Accepted: 06/18/2021] [Indexed: 02/06/2023]
Abstract
Autism spectrum disorder (ASD) and schizophrenia have been conceived as partly opposing disorders in terms of systemizing vs. empathizing cognitive styles, with resemblances to male vs. female average sex differences. Left-right asymmetry of the brain is an important aspect of its organization that shows average differences between the sexes and can be altered in both ASD and schizophrenia. Here we mapped multivariate associations of polygenic risk scores for ASD and schizophrenia with asymmetries of regional cerebral cortical surface area, thickness, and subcortical volume measures in 32,256 participants from the UK Biobank. Polygenic risks for the two disorders were positively correlated (r = 0.08, p = 7.13 × 10-50) and both were higher in females compared to males, consistent with biased participation against higher-risk males. Each polygenic risk score was associated with multivariate brain asymmetry after adjusting for sex, ASD r = 0.03, p = 2.17 × 10-9, and schizophrenia r = 0.04, p = 2.61 × 10-11, but the multivariate patterns were mostly distinct for the two polygenic risks and neither resembled average sex differences. Annotation based on meta-analyzed functional imaging data showed that both polygenic risks were associated with asymmetries of regions important for language and executive functions, consistent with behavioral associations that arose in phenome-wide association analysis. Overall, the results indicate that distinct patterns of subtly altered brain asymmetry may be functionally relevant manifestations of polygenic risks for ASD and schizophrenia, but do not support brain masculinization or feminization in their etiologies.
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Affiliation(s)
- Zhiqiang Sha
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Dick Schijven
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Clyde Francks
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
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18
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Where the genome meets the connectome: Understanding how genes shape human brain connectivity. Neuroimage 2021; 244:118570. [PMID: 34508898 DOI: 10.1016/j.neuroimage.2021.118570] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/10/2021] [Accepted: 09/07/2021] [Indexed: 02/07/2023] Open
Abstract
The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.
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19
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Mazumder AH, Barnett J, Isometsä ET, Lindberg N, Torniainen-Holm M, Lähteenvuo M, Lahdensuo K, Kerkelä M, Ahola-Olli A, Hietala J, Kampman O, Kieseppä T, Jukuri T, Häkkinen K, Cederlöf E, Haaki W, Kajanne R, Wegelius A, Männynsalo T, Niemi-Pynttäri J, Suokas K, Lönnqvist J, Tiihonen J, Paunio T, Vainio SJ, Palotie A, Niemelä S, Suvisaari J, Veijola J. Reaction Time and Visual Memory in Connection to Hazardous Drinking Polygenic Scores in Schizophrenia, Schizoaffective Disorder and Bipolar Disorder. Brain Sci 2021; 11:brainsci11111422. [PMID: 34827421 PMCID: PMC8615595 DOI: 10.3390/brainsci11111422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 11/29/2022] Open
Abstract
The purpose of this study was to explore the association of cognition with hazardous drinking Polygenic Scores (PGS) in 2649 schizophrenia, 558 schizoaffective disorder, and 1125 bipolar disorder patients in Finland. Hazardous drinking PGS was computed using the LDPred program. Participants performed two computerized tasks from the Cambridge Automated Neuropsychological Test Battery (CANTAB) on a tablet computer: the 5-choice serial reaction time task, or Reaction Time (RT) test, and the Paired Associative Learning (PAL) test. The association between hazardous drinking PGS and cognition was measured using four cognition variables. Log-linear regression was used in Reaction Time (RT) assessment, and logistic regression was used in PAL assessment. All analyses were conducted separately for males and females. After adjustment of age, age of onset, education, household pattern, and depressive symptoms, hazardous drinking PGS was not associated with reaction time or visual memory in male or female patients with schizophrenia, schizoaffective, and bipolar disorder.
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Affiliation(s)
- Atiqul Haq Mazumder
- Department of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu, 90014 Oulu, Finland; (M.K.); (T.J.); (J.V.)
- Correspondence:
| | - Jennifer Barnett
- Cambridge Cognition, University of Cambridge, Cambridge CB25 9TU, UK;
| | - Erkki Tapio Isometsä
- Department of Psychiatry, University Hospital and University of Helsinki, 00029 Helsinki, Finland; (E.T.I.); (N.L.); (T.K.); (A.W.); (T.P.)
| | - Nina Lindberg
- Department of Psychiatry, University Hospital and University of Helsinki, 00029 Helsinki, Finland; (E.T.I.); (N.L.); (T.K.); (A.W.); (T.P.)
| | - Minna Torniainen-Holm
- Mental Health Unit, Finnish Institute for Health and Welfare (THL), 00271 Helsinki, Finland; (M.T.-H.); (E.C.); (J.L.); (J.S.)
| | - Markku Lähteenvuo
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, 70240 Kuopio, Finland; (M.L.); (K.H.); (J.T.)
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
| | - Kaisla Lahdensuo
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
- Mehiläinen, Pohjoinen Hesperiankatu 17 C, 00260 Helsinki, Finland
| | - Martta Kerkelä
- Department of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu, 90014 Oulu, Finland; (M.K.); (T.J.); (J.V.)
| | - Ari Ahola-Olli
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
| | - Jarmo Hietala
- Department of Psychiatry, University of Turku, 20014 Turku, Finland; (J.H.); (S.N.)
- Department of Psychiatry, Turku University Hospital, 20521 Turku, Finland
| | - Olli Kampman
- Faculty of Medicine and Health Technology, Tampere University, 33014 Tampere, Finland;
- Department of Psychiatry, Pirkanmaa Hospital District, 33521 Tampere, Finland
| | - Tuula Kieseppä
- Department of Psychiatry, University Hospital and University of Helsinki, 00029 Helsinki, Finland; (E.T.I.); (N.L.); (T.K.); (A.W.); (T.P.)
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
- Mehiläinen, Pohjoinen Hesperiankatu 17 C, 00260 Helsinki, Finland
| | - Tuomas Jukuri
- Department of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu, 90014 Oulu, Finland; (M.K.); (T.J.); (J.V.)
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
| | - Katja Häkkinen
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, 70240 Kuopio, Finland; (M.L.); (K.H.); (J.T.)
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
| | - Erik Cederlöf
- Mental Health Unit, Finnish Institute for Health and Welfare (THL), 00271 Helsinki, Finland; (M.T.-H.); (E.C.); (J.L.); (J.S.)
| | - Willehard Haaki
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
- Department of Psychiatry, University of Turku, 20014 Turku, Finland; (J.H.); (S.N.)
| | - Risto Kajanne
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
| | - Asko Wegelius
- Department of Psychiatry, University Hospital and University of Helsinki, 00029 Helsinki, Finland; (E.T.I.); (N.L.); (T.K.); (A.W.); (T.P.)
- Mental Health Unit, Finnish Institute for Health and Welfare (THL), 00271 Helsinki, Finland; (M.T.-H.); (E.C.); (J.L.); (J.S.)
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
| | - Teemu Männynsalo
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
- Social Services and Health Care Sector, City of Helsinki, 00099 Helsinki, Finland
| | - Jussi Niemi-Pynttäri
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
- Social Services and Health Care Sector, City of Helsinki, 00099 Helsinki, Finland
| | - Kimmo Suokas
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
- Faculty of Medicine and Health Technology, Tampere University, 33014 Tampere, Finland;
| | - Jouko Lönnqvist
- Mental Health Unit, Finnish Institute for Health and Welfare (THL), 00271 Helsinki, Finland; (M.T.-H.); (E.C.); (J.L.); (J.S.)
- Department of Psychiatry, University of Helsinki, 00014 Helsinki, Finland
| | - Jari Tiihonen
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, 70240 Kuopio, Finland; (M.L.); (K.H.); (J.T.)
- Department of Clinical Neuroscience, Karolinska Institute, 17177 Stockholm, Sweden
- Center for Psychiatry Research, Stockholm City Council, 11364 Stockholm, Sweden
| | - Tiina Paunio
- Department of Psychiatry, University Hospital and University of Helsinki, 00029 Helsinki, Finland; (E.T.I.); (N.L.); (T.K.); (A.W.); (T.P.)
- Mental Health Unit, Finnish Institute for Health and Welfare (THL), 00271 Helsinki, Finland; (M.T.-H.); (E.C.); (J.L.); (J.S.)
- Department of Psychiatry, University of Helsinki, 00014 Helsinki, Finland
| | - Seppo Juhani Vainio
- Infotech Oulu, University of Oulu, 90014 Oulu, Finland;
- Northern Finland Biobank Borealis, Oulu University Hospital, 90220 Oulu, Finland
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, 90014 Oulu, Finland
- Kvantum Institute, University of Oulu, 90014 Oulu, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; (K.L.); (A.A.-O.); (W.H.); (R.K.); (T.M.); (J.N.-P.); (K.S.); (A.P.)
- Mehiläinen, Pohjoinen Hesperiankatu 17 C, 00260 Helsinki, Finland
- Stanley Center for Psychiatric Research, The Broad Institute of MIT (Massachusetts Institute of Technology) and Harvard, Cambridge, MA 02142, USA
- Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Solja Niemelä
- Department of Psychiatry, University of Turku, 20014 Turku, Finland; (J.H.); (S.N.)
- Department of Psychiatry, Turku University Hospital, 20521 Turku, Finland
| | - Jaana Suvisaari
- Mental Health Unit, Finnish Institute for Health and Welfare (THL), 00271 Helsinki, Finland; (M.T.-H.); (E.C.); (J.L.); (J.S.)
| | - Juha Veijola
- Department of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu, 90014 Oulu, Finland; (M.K.); (T.J.); (J.V.)
- Department of Psychiatry, Oulu University Hospital, 90220 Oulu, Finland
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20
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Shephard E, Stern ER, van den Heuvel OA, Costa DL, Batistuzzo MC, Godoy PB, Lopes AC, Brunoni AR, Hoexter MQ, Shavitt RG, Reddy JY, Lochner C, Stein DJ, Simpson HB, Miguel EC. Toward a neurocircuit-based taxonomy to guide treatment of obsessive-compulsive disorder. Mol Psychiatry 2021; 26:4583-4604. [PMID: 33414496 PMCID: PMC8260628 DOI: 10.1038/s41380-020-01007-8] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 12/11/2022]
Abstract
An important challenge in mental health research is to translate findings from cognitive neuroscience and neuroimaging research into effective treatments that target the neurobiological alterations involved in psychiatric symptoms. To address this challenge, in this review we propose a heuristic neurocircuit-based taxonomy to guide the treatment of obsessive-compulsive disorder (OCD). We do this by integrating information from several sources. First, we provide case vignettes in which patients with OCD describe their symptoms and discuss different clinical profiles in the phenotypic expression of the condition. Second, we link variations in these clinical profiles to underlying neurocircuit dysfunctions, drawing on findings from neuropsychological and neuroimaging studies in OCD. Third, we consider behavioral, pharmacological, and neuromodulatory treatments that could target those specific neurocircuit dysfunctions. Finally, we suggest methods of testing this neurocircuit-based taxonomy as well as important limitations to this approach that should be considered in future research.
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Affiliation(s)
- Elizabeth Shephard
- Department of Psychiatry, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil. .,Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK.
| | - Emily R. Stern
- Department of Psychiatry, The New York University School of Medicine, New York, USA.,Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, USA
| | - Odile A. van den Heuvel
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Daniel L.C. Costa
- Department of Psychiatry, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Marcelo C. Batistuzzo
- Department of Psychiatry, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Priscilla B.G. Godoy
- Department of Psychiatry, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Antonio C. Lopes
- Department of Psychiatry, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Andre R. Brunoni
- Department of Psychiatry, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Marcelo Q. Hoexter
- Department of Psychiatry, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Roseli G. Shavitt
- Department of Psychiatry, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Janardhan Y.C Reddy
- Department of Psychiatry OCD Clinic, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India
| | - Christine Lochner
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Dan J. Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - H. Blair Simpson
- Center for OCD and Related Disorders, New York State Psychiatric Institute and the Department of Psychiatry, Columbia University Irving Medical Center, New York New York
| | - Euripedes C. Miguel
- Department of Psychiatry, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
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21
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Lafferty CK, Christinck TD, Britt JP. All-optical approaches to studying psychiatric disease. Methods 2021; 203:46-55. [PMID: 34314828 DOI: 10.1016/j.ymeth.2021.07.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 07/20/2021] [Accepted: 07/22/2021] [Indexed: 11/17/2022] Open
Abstract
Improvements in all-optical means of monitoring and manipulating neural activity have generated new ways of studying psychiatric disease. The combination of calcium imaging techniques with optogenetics to concurrently record and manipulate neural activity has been used to create new disease models that link distinct circuit abnormalities to specific disease dimensions. These approaches represent a new path towards the development of more effective treatments, as they allow researchers to identify circuit manipulations that normalize pathological network activity. In this review we highlight the utility of all-optical approaches to generate new psychiatric disease models where the specific circuit abnormalities associated with disease symptomology can be assessed in vivo and in response to manipulations designed to normalize disease states. We then outline the principles underlying all-optical interrogations of neural circuits and discuss practical considerations for experimental design.
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Affiliation(s)
- Christopher K Lafferty
- Department of Psychology, McGill University, Montreal, QC, Canada; Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
| | - Thomas D Christinck
- Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada; Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Jonathan P Britt
- Department of Psychology, McGill University, Montreal, QC, Canada; Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada; Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada.
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22
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Mooney MA, Bhatt P, Hermosillo RJM, Ryabinin P, Nikolas M, Faraone SV, Fair DA, Wilmot B, Nigg JT. Smaller total brain volume but not subcortical structure volume related to common genetic risk for ADHD. Psychol Med 2021; 51:1279-1288. [PMID: 31973781 PMCID: PMC7461955 DOI: 10.1017/s0033291719004148] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Mechanistic endophenotypes can inform process models of psychopathology and aid interpretation of genetic risk factors. Smaller total brain and subcortical volumes are associated with attention-deficit hyperactivity disorder (ADHD) and provide clues to its development. This study evaluates whether common genetic risk for ADHD is associated with total brain volume (TBV) and hypothesized subcortical structures in children. METHODS Children 7-15 years old were recruited for a case-control study (N = 312, N = 199 ADHD). Children were assessed with a multi-informant, best-estimate diagnostic procedure and motion-corrected MRI measured brain volumes. Polygenic scores were computed based on discovery data from the Psychiatric Genomics Consortium (N = 19 099 ADHD, N = 34 194 controls) and the ENIGMA + CHARGE consortium (N = 26 577). RESULTS ADHD was associated with smaller TBV, and altered volumes of caudate, cerebellum, putamen, and thalamus after adjustment for TBV; however, effects were larger and statistically reliable only in boys. TBV was associated with an ADHD polygenic score [β = -0.147 (-0.27 to -0.03)], and mediated a small proportion of the effect of polygenic risk on ADHD diagnosis (average ACME = 0.0087, p = 0.012). This finding was stronger in boys (average ACME = 0.019, p = 0.008). In addition, we confirm genetic variation associated with whole brain volume, via an intracranial volume polygenic score. CONCLUSION Common genetic risk for ADHD is not expressed primarily as developmental alterations in subcortical brain volumes, but appears to alter brain development in other ways, as evidenced by TBV differences. This is among the first demonstrations of this effect using molecular genetic data. Potential sex differences in these effects warrant further examination.
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Affiliation(s)
- Michael A Mooney
- Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
- OHSU Knight Cancer Institute, Portland, Oregon, USA
| | - Priya Bhatt
- Department of Psychiatry, Oregon Health & Science University, Portland, Oregon, USA
| | - Robert J M Hermosillo
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA
| | - Peter Ryabinin
- Oregon Clinical and Translational Research Institute, Portland, Oregon, USA
| | - Molly Nikolas
- Department of Psychological and Brain Sciences, The University of Iowa, Iowa City, Iowa, USA
| | - Stephen V Faraone
- Departments of Psychiatry and Neuroscience & Physiology, State University of New York Upstate Medical University, Syracuse, New York, USA
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA
- Advanced Imaging Research Center, OHSU, Portland, Oregon, USA
| | - Beth Wilmot
- Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
- Oregon Clinical and Translational Research Institute, Portland, Oregon, USA
| | - Joel T Nigg
- Department of Psychiatry, Oregon Health & Science University, Portland, Oregon, USA
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA
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23
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Functional connectome-wide associations of schizophrenia polygenic risk. Mol Psychiatry 2021; 26:2553-2561. [PMID: 32127647 PMCID: PMC9557214 DOI: 10.1038/s41380-020-0699-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Revised: 02/15/2020] [Accepted: 02/20/2020] [Indexed: 01/29/2023]
Abstract
Schizophrenia is a highly heritable mental disorder characterized by functional dysconnectivity across the brain. However, the relationships between polygenic risk factors and connectome-wide neural mechanisms are unclear. Here, combining genetic and multiparadigm fMRI data of 623 healthy Caucasian adults drawn from the Human Connectome Project, we found that higher schizophrenia polygenic risk scores were significantly correlated with lower functional connectivity in a large-scale brain network primarily encompassing the visual system, default-mode system, and frontoparietal system. Such correlation was robustly observed across multiple fMRI paradigms, suggesting a brain-state-independent neural phenotype underlying individual genetic liability to schizophrenia. Moreover, using an independent clinical dataset acquired from the Consortium for Neuropsychiatric Phenomics, we further demonstrated that the connectivity of the identified network was reduced in patients with schizophrenia and significantly correlated with general cognitive ability. These findings provide the first evidence for connectome-wide associations of schizophrenia polygenic risk at the systems level and suggest that disrupted integration of sensori-cognitive information may be a hallmark of genetic effects on the brain that contributes to the pathogenesis of schizophrenia.
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24
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Lawrence KE, Hernandez LM, Fuster E, Padgaonkar NT, Patterson G, Jung J, Okada NJ, Lowe JK, Hoekstra JN, Jack A, Aylward E, Gaab N, Van Horn JD, Bernier RA, McPartland JC, Webb SJ, Pelphrey KA, Green SA, Bookheimer SY, Geschwind DH, Dapretto M. Impact of autism genetic risk on brain connectivity: a mechanism for the female protective effect. Brain 2021; 145:378-387. [PMID: 34050743 PMCID: PMC8967090 DOI: 10.1093/brain/awab204] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 04/23/2021] [Accepted: 05/11/2021] [Indexed: 01/27/2023] Open
Abstract
The biological mechanisms underlying the greater prevalence of autism spectrum disorder in males than females remain poorly understood. One hypothesis posits that this female protective effect arises from genetic load for autism spectrum disorder differentially impacting male and female brains. To test this hypothesis, we investigated the impact of cumulative genetic risk for autism spectrum disorder on functional brain connectivity in a balanced sample of boys and girls with autism spectrum disorder and typically developing boys and girls (127 youth, ages 8-17). Brain connectivity analyses focused on the salience network, a core intrinsic functional connectivity network which has previously been implicated in autism spectrum disorder. The effects of polygenic risk on salience network functional connectivity were significantly modulated by participant sex, with genetic load for autism spectrum disorder influencing functional connectivity in boys with and without autism spectrum disorder but not girls. These findings support the hypothesis that autism spectrum disorder risk genes interact with sex differential processes, thereby contributing to the male bias in autism prevalence and proposing an underlying neurobiological mechanism for the female protective effect.
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Affiliation(s)
- Katherine E Lawrence
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA,Correspondence to: Mirella Dapretto Ahmanson-Lovelace Brain Mapping Center 660 Charles E. Young Drive South Los Angeles, CA 90095, USA E-mail:
| | - Leanna M Hernandez
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Emily Fuster
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Namita T Padgaonkar
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Genevieve Patterson
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jiwon Jung
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Nana J Okada
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jennifer K Lowe
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jackson N Hoekstra
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Allison Jack
- Department of Psychology, George Mason University, Fairfax, VA 22030, USA
| | - Elizabeth Aylward
- Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Nadine Gaab
- Harvard Graduate School of Education, Cambridge, MA 02138, USA
| | - John D Van Horn
- Department of Psychology and School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Raphael A Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98195, USA
| | | | - Sara J Webb
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98195, USA,Center on Child Health, Behavior, and Development, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Kevin A Pelphrey
- Department of Neurology, University of Virginia, Charlottesville, VA 22904, USA
| | - Shulamite A Green
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Daniel H Geschwind
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA,Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
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25
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Zhang YN, Li H, Shen ZW, Xu C, Huang YJ, Wu RH. Healthy individuals vs patients with bipolar or unipolar depression in gray matter volume. World J Clin Cases 2021; 9:1304-1317. [PMID: 33644197 PMCID: PMC7896697 DOI: 10.12998/wjcc.v9.i6.1304] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 12/14/2020] [Accepted: 12/23/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Previous studies using voxel-based morphometry (VBM) revealed changes in gray matter volume (GMV) of patients with depression, but the differences between patients with bipolar disorder (BD) and unipolar depression (UD) are less known.
AIM To analyze the whole-brain GMV data of patients with untreated UD and BD compared with healthy controls.
METHODS Fourteen patients with BD and 20 with UD were recruited from the Mental Health Center of Shantou University between August 2014 and July 2015, and 20 non-depressive controls were recruited. After routine three-plane positioning, axial T2WI scanning was performed. The connecting line between the anterior and posterior commissures was used as the scanning baseline. The scanning range extended from the cranial apex to the foramen magnum. Categorical data are presented as frequencies and were analyzed using the Fisher exact test.
RESULTS There were no significant intergroup differences in gender, age, or years of education. Disease course, age at the first episode, and Hamilton depression rating scale scores were similar between patients with UD and those with BD. Compared with the non-depressive controls, patients with BD showed smaller GMVs in the right inferior temporal gyrus, left middle temporal gyrus, right middle occipital gyrus, and right superior parietal gyrus and larger GMVs in the midbrain, left superior frontal gyrus, and right cerebellum. In contrast, UD patients showed smaller GMVs than the controls in the right fusiform gyrus, left inferior occipital gyrus, left paracentral lobule, right superior and inferior temporal gyri, and the right posterior lobe of the cerebellum, and larger GMVs than the controls in the left posterior central gyrus and left middle frontal gyrus. There was no difference in GMV between patients with BD and UD.
CONCLUSION Using VBM, the present study revealed that patients with UD and BD have different patterns of changes in GMV when compared with healthy controls.
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Affiliation(s)
- Yin-Nan Zhang
- Department of Rehabilitation Medicine, Mental Health Center of Shantou University, Shantou 515000, Guangdong Province, China
| | - Hui Li
- Mental Health Center of Shantou University, Shantou 515000, Guangdong Province, China
| | | | - Chang Xu
- Mental Health Center of Shantou University, Shantou 515000, Guangdong Province, China
| | - Yue-Jun Huang
- Department of Pediatrics, The Second Affiliated Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China
| | - Ren-Hua Wu
- Department of Medical Imaging, The Second Affiliated Hospital of Shantou University Medical College, Shantou 515041, Guangdong Province, China
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26
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Spronk M, Keane BP, Ito T, Kulkarni K, Ji JL, Anticevic A, Cole MW. A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction. Cereb Cortex 2020; 31:547-561. [PMID: 32909037 DOI: 10.1093/cercor/bhaa242] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 07/07/2020] [Accepted: 08/01/2020] [Indexed: 12/13/2022] Open
Abstract
A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations appear to support theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in a broad, whole-brain perspective. Using a graph distance approach-connectome-wide similarity-we found that whole-brain resting-state functional network organization is highly similar across groups of individuals with and without a variety of mental diseases. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease, those differences are informative. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases. Such small network alterations suggest the possibility that small, well-targeted alterations to brain network organization may provide meaningful improvements for a variety of mental disorders.
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Affiliation(s)
- Marjolein Spronk
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Brian P Keane
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA.,Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Kaustubh Kulkarni
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
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27
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Maj C, Tosato S, Zanardini R, Lasalvia A, Favaro A, Leuci E, De Girolamo G, Ruggeri M, Gennarelli M, Bocchio-Chiavetto L. Correlations between immune and metabolic serum markers and schizophrenia/bipolar disorder polygenic risk score in first-episode psychosis. Early Interv Psychiatry 2020; 14:507-511. [PMID: 31749237 DOI: 10.1111/eip.12906] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 10/09/2019] [Accepted: 10/31/2019] [Indexed: 01/01/2023]
Abstract
AIMS There is a strong interest in identifying the biological mechanisms involved in the genetic risk for psychotic disorders. In this study, we evaluated the correlation between serum concentrations of specific molecular markers and the genetic component for schizophrenia and bipolar disorder. METHODS We analysed the association between the polygenic risk score (PRS) and the serum levels of different inflammatory/metabolic markers in a sample of 81 first-episode psychosis patients (FEP) with a diagnosis of schizophrenia or bipolar disorder and 33 controls. RESULTS A positive correlation of schizophrenia and bipolar disorder PRS with the inflammatory marker C-C Motif Chemokine Ligand 4 serum concentration (ρ = 0.42, P = 1.56 × 10-04 and ρ = 0.40, P = 1.65 × 10-03 , respectively) and a negative correlation with the serum ghrelin content (ρ = - 0.35, P = 4.27 × 10-03 and ρ = - 0.45, P = 6.05 × 10-04 , respectively) were observed. CONCLUSION These findings provide new insight into the biological underpinnings of the PRS component, thus supporting a role of the genetic liability on the inflammatory and metabolic alterations that characterize psychosis onset.
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Affiliation(s)
- Carlo Maj
- Institute for Genomic Statistics and Bioinformatics, University Hospital, Bonn, Germany.,Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Sarah Tosato
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Italy
| | - Roberta Zanardini
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Antonio Lasalvia
- UOC Psichiatria, Azienda Ospedaliera Universitaria Integrata (AOUI) di Verona, Verona, Italy
| | - Angela Favaro
- Department of Neurosciences, University of Padua and Azienda Ospedaliera, Padua, Italy
| | | | - Giovanni De Girolamo
- Psychiatric Epidemiology and Evaluation Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Mirella Ruggeri
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Italy
| | - Massimo Gennarelli
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Department of Molecular and Translational Medicine, Division of Biology and Genetics, University of Brescia, Italy
| | | | - Luisella Bocchio-Chiavetto
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Faculty of Psychology, eCampus University, Novedrate (Como), Italy
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28
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Feczko E, Fair DA. Methods and Challenges for Assessing Heterogeneity. Biol Psychiatry 2020; 88:9-17. [PMID: 32386742 PMCID: PMC8404882 DOI: 10.1016/j.biopsych.2020.02.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 12/30/2019] [Accepted: 02/07/2020] [Indexed: 01/14/2023]
Abstract
The widely acknowledged homogeneity assumption limits progress in refining clinical diagnosis, understanding mechanisms, and developing new treatments for mental health disorders. This homogeneity assumption drives both a comorbidity and a heterogeneity problem, where two different approaches tackle the problems. One, a unifying approach, tackles the comorbidity problem by assuming that a single general psychopathology factor underlies multiple disorders. Another, a multifactorial approach, tackles the heterogeneity problem by assuming that disorders comprise multiple subtypes driven by multiple discrete factors. We show how each of these approaches can make useful contributions to mental health-related research and clinical practice. For example, the unifying approach can develop a rapid assessment tool that may be clinically valuable for triaging cases. The multifactorial approach can reveal subtypes that are differentially responsive to treatments and highlight distinct mechanisms leading to similar phenotypes. Because both approaches tackle different problems, both have different limitations. We describe the statistical frameworks that incorporate and adjudicate between both approaches (e.g., the bifactor model, normative modeling, and the functional random forest). Such frameworks can identify whether sets of disorders are more affected by heterogeneity or comorbidity. Therefore, future studies that incorporate such frameworks can provide further insight into the nature of psychopathology.
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Affiliation(s)
- Eric Feczko
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon.
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon; Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon
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29
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High polygenic burden is associated with blood DNA methylation changes in individuals with suicidal behavior. J Psychiatr Res 2020; 123:62-71. [PMID: 32036075 DOI: 10.1016/j.jpsychires.2020.01.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 01/06/2020] [Accepted: 01/24/2020] [Indexed: 12/27/2022]
Abstract
Suicidal behavior is result of the interaction of several contributors, including genetic and environmental factors. The integration of approaches considering the polygenic component of suicidal behavior, such as polygenic risk scores (PRS) and DNA methylation is promising for improving our understanding of the complex interplay between genetic and environmental factors in this behavior. The aim of this study was the evaluation of DNA methylation differences between individuals with high and low genetic burden for suicidality. The present study was divided into two phases. In the first phase, genotyping with the Psycharray chip was performed in a discovery sample of 568 Mexican individuals, of which 149 had suicidal behavior (64 individuals with suicidal ideation, 50 with suicide attempt and 35 with completed suicide). Then, a PRS analysis based on summary statistics from the Psychiatric Genomic Consortium was performed in the discovery sample. In a second phase, we evaluated DNA methylation differences between individuals with high and low genetic burden for suicidality in a sub-sample of the discovery sample (target sample) of 94 subjects. We identified 153 differentially methylated sites between individuals with low and high-PRS. Among genes mapped to differentially methylated sites, we found genes involved in neurodevelopment (CHD7, RFX4, KCNA1, PLCB1, PITX1, NUMBL) and ATP binding (KIF7, NUBP2, KIF6, ATP8B1, ATP11A, CLCN7, MYLK, MAP2K5). Our results suggest that genetic variants might increase the predisposition to epigenetic variations in genes involved in neurodevelopment. This study highlights the possible implication of polygenic burden in the alteration of epigenetic changes in suicidal behavior.
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30
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Abstract
PURPOSE OF REVIEW Attention deficit hyperactivity disorder (ADHD) shows high heritability in formal genetic studies. In our review article, we provide an overview on common and rare genetic risk variants for ADHD and their link to clinical practice. RECENT FINDINGS The formal heritability of ADHD is about 80% and therefore higher than most other psychiatric diseases. However, recent studies estimate the proportion of heritability based on singlenucleotide variants (SNPs) at 22%. It is a matter of debate which genetic mechanisms explain this huge difference. While frequent variants in first mega-analyses of genome-wideassociation study data containing several thousand patients give the first genome-wide results, explaining only little variance, the methodologically more difficult analyses of rare variants are still in their infancy. Some rare genetic syndromes show higher prevalence for ADHD indicating a potential role for a small number of patients. In contrast, polygenic risk scores (PRS) could potentially be applied to every patient. We give an overview how PRS explain different behavioral phenotypes in ADHD and how they could be used for diagnosis and therapy prediction. Knowledge about a patient's genetic makeup is not yet mandatory for ADHD therapy or diagnosis. PRS however have been introduced successfully in other areas of clinical medicine, and their application in psychiatry will begin within the next years. In order to ensure competent advice for patients, knowledge of the current state of research is useful forpsychiatrists.
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Affiliation(s)
- Oliver Grimm
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Thorsten M Kranz
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany.
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31
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Proof-of-concept study of a multi-gene risk score in adolescent bipolar disorder. J Affect Disord 2020; 262:211-222. [PMID: 31727397 DOI: 10.1016/j.jad.2019.11.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 10/07/2019] [Accepted: 11/02/2019] [Indexed: 11/21/2022]
Abstract
BACKGROUND Few studies have examined multiple genetic variants concurrently for the purpose of classifying bipolar disorder (BD); the literature among youth is particularly sparse. We selected 35 genetic variants, previously implicated in BD or associated characteristics, from which to identify the most robustly predictive group of genes. METHODS 215 Caucasian adolescents (114 BD and 101 healthy controls (HC), ages 13-20 years) were included. Psychiatric diagnoses were determined based on semi-structured diagnostic interviews. Genomic DNA was extracted from saliva for genotyping. Two models were used to calculate a multi-gene risk score (MGRS). Model 1 used forward and backward regressions, and model 2 used a PLINK generated method. RESULTS In model 1, GPX3 rs3792797 was significant in the forward regression, DRD4 exonIII was significant in the backward regression; IL1β rs16944 and DISC1 rs821577 were significant in both the forward and backward regressions. These variants are involved in dopamine neurotransmission; inflammation and oxidative stress; and neuronal development. Model 1 MGRS did not significantly discriminate between BD and HC. In model 2, ZNF804A rs1344706 was significantly associated with BD; however, this association did not predict diagnosis when entered into the weighted model. LIMITATIONS This study was limited by the number of genetic variants examined and the modest sample size. CONCLUSIONS Whereas regression approaches identified four genetic variants that significantly discriminated between BD and HC, those same variants no longer discriminated between BD and HC when computed as a MGRS. Future larger studies are needed evaluating intermediate phenotypes such as neuroimaging and blood-based biomarkers.
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32
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Jalbrzikowski M, Freedman D, Hegarty CE, Mennigen E, Karlsgodt KH, Olde Loohuis LM, Ophoff RA, Gur RE, Bearden CE. Structural Brain Alterations in Youth With Psychosis and Bipolar Spectrum Symptoms. J Am Acad Child Adolesc Psychiatry 2019; 58:1079-1091. [PMID: 30768396 PMCID: PMC7110691 DOI: 10.1016/j.jaac.2018.11.012] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 11/26/2018] [Accepted: 01/10/2019] [Indexed: 11/17/2022]
Abstract
OBJECTIVE Adults with established diagnoses of serious mental illness (bipolar disorder and schizophrenia) exhibit structural brain abnormalities, yet less is known about how such abnormalities manifest earlier in development. METHOD Cross-sectional data publicly available from the Philadelphia Neurodevelopmental Cohort (PNC) were analyzed. Structural magnetic resonance neuroimaging data were collected on a subset of the PNC (N = 989; 9-22 years old). Cortical thickness, surface area (SA), and subcortical volumes were calculated. Study participants were assessed for psychiatric symptomatology using a structured interview and the following groups were created: typically developing (n = 376), psychosis spectrum (PS; n = 113), bipolar spectrum (BP; n = 117), and BP + PS (n = 109). Group and developmental differences in structural magnetic resonance neuroimaging measures were examined. In addition, the extent to which any structural aberration was related to neurocognition, global functioning, and clinical symptomatology was examined. RESULTS Compared with other groups, PS youth exhibited significantly decreased SA in the orbitofrontal, cingulate, precentral, and postcentral regions. PS youth also exhibited deceased thalamic volume compared with all other groups. The strongest effects for precentral and posterior cingulate SA decreases were seen during early adolescence (13-15 years old) in PS youth. The strongest effects for decreases in thalamic volume and orbitofrontal and postcentral SA were observed in mid-adolescence (16-18 years) in PS youth. Across groups, better overall functioning was associated with increased lateral orbitofrontal SA. Increased postcentral SA was associated with better executive cognition and less severe negative symptoms in the entire sample. CONCLUSION In a community-based sample, decreased cortical SA and thalamic volume were present early in adolescent development in youth with PS symptoms, but not in youth with BP symptoms or with BP and PS symptoms. These findings point to potential biological distinctions between PS and BP conditions, which could suggest additional biomarkers relevant to early identification.
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Affiliation(s)
| | - David Freedman
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
| | | | - Eva Mennigen
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
| | | | | | - Roel A Ophoff
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles; Center for Neurobehavioral Genetics, University of California, Los Angeles
| | - Raquel E Gur
- Lifespan Brain Institute, Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, PA
| | - Carrie E Bearden
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles; Center for Neurobehavioral Genetics, University of California, Los Angeles; University of California, Los Angeles
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33
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van den Heuvel MP, Scholtens LH, Kahn RS. Multiscale Neuroscience of Psychiatric Disorders. Biol Psychiatry 2019; 86:512-522. [PMID: 31320130 DOI: 10.1016/j.biopsych.2019.05.015] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Revised: 05/16/2019] [Accepted: 05/17/2019] [Indexed: 12/11/2022]
Abstract
The human brain comprises a multiscale network with multiple levels of organization. Neurons with dendritic and axonal connections form the microscale fabric of brain circuitry, and macroscale brain regions and white matter connections form the infrastructure for system-level brain communication and information integration. In this review, we discuss the emerging trend of multiscale neuroscience, the multidisciplinary field that brings together data from these different levels of nervous system organization to form a better understanding of between-scale relationships of brain structure, function, and behavior in health and disease. We provide a broad overview of this developing field and discuss recent findings of exemplary multiscale neuroscience studies that illustrate the importance of studying cross-scale interactions among the genetic, molecular, cellular, and macroscale levels of brain circuitry and connectivity and behavior. We particularly consider a central, overarching goal of these multiscale neuroscience studies of human brain connectivity: to obtain insight into how disease-related alterations at one level of organization may underlie alterations observed at other scales of brain network organization in mental disorders. We conclude by discussing the current limitations, challenges, and future directions of the field.
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Affiliation(s)
- Martijn P van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands; Department of Clinical Genetics, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands.
| | - Lianne H Scholtens
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - René S Kahn
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
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34
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Feczko E, Miranda-Dominguez O, Marr M, Graham AM, Nigg JT, Fair DA. The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes. Trends Cogn Sci 2019; 23:584-601. [PMID: 31153774 PMCID: PMC6821457 DOI: 10.1016/j.tics.2019.03.009] [Citation(s) in RCA: 200] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 03/28/2019] [Accepted: 03/29/2019] [Indexed: 12/12/2022]
Abstract
The imprecise nature of psychiatric nosology restricts progress towards characterizing and treating mental health disorders. One issue is the 'heterogeneity problem': different causal mechanisms may relate to the same disorder, and multiple outcomes of interest can occur within one individual. Our review tackles this heterogeneity problem, providing considerations, concepts, and approaches for investigators examining human cognition and mental health. We highlight the difficulty of pure dimensional approaches due to 'the curse of dimensionality'. Computationally, we consider supervised and unsupervised statistical approaches to identify putative subtypes within a population. However, we emphasize that subtype identification should be linked to a particular outcome or question. We conclude with novel hybrid approaches that can identify subtypes tied to outcomes, and may help advance precision diagnostic and treatment tools.
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Affiliation(s)
- Eric Feczko
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University, Portland, OR 97239, USA.
| | - Oscar Miranda-Dominguez
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - Mollie Marr
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - Alice M Graham
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA
| | - Joel T Nigg
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA; Advanced Imaging Research Center Oregon Health & Science University, Portland, OR 97239, USA.
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35
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Dezhina Z, Ranlund S, Kyriakopoulos M, Williams SCR, Dima D. A systematic review of associations between functional MRI activity and polygenic risk for schizophrenia and bipolar disorder. Brain Imaging Behav 2019; 13:862-877. [PMID: 29748770 PMCID: PMC6538577 DOI: 10.1007/s11682-018-9879-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Genetic factors account for up to 80% of the liability for schizophrenia (SCZ) and bipolar disorder (BD). Genome-wide association studies have successfully identified several genes associated with increased risk for both disorders. This has allowed researchers to model the aggregate effect of genes associated with disease status and create a polygenic risk score (PGRS) for each individual. The interest in imaging genetics using PGRS has grown in recent years, with several studies now published. We have conducted a systematic review to examine the effects of PGRS of SCZ, BD and cross psychiatric disorders on brain function and connectivity using fMRI data. Results indicate that the effect of genetic load for SCZ and BD on brain function affects task-related recruitment, with frontal areas having a more prominent role, independent of task. Additionally, the results suggest that the polygenic architecture of psychotic disorders is not regionally confined but impacts on the task-dependent recruitment of multiple brain regions. Future imaging genetics studies with large samples, especially population studies, would be uniquely informative in mapping the spatial distribution of the genetic risk to psychiatric disorders on brain processes during various cognitive tasks and may lead to the discovery of biological pathways that could be crucial in mediating the link between genetic factors and alterations in brain networks.
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Affiliation(s)
- Zalina Dezhina
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Siri Ranlund
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Marinos Kyriakopoulos
- National and Specialist Acorn Lodge Inpatient Children Unit, South London and Maudsley NHS Foundation Trust, London, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Steve C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Danai Dima
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Department of Psychology, School of Arts and Social Sciences, City, University of London, 10 Northampton Square, London, EC1V 0HB, UK.
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36
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Balicza P, Varga NÁ, Bolgár B, Pentelényi K, Bencsik R, Gál A, Gézsi A, Prekop C, Molnár V, Molnár MJ. Comprehensive Analysis of Rare Variants of 101 Autism-Linked Genes in a Hungarian Cohort of Autism Spectrum Disorder Patients. Front Genet 2019; 10:434. [PMID: 31134136 PMCID: PMC6517558 DOI: 10.3389/fgene.2019.00434] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 04/24/2019] [Indexed: 11/16/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is genetically and phenotypically heterogeneous. Former genetic studies suggested that both common and rare genetic variants play a role in the etiology. In this study, we aimed to analyze rare variants detected by next generation sequencing (NGS) in an autism cohort from Hungary. Methods We investigated the yield of NGS panel sequencing of an unselected ASD cohort (N = 174 ) for the detection of ASD associated syndromes. Besides, we analyzed rare variants in a common disease-rare variant framework and performed rare variant burden analysis and gene enrichment analysis in phenotype based clusters. Results We have diagnosed 13 molecularly proven syndromic autism cases. Strongest indicators of syndromic autism were intellectual disability, epilepsy or other neurological plus symptoms. Rare variant analysis on a cohort level confirmed the association of five genes with autism (AUTS2, NHS, NSD1, SLC9A9, and VPS13). We found no correlation between rare variant burden and number of minor malformation or autism severity. We identified four phenotypic clusters, but no specific gene was enriched in a given cluster. Conclusion Our study indicates that NGS panel gene sequencing can be useful, where the clinical picture suggests a clinically defined syndromic autism. In this group, targeted panel sequencing may provide reasonable diagnostic yield. Unselected NGS panel screening in the clinic remains controversial, because of uncertain utility, and difficulties of the variant interpretation. However, the detected rare variants may still significantly influence autism risk and subphenotypes in a polygenic model, but to detect the effects of these variants larger cohorts are needed.
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Affiliation(s)
- Péter Balicza
- Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Budapest, Hungary
| | - Noémi Ágnes Varga
- Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Budapest, Hungary
| | - Bence Bolgár
- Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Klára Pentelényi
- Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Budapest, Hungary
| | - Renáta Bencsik
- Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Budapest, Hungary
| | - Anikó Gál
- Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Budapest, Hungary
| | - András Gézsi
- Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Csilla Prekop
- Vadaskert Foundation for Children's Mental Health, Budapest, Hungary
| | - Viktor Molnár
- Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Budapest, Hungary
| | - Mária Judit Molnár
- Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Budapest, Hungary
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37
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Singh K, Jayaram M, Kaare M, Leidmaa E, Jagomäe T, Heinla I, Hickey MA, Kaasik A, Schäfer MK, Innos J, Lilleväli K, Philips MA, Vasar E. Neural cell adhesion molecule Negr1 deficiency in mouse results in structural brain endophenotypes and behavioral deviations related to psychiatric disorders. Sci Rep 2019; 9:5457. [PMID: 30932003 PMCID: PMC6443666 DOI: 10.1038/s41598-019-41991-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 03/21/2019] [Indexed: 12/24/2022] Open
Abstract
Neuronal growth regulator 1 (NEGR1) belongs to the immunoglobulin (IgLON) superfamily of cell adhesion molecules involved in cortical layering. Recent functional and genomic studies implicate the role of NEGR1 in a wide spectrum of psychiatric disorders, such as major depression, schizophrenia and autism. Here, we investigated the impact of Negr1 deficiency on brain morphology, neuronal properties and social behavior of mice. In situ hybridization shows Negr1 expression in the brain nuclei which are central modulators of cortical-subcortical connectivity such as the island of Calleja and the reticular nucleus of thalamus. Brain morphological analysis revealed neuroanatomical abnormalities in Negr1−/− mice, including enlargement of ventricles and decrease in the volume of the whole brain, corpus callosum, globus pallidus and hippocampus. Furthermore, decreased number of parvalbumin-positive inhibitory interneurons was evident in Negr1−/− hippocampi. Behaviorally, Negr1−/− mice displayed hyperactivity in social interactions and impairments in social hierarchy. Finally, Negr1 deficiency resulted in disrupted neurite sprouting during neuritogenesis. Our results provide evidence that NEGR1 is required for balancing the ratio of excitatory/inhibitory neurons and proper formation of brain structures, which is prerequisite for adaptive behavioral profiles. Therefore, Negr1−/− mice have a high potential to provide new insights into the neural mechanisms of neuropsychiatric disorders.
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Affiliation(s)
- Katyayani Singh
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia. .,Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia.
| | - Mohan Jayaram
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia.,Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Maria Kaare
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia.,Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Este Leidmaa
- Institute of Molecular Psychiatry, University of Bonn, Sigmund-Freud-Str.25, 53127, Bonn, Germany
| | - Toomas Jagomäe
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia.,Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Indrek Heinla
- Department of Psychology, UiT The Arctic University of Norway, Postboks 6050 Langnes, 9037, Tromso, Norway
| | - Miriam A Hickey
- Department of Pharmacology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Allen Kaasik
- Department of Pharmacology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Michael K Schäfer
- Department for Anesthesiology, University Medical Center and Focus Program Translational Neuroscience (FTN), Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Jürgen Innos
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia.,Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Kersti Lilleväli
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia.,Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Mari-Anne Philips
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia.,Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Eero Vasar
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia.,Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
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38
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Polygenic Scores for Neuropsychiatric Traits and White Matter Microstructure in the Pediatric Population. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:243-250. [DOI: 10.1016/j.bpsc.2018.07.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 07/08/2018] [Accepted: 07/09/2018] [Indexed: 12/31/2022]
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39
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Fries GR, Walss-Bass C, Bauer ME, Teixeira AL. Revisiting inflammation in bipolar disorder. Pharmacol Biochem Behav 2019; 177:12-19. [DOI: 10.1016/j.pbb.2018.12.006] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 12/05/2018] [Accepted: 12/20/2018] [Indexed: 01/11/2023]
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40
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Bandres-Ciga S, Saez-Atienzar S, Bonet-Ponce L, Billingsley K, Vitale D, Blauwendraat C, Gibbs JR, Pihlstrøm L, Gan-Or Z, Cookson MR, Nalls MA, Singleton AB. The endocytic membrane trafficking pathway plays a major role in the risk of Parkinson's disease. Mov Disord 2019; 34:460-468. [PMID: 30675927 DOI: 10.1002/mds.27614] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 12/05/2018] [Accepted: 12/23/2018] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND PD is a complex polygenic disorder. In recent years, several genes from the endocytic membrane-trafficking pathway have been suggested to contribute to disease etiology. However, a systematic analysis of pathway-specific genetic risk factors is yet to be performed. OBJECTIVES To comprehensively study the role of the endocytic membrane-trafficking pathway in the risk of PD. METHODS Linkage disequilibrium score regression was used to estimate PD heritability explained by 252 genes involved in the endocytic membrane-trafficking pathway including genome-wide association studies data from 18,869 cases and 22,452 controls. We used pathway-specific single-nucleotide polymorphisms to construct a polygenic risk score reflecting the cumulative risk of common variants. To prioritize genes for follow-up functional studies, summary-data based Mendelian randomization analyses were applied to explore possible functional genomic associations with expression or methylation quantitative trait loci. RESULTS The heritability estimate attributed to endocytic membrane-trafficking pathway was 3.58% (standard error = 1.17). Excluding previously nominated PD endocytic membrane-trafficking pathway genes, the missing heritability was 2.21% (standard error = 0.42). Random heritability simulations were estimated to be 1.44% (standard deviation = 0.54), indicating that the unbiased total heritability explained by the endocytic membrane-trafficking pathway was 2.14%. Polygenic risk score based on endocytic membrane-trafficking pathway showed a 1.25 times increase of PD risk per standard deviation of genetic risk. Finally, Mendelian randomization identified 11 endocytic membrane-trafficking pathway genes showing functional consequence associated to PD risk. CONCLUSIONS We provide compelling genetic evidence that the endocytic membrane-trafficking pathway plays a relevant role in disease etiology. Further research on this pathway is warranted given that critical effort should be made to identify potential avenues within this biological process suitable for therapeutic interventions. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Sara Bandres-Ciga
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA.,Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), Granada, Spain
| | - Sara Saez-Atienzar
- Transgenics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Luis Bonet-Ponce
- Cell Biology and Gene Expression Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Kimberley Billingsley
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA.,Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom.,Department of Pathophysiology, University of Tartu, Tartu, Estonia
| | - Dan Vitale
- Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Cornelis Blauwendraat
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Jesse Raphael Gibbs
- Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Ziv Gan-Or
- Department of Neurology and Neurosurgery, Department of Human Genetics, McGill University, Montréal, Quebec, Canada.,Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | | | - Mark R Cookson
- Cell Biology and Gene Expression Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Mike A Nalls
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA.,Data Tecnica International, Glen Echo, Maryland, USA
| | - Andrew B Singleton
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
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41
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Park DI, Turck CW. Interactome Studies of Psychiatric Disorders. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1118:163-173. [PMID: 30747422 DOI: 10.1007/978-3-030-05542-4_8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
High comorbidity and complexity have precluded reliable diagnostic assessment and treatment of psychiatric disorders. Impaired molecular interactions may be relevant for underlying mechanisms of psychiatric disorders but by and large remain unknown. With the help of a number of publicly available databases and various technological tools, recent research has filled the paucity of information by generating a novel dataset of psychiatric interactomes. Different technological platforms including yeast two-hybrid screen, co-immunoprecipitation-coupled with mass spectrometry-based proteomics, and transcriptomics have been widely used in combination with cellular and molecular techniques to interrogate the psychiatric interactome. Novel molecular interactions have been identified in association with different psychiatric disorders including autism spectrum disorders, schizophrenia, bipolar disorder, and major depressive disorder. However, more extensive and sophisticated interactome research needs to be conducted to overcome the current limitations such as incomplete interactome databases and a lack of functional information among components. Ultimately, integrated psychiatric interactome databases will contribute to the implementation of biomarkers and therapeutic intervention.
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Affiliation(s)
- Dong Ik Park
- Danish Research Institute of Translational Neuroscience (DANDRITE), Department of Biomedicine, Aarhus University, Aarhus, Denmark.
| | - Christoph W Turck
- Proteomics and Biomarkers, Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
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42
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Volgin AD, Yakovlev OA, Demin KA, de Abreu MS, Alekseeva PA, Friend AJ, Lakstygal AM, Amstislavskaya TG, Bao W, Song C, Kalueff AV. Zebrafish models for personalized psychiatry: Insights from individual, strain and sex differences, and modeling gene x environment interactions. J Neurosci Res 2018; 97:402-413. [PMID: 30320468 DOI: 10.1002/jnr.24337] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 08/16/2018] [Accepted: 09/17/2018] [Indexed: 12/30/2022]
Abstract
Currently becoming widely recognized, personalized psychiatry focuses on unique physiological and genetic profiles of patients to best tailor their therapy. However, the role of individual differences, as well as genetic and environmental factors, in human psychiatric disorders remains poorly understood. Animal experimental models are a valuable tool to improve our understanding of disease pathophysiology and its molecular mechanisms. Due to high reproduction capability, fully sequenced genome, easy gene editing, and high genetic and physiological homology with humans, zebrafish (Danio rerio) are emerging as a novel powerful model in biomedicine. Mounting evidence supports zebrafish as a useful model organism in CNS research. Robustly expressed in these fish, individual, strain, and sex differences shape their CNS responses to genetic, environmental, and pharmacological manipulations. Here, we discuss zebrafish as a promising complementary translational tool to further advance patient-centered personalized psychiatry.
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Affiliation(s)
- Andrey D Volgin
- Almazov National Medical Research Centre, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia.,Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.,Military Medical Academy, St Petersburg, Russia
| | - Oleg A Yakovlev
- Almazov National Medical Research Centre, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia.,Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.,Military Medical Academy, St Petersburg, Russia
| | - Konstantin A Demin
- Almazov National Medical Research Centre, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia.,Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Murilo S de Abreu
- Bioscience Institute, University of Passo Fundo (UPF), Passo Fundo, Brazil.,Postgraduate Program in Pharmacology, Federal University of Santa Maria, Santa Maria, Brazil
| | - Polina A Alekseeva
- Almazov National Medical Research Centre, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia
| | - Ashton J Friend
- Tulane University School of Science and Engineering, New Orleans, Louisiana
| | - Anton M Lakstygal
- Almazov National Medical Research Centre, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia.,Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Tamara G Amstislavskaya
- Laboratory of Translational Biopsychiatry, Scientific Research Institute of Physiology and Basic Medicine, Novosibirsk, Russia
| | - Wandong Bao
- School of Pharmacy, Southwest University, Chongqing, China
| | - Cai Song
- Research Institute of Marine Drugs and Nutrition, Guangdong Ocean University, Zhanjiang, China
| | - Allan V Kalueff
- School of Pharmacy, Southwest University, Chongqing, China.,Ural Federal University, Ekaterinburg, Russia.,ZENEREI Research Center, Slidell, Louisiana.,Institute of Experimental Medicine, Almazov National Medical Research Centre, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia.,Granov Russian Scientific Center of Radiology and Surgical Technologies, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia.,Scientific Research Institute of Physiology and Basic Medicine, Novosibirsk, Russia.,Laboratory of Biological Psychiatry, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
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43
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Yazici E, Cimen Z, Akyollu IIU, Yazici AB, Turkmen BA, Erol A. Depressive Temperament in Relatives of Patients with Schizophrenia Is Associated with Suicidality in Patients with Schizophrenia. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE 2018; 16:302-309. [PMID: 30121980 PMCID: PMC6124869 DOI: 10.9758/cpn.2018.16.3.302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 07/16/2017] [Accepted: 08/11/2017] [Indexed: 01/22/2023]
Abstract
Objective Suicide is a major cause of death in patients with schizophrenia; thus, predicting and preventing suicide in patients with schizophrenia is examined in various studies. Affective temperaments which are accepted as precursors of mood disorders may be an important factor in predicting suicidality. This study investigated the relationship between affective temperaments of relatives of schizophrenia patients and suicidal thoughts and other clinical correlates of patients with schizophrenia. Methods Patients with schizophrenia and their first degree relatives are included to the study. All of the participants were evaluated with Structured Clinical Interview for DSM-IV axis I disorders and relatives with active psychiatric diagnosis were excluded. Positive and Negative Symptom Scale, Clinical Global Impression Scale, Turkish version of cognitive assessment interview were administered congruently to the patients. Relatives of the patients were evaluated with Temperament Evaluation of Memphis, Pisa, Paris and San Diego-Auto-questionnaire. Results Depressive temperament scores of relatives of schizophrenic patients who had suicidal thoughts were higher than the scores of the relatives of the patients who did not have suicidal thoughts. Depressive temperament also predicted number of suicide attempts in regression analysis. Number of suicide attempts was also related with number of hospitalization and functionality of the patient. Conclusion Suicidality in schizophrenia is related with relatives’ affective temperaments and patients’ own positive symptom scores. The relationship between suicidal thoughts and depressive temperament is high lightened in this study
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Affiliation(s)
- Esra Yazici
- Department of Psychiatry, Faculty of Medicine, Sakarya University, Sakarya, Turkey
| | - Zerrin Cimen
- Department of Psychiatry, Sakarya University Training and Research Hospital, Sakarya, Turkey
| | | | - Ahmet Bulent Yazici
- Department of Psychiatry, Sakarya University Training and Research Hospital, Sakarya, Turkey
| | - Betul Aslan Turkmen
- Department of Psychiatry, Sakarya University Training and Research Hospital, Sakarya, Turkey
| | - Atila Erol
- Department of Psychiatry, Faculty of Medicine, Sakarya University, Sakarya, Turkey
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44
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Miller JA, Scult MA, Conley ED, Chen Q, Weinberger DR, Hariri AR. Effects of Schizophrenia Polygenic Risk Scores on Brain Activity and Performance During Working Memory Subprocesses in Healthy Young Adults. Schizophr Bull 2018; 44:844-853. [PMID: 29040762 PMCID: PMC6007653 DOI: 10.1093/schbul/sbx140] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Recent work has begun to shed light on the neural correlates and possible mechanisms of polygenic risk for schizophrenia. Here, we map a schizophrenia polygenic risk profile score (PRS) based on genome-wide association study significant loci onto variability in the activity and functional connectivity of a frontoparietal network supporting the manipulation versus maintenance of information during a numerical working memory (WM) task in healthy young adults (n = 99, mean age = 19.8). Our analyses revealed that higher PRS was associated with hypoactivity of the dorsolateral prefrontal cortex (dlPFC) during the manipulation but not maintenance of information in WM (r2 = .0576, P = .018). Post hoc analyses revealed that PRS-modulated dlPFC hypoactivity correlated with faster reaction times during WM manipulation (r2 = .0967, P = .002), and faster processing speed (r2 = .0967, P = .003) on a separate behavioral task. These PRS-associated patterns recapitulate dlPFC hypoactivity observed in patients with schizophrenia during central executive manipulation of information in WM on this task.
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Affiliation(s)
- Jacob A Miller
- UC Berkeley Graduate Neuroscience Program, Helen Wills Neuroscience Institute, Berkeley, CA
| | - Matthew A Scult
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC,To whom correspondence should be addressed; tel: 919-684-1039, fax: 919-660-5726, e-mail:
| | | | - Qiang Chen
- Lieber Institute for Brain Development, Baltimore, MD,Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD,Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD,Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD,Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD,Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD,Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD,Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD,Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD
| | - Ahmad R Hariri
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC
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45
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Shadrina M, Bondarenko EA, Slominsky PA. Genetics Factors in Major Depression Disease. Front Psychiatry 2018; 9:334. [PMID: 30083112 PMCID: PMC6065213 DOI: 10.3389/fpsyt.2018.00334] [Citation(s) in RCA: 136] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 07/02/2018] [Indexed: 12/22/2022] Open
Abstract
Depressive disorders (DDs) are one of the most widespread forms of psychiatric pathology. According to the World Health Organization, about 350 million people in the world are affected by this condition. Family and twin studies have demonstrated that the contribution of genetic factors to the risk of the onset of DDs is quite large. Various methodological approaches (analysis of candidate genes, genome-wide association analysis, genome-wide sequencing) have been used, and a large number of the associations between genes and different clinical DD variants and DD subphenotypes have been published. However, in most cases, these associations have not been confirmed in replication studies, and only a small number of genes have been proven to be associated with DD development risk. To ascertain the role of genetic factors in DD pathogenesis, further investigations of the relevant conditions are required. Special consideration should be given to the polygenic characteristics noted in whole-genome studies of the heritability of the disorder without a pronounced effect of the major gene. These observations accentuate the relevance of the analysis of gene-interaction roles in DD development and progression. It is important that association studies of the inherited variants of the genome should be supported by analysis of dynamic changes during DD progression. Epigenetic changes that cause modifications of a gene's functional state without changing its coding sequence are of primary interest. However, the opportunities for studying changes in the epigenome, transcriptome, and proteome during DD are limited by the nature of the disease and the need for brain tissue analysis, which is possible only postmortem. Therefore, any association studies between DD pathogenesis and epigenetic factors must be supplemented through the use of different animal models of depression. A threefold approach comprising the combination of gene association studies, assessment of the epigenetic state in DD patients, and analysis of different "omic" changes in animal depression models will make it possible to evaluate the contribution of genetic, epigenetic, and environmental factors to the development of different forms of depression and to help develop ways to decrease the risk of depression and improve the treatment of DD.
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Affiliation(s)
- Maria Shadrina
- Laboratory of Molecular Genetics of Hereditary Diseases, Institute of Molecular Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Elena A Bondarenko
- Laboratory of Molecular Genetics of Hereditary Diseases, Institute of Molecular Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Petr A Slominsky
- Laboratory of Molecular Genetics of Hereditary Diseases, Institute of Molecular Genetics, Russian Academy of Sciences, Moscow, Russia
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Abstract
Recent large-scale genomic studies have confirmed that schizophrenia is a polygenic syndrome and have implicated a number of biological pathways in its aetiology. Both common variants individually of small effect and rarer but more penetrant genetic variants have been shown to play a role in the pathogenesis of the disorder. No simple Mendelian forms of the condition have been identified, but progress has been made in stratifying risk on the basis of the polygenic burden of common variants individually of small effect, and the contribution of rarer variants of larger effect such as Copy Number Variants (CNVs). Pathway analysis of risk-associated variants has begun to identify specific biological processes implicated in risk for the disorder, including elements of the glutamatergic NMDA receptor complex and post synaptic density, voltage-gated calcium channels, targets of the Fragile X Mental Retardation Protein (FMRP targets) and immune pathways. Genetic studies have also been used to drive genomic imaging approaches to the investigation of brain markers associated with risk for the disorder. Genomic imaging approaches have been applied both to investigate the effect of polygenic risk and to study the impact of individual higher-penetrance variants such as CNVs. Both genomic and genomic imaging approaches offer potential for the stratification of patients and at-risk groups and the development of better biomarkers of risk and treatment response; however, further research is needed to integrate this work and realise the full potential of these approaches.
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Liu J, Zhang HX, Li ZQ, Li T, Li JY, Wang T, Li Y, Feng GY, Shi YY, He L. The YWHAE gene confers risk to major depressive disorder in the male group of Chinese Han population. Prog Neuropsychopharmacol Biol Psychiatry 2017; 77:172-177. [PMID: 28414084 DOI: 10.1016/j.pnpbp.2017.04.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Revised: 04/11/2017] [Accepted: 04/13/2017] [Indexed: 12/14/2022]
Abstract
Schizophrenia and major depressive disorder are two major psychiatric illnesses that may share specific genetic risk factors to a certain extent. Increasing evidence suggests that the two disorders might be more closely related than previously considered. To investigate whether YWHAE gene plays a significant role in major depressive disorder in Han Chinese population, we recruited 1135 unrelated major depressive disorder patients (485 males, 650 females) and 989 unrelated controls (296 males, 693 females) of Chinese Han origin. Eleven common SNPs were genotyped using TaqMan® technology. In male-group, the allele and genotype frequencies of rs34041110 differed significantly between patients and control (Pallele=0.036486, OR[95%CI]: 1.249442(1.013988-1.539571); Pgenotype=0.045301). Also in this group, allele and genotype frequencies of rs1532976 differed significantly (Pallele=0.013242, OR[95%CI]: 1.302007(1.056501-1.604563); genotype: P=0.039152). Haplotype-analyses showed that, in male-group, positive association with major depressive disorder was found for the A-A-C-G haplotype of rs3752826-rs2131431-rs1873827-rs12452627 (χ2=20.397, P=6.38E-06, OR[95%CI]: 7.442 [2.691-20.583]), its C-A-C-G haplotype (χ2=19.122, P=1.24E-05, OR and 95%CI: 0.402 [0.264-0.612]), its C-C-T-G haplotype (χ2=9.766, P=0.001785, OR[95%CI]: 5.654 [1.664-19.211]). In female-group, positive association was found for the A-A-C-G haplotype of rs3752826-rs2131431-rs1873827-rs12452627 (χ2=78.628, P=7.94E-19, OR[95%CI]: 50.043 [11.087-225.876]), its A-C-T-G haplotype (χ2=38.806, P=4.83E-10, OR[95%CI]: 0.053 [0.015-0.192]), the C-A-C-G haplotype (χ2=18.930, P=1.37E-05, OR[95%CI]: 0.526 [0.392-0.705]), and the C-C-T-G haplotype (χ2=38.668, P=5.18E-10, OR[95%CI]: 6.130 [3.207-11.716]). Our findings support YWHAE being a risk gene for Major Depressive Disorder in the Han Chinese population.
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Affiliation(s)
- Jie Liu
- Shanghai Institute of Orthopaedics and Traumatology, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Bio-X Institute, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Hong-Xin Zhang
- Research Center for Experimental Medicine, State Key Laboratory of Medical Genomics, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Zhi-Qiang Li
- Bio-X Institute, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Tao Li
- Bio-X Institute, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jun-Yan Li
- Bio-X Institute, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Ti Wang
- Bio-X Institute, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - You Li
- Bio-X Institute, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Guo-Yin Feng
- Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yong-Yong Shi
- Bio-X Institute, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Lin He
- Bio-X Institute, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China.
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