1
|
d'Oleire Uquillas F, Sefik E, Li B, Trotter MA, Steele KA, Seidlitz J, Gesue R, Latif M, Fasulo T, Zhang V, Kislin M, Verpeut JL, Cohen JD, Sepulcre J, Wang SSH, Gomez J. Multimodal evidence for cerebellar influence on cortical development in autism: structural growth amidst functional disruption. Mol Psychiatry 2024:10.1038/s41380-024-02769-1. [PMID: 39390225 DOI: 10.1038/s41380-024-02769-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 09/23/2024] [Accepted: 09/25/2024] [Indexed: 10/12/2024]
Abstract
Despite perinatal damage to the cerebellum being one of the highest risk factors for later being diagnosed with autism spectrum disorder (ASD), it is not yet clear how the cerebellum might influence the development of cerebral cortex and whether this co-developmental process is distinct between neurotypical and ASD children. Leveraging a large structural brain MRI dataset of neurotypical children and those diagnosed with ASD, we examined whether structural variation in cerebellar tissue across individuals was correlated with neocortical variation during development, including the thalamus as a coupling factor. We found that the thalamus plays a distinct role in moderating cerebro-cerebellar structural coordination in ASD. Notably, structural coupling between cerebellum, thalamus, and neocortex was strongest in younger childhood and waned by early adolescence, mirroring a previously undescribed trajectory of behavioral development between ASD and neurotypical children. Complementary functional connectivity analyses likewise revealed atypical connectivity between cerebellum and neocortex in ASD. This relationship was particularly prominent in a model of cerebellar structure predicting functional connectivity, where ASD and neurotypical children showed divergent patterns. Interestingly, these functional-structural relationships became more prominent with age, while structural effects were most prominent earlier in childhood, and showed significant lateralization. This pattern may suggest a developmental sequence where early uncoordinated structural growth amongst regions is followed by increasingly atypical functional synchronization. These findings provide multimodal evidence in the living brain for a cerebellar diaschisis model of autism, where both increased cerebellar-cerebral structural coupling and altered functional connectivity in cerebro-cerebellar pathways contribute to the ontogeny of this neurodevelopmental disorder.
Collapse
Affiliation(s)
| | - Esra Sefik
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Bing Li
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Matthew A Trotter
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Kara A Steele
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jakob Seidlitz
- Lifespan Brain Institute, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Rowen Gesue
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mariam Latif
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Tristano Fasulo
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Veronica Zhang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mikhail Kislin
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jessica L Verpeut
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Department of Psychology, Arizona State University, Tempe, AZ, USA
| | - Jonathan D Cohen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Samuel S-H Wang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jesse Gomez
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| |
Collapse
|
2
|
Fu X, Chen Y, Luo X, Ide JS, Li CSR. Gray matter volumetric correlates of the polygenic risk of depression: A study of the Human Connectome Project data. Eur Neuropsychopharmacol 2024; 87:2-12. [PMID: 38936229 DOI: 10.1016/j.euroneuro.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 06/04/2024] [Accepted: 06/07/2024] [Indexed: 06/29/2024]
Abstract
Genetic factors confer risks for depression. Understanding the neural endophenotypes, including brain morphometrics, of genetic predisposition to depression would help in unraveling the pathophysiology of depression. We employed voxel-based morphometry (VBM) to examine how gray matter volumes (GMVs) were correlated with the polygenic risk score (PRS) for depression in 993 young adults of the Human Connectome Project. The phenotype of depression was quantified with a DSM-oriented scale of the Achenbach Adult Self-Report. The PRS for depression was computed for each subject using the Psychiatric Genomics Association Study as the base sample. In multiple regression with age, sex, race, drinking severity, and total intracranial volume as covariates, regional GMVs in positive correlation with the PRS were observed in bilateral hippocampi and right gyrus rectus. Regional GMVs in negative correlation with the PRS were observed in a wide swath of brain regions, including bilateral frontal and temporal lobes, anterior cingulate cortex, thalamus, lingual gyri, cerebellum, and the left postcentral gyrus, cuneus, and parahippocampal gyrus. We also found sex difference in anterior cingulate volumes in manifesting the genetic risk of depression. In addition, the GMV of the right cerebellum crus I partially mediated the link from PRS to depression severity. These findings add to the literature by highlighting 1) a more diverse pattern of the volumetric markers of depression, with most regions showing lower but others higher GMVs in association with the genetic risks of depression, and 2) the cerebellar GMV as a genetically informed neural phenotype of depression, in neurotypical individuals.
Collapse
Affiliation(s)
- Xiaoya Fu
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Yu Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Xingguang Luo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Jaime S Ide
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA; Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT 06520, USA; Wu Tsai Institute, Yale University, New Haven, CT 06520, USA.
| |
Collapse
|
3
|
Wang Y, Zhang S, Gong W, Liu X, Mo Q, Shen L, Zhao Y, Wang S, Yuan Z. Multi-Omics Integration Analysis Pinpoint Proteins Influencing Brain Structure and Function: Toward Drug Targets and Neuroimaging Biomarkers for Neuropsychiatric Disorders. Int J Mol Sci 2024; 25:9223. [PMID: 39273172 PMCID: PMC11395524 DOI: 10.3390/ijms25179223] [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: 07/23/2024] [Revised: 08/20/2024] [Accepted: 08/23/2024] [Indexed: 09/15/2024] Open
Abstract
Integrating protein quantitative trait loci (pQTL) data and summary statistics from genome-wide association studies (GWAS) of brain image-derived phenotypes (IDPs) can benefit in identifying IDP-related proteins. Here, we developed a systematic omics-integration analytic framework by sequentially using proteome-wide association study (PWAS), Mendelian randomization (MR), and colocalization (COLOC) analyses to identify the potentially causal brain and plasma proteins for IDPs, followed by pleiotropy analysis, mediation analysis, and drug exploration analysis to investigate potential mediation pathways of pleiotropic proteins to neuropsychiatric disorders (NDs) as well as candidate drug targets. A total of 201 plasma proteins and 398 brain proteins were significantly associated with IDPs from PWAS analysis. Subsequent MR and COLOC analyses further identified 313 potentially causal IDP-related proteins, which were significantly enriched in neural-related phenotypes, among which 91 were further identified as pleiotropic proteins associated with both IDPs and NDs, including EGFR, TMEM106B, GPT, and HLA-B. Drug prioritization analysis showed that 6.33% of unique pleiotropic proteins had drug targets or interactions with medications for NDs. Nine potential mediation pathways were identified to illustrate the mediating roles of the IDPs in the causal effect of the pleiotropic proteins on NDs, including the indirect effect of TMEM106B on Alzheimer's disease (AD) risk via radial diffusivity (RD) of the posterior limb of the internal capsule (PLIC), with the mediation proportion being 11.18%, and the indirect effect of EGFR on AD through RD of PLIC, RD of splenium of corpus callosum (SCC), and fractional anisotropy (FA) of SCC, with the mediation proportion being 18.99%, 22.79%, and 19.91%, respectively. These findings provide novel insights into pathogenesis, drug targets, and neuroimaging biomarkers of NDs.
Collapse
Affiliation(s)
- Yunzhuang Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan 250012, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan 250003, China
| | - Sunjie Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan 250012, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan 250003, China
| | - Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan 250012, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan 250003, China
| | - Xinyu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan 250012, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan 250003, China
| | - Qinyou Mo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan 250012, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan 250003, China
| | - Lujia Shen
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan 250012, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan 250003, China
| | - Yansong Zhao
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan 250012, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan 250003, China
| | - Shukang Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan 250012, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan 250003, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan 250012, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan 250003, China
| |
Collapse
|
4
|
Ge YJ, Fu Y, Gong W, Cheng W, Yu JT. Genetic architecture of brain morphology and overlap with neuropsychiatric traits. Trends Genet 2024; 40:706-717. [PMID: 38702264 DOI: 10.1016/j.tig.2024.04.005] [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: 02/12/2024] [Revised: 04/05/2024] [Accepted: 04/12/2024] [Indexed: 05/06/2024]
Abstract
Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.
Collapse
Affiliation(s)
- Yi-Jun Ge
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, 266071, China
| | - Weikang Gong
- School of Data Science, Fudan University, Shanghai, China; Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| |
Collapse
|
5
|
Strike LT, Kerestes R, McMahon KL, de Zubicaray GI, Harding IH, Medland SE. Heritability of cerebellar subregion volumes in adolescent and young adult twins. Hum Brain Mapp 2024; 45:e26717. [PMID: 38798116 PMCID: PMC11128777 DOI: 10.1002/hbm.26717] [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] [Revised: 04/23/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024] Open
Abstract
Twin studies have found gross cerebellar volume to be highly heritable. However, whether fine-grained regional volumes within the cerebellum are similarly heritable is still being determined. Anatomical MRI scans from two independent datasets (QTIM: Queensland Twin IMaging, N = 798, mean age 22.1 years; QTAB: Queensland Twin Adolescent Brain, N = 396, mean age 11.3 years) were combined with an optimised and automated cerebellum parcellation algorithm to segment and measure 28 cerebellar regions. We show that the heritability of regional volumetric measures varies widely across the cerebellum (h 2 $$ {h}^2 $$ 47%-91%). Additionally, the good to excellent test-retest reliability for a subsample of QTIM participants suggests that non-genetic variance in cerebellar volumes is due primarily to unique environmental influences rather than measurement error. We also show a consistent pattern of strong associations between the volumes of homologous left and right hemisphere regions. Associations were predominantly driven by genetic effects shared between lobules, with only sparse contributions from environmental effects. These findings are consistent with similar studies of the cerebrum and provide a first approximation of the upper bound of heritability detectable by genome-wide association studies.
Collapse
Affiliation(s)
- Lachlan T. Strike
- Psychiatric Genetics, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
- School of Psychology and Counselling, Faculty of HealthQueensland University of TechnologyKelvin GroveQueenslandAustralia
- School of Biomedical Sciences, Faculty of MedicineUniversity of QueenslandBrisbaneAustralia
| | - Rebecca Kerestes
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneAustralia
| | - Katie L. McMahon
- School of Clinical Sciences, Centre for Biomedical TechnologiesQueensland University of TechnologyBrisbaneQueenslandAustralia
| | - Greig I. de Zubicaray
- School of Psychology and Counselling, Faculty of HealthQueensland University of TechnologyKelvin GroveQueenslandAustralia
| | - Ian H. Harding
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneAustralia
- Cerebellum and Neurodegeneration, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
| | - Sarah E. Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
- School of Psychology and Counselling, Faculty of HealthQueensland University of TechnologyKelvin GroveQueenslandAustralia
- School of PsychologyUniversity of QueenslandBrisbaneAustralia
| |
Collapse
|
6
|
Deng Q, Song C, Lin S. An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics. Eur J Hum Genet 2024; 32:681-690. [PMID: 37237036 PMCID: PMC11153499 DOI: 10.1038/s41431-023-01389-7] [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: 05/19/2022] [Revised: 05/01/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. In contrast to individual-level data, GWAS summary statistics are usually publicly available, and thus methods using only summary statistics have greater usage. Although many methods have been developed for joint analysis of multiple traits using summary statistics, there are many issues, including inconsistent performance, computational inefficiency, and numerical problems when considering lots of traits. To address these challenges, we propose a multi-trait adaptive Fisher method for summary statistics (MTAFS), a computationally efficient method with robust power performance. We applied MTAFS to two sets of brain imaging derived phenotypes (IDPs) from the UK Biobank, including a set of 58 Volumetric IDPs and a set of 212 Area IDPs. Through annotation analysis, the underlying genes of the SNPs identified by MTAFS were found to exhibit higher expression and are significantly enriched in brain-related tissues. Together with results from a simulation study, MTAFS shows its advantage over existing multi-trait methods, with robust performance across a range of underlying settings. It controls type 1 error well and can efficiently handle a large number of traits.
Collapse
Affiliation(s)
- Qiaolan Deng
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
- Department of Statistics, College of Arts and Sciences, The Ohio State University, Columbus, OH, USA
| | - Chi Song
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Shili Lin
- Department of Statistics, College of Arts and Sciences, The Ohio State University, Columbus, OH, USA.
| |
Collapse
|
7
|
胡 云, 田 耕, 刘 蒙, 汪 鸿. [Advances in Mendelian randomization studies on autism spectrum disorder]. ZHONGGUO DANG DAI ER KE ZA ZHI = CHINESE JOURNAL OF CONTEMPORARY PEDIATRICS 2024; 26:535-540. [PMID: 38802917 PMCID: PMC11135060 DOI: 10.7499/j.issn.1008-8830.2311030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/03/2024] [Indexed: 05/29/2024]
Abstract
Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder with onset in infancy or early childhood. Mendelian randomization (MR) is a statistical method used to infer causal relationships between exposures and outcomes. This article summarizes MR studies related to ASD. Existing research supports a causal relationship between maternal inflammatory bowel disease in children with ASD, parental education levels, screen time exposure, obesity, insomnia, serum transferrin, decreased blood selenium, abnormal signals in brain functional MRI, interleukin-6, phosphodiesterase 2A, mitogen-activated protein kinase kinase 3, mitochondrial ribosomal protein L33, serotonin, and ASD. However, it does not support a causal relationship between parental rheumatoid arthritis, systemic lupus erythematosus, neonatal jaundice in children with ASD, cytomegalovirus infection, asthma, oral ulcers, vitamin D levels, and ASD. This article reviews the etiological factors related to ASD and MR studies, aiming to explore and deepen the understanding of the pathophysiology of ASD. It provides strong statistical support for the prevention, diagnosis, and treatment of ASD, and offers new methods and strategies for the etiological analysis of complex traits.
Collapse
|
8
|
Sefik E, Duan K, Li Y, Sholar B, Evans L, Pincus J, Ammar Z, Murphy MM, Klaiman C, Saulnier CA, Pulver SL, Goldman-Yassen AE, Guo Y, Walker EF, Li L, Mulle JG, Shultz S. Structural deviations of the posterior fossa and the cerebellum and their cognitive links in a neurodevelopmental deletion syndrome. Mol Psychiatry 2024:10.1038/s41380-024-02584-8. [PMID: 38744992 DOI: 10.1038/s41380-024-02584-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 04/16/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
High-impact genetic variants associated with neurodevelopmental disorders provide biologically-defined entry points for mechanistic investigation. The 3q29 deletion (3q29Del) is one such variant, conferring a 40-100-fold increased risk for schizophrenia, as well as high risk for autism and intellectual disability. However, the mechanisms leading to neurodevelopmental disability remain largely unknown. Here, we report the first in vivo quantitative neuroimaging study in individuals with 3q29Del (N = 24) and neurotypical controls (N = 1608) using structural MRI. Given prior radiology reports of posterior fossa abnormalities in 3q29Del, we focused our investigation on the cerebellum and its tissue-types and lobules. Additionally, we compared the prevalence of cystic/cyst-like malformations of the posterior fossa between 3q29Del and controls and examined the association between neuroanatomical findings and quantitative traits to probe gene-brain-behavior relationships. 3q29Del participants had smaller cerebellar cortex volumes than controls, before and after correction for intracranial volume (ICV). An anterior-posterior gradient emerged in finer grained lobule-based and voxel-wise analyses. 3q29Del participants also had larger cerebellar white matter volumes than controls following ICV-correction and displayed elevated rates of posterior fossa arachnoid cysts and mega cisterna magna findings independent of cerebellar volume. Cerebellar white matter and subregional gray matter volumes were associated with visual-perception and visual-motor integration skills as well as IQ, while cystic/cyst-like malformations yielded no behavioral link. In summary, we find that abnormal development of cerebellar structures may represent neuroimaging-based biomarkers of cognitive and sensorimotor function in 3q29Del, adding to the growing evidence identifying cerebellar pathology as an intersection point between syndromic and idiopathic forms of neurodevelopmental disabilities.
Collapse
Affiliation(s)
- Esra Sefik
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Kuaikuai Duan
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Yiheng Li
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Brittney Sholar
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Lindsey Evans
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Jordan Pincus
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Zeena Ammar
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Melissa M Murphy
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Cheryl Klaiman
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Celine A Saulnier
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Neurodevelopmental Assessment & Consulting Services, Atlanta, GA, USA
| | - Stormi L Pulver
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Adam E Goldman-Yassen
- Department of Radiology, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Longchuan Li
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Jennifer G Mulle
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.
| | - Sarah Shultz
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA.
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA.
| |
Collapse
|
9
|
Carrión-Castillo A, Boeckx C. Insights into the genetic architecture of cerebellar lobules derived from the UK Biobank. Sci Rep 2024; 14:9488. [PMID: 38664414 DOI: 10.1038/s41598-024-59699-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/15/2024] [Indexed: 06/19/2024] Open
Abstract
In this work we endeavor to further understand the genetic architecture of the cerebellum by examining the genetic underpinnings of the different cerebellar lob(ul)es, identifying their genetic relation to cortical and subcortical regions, as well as to psychiatric disorders, as well as traces of their evolutionary trajectories. We confirm the moderate heritability of cerebellar volumes, and reveal genetic clustering and variability across their different substructures, which warranted a detailed analysis using this higher structural resolution. We replicated known genetic correlations with several subcortical volumes, and report new cortico-cerebellar genetic correlations, including negative genetic correlations between anterior cerebellar lobules and cingulate, and positive ones between lateral Crus I and lobule VI with cortical measures in the fusiform region. Heritability partitioning for evolutionary annotations highlighted that the vermis of Crus II has depleted heritability in genomic regions of "archaic introgression deserts", but no enrichment/depletion of heritability in any other cerebellar regions. Taken together, these findings reveal novel insights into the genetic underpinnings of the different cerebellar lobules.
Collapse
Affiliation(s)
- Amaia Carrión-Castillo
- Basque Center on Cognition, Brain and Language (BCBL), Donostia-San Sebastián, Spain.
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain.
| | - Cedric Boeckx
- Universitat de Barcelona, Barcelona, Spain.
- Universitat de Barcelona Institute of Complex Systems, Barcelona, Spain.
- Universitat de Barcelona Institute of Neurosciences, Barcelona, Spain.
- Catalan Institute for Research and Advanced Studies (ICREA), Barcelona, Spain.
| |
Collapse
|
10
|
Wainberg M, Forde NJ, Mansour S, Kerrebijn I, Medland SE, Hawco C, Tripathy SJ. Genetic architecture of the structural connectome. Nat Commun 2024; 15:1962. [PMID: 38438384 PMCID: PMC10912129 DOI: 10.1038/s41467-024-46023-2] [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: 09/13/2022] [Accepted: 02/12/2024] [Indexed: 03/06/2024] Open
Abstract
Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.
Collapse
Affiliation(s)
- Michael Wainberg
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
| | - Natalie J Forde
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Salim Mansour
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Isabel Kerrebijn
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Colin Hawco
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | - Shreejoy J Tripathy
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
| |
Collapse
|
11
|
Li H, Cui J, Hu C, Li H, Luo X, Hao Y. Identification and Analysis of ZIC-Related Genes in Cerebellum of Autism Spectrum Disorders. Neuropsychiatr Dis Treat 2024; 20:325-339. [PMID: 38410689 PMCID: PMC10895985 DOI: 10.2147/ndt.s444138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 02/09/2024] [Indexed: 02/28/2024] Open
Abstract
Objective Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with significant genetic heterogeneity. The ZIC gene family can regulate neurodevelopment, especially in the cerebellum, and has been implicated in ASD-like behaviors in mice. We performed bioinformatic analysis to identify the ZIC gene family in the ASD cerebellum. Methods We explored the roles of ZIC family genes in ASD by investigating (i) the association of ZIC genes with ASD risk genes from the Simons Foundation Autism Research Initiative (SFARI) database and ZIC genes in the brain regions of the Human Protein Atlas (HPA) database; (ii) co-expressed gene networks of genes positively and negatively correlated with ZIC1, ZIC2, and ZIC3, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and receiver operating characteristic (ROC) curve analysis of genes in these networks; and (iii) the relationship between ZIC1, ZIC2, ZIC3, and their related genes with cerebellar immune cells and stromal cells in ASD patients. Results (i) ZIC1, ZIC2, and ZIC3 were associated with neurodevelopmental disorders and risk genes related to ASD in the human cerebellum and (ii) ZIC1, ZIC2, and ZIC3 were highly expressed in the cerebellum, which may play a pathogenic role by affecting neuronal development and the cerebellar internal environment in patients with ASD, including immune cells, astrocytes, and endothelial cells. (iii) OLFM3, SLC27A4, GRB2, TMED1, NR2F1, and STRBP are closely related to ZIC1, ZIC2, and ZIC3 in ASD cerebellum and have good diagnostic accuracy. (iv) ASD mice in the maternal immune activation model demonstrated that Zic3 and Nr2f1 levels were decreased in the immune-activated cerebellum. Conclusion Our study supports the role of ZIC1, ZIC2, and ZIC3 in ASD pathogenesis and provides potential targets for early and accurate prediction of ASD.
Collapse
Affiliation(s)
- Heli Li
- Division of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China
| | - Jinru Cui
- Division of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China
| | - Cong Hu
- Division of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China
| | - Hao Li
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China
| | - Xiaoping Luo
- Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China
| | - Yan Hao
- Division of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China
| |
Collapse
|
12
|
Xu Y, Han S, Wei Y, Zheng R, Cheng J, Zhang Y. Abnormal resting-state effective connectivity in large-scale networks among obsessive-compulsive disorder. Psychol Med 2024; 54:350-358. [PMID: 37310178 DOI: 10.1017/s0033291723001228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND Obsessive-compulsive disorder (OCD) is a chronic mental illness characterized by abnormal functional connectivity among distributed brain regions. Previous studies have primarily focused on undirected functional connectivity and rarely reported from network perspective. METHODS To better understand between or within-network connectivities of OCD, effective connectivity (EC) of a large-scale network is assessed by spectral dynamic causal modeling with eight key regions of interests from default mode (DMN), salience (SN), frontoparietal (FPN) and cerebellum networks, based on large sample size including 100 OCD patients and 120 healthy controls (HCs). Parametric empirical Bayes (PEB) framework was used to identify the difference between the two groups. We further analyzed the relationship between connections and Yale-Brown Obsessive Compulsive Scale (Y-BOCS). RESULTS OCD and HCs shared some similarities of inter- and intra-network patterns in the resting state. Relative to HCs, patients showed increased ECs from left anterior insula (LAI) to medial prefrontal cortex, right anterior insula (RAI) to left dorsolateral prefrontal cortex (L-DLPFC), right dorsolateral prefrontal cortex (R-DLPFC) to cerebellum anterior lobe (CA), CA to posterior cingulate cortex (PCC) and to anterior cingulate cortex (ACC). Moreover, weaker from LAI to L-DLPFC, RAI to ACC, and the self-connection of R-DLPFC. Connections from ACC to CA and from L-DLPFC to PCC were positively correlated with compulsion and obsession scores (r = 0.209, p = 0.037; r = 0.199, p = 0.047, uncorrected). CONCLUSIONS Our study revealed dysregulation among DMN, SN, FPN, and cerebellum in OCD, emphasizing the role of these four networks in achieving top-down control for goal-directed behavior. There existed a top-down disruption among these networks, constituting the pathophysiological and clinical basis.
Collapse
Affiliation(s)
- Yinhuan Xu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China and Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China and Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China and Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China and Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China and Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yan Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China and Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| |
Collapse
|
13
|
Cao H, Shinn AK, Guo W. Editorial: Cerebellar structure and function in psychotic disorders: from mechanisms to clinics. Front Psychiatry 2023; 14:1344882. [PMID: 38146281 PMCID: PMC10749355 DOI: 10.3389/fpsyt.2023.1344882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/27/2023] Open
Affiliation(s)
- Hengyi Cao
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, United States
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, United States
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Ann K. Shinn
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| |
Collapse
|
14
|
Thomaidis GV, Papadimitriou K, Michos S, Chartampilas E, Tsamardinos I. A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning. IBRO Neurosci Rep 2023; 15:77-89. [PMID: 38025660 PMCID: PMC10668096 DOI: 10.1016/j.ibneur.2023.06.008] [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: 01/06/2023] [Revised: 05/19/2023] [Accepted: 06/29/2023] [Indexed: 12/01/2023] Open
Abstract
Background Transcriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar disorder.With this aim, user-friendly, fully automated machine learning algorithms can achieve extremely high classification scores and disease-related predictive biosignature identification, in short time frames and scaled down to small datasets. Method A fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper-parameter values, was applied on a preprocessed transcriptomics dataset, in order to produce a model for biosignature selection and to classify subjects into groups of patients and controls. The parent GEO datasets were originally produced from the cerebellar and parietal lobe tissue of deceased bipolar patients and healthy controls, using Affymetrix Human Gene 1.0 ST Array. Results Patients and controls were classified into two separate groups, with no close-to-the-boundary cases, and this classification was based on the cerebellar transcriptomic biosignature of 25 features (genes), with Area Under Curve 0.929 and Average Precision 0.955. The biosignature includes both genes connected before to bipolar disorder, depression, psychosis or epilepsy, as well as genes not linked before with any psychiatric disease. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed participation of 4 identified features in 6 pathways which have also been associated with bipolar disorder. Conclusion Automated machine learning (AutoML) managed to identify accurately 25 genes that can jointly - in a multivariate-fashion - separate bipolar patients from healthy controls with high predictive power. The discovered features lead to new biological insights. Machine Learning (ML) analysis considers the features in combination (in contrast to standard differential expression analysis), removing both irrelevant as well as redundant markers, and thus, focusing to biological interpretation.
Collapse
Affiliation(s)
- Georgios V. Thomaidis
- Greek National Health System, Psychiatric Department, Katerini General Hospital, Katerini, Greece
| | - Konstantinos Papadimitriou
- Greek National Health System, G. Papanikolaou General Hospital, Organizational Unit - Psychiatric Hospital of Thessaloniki, Thessaloniki, Greece
| | | | - Evangelos Chartampilas
- Laboratory of Radiology, AHEPA General Hospital, University of Thessaloniki, Thessaloniki, Greece
| | | |
Collapse
|
15
|
Wu BS, Ge YJ, Zhang W, Chen SD, Xiang ST, Zhang YR, Ou YN, Jiang YC, Tan L, Cheng W, Suckling J, Feng JF, Yu JT, Mao Y. Genome-wide association study of cerebellar white matter microstructure and genetic overlap with common brain disorders. Neuroimage 2023; 269:119928. [PMID: 36740028 DOI: 10.1016/j.neuroimage.2023.119928] [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: 11/23/2022] [Revised: 01/12/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The cerebellum is recognized as being involved in neurocognitive and motor functions with communication with extra-cerebellar regions relying on the white matter integrity of the cerebellar peduncles. However, the genetic determinants of cerebellar white matter integrity remain largely unknown. METHODS We conducted a genome-wide association analysis of cerebellar white matter microstructure using diffusion tensor imaging data from 25,415 individuals from UK Biobank. The integrity of cerebellar white matter microstructure was measured as fractional anisotropy (FA) and mean diffusivity (MD). Identification of independent genomic loci, functional annotation, and tissue and cell-type analysis were conducted with FUMA. The linkage disequilibrium score regression (LDSC) was used to calculate genetic correlations between cerebellar white matter microstructure and regional brain volumes and brain-related traits. Furthermore, the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was employed to identify the shared genetic basis between cerebellar white matter microstructure and common brain disorders. RESULTS We identified 11 genetic loci (P < 8.3 × 10-9) and 86 genes associated with cerebellar white matter microstructure. Further functional enrichment analysis implicated the involvement of GABAergic neurons and cholinergic pathways. Significant polygenetic overlap between cerebellar white matter tracts and their anatomically connected or adjacent brain regions was detected. In addition, we report the overall genetic correlation and specific loci shared between cerebellar white matter microstructural integrity and brain-related traits, including movement, cognitive, psychiatric, and cerebrovascular categories. CONCLUSIONS Collectively, this study represents a step forward in understanding the genetics of cerebellar white matter microstructure and its shared genetic etiology with common brain disorders.
Collapse
Affiliation(s)
- Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Tong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yu-Chao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - John Suckling
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Ying Mao
- Department of Neurosurgery and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| |
Collapse
|
16
|
Andreassen OA, Hindley GFL, Frei O, Smeland OB. New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications. World Psychiatry 2023; 22:4-24. [PMID: 36640404 PMCID: PMC9840515 DOI: 10.1002/wps.21034] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 01/15/2023] Open
Abstract
Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.
Collapse
Affiliation(s)
- Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Guy F L Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| |
Collapse
|
17
|
Tissink E, de Lange SC, Savage JE, Wightman DP, de Leeuw CA, Kelly KM, Nagel M, van den Heuvel MP, Posthuma D. Genome-wide association study of cerebellar volume provides insights into heritable mechanisms underlying brain development and mental health. Commun Biol 2022; 5:710. [PMID: 35842455 PMCID: PMC9288439 DOI: 10.1038/s42003-022-03672-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
Cerebellar volume is highly heritable and associated with neurodevelopmental and neurodegenerative disorders. Understanding the genetic architecture of cerebellar volume may improve our insight into these disorders. This study aims to investigate the convergence of cerebellar volume genetic associations in close detail. A genome-wide associations study for cerebellar volume was performed in a discovery sample of 27,486 individuals from UK Biobank, resulting in 30 genome-wide significant loci and a SNP heritability of 39.82%. We pinpoint the likely causal variants and those that have effects on amino acid sequence or cerebellar gene-expression. Additionally, 85 genome-wide significant genes were detected and tested for convergence onto biological pathways, cerebellar cell types, human evolutionary genes or developmental stages. Local genetic correlations between cerebellar volume and neurodevelopmental and neurodegenerative disorders reveal shared loci with Parkinson's disease, Alzheimer's disease and schizophrenia. These results provide insights into the heritable mechanisms that contribute to developing a brain structure important for cognitive functioning and mental health.
Collapse
Affiliation(s)
- Elleke Tissink
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - Siemon C de Lange
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands.,Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - Douglas P Wightman
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - Christiaan A de Leeuw
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - Kristen M Kelly
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Mats Nagel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands.,Department of Child and Adolescent Psychiatry, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands. .,Department of Child and Adolescent Psychiatry, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands.
| |
Collapse
|