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Mahmoodi M, Mehrgardi AA, Momen M, Serpell JA, Esmailizadeh A. Deciphering the Genetic Basis of Behavioral Traits in Dogs: Observed-trait GWAS and Latent-trait GWAS Analysis Reveal Key Genes and Variants. Vet J 2024:106251. [PMID: 39368730 DOI: 10.1016/j.tvjl.2024.106251] [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: 07/25/2024] [Revised: 09/21/2024] [Accepted: 09/26/2024] [Indexed: 10/07/2024]
Abstract
Dogs exhibit remarkable phenotypic diversity, particularly in behavioral traits, making them an excellent model for studying the genetic basis of complex behaviors. Behavioral traits such as aggression and fear are highly heritable among different dog breeds, but their genetic basis is largely unknown. We used the genome-wide association study (GWAS) to identify candidate genes associated with nine behavioral traits including; stranger-directed aggression (SDA), owner-directed aggression (ODA), dog-directed aggression (DDA), stranger-directed fear (SDF), nonsocial fear (NF), dog-directed fear (DDF), touch sensitivity (TS), separation-related behavior (SRB) and attachment attention-seeking (AAS). The observed behavioral traits were collected from 38,714 to 40,460 individuals across 108 modern dog breeds. We performed a GWAS based on a latent trait extracted using the confirmatory factor analysis (CFA) method with nine observable behavioral traits and compared the results with those from the GWAS of the observed traits. Using both observed-trait and latent-trait GWAS, we identified 41 significant SNPs that were common between both GWAS methods, of which 26 were pleiotropic, as well as 10 SNPs unique to the latent-trait GWAS, and 5 SNPs unique to the observed-trait GWAS discovered. These SNPs were associated with 21 genes in latent-trait GWAS and 22 genes in the observed-trait GWAS, with 19 genes shared by both. According to previous studies, some of the genes from this study have been reported to be related to behavioral and neurological functions in dogs. In the human population, these identified genes play a role in either the formation of the nervous system or are linked to various mental health conditions. Taken together, our findings suggest that latent-trait GWAS for behavioral traits in dogs identifies significant latent genes that are neurologically prioritized.
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Affiliation(s)
- Maryam Mahmoodi
- Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.
| | - Ahmad Ayatollahi Mehrgardi
- Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.
| | - Mehdi Momen
- Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - James A Serpell
- Center for the Interaction of Animals and Society, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ali Esmailizadeh
- Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
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Zhou J, Gong L, Liu X, Chen L, Yang Z. Mendelian randomization in Alzheimer's disease and mild cognitive impairment: Hippocampal volume associations. Neuroscience 2024:S0306-4522(24)00510-4. [PMID: 39368607 DOI: 10.1016/j.neuroscience.2024.10.007] [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: 04/11/2024] [Revised: 09/30/2024] [Accepted: 10/02/2024] [Indexed: 10/07/2024]
Abstract
This study investigates the association between cognitive dysfunction and hippocampal volumes in Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) using Mendelian randomization. A meta-analysis of 503 healthy controls, 562 MCI patients, and 389 CE patients revealed significant reductions in hippocampal and subregion volumes in MCI and AD compared to controls. While various subregions showed volume reductions, no causal relationship between hippocampal volume and AD was established through Mendelian randomization analysis. In conclusion, significant volume reductions were observed in MCI and AD patients, highlighting the complexity of the relationship between hippocampal volume and cognitive impairment.
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Affiliation(s)
- Jianguo Zhou
- Department of Radiology, Lianyungang Affiliated Hospital of Nanjing University of Chinese Medicine, Lianyungang 222004, PR China
| | - Lei Gong
- Department of Radiology, The Fourth People's Hospital of Lianyungang, Affiliated Hospital of Nanjing Medical University Kangda, Lianyungang 222000, PR China
| | - Xiaoli Liu
- Department of Rehabilitation, Lianyungang Affiliated Hospital of Nanjing University of Chinese Medicine, Lianyungang 222004, PR China
| | - Liping Chen
- Department of Rehabilitation, Lianyungang Affiliated Hospital of Nanjing University of Chinese Medicine, Lianyungang 222004, PR China
| | - Zhou Yang
- Department of Rehabilitation, Lianyungang Affiliated Hospital of Nanjing University of Chinese Medicine, Lianyungang 222004, PR China.
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3
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Li R, Fan YR, Wang YZ, Lu HY, Li PX, Dong Q, Jiang YF, Chen XD, Cui M. Brain Iron in signature regions relating to cognitive aging in older adults: the Taizhou Imaging Study. Alzheimers Res Ther 2024; 16:211. [PMID: 39358805 PMCID: PMC11448274 DOI: 10.1186/s13195-024-01575-9] [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: 05/15/2024] [Accepted: 09/12/2024] [Indexed: 10/04/2024]
Abstract
BACKGROUND Recent magnetic resonance imaging (MRI) studies have established that brain iron accumulation might accelerate cognitive decline in Alzheimer's disease (AD) patients. Both normal aging and AD are associated with cerebral atrophy in specific regions. However, no studies have investigated aging- and AD-selective iron deposition-related cognitive changes during normal aging. Here, we applied quantitative susceptibility mapping (QSM) to detect iron levels in cortical signature regions and assessed the relationships among iron, atrophy, and cognitive changes in older adults. METHODS In this Taizhou Imaging Study, 770 older adults (mean age 62.0 ± 4.93 years, 57.5% women) underwent brain MRI to measure brain iron and atrophy, of whom 219 underwent neuropsychological tests nearly every 12 months for up to a mean follow-up of 2.68 years. Global cognition was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Domain-specific cognitive scores were obtained from MoCA subscore components. Regional analyses were performed for cortical regions and 2 signature regions where atrophy affected by aging and AD only: Aging (AG) -specific and AD signature meta-ROIs. The QSM and cortical morphometry means of the above ROIs were also computed. RESULTS Significant associations were found between QSM levels and cognitive scores. In particular, after adjusting for cortical thickness of regions of interest (ROIs), participants in the upper tertile of the cortical and AG-specific signature QSM exhibited worse ZMMSE than did those in the lower tertile [β = -0.104, p = 0.026;β = -0.118, p = 0.021, respectively]. Longitudinal analysis suggested that QSM values in all ROIs might predict decline in ZMoCA and key domains such as attention and visuospatial function (all p < 0.05). Furthermore, iron levels were negatively correlated with classic MRI markers of cortical atrophy (cortical thickness, gray matter volume, and local gyrification index) in total, AG-specific signature and AD signature regions (all p < 0.05). CONCLUSION AG- and AD-selective iron deposition was associated with atrophy and cognitive decline in elderly people, highlighting its potential as a neuroimaging marker for cognitive aging.
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Affiliation(s)
- Rui Li
- Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China
| | - Yi-Ren Fan
- Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China
| | - Ying-Zhe Wang
- Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China
| | - He-Yang Lu
- Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China
| | - Pei-Xi Li
- Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China
| | - Qiang Dong
- Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China
| | - Yan-Feng Jiang
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, 2005 Songhu Road, Shanghai, 200438, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China
| | - Xing-Dong Chen
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, 2005 Songhu Road, Shanghai, 200438, China.
- Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China.
| | - Mei Cui
- Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China.
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Bahrami S, Nordengen K, Rokicki J, Shadrin AA, Rahman Z, Smeland OB, Jaholkowski PP, Parker N, Parekh P, O'Connell KS, Elvsåshagen T, Toft M, Djurovic S, Dale AM, Westlye LT, Kaufmann T, Andreassen OA. The genetic landscape of basal ganglia and implications for common brain disorders. Nat Commun 2024; 15:8476. [PMID: 39353893 PMCID: PMC11445552 DOI: 10.1038/s41467-024-52583-0] [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: 08/22/2023] [Accepted: 09/13/2024] [Indexed: 10/03/2024] Open
Abstract
The basal ganglia are subcortical brain structures involved in motor control, cognition, and emotion regulation. We conducted univariate and multivariate genome-wide association analyses (GWAS) to explore the genetic architecture of basal ganglia volumes using brain scans obtained from 34,794 Europeans with replication in 4,808 white and generalization in 5,220 non-white Europeans. Our multivariate GWAS identified 72 genetic loci associated with basal ganglia volumes with a replication rate of 55.6% at P < 0.05 and 87.5% showed the same direction, revealing a distributed genetic architecture across basal ganglia structures. Of these, 50 loci were novel, including exonic regions of APOE, NBR1 and HLAA. We examined the genetic overlap between basal ganglia volumes and several neurological and psychiatric disorders. The strongest genetic overlap was between basal ganglia and Parkinson's disease, as supported by robust LD-score regression-based genetic correlations. Mendelian randomization indicated genetic liability to larger striatal volume as potentially causal for Parkinson's disease, in addition to a suggestive causal effect of greater genetic liability to Alzheimer's disease on smaller accumbens. Functional analyses implicated neurogenesis, neuron differentiation and development in basal ganglia volumes. These results enhance our understanding of the genetic architecture and molecular associations of basal ganglia structure and their role in brain disorders.
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Grants
- R01 MH129742 NIMH NIH HHS
- Stiftelsen Kristian Gerhard Jebsen (Kristian Gerhard Jebsen Foundation)
- Norwegian Health Association (22731, 25598), the South-Eastern Norway Regional Health Authority (2013-123, 2017-112, 2019-108, 2014-097, 2015-073, 2016-083), the Research Council of Norway (276082, 323961. 213837, 223273, 248778, 273291, 262656, 229129, 283798, 311993, 324499. 204966, 249795, 273345).
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Affiliation(s)
- Shahram Bahrami
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway.
| | - Kaja Nordengen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Jaroslav Rokicki
- Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Alexey A Shadrin
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Zillur Rahman
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Nadine Parker
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pravesh Parekh
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Torbjørn Elvsåshagen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Department of Behavioral Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Mathias Toft
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Srdjan Djurovic
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Anders M Dale
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Lars T Westlye
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- German Center for Mental Health (DZPG), Tübingen, Germany
| | - Ole A Andreassen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway.
- Department of Psychiatry, Oslo University Hospital, Oslo, Norway.
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5
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Yao S, Han JZ, Guo J, Wang X, Qian L, Wu H, Shi W, Zhu RJ, Wang JH, Dong SS, Cui LL, Wang Y, Guo Y, Yang TL. The Causal Relationships Between Gut Microbiota, Brain Volume, and Intelligence: A Two-Step Mendelian Randomization Analysis. Biol Psychiatry 2024; 96:463-472. [PMID: 38432522 DOI: 10.1016/j.biopsych.2024.02.1012] [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: 10/10/2023] [Revised: 02/05/2024] [Accepted: 02/23/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND Growing evidence indicates that dynamic changes in gut microbiome can affect intelligence; however, whether these relationships are causal remains elusive. We aimed to disentangle the poorly understood causal relationship between gut microbiota and intelligence. METHODS We performed a 2-sample Mendelian randomization (MR) analysis using genetic variants from the largest available genome-wide association studies of gut microbiota (N = 18,340) and intelligence (N = 269,867). The inverse-variance weighted method was used to conduct the MR analyses complemented by a range of sensitivity analyses to validate the robustness of the results. Considering the close relationship between brain volume and intelligence, we applied 2-step MR to evaluate whether the identified effect was mediated by regulating brain volume (N = 47,316). RESULTS We found a risk effect of the genus Oxalobacter on intelligence (odds ratio = 0.968 change in intelligence per standard deviation increase in taxa; 95% CI, 0.952-0.985; p = 1.88 × 10-4) and a protective effect of the genus Fusicatenibacter on intelligence (odds ratio = 1.053; 95% CI, 1.024-1.082; p = 3.03 × 10-4). The 2-step MR analysis further showed that the effect of genus Fusicatenibacter on intelligence was partially mediated by regulating brain volume, with a mediated proportion of 33.6% (95% CI, 6.8%-60.4%; p = .014). CONCLUSIONS Our results provide causal evidence indicating the role of the microbiome in intelligence. Our findings may help reshape our understanding of the microbiota-gut-brain axis and development of novel intervention approaches for preventing cognitive impairment.
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Affiliation(s)
- Shi Yao
- Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China; National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Ji-Zhou Han
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jing Guo
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xin Wang
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Long Qian
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Hao Wu
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Wei Shi
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Ren-Jie Zhu
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jia-Hao Wang
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Li-Li Cui
- Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Yan Wang
- Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China; National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
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6
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Shafee R, Moraczewski D, Liu S, Mallard T, Thomas A, Raznahan A. A sex-stratified analysis of the genetic architecture of human brain anatomy. Nat Commun 2024; 15:8041. [PMID: 39271676 PMCID: PMC11399304 DOI: 10.1038/s41467-024-52244-2] [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: 08/28/2023] [Accepted: 08/30/2024] [Indexed: 09/15/2024] Open
Abstract
Large biobanks have dramatically advanced our understanding of genetic influences on human brain anatomy. However, most studies have combined rather than compared male and female participants. Here we screen for sex differences in the common genetic architecture of over 1000 neuroanatomical phenotypes in the UK Biobank and establish a general concordance between male and female participants in heritability estimates, genetic correlations, and variant-level effects. Notable exceptions include higher mean heritability in the female group for regional volume and surface area phenotypes; between-sex genetic correlations that are significantly below 1 in the insula and parietal cortex; and a common variant with stronger effect in male participants mapping to RBFOX1 - a gene linked to multiple neuropsychiatric disorders more common in men. This work suggests that common variant influences on human brain anatomy are largely consistent between males and females, with a few exceptions that will guide future research in growing datasets.
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Affiliation(s)
- Rebecca Shafee
- Section on Developmental Neurogenomics, Human Genetics Branch, NIMH Intramural Research Program, NIH, Bethesda, MD, USA.
| | | | - Siyuan Liu
- Section on Developmental Neurogenomics, Human Genetics Branch, NIMH Intramural Research Program, NIH, Bethesda, MD, USA
| | - Travis Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, USA
| | - Adam Thomas
- Data Science and Sharing Team, NIMH, NIH, Bethesda, MD, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, NIMH Intramural Research Program, NIH, Bethesda, MD, USA.
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7
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Auvergne A, Traut N, Henches L, Troubat L, Frouin A, Boetto C, Kazem S, Julienne H, Toro R, Aschard H. Multitrait analysis to decipher the intertwined genetic architecture of neuroanatomical phenotypes and psychiatric disorders. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00266-0. [PMID: 39260564 DOI: 10.1016/j.bpsc.2024.08.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/28/2024] [Accepted: 08/12/2024] [Indexed: 09/13/2024]
Abstract
BACKGROUND There is increasing evidence of shared genetic factors between psychiatric disorders and brain magnetic resonance imaging (MRI) phenotypes. However, deciphering the joint genetic architecture of these outcomes has proven challenging, and new approaches are needed to infer potential genetic structure underlying those phenotypes. Multivariate analyses is arising as a meaningful approach to reveal links between MRI phenotypes and psychiatric disorders missed by univariate approaches. METHODS We first conducted univariate and multivariate genome-wide association studies (GWAS) for nine MRI-derived brain volume phenotypes in 20K UK Biobank participants. We next performed various complementary enrichment analyses to assess whether and how univariate and multitrait approaches can distinguish disorder-associated and non-disorder-associated variants from six psychiatric disorders: bipolarity, attention-deficit/hyperactivity disorder (ADHD), autism, schizophrenia, obsessive-compulsive disorder, and major depressive disorder. Finally, we conducted a clustering analysis of top associated variants based on their MRI multitrait association using an optimized k-medoids approach. RESULTS Univariate MRI GWAS displayed only negligible genetic correlation with psychiatric disorders, while multitrait GWAS identified multiple new associations and showed significant enrichment for variants related to both ADHD and schizophrenia. Clustering analyses further detected two clusters displaying not only enrichment for association with ADHD and schizophrenia, but also consistent direction of effects. Functional annotation analyses of those clusters pointed to multiple potential mechanisms, suggesting in particular a role of neurotrophins pathways on both MRI and schizophrenia. CONCLUSIONS Our results show that multitrait association signature can be used to infer genetically-driven latent MRI variables associated with psychiatric disorders, opening paths for future biomarker development.
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Affiliation(s)
- Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France.
| | - Nicolas Traut
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Léo Henches
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Arthur Frouin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Sayeh Kazem
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Roberto Toro
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA.
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8
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Casamitjana A, Mancini M, Robinson E, Peter L, Annunziata R, Althonayan J, Crampsie S, Blackburn E, Billot B, Atzeni A, Puonti O, Balbastre Y, Schmidt P, Hughes J, Augustinack JC, Edlow BL, Zöllei L, Thomas DL, Kliemann D, Bocchetta M, Strand C, Holton JL, Jaunmuktane Z, Iglesias JE. A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.05.579016. [PMID: 39282320 PMCID: PMC11398399 DOI: 10.1101/2024.02.05.579016] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/21/2024]
Abstract
Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next-generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct ~10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimer's disease and healthy brain ageing based on ~4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 μm isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.
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Affiliation(s)
- Adrià Casamitjana
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Matteo Mancini
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Department of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Italian National Institute of Health, Rome, Italy
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Eleanor Robinson
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Loïc Peter
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Roberto Annunziata
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Juri Althonayan
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Shauna Crampsie
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Emily Blackburn
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Benjamin Billot
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Alessia Atzeni
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Oula Puonti
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Yaël Balbastre
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Peter Schmidt
- Advanced Research Computing Centre, University College London, London, United Kingdom
| | - James Hughes
- Advanced Research Computing Centre, University College London, London, United Kingdom
| | - Jean C Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Brian L Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - David L Thomas
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Dorit Kliemann
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, United States
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, United Kingdom
| | - Catherine Strand
- Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Janice L Holton
- Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Zane Jaunmuktane
- Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Juan Eugenio Iglesias
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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9
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Zhou D, Wang W, Gu J, Lu Q. Causal effects of sepsis on structural changes in cerebral cortex: A Mendelian randomization investigation. Medicine (Baltimore) 2024; 103:e39404. [PMID: 39252275 PMCID: PMC11383497 DOI: 10.1097/md.0000000000039404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/11/2024] Open
Abstract
Previous research has shown a strong correlation between sepsis and brain structure. However, whether this relationship represents a causality remains elusive. In this study, we employed Mendelian randomization (MR) to probe the associations of genetically predicted sepsis and sepsis-related death with structural changes in specific brain regions. Genome-wide association study (GWAS) data for sepsis phenotypes (sepsis and sepsis-related death) were obtained from the IEU OpenGWAS. Correspondingly, GWAS data for brain structural traits (volume of the subcortical structure, cortical thickness, and surface area) were derived from the ENIGMA consortium. Inverse variance weighted was mainly utilized to assess the causal effects, while weighted median and MR-Egger regression served as complementary methods. Sensitivity analyses were implemented with Cochran Q test, MR-Egger regression, and MR-PRESSO. In addition, a reverse MR analysis was carried out to assess the possibility of reverse causation. We identified that genetic liability to sepsis was normally significantly associated with a reduced surface area of the postcentral gyrus (β = -35.5280, SE = 13.7465, P = .0096). The genetic liability to sepsis-related death showed a suggestive positive correlation with the surface area of fusiform gyrus (β = 11.0920, SE = 3.6412, P = .0023) and posterior cingulate gyrus (β = 3.6530, SE = 1.6684, P = .0286), While it presented a suggestive negative correlation with surface area of the caudal middle frontal gyrus (β = -11.4586, SE = 5.1501, P = .0261) and frontal pole (β = -1.0024, SE = 0.4329, P = .0206). We also indicated a possible bidirectional causal association between genetic liability to sepsis-related death and the thickness of the transverse temporal gyrus. Sensitivity analyses verified the robustness of the above associations. These findings suggested that genetically determined liability to sepsis might influence the specific brain structure in a causal way, offering new perspectives to investigate the mechanism of sepsis-related neuropsychiatric disorders.
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Affiliation(s)
- Dengfeng Zhou
- Department of Respiratory and Critical Care Medicine, Wuhan Fourth Hospital, Wuhan, Hubei Province, China
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10
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Liu M, Wang L, Zhang Y, Dong H, Wang C, Chen Y, Qian Q, Zhang N, Wang S, Zhao G, Zhang Z, Lei M, Wang S, Zhao Q, Liu F. Investigating the shared genetic architecture between depression and subcortical volumes. Nat Commun 2024; 15:7647. [PMID: 39223129 PMCID: PMC11368965 DOI: 10.1038/s41467-024-52121-y] [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: 01/22/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024] Open
Abstract
Depression, a widespread and highly heritable mental health condition, profoundly affects millions of individuals worldwide. Neuroimaging studies have consistently revealed volumetric abnormalities in subcortical structures associated with depression. However, the genetic underpinnings shared between depression and subcortical volumes remain inadequately understood. Here, we investigate the extent of polygenic overlap using the bivariate causal mixture model (MiXeR), leveraging summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 14 subcortical volumetric phenotypes (N = 33,224). Additionally, we identify shared genomic loci through conditional/conjunctional FDR analyses. MiXeR shows that subcortical volumetric traits share a substantial proportion of genetic variants with depression, with 44 distinct shared loci identified by subsequent conjunctional FDR analysis. These shared loci are predominantly located in intronic regions (58.7%) and non-coding RNA intronic regions (25.4%). The 269 protein-coding genes mapped by these shared loci exhibit specific developmental trajectories, with the expression level of 55 genes linked to both depression and subcortical volumes, and 30 genes linked to cognitive abilities and behavioral symptoms. These findings highlight a shared genetic architecture between depression and subcortical volumetric phenotypes, enriching our understanding of the neurobiological underpinnings of depression.
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Affiliation(s)
- Mengge Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Lu Wang
- Department of Geriatrics and Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujie Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Haoyang Dong
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Caihong Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yayuan Chen
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Qian Qian
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Nannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Shaoying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Guoshu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhihui Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Minghuan Lei
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Sijia Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China.
| | - Qiyu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China.
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11
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Guo J, He C, Song H, Gao H, Yao S, Dong SS, Yang TL. Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives. Neurosci Bull 2024; 40:1333-1352. [PMID: 38703276 PMCID: PMC11365900 DOI: 10.1007/s12264-024-01214-1] [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: 07/14/2023] [Accepted: 01/08/2024] [Indexed: 05/06/2024] Open
Abstract
Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.
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Affiliation(s)
- Jing Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Changyi He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huimiao Song
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huiwu Gao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Shi Yao
- Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
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12
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Hoang N, Sardaripour N, Ramey GD, Schilling K, Liao E, Chen Y, Park JH, Bledsoe X, Landman BA, Gamazon ER, Benton ML, Capra JA, Rubinov M. Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale. PLoS Biol 2024; 22:e3002782. [PMID: 39269986 PMCID: PMC11424006 DOI: 10.1371/journal.pbio.3002782] [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: 03/22/2024] [Revised: 09/25/2024] [Accepted: 08/01/2024] [Indexed: 09/15/2024] Open
Abstract
An understanding of human brain individuality requires the integration of data on brain organization across people and brain regions, molecular and systems scales, as well as healthy and clinical states. Here, we help advance this understanding by leveraging methods from computational genomics to integrate large-scale genomic, transcriptomic, neuroimaging, and electronic-health record data sets. We estimated genetically regulated gene expression (gr-expression) of 18,647 genes, across 10 cortical and subcortical regions of 45,549 people from the UK Biobank. First, we showed that patterns of estimated gr-expression reflect known genetic-ancestry relationships, regional identities, as well as inter-regional correlation structure of directly assayed gene expression. Second, we performed transcriptome-wide association studies (TWAS) to discover 1,065 associations between individual variation in gr-expression and gray-matter volumes across people and brain regions. We benchmarked these associations against results from genome-wide association studies (GWAS) of the same sample and found hundreds of novel associations relative to these GWAS. Third, we integrated our results with clinical associations of gr-expression from the Vanderbilt Biobank. This integration allowed us to link genes, via gr-expression, to neuroimaging and clinical phenotypes. Fourth, we identified associations of polygenic gr-expression with structural and functional MRI phenotypes in the Human Connectome Project (HCP), a small neuroimaging-genomic data set with high-quality functional imaging data. Finally, we showed that estimates of gr-expression and magnitudes of TWAS were generally replicable and that the p-values of TWAS were replicable in large samples. Collectively, our results provide a powerful new resource for integrating gr-expression with population genetics of brain organization and disease.
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Affiliation(s)
- Nhung Hoang
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Neda Sardaripour
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Grace D. Ramey
- Biological and Medical Informatics Division, University of California, San Francisco, California, United States of America
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
| | - Kurt Schilling
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Emily Liao
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Yiting Chen
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Jee Hyun Park
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Xavier Bledsoe
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Bennett A. Landman
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Eric R. Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Mary Lauren Benton
- Department of Computer Science, Baylor University, Waco, Texas, United States of America
| | - John A. Capra
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
- Bakar Computational Health Sciences Institute, University of California, San Francisco, California, United States of America
| | - Mikail Rubinov
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Psychology, Vanderbilt University, Nashville, Tennessee, United States of America
- Howard Hughes Medical Institute Janelia Research Campus, Ashburn, Virginia, United States of America
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13
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García-Marín LM, Campos AI, Diaz-Torres S, Rabinowitz JA, Ceja Z, Mitchell BL, Grasby KL, Thorp JG, Agartz I, Alhusaini S, Ames D, Amouyel P, Andreassen OA, Arfanakis K, Arias Vasquez A, Armstrong NJ, Athanasiu L, Bastin ME, Beiser AS, Bennett DA, Bis JC, Boks MP, Boomsma DI, Brodaty H, Brouwer RM, Buitelaar JK, Burkhardt R, Cahn W, Calhoun VD, Carmichael OT, Chakravarty M, Chen Q, Ching CRK, Cichon S, Crespo-Facorro B, Crivello F, Dale AM, Davey Smith G, de Geus EJ, De Jager PL, de Zubicaray GI, Debette S, DeCarli C, Depondt C, Desrivières S, Djurovic S, Ehrlich S, Erk S, Espeseth T, Fernández G, Filippi I, Fisher SE, Fleischman DA, Fletcher E, Fornage M, Forstner AJ, Francks C, Franke B, Ge T, Goldman AL, Grabe HJ, Green RC, Grimm O, Groenewold NA, Gruber O, Gudnason V, Håberg AK, Haukvik UK, Heinz A, Hibar DP, Hilal S, Himali JJ, Ho BC, Hoehn DF, Hoekstra PJ, Hofer E, Hoffmann W, Holmes AJ, Homuth G, Hosten N, Ikram MK, Ipser JC, Jack CR, Jahanshad N, Jönsson EG, Kahn RS, Kanai R, Klein M, Knol MJ, Launer LJ, Lawrie SM, Le Hellard S, Lee PH, Lemaître H, Li S, Liewald DC, Lin H, Longstreth WT, Lopez OL, Luciano M, Maillard P, Marquand AF, Martin NG, Martinot JL, Mather KA, Mattay VS, McMahon KL, Mecocci P, Melle I, Meyer-Lindenberg A, Mirza-Schreiber N, Milaneschi Y, Mosley TH, Mühleisen TW, Müller-Myhsok B, Muñoz Maniega S, Nauck M, Nho K, Niessen WJ, Nöthen MM, Nyquist PA, Oosterlaan J, Pandolfo M, Paus T, Pausova Z, Penninx BW, Pike GB, Psaty BM, Pütz B, Reppermund S, Rietschel MD, Risacher SL, Romanczuk-Seiferth N, Romero-Garcia R, Roshchupkin GV, Rotter JI, Sachdev PS, Sämann PG, Saremi A, Sargurupremraj M, Saykin AJ, Schmaal L, Schmidt H, Schmidt R, Schofield PR, Scholz M, Schumann G, Schwarz E, Shen L, Shin J, Sisodiya SM, Smith AV, Smoller JW, Soininen HS, Steen VM, Stein DJ, Stein JL, Thomopoulos SI, Toga AW, Tordesillas-Gutiérrez D, Trollor JN, Valdes-Hernandez MC, van 't Ent D, van Bokhoven H, van der Meer D, van der Wee NJ, Vázquez-Bourgon J, Veltman DJ, Vernooij MW, Villringer A, Vinke LN, Völzke H, Walter H, Wardlaw JM, Weinberger DR, Weiner MW, Wen W, Westlye LT, Westman E, White T, Witte AV, Wolf C, Yang J, Zwiers MP, Ikram MA, Seshadri S, Thompson PM, Satizabal CL, Medland SE, Rentería ME. Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.13.24311922. [PMID: 39371125 PMCID: PMC11451674 DOI: 10.1101/2024.08.13.24311922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/08/2024]
Abstract
Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. We performed GWAS meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and, for the first time, the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signalling and brain ageing-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and ADHD. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.
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14
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Zhu J, Chen X, Lu B, Li XY, Wang ZH, Cao LP, Chen GM, Chen JS, Chen T, Chen TL, Cheng YQ, Chu ZS, Cui SX, Cui XL, Deng ZY, Gong QY, Guo WB, He CC, Hu ZJY, Huang Q, Ji XL, Jia FN, Kuang L, Li BJ, Li F, Li HX, Li T, Lian T, Liao YF, Liu XY, Liu YS, Liu ZN, Long YC, Lu JP, Qiu J, Shan XX, Si TM, Sun PF, Wang CY, Wang HN, Wang X, Wang Y, Wang YW, Wu XP, Wu XR, Wu YK, Xie CM, Xie GR, Xie P, Xu XF, Xue ZP, Yang H, Yu H, Yuan ML, Yuan YG, Zhang AX, Zhao JP, Zhang KR, Zhang W, Zhang ZJ, Yan CG, Yu Y. Transcriptomic decoding of regional cortical vulnerability to major depressive disorder. Commun Biol 2024; 7:960. [PMID: 39117859 PMCID: PMC11310478 DOI: 10.1038/s42003-024-06665-w] [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: 01/17/2024] [Accepted: 07/31/2024] [Indexed: 08/10/2024] Open
Abstract
Previous studies in small samples have identified inconsistent cortical abnormalities in major depressive disorder (MDD). Despite genetic influences on MDD and the brain, it is unclear how genetic risk for MDD is translated into spatially patterned cortical vulnerability. Here, we initially examined voxel-wise differences in cortical function and structure using the largest multi-modal MRI data from 1660 MDD patients and 1341 controls. Combined with the Allen Human Brain Atlas, we then adopted transcription-neuroimaging spatial correlation and the newly developed ensemble-based gene category enrichment analysis to identify gene categories with expression related to cortical changes in MDD. Results showed that patients had relatively circumscribed impairments in local functional properties and broadly distributed disruptions in global functional connectivity, consistently characterized by hyper-function in associative areas and hypo-function in primary regions. Moreover, the local functional alterations were correlated with genes enriched for biological functions related to MDD in general (e.g., endoplasmic reticulum stress, mitogen-activated protein kinase, histone acetylation, and DNA methylation); and the global functional connectivity changes were associated with not only MDD-general, but also brain-relevant genes (e.g., neuron, synapse, axon, glial cell, and neurotransmitters). Our findings may provide important insights into the transcriptomic signatures of regional cortical vulnerability to MDD.
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Affiliation(s)
- Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China
- Anhui Provincial Institute of Translational Medicine, Hefei, 230032, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xue-Ying Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zi-Han Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Li-Ping Cao
- Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, 510370, China
| | - Guan-Mao Chen
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 250024, China
| | - Jian-Shan Chen
- Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, 510370, China
| | - Tao Chen
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Tao-Lin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610044, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, 610052, China
| | - Yu-Qi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, 650032, China
| | - Zhao-Song Chu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, 650032, China
| | - Shi-Xian Cui
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 101408, China
- Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Xi-Long Cui
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Zhao-Yu Deng
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qi-Yong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610044, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, 610052, China
| | - Wen-Bin Guo
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Can-Can He
- Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing, Jiangsu, 210009, China
| | - Zheng-Jia-Yi Hu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 101408, China
- Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Qian Huang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400042, China
| | - Xin-Lei Ji
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Feng-Nan Jia
- Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu, 215003, China
| | - Li Kuang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400042, China
| | - Bao-Juan Li
- Xijing Hospital of Air Force Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Feng Li
- Beijing Anding Hospital, Capital Medical University, Beijing, 100120, China
| | - Hui-Xian Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tao Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310063, China
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, Sichuan, 610044, China
| | - Tao Lian
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yi-Fan Liao
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xiao-Yun Liu
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Yan-Song Liu
- Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu, 215003, China
| | - Zhe-Ning Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Yi-Cheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Jian-Ping Lu
- Shenzhen Kangning Hospital Shenzhen, Guangzhou, 518020, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Xiao-Xiao Shan
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Tian-Mei Si
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Peng-Feng Sun
- Xi'an Central Hospital, Xi'an, Shaanxi, 710004, China
| | - Chuan-Yue Wang
- Beijing Anding Hospital, Capital Medical University, Beijing, 100120, China
| | - Hua-Ning Wang
- Xijing Hospital of Air Force Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Xiang Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Ying Wang
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 250024, China
| | - Yu-Wei Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiao-Ping Wu
- Xi'an Central Hospital, Xi'an, Shaanxi, 710004, China
| | - Xin-Ran Wu
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Yan-Kun Wu
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Chun-Ming Xie
- Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing, Jiangsu, 210009, China
| | - Guang-Rong Xie
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Peng Xie
- Institute of Neuroscience, Chongqing Medical University, Chongqing, 400016, China
- Chongqing Key Laboratory of Neurobiology, Chongqing, 400000, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400042, China
| | - Xiu-Feng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, 650032, China
| | - Zhen-Peng Xue
- Shenzhen Kangning Hospital Shenzhen, Guangzhou, 518020, China
| | - Hong Yang
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Hua Yu
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310063, China
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, Sichuan, 610044, China
| | - Min-Lan Yuan
- West China Hospital of Sichuan University, Chengdu, Sichuan, 610044, China
| | - Yong-Gui Yuan
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Ai-Xia Zhang
- First Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030001, China
| | - Jing-Ping Zhao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Ke-Rang Zhang
- First Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030001, China
| | - Wei Zhang
- West China Hospital of Sichuan University, Chengdu, Sichuan, 610044, China
| | - Zi-Jing Zhang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 101408, China
- Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.
- Anhui Provincial Institute of Translational Medicine, Hefei, 230032, China.
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15
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Tesfaye M, Spindola LM, Stavrum AK, Shadrin A, Melle I, Andreassen OA, Le Hellard S. Sex effects on DNA methylation affect discovery in epigenome-wide association study of schizophrenia. Mol Psychiatry 2024; 29:2467-2477. [PMID: 38503926 DOI: 10.1038/s41380-024-02513-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/27/2024] [Accepted: 03/01/2024] [Indexed: 03/21/2024]
Abstract
Sex differences in the epidemiology and clinical characteristics of schizophrenia are well-known; however, the molecular mechanisms underlying these differences remain unclear. Further, the potential advantages of sex-stratified meta-analyses of epigenome-wide association studies (EWAS) of schizophrenia have not been investigated. Here, we performed sex-stratified EWAS meta-analyses to investigate whether sex stratification improves discovery, and to identify differentially methylated regions (DMRs) in schizophrenia. Peripheral blood-derived DNA methylation data from 1519 cases of schizophrenia (male n = 989, female n = 530) and 1723 controls (male n = 997, female n = 726) from three publicly available datasets, and the TOP cohort were meta-analyzed to compare sex-specific, sex-stratified, and sex-adjusted EWAS. The predictive power of each model was assessed by polymethylation score (PMS). The number of schizophrenia-associated differentially methylated positions identified was higher for the sex-stratified model than for the sex-adjusted one. We identified 20 schizophrenia-associated DMRs in the sex-stratified analysis. PMS from sex-stratified analysis outperformed that from sex-adjusted analysis in predicting schizophrenia. Notably, PMSs from the sex-stratified and female-only analyses, but not those from sex-adjusted or the male-only analyses, significantly predicted schizophrenia in males. The findings suggest that sex-stratified EWAS meta-analyses improve the identification of schizophrenia-associated epigenetic changes and highlight an interaction between sex and schizophrenia status on DNA methylation. Sex-specific DNA methylation may have potential implications for precision psychiatry and the development of stratified treatments for schizophrenia.
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Affiliation(s)
- Markos Tesfaye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway.
| | - Leticia M Spindola
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
- Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway
| | - Anne-Kristin Stavrum
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Alexey Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Ingrid Melle
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Stephanie Le Hellard
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway.
- Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway.
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16
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Bandeira CE, Grevet EH, Vitola ES, da Silva BS, Cupertino RB, Picon FA, Ito LT, Tavares MEDA, Rovaris DL, Grimm O, Bau CHD. Exploring Neuroimaging Association Scores in adulthood ADHD and middle-age trajectories. J Psychiatr Res 2024; 176:348-353. [PMID: 38936238 DOI: 10.1016/j.jpsychires.2024.06.025] [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: 12/17/2023] [Revised: 05/29/2024] [Accepted: 06/17/2024] [Indexed: 06/29/2024]
Abstract
Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder associated with brain differences in children, but not in adults. A combined evaluation of the regional brain differences could improve statistical power and, consequently, allow the detection of possible effects in adults. Thus, our aim is to verify whether Neuroimaging Association Scores (NAS) are associated with adulthood ADHD and clinical trajectories of the disorder in midlife. Clinical and neuroimaging data were collected for 121 subjects with ADHD (mean age: 47.1 ± 10.5; 43% male) and 82 controls (mean age: 38.2 ± 9.0; 54.9% male). Cases were assessed seven and thirteen years after baseline diagnosis, and their clinical trajectories were classified as stable if they fulfilled ADHD diagnosis in all assessments or unstable if they presented remission and recurrence of symptoms. Neuroimaging data were acquired in the last clinical assessment (thirteen years after baseline) and NAS were calculated as a weighted sum of the associations previously reported by meta-analyses for three types of structural brain modalities: cortical thickness, cortical surface area, and subcortical volume. The NAS for cortical surface area was higher in cases compared to controls. No association was found for NAS and number of symptoms of ADHD or clinical trajectories. The fact that differences were restricted to ADHD diagnostic status suggests a susceptibility effect that is not extended to subtle aspects of the disorder. Our results also suggest that evaluating overall effects may have advantages especially when applied to adult ADHD samples.
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Affiliation(s)
- Cibele Edom Bandeira
- ADHD Outpatient Program, Clinical Research Center, Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas da Universidade de São Paulo, São Paulo, Brazil
| | - Eugenio Horacio Grevet
- ADHD Outpatient Program, Clinical Research Center, Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Department of Psychiatry, Faculty of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Eduardo Schneider Vitola
- ADHD Outpatient Program, Clinical Research Center, Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas da Universidade de São Paulo, São Paulo, Brazil
| | - Bruna Santos da Silva
- ADHD Outpatient Program, Clinical Research Center, Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Department of Basic Health Sciences, Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre, Brazil
| | | | - Felipe Almeida Picon
- ADHD Outpatient Program, Clinical Research Center, Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Lucas Toshio Ito
- Department of Biochemistry, Universidade Federal de São Paulo, São Paulo, Brazil; Laboratory of Integrative Neuroscience, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Maria Eduarda de Araujo Tavares
- ADHD Outpatient Program, Clinical Research Center, Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Graduate Program in Genetics and Molecular Biology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Diego Luiz Rovaris
- ADHD Outpatient Program, Clinical Research Center, Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas da Universidade de São Paulo, São Paulo, Brazil
| | - Oliver Grimm
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Claiton Henrique Dotto Bau
- ADHD Outpatient Program, Clinical Research Center, Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Graduate Program in Genetics and Molecular Biology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
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17
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Liu A, Wang J, Jin T, Jiang Z, Huang S, Li S, Ying Z, Jiang H. Identifying the genetic association between the cerebral cortex and fibromyalgia. Cereb Cortex 2024; 34:bhae318. [PMID: 39106177 DOI: 10.1093/cercor/bhae318] [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: 06/29/2024] [Revised: 07/08/2024] [Indexed: 08/09/2024] Open
Abstract
Fibromyalgia (FM) is a central sensitization syndrome that is strongly associated with the cerebral cortex. This study used bidirectional two-sample Mendelian randomization (MR) analysis to investigate the bidirectional causality between FM and the cortical surface area and cortical thickness of 34 brain regions. Inverse variance weighted (IVW) was used as the primary method for this study, and sensitivity analyses further supported the results. The forward MR analysis revealed that genetically determined thinner cortical thickness in the parstriangularis (OR = 0.0567 mm, PIVW = 0.0463), caudal middle frontal (OR = 0.0346 mm, PIVW = 0.0433), and rostral middle frontal (OR = 0.0285 mm, PIVW = 0.0463) was associated with FM. Additionally, a reduced genetically determined cortical surface area in the pericalcarine (OR = 0.9988 mm2, PIVW = 0.0085) was associated with an increased risk of FM. Conversely, reverse MR indicated that FM was associated with cortical thickness in the caudal middle frontal region (β = -0.0035 mm, PIVW = 0.0265), fusiform region (β = 0.0024 mm, SE = 0.0012, PIVW = 0.0440), the cortical surface area in the supramarginal (β = -9.3938 mm2, PIVW = 0.0132), and postcentral regions (β = -6.3137 mm2, PIVW = 0.0360). Reduced cortical thickness in the caudal middle frontal gyrus is shown to have a significant relationship with FM prevalence in a bidirectional causal analysis.
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Affiliation(s)
- Aihui Liu
- Department of Rheumatology and Immunology, Center for General Practice Medicine, Hangzhou Medical College, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang 310000, China
- Zhejiang Provincial Key Laboratory of Traditional Chinese Medicine Cultivation for Arthritis Diagnosis and Treatment, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 31000, China
- Rheumatology and Immunology Research Institute, Hangzhou Medical College, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 310000, China
| | - Jing Wang
- Department of Rheumatology and Immunology, Center for General Practice Medicine, Hangzhou Medical College, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang 310000, China
- Zhejiang Provincial Key Laboratory of Traditional Chinese Medicine Cultivation for Arthritis Diagnosis and Treatment, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 31000, China
- Rheumatology and Immunology Research Institute, Hangzhou Medical College, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 310000, China
| | - Tianyu Jin
- China Rehabilitation Research center, No. 10, Jiaomen North Road, Fengtai District, Beijing 100068, China
| | - Zhaoyu Jiang
- Department of Rheumatology and Immunology, Center for General Practice Medicine, Hangzhou Medical College, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang 310000, China
- Zhejiang Provincial Key Laboratory of Traditional Chinese Medicine Cultivation for Arthritis Diagnosis and Treatment, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 31000, China
- Rheumatology and Immunology Research Institute, Hangzhou Medical College, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 310000, China
| | - Shan Huang
- Department of Rheumatology and Immunology, Center for General Practice Medicine, Hangzhou Medical College, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang 310000, China
- Zhejiang Provincial Key Laboratory of Traditional Chinese Medicine Cultivation for Arthritis Diagnosis and Treatment, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 31000, China
- Rheumatology and Immunology Research Institute, Hangzhou Medical College, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 310000, China
| | - Shinan Li
- Department of Rheumatology and Immunology, Center for General Practice Medicine, Hangzhou Medical College, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang 310000, China
- Zhejiang Provincial Key Laboratory of Traditional Chinese Medicine Cultivation for Arthritis Diagnosis and Treatment, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 31000, China
- Rheumatology and Immunology Research Institute, Hangzhou Medical College, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 310000, China
| | - Zhenhua Ying
- Department of Rheumatology and Immunology, Center for General Practice Medicine, Hangzhou Medical College, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang 310000, China
- Zhejiang Provincial Key Laboratory of Traditional Chinese Medicine Cultivation for Arthritis Diagnosis and Treatment, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 31000, China
- Rheumatology and Immunology Research Institute, Hangzhou Medical College, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 310000, China
| | - Hongyang Jiang
- Zhejiang Provincial Key Laboratory of Traditional Chinese Medicine Cultivation for Arthritis Diagnosis and Treatment, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 31000, China
- Rheumatology and Immunology Research Institute, Hangzhou Medical College, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang Province 310000, China
- Department of Radiology, Center for Rehabilitation Medicine, Hangzhou Medical College, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, No. 158 Shangtang Road, Gongshu District, Hangzhou, Zhejiang 310000, China
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18
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Qu J, Qu Y, Zhu R, Wu Y, Xu G, Wang D. Transcriptional expression patterns of the cortical morphometric similarity network in progressive supranuclear palsy. CNS Neurosci Ther 2024; 30:e14901. [PMID: 39097922 PMCID: PMC11298202 DOI: 10.1111/cns.14901] [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: 06/10/2024] [Revised: 07/09/2024] [Accepted: 07/24/2024] [Indexed: 08/06/2024] Open
Abstract
BACKGROUND It has been demonstrated that progressive supranuclear palsy (PSP) correlates with structural abnormalities in several distinct regions of the brain. However, whether there are changes in the morphological similarity network (MSN) and the relationship between changes in brain structure and gene expression remain largely unknown. METHODS We used two independent cohorts (discovery dataset: PSP: 51, healthy controls (HC): 82; replication dataset: PSP: 53, HC: 55) for MSN analysis and comparing the longitudinal changes in the MSN of PSP. Then, we applied partial least squares regression to determine the relationships between changes in MSN and spatial transcriptional features and identified specific genes associated with MSN differences in PSP. We further investigated the biological processes enriched in PSP-associated genes and the cellular characteristics of these genes, and finally, we performed an exploratory analysis of the relationship between MSN changes and neurotransmitter receptors. RESULTS We found that the MSN in PSP patients was mainly decreased in the frontal and temporal cortex but increased in the occipital cortical region. This difference is replicable. In longitudinal studies, MSN differences are mainly manifested in the frontal and parietal regions. Furthermore, the expression pattern associated with MSN changes in PSP involves genes implicated in astrocytes and excitatory and inhibitory neurons and is functionally enriched in neuron-specific biological processes related to synaptic signaling. Finally, we found that the changes in MSN were mainly negatively correlated with the levels of serotonin, norepinephrine, and opioid receptors. CONCLUSIONS These results have enhanced our understanding of the microscale genetic and cellular mechanisms responsible for large-scale morphological abnormalities in PSP patients, suggesting potential targets for future therapeutic trials.
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Affiliation(s)
- Junyu Qu
- Department of RadiologyQilu Hospital of Shandong University, Qilu Medical Imaging Institute of Shandong UniversityJinanChina
| | - Yancai Qu
- Department of NeurosurgeryTraditional Chinese Medicine Hospital of Muping DistrictYantaiChina
| | - Rui Zhu
- Department of RadiologyQilu Hospital of Shandong University, Qilu Medical Imaging Institute of Shandong UniversityJinanChina
| | - Yongsheng Wu
- Department of RadiologyQilu Hospital of Shandong University, Qilu Medical Imaging Institute of Shandong UniversityJinanChina
| | - Guihua Xu
- Department of RadiologyQilu Hospital of Shandong University, Qilu Medical Imaging Institute of Shandong UniversityJinanChina
| | - Dawei Wang
- Department of RadiologyQilu Hospital of Shandong University, Qilu Medical Imaging Institute of Shandong UniversityJinanChina
- Magnetic Field‐free Medicine & Functional ImagingResearch Institute of Shandong UniversityJinanChina
- Magnetic Field‐free Medicine & Functional Imaging (MF)Shandong Key LaboratoryJinanChina
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19
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van der Es T, Soheili-Nezhad S, Roth Mota N, Franke B, Buitelaar J, Sprooten E. Exploring the genetic architecture of brain structure and ADHD using polygenic neuroimaging-derived scores. Am J Med Genet B Neuropsychiatr Genet 2024:e32987. [PMID: 39016115 DOI: 10.1002/ajmg.b.32987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 04/24/2024] [Accepted: 05/11/2024] [Indexed: 07/18/2024]
Abstract
Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of neuropsychiatric disorders and highlighted their complexity. Careful consideration of the polygenicity and complex genetic architecture could aid in the understanding of the underlying brain mechanisms. We introduce an innovative approach to polygenic scoring, utilizing imaging-derived phenotypes (IDPs) to predict a clinical phenotype. We leveraged IDP GWAS data from the UK Biobank, to create polygenic imaging-derived scores (PIDSs). As a proof-of-concept, we assessed genetic variations in brain structure between individuals with ADHD and unaffected controls across three NeuroIMAGE waves (n = 954). Out of the 94 PIDS, 72 exhibited significant associations with their corresponding IDPs in an independent sample. Notably, several global measures, including cerebellum white matter, cerebellum cortex, and cerebral white matter, displayed substantial variance explained for their respective IDPs, ranging from 3% to 5.7%. Conversely, the associations between each IDP and the clinical ADHD phenotype were relatively weak. These findings highlight the growing power of GWAS in structural neuroimaging traits, enabling the construction of polygenic scores that accurately reflect the underlying polygenic architecture. However, to establish robust connections between PIDS and behavioral or clinical traits such as ADHD, larger samples are needed. Our novel approach to polygenic risk scoring offers a valuable tool for researchers in the field of psychiatric genetics.
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Affiliation(s)
- Tim van der Es
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | | | - Nina Roth Mota
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jan Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Emma Sprooten
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
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20
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Pawlak M, Kemp J, Bray S, Chenji S, Noel M, Birnie KA, MacMaster FP, Miller JV, Kopala-Sibley DC. Macrostructural Brain Morphology as Moderator of the Relationship Between Pandemic-Related Stress and Internalizing Symptomology During COVID-19 in High-Risk Adolescents. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00190-3. [PMID: 39019399 DOI: 10.1016/j.bpsc.2024.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 06/30/2024] [Accepted: 07/02/2024] [Indexed: 07/19/2024]
Abstract
BACKGROUND According to person-by-environment models, individual differences in traits may moderate the association between stressors and the development of psychopathology; however, findings in the literature have been inconsistent and little literature has examined adolescent brain structure as a moderator of the effects of stress on adolescent internalizing symptoms. The COVID-19 pandemic presented a unique opportunity to examine the associations between stress, brain structure, and psychopathology. Given links of cortical morphology with adolescent depression and anxiety, the current study investigated whether cortical morphology moderated the relationship between stress from the COVID-19 pandemic and the development of internalizing symptoms in familial high-risk adolescents. METHODS Prior to the COVID-19 pandemic, 72 adolescents (27 male) completed a measure of depressive and anxiety symptoms and underwent magnetic resonance imaging. T1-weighted images were acquired to assess cortical thickness and surface area. Approximately 6 to 8 months after COVID-19 was declared a global pandemic, adolescents reported their depressive and anxiety symptoms and pandemic-related stress. RESULTS Adjusting for pre-pandemic depressive and anxiety symptoms and stress, increased pandemic-related stress was associated with increased depressive but not anxiety symptoms. This relationship was moderated by cortical thickness and surface area in the anterior cingulate and cortical thickness in the medial orbitofrontal cortex such that increased stress was only associated with increased depressive and anxiety symptoms among adolescents with lower cortical surface area and higher cortical thickness in these regions. CONCLUSIONS Results further our understanding of neural vulnerabilities to the associations between stress and internalizing symptoms in general and during the COVID-19 pandemic in particular.
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Affiliation(s)
- McKinley Pawlak
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Alberta Children Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta, Canada.
| | - Jennifer Kemp
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta, Canada
| | - Signe Bray
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Alberta Children Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Department of Radiology, University of Calgary, Calgary, Alberta, Canada; Department of Pediatrics, University of Calgary, Calgary, Alberta, Canada
| | - Sneha Chenji
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Melanie Noel
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Alberta Children Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Department of Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Kathryn A Birnie
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Alberta Children Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta, Canada; Department of Anesthesiology, Perioperative, and Pain Medicine, University of Calgary, Calgary, Alberta, Canada; Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Frank P MacMaster
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada; IWK Health, Halifax, Nova Scotia, Canada
| | - Jillian Vinall Miller
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Alberta Children Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Department of Anesthesiology, Perioperative, and Pain Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Daniel C Kopala-Sibley
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Alberta Children Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta, Canada; Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
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21
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Zhao S, Huang Y, Shi S, Chen W, Chen R, Wang Z, Wang D. Causal effects of hypertensive disorders of pregnancy on structural changes in specific brain regions: a Mendelian randomization study. Cereb Cortex 2024; 34:bhae282. [PMID: 38984704 DOI: 10.1093/cercor/bhae282] [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: 02/20/2024] [Revised: 06/16/2024] [Accepted: 06/21/2024] [Indexed: 07/11/2024] Open
Abstract
This study utilized Mendelian randomization to explore the impact of hypertensive disorders of pregnancy and their subtypes on brain structures, using genome-wide association study data from the FinnGen consortium for hypertensive disorders of pregnancy exposure and brain structure data from the ENIGMA consortium as outcomes. The inverse-variance weighted method, along with Cochran's Q test, Mendelian randomization-Egger regression, Mendelian randomization-PRESSO global test, and the leave-one-out approach, were applied to infer causality and assess heterogeneity and pleiotropy. Findings indicate hypertensive disorders of pregnancy are associated with structural brain alterations, including reduced cortical thickness in areas like the insula, isthmus cingulate gyrus, superior temporal gyrus, temporal pole, and transverse temporal gyrus, and an increased surface area in the superior frontal gyrus. Specific associations were found for hypertensive disorders of pregnancy subtypes: chronic hypertension with superimposed preeclampsia increased cortical thickness in the supramarginal gyrus; preeclampsia/eclampsia led to thinner cortex in the lingual gyrus and larger hippocampal volume and superior parietal lobule surface area. Chronic hypertension was associated with reduced cortical thickness in the caudal and rostral anterior cingulate and increased surface area of the cuneus and thickness of the pars orbitalis cortex. Gestational hypertension showed no significant brain region changes. These insights clarify hypertensive disorders of pregnancies' neurological and cognitive effects by identifying affected brain regions.
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Affiliation(s)
- Shanshan Zhao
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road II, Guangzhou 510080, China
- Guangdong Provincial Clinical Research Center for Obstetrical and Gynecological Diseases, 58 Zhongshan Road II, Guangzhou 510080, China
| | - Yihong Huang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road II, Guangzhou 510080, China
- Guangdong Provincial Clinical Research Center for Obstetrical and Gynecological Diseases, 58 Zhongshan Road II, Guangzhou 510080, China
| | - Shaole Shi
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road II, Guangzhou 510080, China
- Guangdong Provincial Clinical Research Center for Obstetrical and Gynecological Diseases, 58 Zhongshan Road II, Guangzhou 510080, China
| | - Wei Chen
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road II, Guangzhou 510080, China
- Guangdong Provincial Clinical Research Center for Obstetrical and Gynecological Diseases, 58 Zhongshan Road II, Guangzhou 510080, China
| | - Run Chen
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road II, Guangzhou 510080, China
- Guangdong Provincial Clinical Research Center for Obstetrical and Gynecological Diseases, 58 Zhongshan Road II, Guangzhou 510080, China
| | - Zilian Wang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road II, Guangzhou 510080, China
- Guangdong Provincial Clinical Research Center for Obstetrical and Gynecological Diseases, 58 Zhongshan Road II, Guangzhou 510080, China
| | - Dongyu Wang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road II, Guangzhou 510080, China
- Guangdong Provincial Clinical Research Center for Obstetrical and Gynecological Diseases, 58 Zhongshan Road II, Guangzhou 510080, China
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22
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Ohi K, Tanaka Y, Otowa T, Shimada M, Kaiya H, Nishimura F, Sasaki T, Tanii H, Shioiri T, Hara T. Discrimination between healthy participants and people with panic disorder based on polygenic scores for psychiatric disorders and for intermediate phenotypes using machine learning. Aust N Z J Psychiatry 2024; 58:603-614. [PMID: 38581251 DOI: 10.1177/00048674241242936] [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: 04/08/2024]
Abstract
OBJECTIVE Panic disorder is a modestly heritable condition. Currently, diagnosis is based only on clinical symptoms; identifying objective biomarkers and a more reliable diagnostic procedure is desirable. We investigated whether people with panic disorder can be reliably diagnosed utilizing combinations of multiple polygenic scores for psychiatric disorders and their intermediate phenotypes, compared with single polygenic score approaches, by applying specific machine learning techniques. METHODS Polygenic scores for 48 psychiatric disorders and intermediate phenotypes based on large-scale genome-wide association studies (n = 7556-1,131,881) were calculated for people with panic disorder (n = 718) and healthy controls (n = 1717). Discrimination between people with panic disorder and healthy controls was based on the 48 polygenic scores using five methods for classification: logistic regression, neural networks, quadratic discriminant analysis, random forests and a support vector machine. Differences in discrimination accuracy (area under the curve) due to an increased number of polygenic score combinations and differences in the accuracy across five classifiers were investigated. RESULTS All five classifiers performed relatively well for distinguishing people with panic disorder from healthy controls by increasing the number of polygenic scores. Of the 48 polygenic scores, the polygenic score for anxiety UK Biobank was the most useful for discrimination by the classifiers. In combinations of two or three polygenic scores, the polygenic score for anxiety UK Biobank was included as one of polygenic scores in all classifiers. When all 48 polygenic scores were used in combination, the greatest areas under the curve significantly differed among the five classifiers. Support vector machine and logistic regression had higher accuracy than quadratic discriminant analysis and random forests. For each classifier, the greatest area under the curve was 0.600 ± 0.030 for logistic regression (polygenic score combinations N = 14), 0.591 ± 0.039 for neural networks (N = 9), 0.603 ± 0.033 for quadratic discriminant analysis (N = 10), 0.572 ± 0.039 for random forests (N = 25) and 0.617 ± 0.041 for support vector machine (N = 11). The greatest areas under the curve at the best polygenic score combination significantly differed among the five classifiers. Random forests had the lowest accuracy among classifiers. Support vector machine had higher accuracy than neural networks. CONCLUSIONS These findings suggest that increasing the number of polygenic score combinations up to approximately 10 effectively improved the discrimination accuracy and that support vector machine exhibited greater accuracy among classifiers. However, the discrimination accuracy for panic disorder, when based solely on polygenic score combinations, was found to be modest.
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Affiliation(s)
- Kazutaka Ohi
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
- Department of General Internal Medicine, Kanazawa Medical University, Ishikawa, Japan
| | - Yuta Tanaka
- Department of Intelligence Science and Engineering, Gifu University Graduate School of Natural Science and Technology, Gifu, Japan
| | - Takeshi Otowa
- Department of Psychiatry, East Medical Center, Nagoya City University, Nagoya, Japan
| | - Mihoko Shimada
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine (NCGM), Tokyo, Japan
| | - Hisanobu Kaiya
- Panic Disorder Research Center, Warakukai Medical Corporation, Tokyo, Japan
| | - Fumichika Nishimura
- Center for Research on Counseling and Support Services, The University of Tokyo, Tokyo, Japan
| | - Tsukasa Sasaki
- Department of Physical and Health Education, Graduate School of Education, The University of Tokyo, Tokyo, Japan
| | - Hisashi Tanii
- Center for Physical and Mental Health, Mie University, Mie, Japan
- Graduate School of Medicine, Department of Health Promotion and Disease Prevention, Mie University, Mie, Japan
| | - Toshiki Shioiri
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Takeshi Hara
- Department of Intelligence Science and Engineering, Gifu University Graduate School of Natural Science and Technology, Gifu, Japan
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23
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Yang HH, Han KM, Kim A, Kang Y, Tae WS, Han MR, Ham BJ. Neuroimaging and epigenetic analysis reveal novel epigenetic loci in major depressive disorder. Psychol Med 2024; 54:2585-2598. [PMID: 38721773 DOI: 10.1017/s0033291724000709] [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: 10/10/2024]
Abstract
BACKGROUND Epigenetic modifications, such as DNA methylation, contribute to the pathophysiology of major depressive disorder (MDD). This study aimed to identify novel MDD-associated epigenetic loci using DNA methylation profiles and explore the correlations between epigenetic loci and cortical thickness changes in patients with MDD. METHODS A total of 350 patients with MDD and 161 healthy controls (HCs) were included in the epigenome-wide association studies (EWAS). We analyzed methylation, copy number alteration (CNA), and gene network profiles in the MDD group. A total of 234 patients with MDD and 135 HCs were included in neuroimaging methylation analysis. Pearson's partial correlation analysis was used to estimate the correlation between cortical thickness of brain regions and DNA methylation levels of the loci. RESULTS In total, 2018 differentially methylated probes (DMPs) and 351 differentially methylated regions (DMRs) were identified. DMP-related genes were enriched in two networks involved in the central nervous system. In neuroimaging analysis, patients with MDD showed cortical thinning in the prefrontal regions and cortical thickening in several occipital regions. Cortical thickness of the left ventrolateral prefrontal cortex (VLPFC, i.e. pars triangularis) was negatively correlated with eight DMPs associated with six genes (EML6, ZFP64, CLSTN3, KCNMA1, TAOK2, and NT5E). CONCLUSION Through combining DNA methylation and neuroimaging analyses, negative correlations were identified between the cortical thickness of the left VLPFC and DNA methylation levels of eight DMPs. Our findings could improve our understanding of the pathophysiology of MDD.
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Affiliation(s)
- Hyun-Ho Yang
- Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea
| | - Kyu-Man Han
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
| | - Aram Kim
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Youbin Kang
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Woo-Suk Tae
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
| | - Mi-Ryung Han
- Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
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24
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Guo X, Luo X, Huang X, Zhang Y, Ji J, Wang X, Wang K, Wang J, Pan X, Chen B, Tan Y, Luo X. The Role of 3' Regulatory Region Flanking Kinectin 1 Gene in Schizophrenia. ALPHA PSYCHIATRY 2024; 25:413-420. [PMID: 39148597 PMCID: PMC11322729 DOI: 10.5152/alphapsychiatry.2024.241616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 04/18/2024] [Indexed: 08/17/2024]
Abstract
Objective Schizophrenia is often associated with volumetric reductions in cortices and expansions in basal ganglia, particularly the putamen. Recent genome-wide association studies have highlighted the significance of variants in the 3' regulatory region adjacent to the kinectin 1 gene (KTN1) in regulating gray matter volume (GMV) of the putamen. This study aimed to comprehensively investigate the involvement of this region in schizophrenia. Methods We analyzed 1136 single-nucleotide polymorphisms (SNPs) covering the entire 3' regulatory region in 4 independent dbGaP samples (4604 schizophrenia patients vs. 4884 healthy subjects) and 3 independent Psychiatric Genomics Consortium samples (107 240 cases vs. 210 203 controls) to identify consistent associations. Additionally, we examined the regulatory effects of schizophrenia-associated alleles on KTN1 mRNA expression in 16 brain areas among 348 subjects, as well as GMVs of 7 subcortical nuclei in 38 258 subjects, and surface areas (SA) and thickness (TH) of the entire cortex and 34 cortical areas in 36 936 subjects. Results The major alleles (f > 0.5) of 25 variants increased (β > 0) the risk of schizophrenia across 2 to 5 independent samples (8.4 × 10-4 ≤ P ≤ .049). These schizophrenia-associated alleles significantly elevated (β > 0) GMVs of basal ganglia, including the putamen (6.0 × 10-11 ≤ P ≤ 1.1 × 10-4), caudate (8.7 × 10-4 ≤ P ≤ 9.4 × 10-3), pallidum (P = 6.0 × 10-4), and nucleus accumbens (P = 2.7 × 10-5). Moreover, they potentially augmented (β > 0) the SA of posterior cingulate and insular cortices, as well as the TH of frontal (pars triangularis and medial orbitofrontal), parietal (superior, precuneus, and inferior), and temporal (transverse) cortices, but potentially reduced (β < 0) the SA of the whole, frontal (medial orbitofrontal), and temporal (pole, superior, middle, and entorhinal) cortices, as well as the TH of rostral middle frontal and superior frontal cortices (8.9 × 10-4 ≤ P ≤ .050). Conclusion Our findings identify significant and functionally relevant risk alleles in the 3' regulatory region adjacent to KTN1, implicating their crucial roles in the development of schizophrenia.
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Affiliation(s)
- Xiaoyun Guo
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xinqun Luo
- Department of Neurosurgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Xiaoyi Huang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yong Zhang
- Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China
| | - Jiawu Ji
- Department of Psychiatry, Fujian Medical University Affiliated Fuzhou Neuropsychiatric Hospital, Fuzhou, Fujian, China
| | - Xiaoping Wang
- Department of Neurology, Jiading Branch of Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Kesheng Wang
- Department of Family and Community Health, School of Nursing, Health Sciences Center, West Virginia University, Morgantown, WV, USA
| | - Jijun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xinghua Pan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, and Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Guangzhou, China
| | - Bin Chen
- Department of Cardiovascular Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
| | - Yunlong Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan School of Clinical Medicine, Beijing, China
| | - Xingguang Luo
- Beijing Huilongguan Hospital, Peking University Huilongguan School of Clinical Medicine, Beijing, China
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
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25
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Fu J, Zhang Q, Wang J, Wang M, Zhang B, Zhu W, Qiu S, Geng Z, Cui G, Yu Y, Liao W, Zhang H, Gao B, Xu X, Han T, Yao Z, Qin W, Liu F, Liang M, Wang S, Xu Q, Xu J, Zhang P, Li W, Shi D, Wang C, Lui S, Yan Z, Chen F, Zhang J, Li J, Shen W, Miao Y, Wang D, Xian J, Gao JH, Zhang X, Xu K, Zuo XN, Zhang L, Ye Z, Cheng J, Li MJ, Yu C. Cross-ancestry genome-wide association studies of brain imaging phenotypes. Nat Genet 2024; 56:1110-1120. [PMID: 38811844 DOI: 10.1038/s41588-024-01766-y] [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: 09/21/2022] [Accepted: 04/23/2024] [Indexed: 05/31/2024]
Abstract
Genome-wide association studies of brain imaging phenotypes are mainly performed in European populations, but other populations are severely under-represented. Here, we conducted Chinese-alone and cross-ancestry genome-wide association studies of 3,414 brain imaging phenotypes in 7,058 Chinese Han and 33,224 white British participants. We identified 38 new associations in Chinese-alone analyses and 486 additional new associations in cross-ancestry meta-analyses at P < 1.46 × 10-11 for discovery and P < 0.05 for replication. We pooled significant autosomal associations identified by single- or cross-ancestry analyses into 6,443 independent associations, which showed uneven distribution in the genome and the phenotype subgroups. We further divided them into 44 associations with different effect sizes and 3,557 associations with similar effect sizes between ancestries. Loci of these associations were shared with 15 brain-related non-imaging traits including cognition and neuropsychiatric disorders. Our results provide a valuable catalog of genetic associations for brain imaging phenotypes in more diverse populations.
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Affiliation(s)
- Jilian Fu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Quan Zhang
- Department of Radiology, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, China
| | - Jianhua Wang
- Department of Bioinformatics, the Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital and Zhengzhou University People's Hospital, Zhengzhou, China
- Biomedical Institute, Henan Academy of Sciences, Zhengzhou, China
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shijun Qiu
- Department of Medical Imaging, the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
| | - Zuojun Geng
- Department of Medical Imaging, the Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Guangbin Cui
- Functional and Molecular Imaging Key Lab of Shaanxi Province and Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Yongqiang Yu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- Molecular Imaging Research Center of Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hui Zhang
- Department of Radiology, the First Hospital of Shanxi Medical University, Taiyuan, China
| | - Bo Gao
- Department of Radiology, the Affiliated Hospital of Guizhou Medical University, Guiyang, China
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Wen Qin
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Feng Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Sijia Wang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Qiang Xu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jiayuan Xu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Peng Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Wei Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Dapeng Shi
- Department of Radiology, Henan Provincial People's Hospital and Zhengzhou University People's Hospital, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Su Lui
- Department of Radiology, Huaxi MR Research Center, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Zhihan Yan
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jiance Li
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, Tianjin, China
| | - Yanwei Miao
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Xiaochu Zhang
- Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
| | - Kai Xu
- Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center at IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Mulin Jun Li
- Department of Bioinformatics, the Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
| | - Chunshui Yu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
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Bao J, Zhao Z, Qin S, Cheng M, Wang Y, Li M, Jia P, Li J, Yu H. Elucidating the association of obstructive sleep apnea with brain structure and cognitive performance. BMC Psychiatry 2024; 24:338. [PMID: 38711061 DOI: 10.1186/s12888-024-05789-x] [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: 08/07/2023] [Accepted: 04/25/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Obstructive sleep apnea (OSA) is a pervasive, chronic sleep-related respiratory condition that causes brain structural alterations and cognitive impairments. However, the causal association of OSA with brain morphology and cognitive performance has not been determined. METHODS We conducted a two-sample bidirectional Mendelian randomization (MR) analysis to investigate the causal relationship between OSA and a range of neurocognitive characteristics, including brain cortical structure, brain subcortical structure, brain structural change across the lifespan, and cognitive performance. Summary-level GWAS data for OSA from the FinnGen consortium was used to identify genetically predicted OSA. Data regarding neurocognitive characteristics were obtained from published meta-analysis studies. Linkage disequilibrium score regression analysis was employed to reveal genetic correlations between OSA and related traits. RESULTS Our MR study provided evidence that OSA was found to significantly increase the volume of the hippocampus (IVW β (95% CI) = 158.997 (76.768 to 241.227), P = 1.51e-04), with no heterogeneity and pleiotropy detected. Nominally causal effects of OSA on brain structures, such as the thickness of the temporal pole with or without global weighted, amygdala structure change, and cerebellum white matter change covering lifespan, were observed. Bidirectional causal links were also detected between brain cortical structure, brain subcortical, cognitive performance, and OSA risk. LDSC regression analysis showed no significant correlation between OSA and hippocampus volume. CONCLUSIONS Overall, we observed a positive association between genetically predicted OSA and hippocampus volume. These findings may provide new insights into the bidirectional links between OSA and neurocognitive features, including brain morphology and cognitive performance.
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Affiliation(s)
- Jiahao Bao
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology and Shanghai Research Institute of Stomatology, No. 639 Zhizaoju Road, Shanghai, China
| | - Zhiyang Zhao
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology and Shanghai Research Institute of Stomatology, No. 639 Zhizaoju Road, Shanghai, China
| | - Shanmei Qin
- Department of Neurology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Mengjia Cheng
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology and Shanghai Research Institute of Stomatology, No. 639 Zhizaoju Road, Shanghai, China
| | - Yiming Wang
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology and Shanghai Research Institute of Stomatology, No. 639 Zhizaoju Road, Shanghai, China
| | - Meng Li
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology and Shanghai Research Institute of Stomatology, No. 639 Zhizaoju Road, Shanghai, China
| | - Pingping Jia
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Jinhui Li
- Department of Urology, Stanford University Medical Center, Stanford, CA, USA.
| | - Hongbo Yu
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology and Shanghai Research Institute of Stomatology, No. 639 Zhizaoju Road, Shanghai, China.
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Schleifer CH, O'Hora KP, Fung H, Xu J, Robinson TA, Wu AS, Kushan-Wells L, Lin A, Ching CRK, Bearden CE. Effects of gene dosage and development on subcortical nuclei volumes in individuals with 22q11.2 copy number variations. Neuropsychopharmacology 2024; 49:1024-1032. [PMID: 38431758 PMCID: PMC11039652 DOI: 10.1038/s41386-024-01832-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/16/2024] [Accepted: 02/12/2024] [Indexed: 03/05/2024]
Abstract
The 22q11.2 locus contains genes critical for brain development. Reciprocal Copy Number Variations (CNVs) at this locus impact risk for neurodevelopmental and psychiatric disorders. Both 22q11.2 deletions (22qDel) and duplications (22qDup) are associated with autism, but 22qDel uniquely elevates schizophrenia risk. Understanding brain phenotypes associated with these highly penetrant CNVs can provide insights into genetic pathways underlying neuropsychiatric disorders. Human neuroimaging and animal models indicate subcortical brain alterations in 22qDel, yet little is known about developmental differences across specific nuclei between reciprocal 22q11.2 CNV carriers and typically developing (TD) controls. We conducted a longitudinal MRI study in a total of 385 scans from 22qDel (n = 96, scans = 191, 53.1% female), 22qDup (n = 37, scans = 64, 45.9% female), and TD controls (n = 80, scans = 130, 51.2% female), across a wide age range (5.5-49.5 years). Volumes of the thalamus, hippocampus, amygdala, and anatomical subregions were estimated using FreeSurfer, and the linear effects of 22q11.2 gene dosage and non-linear effects of age were characterized with generalized additive mixed models (GAMMs). Positive gene dosage effects (volume increasing with copy number) were observed for total intracranial and whole hippocampus volumes, but not whole thalamus or amygdala volumes. Several amygdala subregions exhibited similar positive effects, with bi-directional effects found across thalamic nuclei. Distinct age-related trajectories were observed across the three groups. Notably, both 22qDel and 22qDup carriers exhibited flattened development of hippocampal CA2/3 subfields relative to TD controls. This study provides novel insights into the impact of 22q11.2 CNVs on subcortical brain structures and their developmental trajectories.
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Affiliation(s)
- Charles H Schleifer
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.
- David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
| | - Kathleen P O'Hora
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Hoki Fung
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Jennifer Xu
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Taylor-Ann Robinson
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Angela S Wu
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Leila Kushan-Wells
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Amy Lin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.
- Department of Psychology, University of California, Los Angeles, CA, USA.
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28
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Patel K, Xie Z, Yuan H, Islam SMS, Xie Y, He W, Zhang W, Gottlieb A, Chen H, Giancardo L, Knaack A, Fletcher E, Fornage M, Ji S, Zhi D. Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging. Commun Biol 2024; 7:414. [PMID: 38580839 PMCID: PMC10997628 DOI: 10.1038/s42003-024-06096-7] [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: 01/31/2024] [Accepted: 03/22/2024] [Indexed: 04/07/2024] Open
Abstract
Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.
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Affiliation(s)
- Khush Patel
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Ziqian Xie
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Hao Yuan
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | | | - Yaochen Xie
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Wei He
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Wanheng Zhang
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Assaf Gottlieb
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Han Chen
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Luca Giancardo
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Alexander Knaack
- Department of Neurology and Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, 95618, USA
| | - Evan Fletcher
- Department of Neurology and Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, 95618, USA
| | - Myriam Fornage
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
- McGovern Medical School, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Shuiwang Ji
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Degui Zhi
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
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Shi J, Wang Z, Yi M, Xie S, Zhang X, Tao D, Liu Y, Yang Y. Evidence based on Mendelian randomization and colocalization analysis strengthens causal relationships between structural changes in specific brain regions and risk of amyotrophic lateral sclerosis. Front Neurosci 2024; 18:1333782. [PMID: 38505770 PMCID: PMC10948422 DOI: 10.3389/fnins.2024.1333782] [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: 11/06/2023] [Accepted: 02/22/2024] [Indexed: 03/21/2024] Open
Abstract
Background Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the degeneration of motor neurons in the brain and spinal cord with a poor prognosis. Previous studies have observed cognitive decline and changes in brain morphometry in ALS patients. However, it remains unclear whether the brain structural alterations contribute to the risk of ALS. In this study, we conducted a bidirectional two-sample Mendelian randomization (MR) and colocalization analysis to investigate this causal relationship. Methods Summary data of genome-wide association study were obtained for ALS and the brain structures, including surface area (SA), thickness and volume of subcortical structures. Inverse-variance weighted (IVW) method was used as the main estimate approach. Sensitivity analysis was conducted detect heterogeneity and pleiotropy. Colocalization analysis was performed to calculate the posterior probability of causal variation and identify the common genes. Results In the forward MR analysis, we found positive associations between the SA in four cortical regions (lingual, parahippocampal, pericalcarine, and middle temporal) and the risk of ALS. Additionally, decreased thickness in nine cortical regions (caudal anterior cingulate, frontal pole, fusiform, inferior temporal, lateral occipital, lateral orbitofrontal, pars orbitalis, pars triangularis, and pericalcarine) was significantly associated with a higher risk of ALS. In the reverse MR analysis, genetically predicted ALS was associated with reduced thickness in the bankssts and increased thickness in the caudal middle frontal, inferior parietal, medial orbitofrontal, and superior temporal regions. Colocalization analysis revealed the presence of shared causal variants between the two traits. Conclusion Our results suggest that altered brain morphometry in individuals with high ALS risk may be genetically mediated. The causal associations of widespread multifocal extra-motor atrophy in frontal and temporal lobes with ALS risk support the notion of a continuum between ALS and frontotemporal dementia. These findings enhance our understanding of the cortical structural patterns in ALS and shed light on potentially viable therapeutic targets.
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Affiliation(s)
| | | | | | | | | | | | | | - Yuan Yang
- Department of Medical Genetics, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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30
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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.
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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.
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31
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Liu X, Shi L, Li E, Jia S. Associations of hearing loss and structural changes in specific cortical regions: a Mendelian randomization study. Cereb Cortex 2024; 34:bhae084. [PMID: 38494888 DOI: 10.1093/cercor/bhae084] [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: 12/04/2023] [Revised: 02/13/2024] [Accepted: 02/15/2024] [Indexed: 03/19/2024] Open
Abstract
INTRODUCTION Previous studies have suggested a correlation between hearing loss (HL) and cortical alterations, but the specific brain regions that may be affected are unknown. METHODS Genome-wide association study (GWAS) data for 3 subtypes of HL phenotypes, sensorineural hearing loss (SNHL), conductive hearing loss, and mixed hearing loss, were selected as exposures, and GWAS data for brain structure-related traits were selected as outcomes. The inverse variance weighted method was used as the main estimation method. RESULTS Negative associations were identified between genetically predicted SNHL and brain morphometric indicators (cortical surface area, cortical thickness, or volume of subcortical structures) in specific brain regions, including the bankssts (β = -0.006 mm, P = 0.016), entorhinal cortex (β = -4.856 mm2, P = 0.029), and hippocampus (β = -24.819 cm3, P = 0.045), as well as in brain regions functionally associated with visual perception, including the pericalcarine (β = -10.009 cm3, P = 0.013). CONCLUSION Adaptive changes and functional remodeling of brain structures occur in patients with genetically predicted HL. Brain regions functionally associated with auditory perception, visual perception, and memory function are the main brain regions vulnerable in HL.
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Affiliation(s)
- Xiaoduo Liu
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Lubo Shi
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing Digestive Disease Center, 95 Yong'an Road, Xicheng District, Beijing, 100050, China
| | - Enze Li
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, 2 Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Shuo Jia
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, 2 Anzhen Road, Chaoyang District, Beijing, 100029, China
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Shao X, Li A, Wang Z, Xue G, Zhu B. False recall is associated with larger caudate in males but not in females. Memory 2024:1-8. [PMID: 38416016 DOI: 10.1080/09658211.2024.2319314] [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: 06/29/2023] [Accepted: 02/05/2024] [Indexed: 02/29/2024]
Abstract
After learning semantically related words, some people are more likely than others to incorrectly recall unstudied but semantically related lures (i.e., Deese-Roediger-McDermott [DRM] false recall). Previous studies have suggested that neural activity in subcortical regions (e.g., the caudate) is involved in false memory, and that there may be sex differences in the neural basis of false memory. However, sex-specific associations between subcortical volumes and false memory are not well understood. This study investigated whether sex modulates the associations between subcortical volumes and DRM false recall in 400 healthy college students. Volumes of subcortical regions including the caudate, accumbens, amygdala, hippocampus, pallidum, putamen and thalamus were obtained from structural magnetic resonance images and measured using FreeSurfer. The results showed that males had lower true and false recall but larger subcortical volumes than females. Interestingly, higher false recall was associated with a larger caudate in males, but not in females. This association was significant after controlling for age and intracranial volume. This study provides new evidence on the neural basis of false recall. It suggests that the caudate plays a role in false recall in young men, and that future studies of the neural correlates of false memory should consider sex differences.
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Affiliation(s)
- Xuhao Shao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
- Institute of Developmental Psychology, Beijing Normal University, Beijing, People's Republic of China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, People's Republic of China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, People's Republic of China
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, People's Republic of China
| | - Ao Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
| | - Zehua Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
| | - Gui Xue
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
| | - Bi Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
- Institute of Developmental Psychology, Beijing Normal University, Beijing, People's Republic of China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, People's Republic of China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, People's Republic of China
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Moore A, Crea PW, Makarious M, Bandres-Ciga S, Blauwendraat C, Diez-Fairen M. A genetic and transcriptomic assessment of the KTN1 gene in Parkinson's disease risk. Neurobiol Aging 2024; 134:66-73. [PMID: 37992546 PMCID: PMC10843739 DOI: 10.1016/j.neurobiolaging.2023.11.001] [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: 03/22/2023] [Revised: 09/29/2023] [Accepted: 11/04/2023] [Indexed: 11/24/2023]
Abstract
Parkinson's disease (PD) is a progressive neurological disorder caused by both genetic and environmental factors. An association has been described between KTN1 genetic variants and changes in its expression in the putamen and substantia nigra brain regions and an increased risk for PD. Here, we examine the link between PD susceptibility and KTN1 using individual-level genotyping data and summary statistics from the most recent genome-wide association studies (GWAS) for PD risk and age at onset from the International Parkinson's Disease Genomics Consortium (IPDGC), as well as whole-genome sequencing data from the Accelerating Medicines Partnership Parkinson's disease (AMP-PD) initiative. To investigate the potential effect of changes in KTN1 expression on PD compared to unaffected individuals, we further assess publicly available expression quantitative trait loci (eQTL) results from GTEx v8 and BRAINEAC and transcriptomics data from AMP-PD. Overall, we found no genetic associations between KTN1 and PD in our cohorts but found potential evidence of differences in mRNA expression, which needs to be further explored.
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Affiliation(s)
- Anni Moore
- Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, 9000 Rockville Pike, Building 35, Bethesda, MD 20892, USA
| | - Peter Wild Crea
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, 9000 Rockville Pike, Building 35, Bethesda, MD 20892, USA
| | - Mary Makarious
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, 9000 Rockville Pike, Building 35, Bethesda, MD 20892, USA; UCL Movement Disorders Centre, University College London, 33 Queen Square, 6th floor, WC1N 3BG Box 146, London, UK
| | - Sara Bandres-Ciga
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, 9000 Rockville Pike, Building 35, Bethesda, MD 20892, USA; Center for Alzheimer's and Related Dementias, National Institute on Aging, 9000 Rockville Pike, Building T44, Bethesda, MD 20892, USA.
| | - Cornelis Blauwendraat
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, 9000 Rockville Pike, Building 35, Bethesda, MD 20892, USA; Center for Alzheimer's and Related Dementias, National Institute on Aging, 9000 Rockville Pike, Building T44, Bethesda, MD 20892, USA
| | - Monica Diez-Fairen
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, 9000 Rockville Pike, Building 35, Bethesda, MD 20892, USA
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Hu X, Fang Z, Wang F, Mei Z, Huang X, Lin Y, Lin Z. A causal relationship between gut microbiota and subcortical brain structures contributes to the microbiota-gut-brain axis: a Mendelian randomization study. Cereb Cortex 2024; 34:bhae056. [PMID: 38415993 DOI: 10.1093/cercor/bhae056] [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: 12/06/2023] [Revised: 01/27/2024] [Accepted: 01/28/2024] [Indexed: 02/29/2024] Open
Abstract
A correlation between gut microbiota and brain structure, referring to as a component of the gut-brain axis, has been observed in observational studies. However, the causality of this relationship and its specific bacterial taxa remains uncertain. To reveal the causal effects of gut microbiota on subcortical brain volume, we applied Mendelian randomization (MR) studies in this study. Genome-wide association study data were obtained from the MiBioGen Consortium (n = 18,340) and the Enhancing Neuro Imaging Genetics through Meta-Analysis Consortium (n = 13,170). The primary estimate was obtained utilizing the inverse-variance weighted, while heterogeneity and pleiotropy were assessed using the Cochrane Q statistic, MR Pleiotropy RESidual Sum and Outlier, and MR-Egger intercept. Our findings provide strong evidence that a higher abundance of the genus Parasutterella is causally correlated with a decrease in intracranial volume (β = -30,921.33, 95% CI -46,671.78 to -15,170.88, P = 1.19 × 10-4), and the genus FamilyXIIIUCG001 is associated with a decrease in thalamus volume (β = -141.96, 95% CI: -214.81 to -69.12, P = 1.0× 10-4). This MR study offers novel perspectives on the intricate interplay between the gut microbiota and subcortical brain volume, thereby lending some support to the existence of the microbiota-gut-brain axis.
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Affiliation(s)
- Xuequn Hu
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| | - Zhiyong Fang
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| | - Feng Wang
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| | - Zhen Mei
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| | - Xiaofen Huang
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| | - Yuanxiang Lin
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| | - Zhangya Lin
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
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So HC, Xue X, Ma Z, Sham PC. SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates. Int J Mol Sci 2024; 25:1347. [PMID: 38279346 PMCID: PMC10816209 DOI: 10.3390/ijms25021347] [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: 10/13/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 01/28/2024] Open
Abstract
Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the "true" effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.
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Affiliation(s)
- Hon-Cheong So
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China; (X.X.); (Z.M.)
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- CUHK Shenzhen Research Institute, Shenzhen 518057, China
- Margaret K. L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Xiao Xue
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China; (X.X.); (Z.M.)
| | - Zhijie Ma
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China; (X.X.); (Z.M.)
| | - Pak-Chung Sham
- Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong, China;
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Mufford MS, van der Meer D, Kaufmann T, Frei O, Ramesar R, Thompson PM, Jahanshad N, Morey RA, Andreassen OA, Stein DJ, Dalvie S. The Genetic Architecture of Amygdala Nuclei. Biol Psychiatry 2024; 95:72-84. [PMID: 37391117 DOI: 10.1016/j.biopsych.2023.06.022] [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: 03/08/2022] [Revised: 06/02/2023] [Accepted: 06/02/2023] [Indexed: 07/02/2023]
Abstract
BACKGROUND Whereas genetic variants influencing total amygdala volume have been identified, the genetic architecture of its distinct nuclei has yet to be explored. We aimed to investigate whether increased phenotypic specificity through nuclei segmentation aids genetic discoverability and elucidates the extent of shared genetic architecture and biological pathways with related disorders. METHODS T1-weighted brain magnetic resonance imaging scans (N = 36,352, 52% female) from the UK Biobank were segmented into 9 amygdala nuclei with FreeSurfer (version 6.1). Genome-wide association analyses were performed on the entire sample, a European-only subset (n = 31,690), and a generalization (transancestry) subset (n = 4662). We estimated single nucleotide polymorphism-based heritability; derived polygenicity, discoverability, and power estimates; and investigated genetic correlations and shared loci with psychiatric disorders. RESULTS The heritability of the nuclei ranged from 0.17 to 0.33. Across the whole amygdala and the nuclei volumes, we identified 28 novel genome-wide significant (padj < 5 × 10-9) loci in the European analysis, with significant en masse replication for the whole amygdala and central nucleus volumes in the generalization analysis, and we identified 10 additional candidate loci in the combined analysis. The central nucleus had the highest statistical power for discovery. The significantly associated genes and pathways showed unique and shared effects across the nuclei, including immune-related pathways. Shared variants were identified between specific nuclei and autism spectrum disorder, Alzheimer's disease, Parkinson's disease, bipolar disorder, and schizophrenia. CONCLUSIONS Through investigation of amygdala nuclei volumes, we have identified novel candidate loci in the neurobiology of amygdala volume. These nuclei volumes have unique associations with biological pathways and genetic overlap with psychiatric disorders.
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Affiliation(s)
- Mary S Mufford
- South African Medical Research Council Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa; Global Initiative for Neuropsychiatric Genetics Education in Research program, Harvard T.H. Chan School of Public Health and the Stanley Center for Psychiatric Research at the Broad Institute of Harvard and MIT, Boston, Massachusetts; South African Medical Research Council Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Raj Ramesar
- South African Medical Research Council Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, California
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, California
| | - Rajendra A Morey
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, North Carolina
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dan J Stein
- South African Medical Research Council Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Shareefa Dalvie
- South African Medical Research Council Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
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Poudel P, Frost SM, Eslick S, Sohrabi HR, Taddei K, Martins RN, Hone E. Hyperspectral Retinal Imaging as a Non-Invasive Marker to Determine Brain Amyloid Status. J Alzheimers Dis 2024; 100:S131-S152. [PMID: 39121128 DOI: 10.3233/jad-240631] [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] [Indexed: 08/11/2024]
Abstract
Background As an extension of the central nervous system (CNS), the retina shares many similarities with the brain and can manifest signs of various neurological diseases, including Alzheimer's disease (AD). Objective To investigate the retinal spectral features and develop a classification model to differentiate individuals with different brain amyloid levels. Methods Sixty-six participants with varying brain amyloid-β protein levels were non-invasively imaged using a hyperspectral retinal camera in the wavelength range of 450-900 nm in 5 nm steps. Multiple retina features from the central and superior views were selected and analyzed to identify their variability among individuals with different brain amyloid loads. Results The retinal reflectance spectra in the 450-585 nm wavelengths exhibited a significant difference in individuals with increasing brain amyloid. The retinal features in the superior view showed higher inter-subject variability. A classification model was trained to differentiate individuals with varying amyloid levels using the spectra of extracted retinal features. The performance of the spectral classification model was dependent upon retinal features and showed 0.758-0.879 accuracy, 0.718-0.909 sensitivity, 0.764-0.912 specificity, and 0.745-0.891 area under curve for the right eye. Conclusions This study highlights the spectral variation of retinal features associated with brain amyloid loads. It also demonstrates the feasibility of the retinal hyperspectral imaging technique as a potential method to identify individuals in the preclinical phase of AD as an inexpensive alternative to brain imaging.
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Affiliation(s)
- Purna Poudel
- Alzheimer's Research Australia, Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, WA, Australia
- Centre of Excellence for Alzheimer's Disease Research and Care, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Shaun M Frost
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Kensington, WA, Australia
- Australian e-Health Research Centre, Floreat, WA, Australia
| | - Shaun Eslick
- Lifespan Health and Wellbeing Research Centre, Macquarie Medical School, Macquarie University, Macquarie Park, NSW, Australia
| | - Hamid R Sohrabi
- Alzheimer's Research Australia, Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, WA, Australia
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Perth, WA, Australia
| | - Kevin Taddei
- Alzheimer's Research Australia, Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, WA, Australia
- Centre of Excellence for Alzheimer's Disease Research and Care, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Lions Alzheimer's Foundation, Perth, WA, Australia
| | - Ralph N Martins
- Alzheimer's Research Australia, Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, WA, Australia
- Centre of Excellence for Alzheimer's Disease Research and Care, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Lifespan Health and Wellbeing Research Centre, Macquarie Medical School, Macquarie University, Macquarie Park, NSW, Australia
- Lions Alzheimer's Foundation, Perth, WA, Australia
| | - Eugene Hone
- Alzheimer's Research Australia, Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, WA, Australia
- Centre of Excellence for Alzheimer's Disease Research and Care, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Lions Alzheimer's Foundation, Perth, WA, Australia
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Ge YJ, Wu BS, Zhang Y, Chen SD, Zhang YR, Kang JJ, Deng YT, Ou YN, He XY, Zhao YL, Kuo K, Ma Q, Banaschewski T, Barker GJ, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Lemaitre H, Paus T, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Feng JF, Tan L, Dong Q, Schumann G, Cheng W, Yu JT. Genetic architectures of cerebral ventricles and their overlap with neuropsychiatric traits. Nat Hum Behav 2024; 8:164-180. [PMID: 37857874 DOI: 10.1038/s41562-023-01722-6] [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: 04/23/2023] [Accepted: 09/12/2023] [Indexed: 10/21/2023]
Abstract
The cerebral ventricles are recognized as windows into brain development and disease, yet their genetic architectures, underlying neural mechanisms and utility in maintaining brain health remain elusive. Here we aggregated genetic and neuroimaging data from 61,974 participants (age range, 9 to 98 years) in five cohorts to elucidate the genetic basis of ventricular morphology and examined their overlap with neuropsychiatric traits. Genome-wide association analysis in a discovery sample of 31,880 individuals identified 62 unique loci and 785 candidate genes associated with ventricular morphology. We replicated over 80% of loci in a well-matched cohort of lateral ventricular volume. Gene set analysis revealed enrichment of ventricular-trait-associated genes in biological processes and disease pathogenesis during both early brain development and degeneration. We explored the age-dependent genetic associations in cohorts of different age groups to investigate the possible roles of ventricular-trait-associated loci in neurodevelopmental and neurodegenerative processes. We describe the genetic overlap between ventricular and neuropsychiatric traits through comprehensive integrative approaches under correlative and causal assumptions. We propose the volume of the inferior lateral ventricles as a heritable endophenotype to predict the risk of Alzheimer's disease, which might be a consequence of prodromal Alzheimer's disease. Our study provides an advance in understanding the genetics of the cerebral ventricles and demonstrates the potential utility of ventricular measurements in tracking brain disorders and maintaining brain health across the lifespan.
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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
| | - 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 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
| | - 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
| | - 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
| | - Ju-Jiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yue-Ting Deng
- 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
| | - Xiao-Yu He
- 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
| | - Yong-Li Zhao
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Kevin Kuo
- 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
| | - Qing Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, UK
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 'Trajectoires développementales & psychiatrie', University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 'Trajectoires développementales & psychiatrie', University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France
- AP-HP, Sorbonne University, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 'Trajectoires développementales & psychiatrie', University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | | | - Herve Lemaitre
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
- Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, Bordeaux, France
| | - Tomáš Paus
- Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine, Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Jian-Feng Feng
- 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, Beijing, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Qiang Dong
- 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
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine, Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - 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, Beijing, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer 79 Center, 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.
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Liang J, LaFleur B, Hussainy S, Perry G. Gene Co-Expression Analysis of Multiple Brain Tissues Reveals Correlation of FAM222A Expression with Multiple Alzheimer's Disease-Related Genes. J Alzheimers Dis 2024; 99:S249-S263. [PMID: 37092222 PMCID: PMC11091573 DOI: 10.3233/jad-221241] [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] [Accepted: 02/27/2023] [Indexed: 04/25/2023]
Abstract
Background Alzheimer's disease (AD) is the most common form of dementia in the elderly marked by central nervous system (CNS) neuronal loss and amyloid plaques. FAM222A, encoding an amyloid plaque core protein, is an AD brain atrophy susceptibility gene that mediates amyloid-β aggregation. However, the expression interplay between FAM222A and other AD-related pathway genes is unclear. Objective Our goal was to study FAM222A's whole-genome co-expression profile in multiple tissues and investigate its interplay with other AD-related genes. Methods We analyzed gene expression correlations in Genotype-Tissue Expression (GTEx) tissues to identify FAM222A co-expressed genes and performed functional enrichment analysis on identified genes in CNS system. Results Genome-wide gene expression profiling identified 673 genes significantly correlated with FAM222A (p < 2.5×10-6) in 48 human tissues, including 298 from 13 CNS tissues. Functional enrichment analysis revealed that FAM222A co-expressed CNS genes were enriched in multiple AD-related pathways. Gene co-expression network analysis for identified genes in each brain region predicted other disease associated genes with similar biological function. Furthermore, co-expression of 25 out of 31 AD-related pathways genes with FAM222A was replicated in brain samples from 107 aged subjects from the Aging, Dementia and TBI Study. Conclusion This gene co-expression study identified multiple AD-related genes that are associated with FAM222A, indicating that FAM222A and AD-associated genes can be active simultaneously in similar biological processes, providing evidence that supports the association of FAM222A with AD.
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Affiliation(s)
- Jingjing Liang
- Department of Pharmacy Practice and Science, College of Pharmacy, University of Arizona, Tucson, AZ, USA
| | - Bonnie LaFleur
- Department of Pharmacy Practice and Science, College of Pharmacy, University of Arizona, Tucson, AZ, USA
| | - Sadiya Hussainy
- Department of Pharmacy Practice and Science, College of Pharmacy, University of Arizona, Tucson, AZ, USA
| | - George Perry
- College of Sciences, University of Texas at San Antonio, San Antonio, TX, USA
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Jain PR, Yates M, de Celis CR, Drineas P, Jahanshad N, Thompson P, Paschou P. Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes. Neuroimage 2023; 284:120466. [PMID: 37995919 DOI: 10.1016/j.neuroimage.2023.120466] [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: 07/17/2023] [Revised: 10/17/2023] [Accepted: 11/20/2023] [Indexed: 11/25/2023] Open
Abstract
Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes, with nine proteins and five metabolites replicated using independent exposure data. We found causal associations between accumbens volume and plasma protease c1 inhibitor as well as strong association between putamen volume and Agouti signaling protein. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.
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Affiliation(s)
- Pritesh R Jain
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Madison Yates
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Carlos Rubin de Celis
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Petros Drineas
- Department of Computer Science, Purdue University, United States
| | - Neda Jahanshad
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of South California, United States
| | - Paul Thompson
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of South California, United States
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States.
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Panoyan MA, Shi Y, Abbatangelo CL, Adler N, Moo-Choy A, Parra EJ, Polimanti R, Hu P, Wendt FR. Exome-wide tandem repeats confer large effects on subcortical volumes in UK Biobank participants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.11.23299818. [PMID: 38168307 PMCID: PMC10760277 DOI: 10.1101/2023.12.11.23299818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The human subcortex is involved in memory and cognition. Structural and functional changes in subcortical regions is implicated in psychiatric conditions. We performed an association study of subcortical volumes using 15,941 tandem repeats (TRs) derived from whole exome sequencing (WES) data in 16,527 unrelated European ancestry participants. We identified 17 loci, most of which were associated with accumbens volume, and nine of which had fine-mapping probability supporting their causal effect on subcortical volume independent of surrounding variation. The most significant association involved NTN1 -[GCGG] N and increased accumbens volume (β=5.93, P=8.16x10 -9 ). Three exonic TRs had large effects on thalamus volume ( LAT2 -[CATC] N β=-949, P=3.84x10 -6 and SLC39A4 -[CAG] N β=-1599, P=2.42x10 -8 ) and pallidum volume ( MCM2 -[AGG] N β=-404.9, P=147x10 -7 ). These genetic effects were consistent measurements of per-repeat expansion/contraction effects on organism fitness. With 3-dimensional modeling, we reinforced these effects to show that the expanded and contracted LAT2 -[CATC] N repeat causes a frameshift mutation that prevents appropriate protein folding. These TRs also exhibited independent effects on several psychiatric symptoms, including LAT2 -[CATC] N and the tiredness/low energy symptom of depression (β=0.340, P=0.003). These findings link genetic variation to tractable biology in the brain and relevant psychiatric symptoms. We also chart one pathway for TR prioritization in future complex trait genetic studies.
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Xiao Y, Zhao L, Zang X, Xue S. Compressed primary-to-transmodal gradient is accompanied with subcortical alterations and linked to neurotransmitters and cellular signatures in major depressive disorder. Hum Brain Mapp 2023; 44:5919-5935. [PMID: 37688552 PMCID: PMC10619397 DOI: 10.1002/hbm.26485] [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: 02/20/2023] [Revised: 08/18/2023] [Accepted: 08/30/2023] [Indexed: 09/11/2023] Open
Abstract
Major depressive disorder (MDD) has been shown to involve widespread changes in low-level sensorimotor and higher-level cognitive functions. Recent research found that a primary-to-transmodal gradient could capture a cortical hierarchical organization ranging from perception and action to cognition in healthy subjects, but a prominent gradient dysfunction in MDD patients. However, whether and how this cortical gradient is linked to subcortical impairments and whether it is reflected in the microscale neurotransmitter systems and cell type-specific transcriptional signatures remain largely unknown. Data were acquired from 323 MDD patients and 328 sex- and age-matched healthy controls derived from the REST-meta-MDD project, and the human brain neurotransmitter systems density maps and gene expression data were drawn from two publicly available datasets. We investigated alterations of the primary-to-transmodal gradient in MDD patients and their correlations with clinical symptoms of depression and anxiety, as well as their paralleled subcortical impairments. The correlations between MDD-related gradient alterations and densities of the neurotransmitter systems and gene expression information were assessed, respectively. The results demonstrated that MDD patients had a compressed primary-to-transmodal gradient accompanied by paralleled alterations in subcortical regions including the caudate, amygdala, and thalamus. The case-control gradient differences were spatially correlated with the densities of the neurotransmitter systems including the serotonin and dopamine receptors, and meanwhile with gene expression enriched in astrocytes, excitatory and inhibitory neuronal cells. These findings mapped the paralleled subcortical impairments in cortical hierarchical organization and also helped us understand the possible molecular and cellular substrates of the co-occurrence of high-level cognitive impairments with low-level sensorimotor abnormalities in MDD.
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Affiliation(s)
- Yang Xiao
- Center for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Institute of Psychological ScienceHangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouZhejiang ProvincePR China
| | - Lei Zhao
- Center for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Institute of Psychological ScienceHangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouZhejiang ProvincePR China
| | - Xuelian Zang
- Center for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Institute of Psychological ScienceHangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouZhejiang ProvincePR China
| | - Shao‐Wei Xue
- Center for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Institute of Psychological ScienceHangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouZhejiang ProvincePR China
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Ma DR, Li SJ, Shi JJ, Liang YY, Hu ZW, Hao XY, Li MJ, Guo MN, Zuo CY, Yu WK, Mao CY, Tang MB, Zhang C, Xu YM, Wu J, Sun SL, Shi CH. Shared Genetic Architecture between Parkinson's Disease and Brain Structural Phenotypes. Mov Disord 2023; 38:2258-2268. [PMID: 37990409 DOI: 10.1002/mds.29598] [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: 05/10/2023] [Revised: 08/02/2023] [Accepted: 08/21/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGROUND Patients with Parkinson's disease (PD) have consistently demonstrated brain structure abnormalities, indicating the presence of shared etiological and pathological processes between PD and brain structures; however, the genetic relationship remains poorly understood. OBJECTIVE The aim of this study was to investigate the extent of shared genetic architecture between PD and brain structural phenotypes (BSPs) and to identify shared genomic loci. METHODS We used the summary statistics from genome-wide association studies to conduct MiXeR and conditional/conjunctional false discovery rate analyses to investigate the shared genetic signatures between PD and BSPs. Subsequent expression quantitative trait loci mapping in the human brain and enrichment analyses were also performed. RESULTS MiXeR analysis identified genetic overlap between PD and various BSPs, including total cortical surface area, average cortical thickness, and specific brain volumetric structures. Further analysis using conditional false discovery rate (FDR) identified 21 novel PD risk loci on associations with BSPs at conditional FDR < 0.01, and the conjunctional FDR analysis demonstrated that PD shared several genomic loci with certain BSPs at conjunctional FDR < 0.05. Among the shared loci, 16 credible mapped genes showed high expression in the brain tissues and were primarily associated with immune function-related biological processes. CONCLUSIONS We confirmed the polygenic overlap with mixed directions of allelic effects between PD and BSPs and identified multiple shared genomic loci and risk genes, which are likely related to immune-related biological processes. These findings provide insight into the complex genetic architecture associated with PD. © 2023 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Dong-Rui Ma
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Shuang-Jie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Jing-Jing Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Yuan-Yuan Liang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Zheng-Wei Hu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Xiao-Yan Hao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Meng-Jie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Meng-Nan Guo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Chun-Yan Zuo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Wen-Kai Yu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Cheng-Yuan Mao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Mi-Bo Tang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Chan Zhang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Yu-Ming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Jun Wu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Shi-Lei Sun
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Chang-He Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
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Korologou-Linden R, Schuurmans IK, Cecil CAM, White T, Banaschewski T, Bokde ALW, Desrivières S, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Paus T, Poustka L, Holz N, Fröhner JH, Smolka M, Walter H, Winterer J, Whelan R, Schumann G, Howe LD, Ben-Shlomo Y, Davies NM, Anderson EL. The bidirectional effects between cognitive ability and brain morphology: A life course Mendelian randomization analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.17.23297145. [PMID: 38014064 PMCID: PMC10680890 DOI: 10.1101/2023.11.17.23297145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Introduction Little is understood about the dynamic interplay between brain morphology and cognitive ability across the life course. Additionally, most existing research has focused on global morphology measures such as estimated total intracranial volume, mean thickness, and total surface area. Methods Mendelian randomization was used to estimate the bidirectional effects between cognitive ability, global and regional measures of cortical thickness and surface area, estimated total intracranial volume, total white matter, and the volume of subcortical structures (N=37,864). Analyses were stratified for developmental periods (childhood, early adulthood, mid-to-late adulthood; age range: 8-81 years). Results The earliest effects were observed in childhood and early adulthood in the frontoparietal lobes. A bidirectional relationship was identified between higher cognitive ability, larger estimated total intracranial volume (childhood, mid-to-late adulthood) and total surface area (all life stages). A thicker posterior cingulate cortex and a larger surface area in the caudal middle frontal cortex and temporal pole were associated with greater cognitive ability. Contrary, a thicker temporal pole was associated with lower cognitive ability. Discussion Stable effects of cognitive ability on brain morphology across the life course suggests that childhood is potentially an important window for intervention.
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Liu Y, Tian J. Neuroprotective factors affect the progression of Alzheimer's disease. Biochem Biophys Res Commun 2023; 681:276-282. [PMID: 37797415 DOI: 10.1016/j.bbrc.2023.09.042] [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: 06/29/2023] [Revised: 09/05/2023] [Accepted: 09/18/2023] [Indexed: 10/07/2023]
Abstract
Alzheimer's disease(AD) is a neurodegenerative disease that occurs mostly in the elderly and is characterized by chronic progressive cognitive dysfunction, which seriously threatens the health and life-quality of patients. Alterations at the molecular level, which causes pathological changes of AD brain, have impacted the progression of AD. In this review, we illustrate the recent evidence of the alteration of neuroprotective proteins in AD, such as changes in their contents and variants. Furthermore, we elucidate the single nucleotide polymorphism (SNP) and gene changes. Finally, we highlight the epigenetic changes in AD, which helps to display the characteristics of the disease and provides guidance regarding research hot spots in the field against AD.
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Affiliation(s)
- Yan Liu
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, China
| | - Jinzhou Tian
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, China.
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Schleifer CH, O’Hora KP, Fung H, Xu J, Robinson TA, Wu AS, Kushan-Wells L, Lin A, Ching CRK, Bearden CE. Effects of Gene Dosage and Development on Subcortical Nuclei Volumes in Individuals with 22q11.2 Copy Number Variations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.31.564553. [PMID: 37961662 PMCID: PMC10635019 DOI: 10.1101/2023.10.31.564553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The 22q11.2 locus contains genes critical for brain development. Reciprocal Copy Number Variations (CNVs) at this locus impact risk for neurodevelopmental and psychiatric disorders. Both 22q11.2 deletions (22qDel) and duplications (22qDup) are associated with autism, but 22qDel uniquely elevates schizophrenia risk. Understanding brain phenotypes associated with these highly penetrant CNVs can provide insights into genetic pathways underlying neuropsychiatric disorders. Human neuroimaging and animal models indicate subcortical brain alterations in 22qDel, yet little is known about developmental differences across specific nuclei between reciprocal 22q11.2 CNV carriers and typically developing (TD) controls. We conducted a longitudinal MRI study in 22qDel (n=96, 53.1% female), 22qDup (n=37, 45.9% female), and TD controls (n=80, 51.2% female), across a wide age range (5.5-49.5 years). Volumes of the thalamus, hippocampus, amygdala, and anatomical subregions were estimated using FreeSurfer, and the effect of 22q11.2 gene dosage was examined using linear mixed models. Age-related changes were characterized with general additive mixed models (GAMMs). Positive gene dosage effects (22qDel < TD < 22qDup) were observed for total intracranial and whole hippocampus volumes, but not whole thalamus or amygdala volumes. Several amygdala subregions exhibited similar positive effects, with bi-directional effects found across thalamic nuclei. Distinct age-related trajectories were observed across the three groups. Notably, both 22qDel and 22qDup carriers exhibited flattened development of hippocampal CA2/3 subfields relative to TD controls. This study provides novel insights into the impact of 22q11.2 CNVs on subcortical brain structures and their developmental trajectories.
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Affiliation(s)
- Charles H. Schleifer
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kathleen P. O’Hora
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Hoki Fung
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Jennifer Xu
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Taylor-Ann Robinson
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Angela S. Wu
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Leila Kushan-Wells
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Amy Lin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Carrie E. Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Psychology, University of California, Los Angeles, CA, USA
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Antón-Galindo E, Cabana-Domínguez J, Torrico B, Corominas R, Cormand B, Fernàndez-Castillo N. The pleiotropic contribution of genes in dopaminergic and serotonergic pathways to addiction and related behavioral traits. Front Psychiatry 2023; 14:1293663. [PMID: 37937232 PMCID: PMC10627163 DOI: 10.3389/fpsyt.2023.1293663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 09/28/2023] [Indexed: 11/09/2023] Open
Abstract
Introduction Co-occurrence of substance use disorders (SUD) and other behavioral conditions, such as stress-related, aggressive or risk-taking behaviors, in the same individual has been frequently described. As dopamine (DA) and serotonin (5-HT) have been previously identified as key neurotransmitters for some of these phenotypes, we explored the genetic contribution of these pathways to SUD and these comorbid phenotypes in order to better understand the genetic relationship between them. Methods We tested the association of 275 dopaminergic genes and 176 serotonergic genes with these phenotypes by performing gene-based, gene-set and transcriptome-wide association studies in 11 genome-wide association studies (GWAS) datasets on SUD and related behaviors. Results At the gene-wide level, 68 DA and 27 5-HT genes were found to be associated with at least one GWAS on SUD or related behavior. Among them, six genes had a pleiotropic effect, being associated with at least three phenotypes: ADH1C, ARNTL, CHRNA3, HPRT1, HTR1B and DRD2. Additionally, we found nominal associations between the DA gene sets and SUD, opioid use disorder, antisocial behavior, irritability and neuroticism, and between the 5-HT-core gene set and neuroticism. Predicted gene expression correlates in brain were also found for 19 DA or 5-HT genes. Discussion Our study shows a pleiotropic contribution of dopaminergic and serotonergic genes to addiction and related behaviors such as anxiety, irritability, neuroticism and risk-taking behavior, highlighting a role for DA genes, which could explain, in part, the co-occurrence of these phenotypes.
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Affiliation(s)
- Ester Antón-Galindo
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
- Institut de Biomedicina de la Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Judit Cabana-Domínguez
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Bàrbara Torrico
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Roser Corominas
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
- Institut de Biomedicina de la Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Bru Cormand
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
- Institut de Biomedicina de la Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Noèlia Fernàndez-Castillo
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
- Institut de Biomedicina de la Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
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Forsyth L, Aman A, Cullen B, Graham N, Lyall DM, Lyall LM, Pell JP, Ward J, Smith DJ, Strawbridge RJ. Genetic architecture of DCC and influence on psychological, psychiatric and cardiometabolic traits in multiple ancestry groups in UK Biobank. J Affect Disord 2023; 339:943-953. [PMID: 37487843 DOI: 10.1016/j.jad.2023.07.052] [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: 03/17/2023] [Revised: 06/28/2023] [Accepted: 07/08/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND People with severe mental illness have a higher risk of cardiometabolic disease than the general population. Traditionally attributed to sociodemographic, behavioural factors and medication effects, recent genetic studies have provided evidence of shared biological mechanisms underlying mental illness and cardiometabolic disease. We aimed to determine whether signals in the DCC locus, implicated in psychiatric and cardiometabolic traits, were shared or distinct. METHODS In UK Biobank, we systematically assessed genetic variation in the DCC locus for association with metabolic, cardiovascular and psychiatric-related traits in unrelated "white British" participants (N = 402,837). Logistic or linear regression were applied assuming an additive genetic model and adjusting for age, sex, genotyping chip and population structure. Bonferroni correction for the number of independent variants was applied. Conditional analyses (including lead variants as covariates) and trans-ancestry analyses were used to investigate linkage disequilibrium between signals. RESULTS Significant associations were observed between DCC variants and smoking, anhedonia, body mass index (BMI), neuroticism and mood instability. Conditional analyses and linkage disequilibrium structure suggested signals for smoking and BMI were distinct from each other and the mood traits, whilst individual mood traits were inter-related in a complex manner. LIMITATIONS Restricting analyses in non-"white British" individuals to the phenotypes significant in the "white British" sample is not ideal, but the smaller samples sizes restricted the phenotypes possible to analyse. CONCLUSIONS Genetic variation in the DCC locus had distinct effects on BMI, smoking and mood traits, and therefore is unlikely to contribute to shared mechanisms underpinning mental and cardiometabolic traits.
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Affiliation(s)
- Lewis Forsyth
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Alisha Aman
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Breda Cullen
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Nicholas Graham
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Donald M Lyall
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Laura M Lyall
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Jill P Pell
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Joey Ward
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Daniel J Smith
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh E10 5HF, UK
| | - Rona J Strawbridge
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; Health Data Research, Glasgow G12 8RZ, UK; Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm 171 76, Sweden.
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49
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Zeng Y, Guo R, Cao S, Yang H. Causal associations between blood lipids and brain structures: a Mendelian randomization study. Cereb Cortex 2023; 33:10901-10908. [PMID: 37718242 DOI: 10.1093/cercor/bhad334] [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: 07/09/2023] [Revised: 08/26/2023] [Accepted: 08/27/2023] [Indexed: 09/19/2023] Open
Abstract
The potential causal association between dyslipidemia and brain structures remains unclear. Therefore, this study aimed to investigate whether circulating lipids are causally associated with brain structure alterations using Mendelian randomization analysis. Genome-wide association study summary statistics of blood lipids and brain structures were obtained from publicly available databases. Inverse-variance weighted method was used as the primary method to assess causality. In addition, four additional Mendelian randomization methods (MR-Egger, weighted median, simple mode, and weighted mode) were applied to supplement inverse-variance weighted. Furthermore, Cochrane's Q test, MR-Egger intercept test, MR-PRESSO global test, and leave-one-out analysis were performed for sensitivity analyses. After Bonferroni corrections, two causal associations were finally identified: elevated non-high-density lipoprotein cholesterol level leads to higher average cortical thickness (β = 0.0066 mm, 95% confidence interval: 0.0045-0.0087 mm, P = 0.001); and elevated high-density lipoprotein cholesterol level leads to higher inferior temporal surface area (β = 18.6077 mm2, 95% confidence interval: 11.9835-25.2320 mm2, P = 0.005). Four additional Mendelian randomization methods indicated parallel results. Sensitivity tests demonstrated the stability. Overall, the present study showed causal relationships between several lipid profiles and specific brain structures, providing new insights into the link between dyslipidemia and neurological disorders.
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Affiliation(s)
- Youjie Zeng
- Department of Anesthesiology, Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, China
| | - Ren Guo
- Department of Pharmacy, Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, China
| | - Si Cao
- Department of Anesthesiology, Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, China
| | - Heng Yang
- Department of Neurology, Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, China
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50
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Johansen N, Somasundaram S, Travaglini KJ, Yanny AM, Shumyatcher M, Casper T, Cobbs C, Dee N, Ellenbogen R, Ferreira M, Goldy J, Guzman J, Gwinn R, Hirschstein D, Jorstad NL, Keene CD, Ko A, Levi BP, Ojemann JG, Pham T, Shapovalova N, Silbergeld D, Sulc J, Torkelson A, Tung H, Smith K, Lein ES, Bakken TE, Hodge RD, Miller JA. Interindividual variation in human cortical cell type abundance and expression. Science 2023; 382:eadf2359. [PMID: 37824649 DOI: 10.1126/science.adf2359] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 07/30/2023] [Indexed: 10/14/2023]
Abstract
Single-cell transcriptomic studies have identified a conserved set of neocortical cell types from small postmortem cohorts. We extended these efforts by assessing cell type variation across 75 adult individuals undergoing epilepsy and tumor surgeries. Nearly all nuclei map to one of 125 robust cell types identified in the middle temporal gyrus. However, we found interindividual variance in abundances and gene expression signatures, particularly in deep-layer glutamatergic neurons and microglia. A minority of donor variance is explainable by age, sex, ancestry, disease state, and cell state. Genomic variation was associated with expression of 150 to 250 genes for most cell types. This characterization of cellular variation provides a baseline for cell typing in health and disease.
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Affiliation(s)
| | | | | | | | | | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Charles Cobbs
- Swedish Neuroscience Institute, Seattle,WA 98122, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Richard Ellenbogen
- Department of Neurological Surgery, University of Washington, Seattle, WA 98104, USA
| | - Manuel Ferreira
- Department of Neurological Surgery, University of Washington, Seattle, WA 98104, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Junitta Guzman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ryder Gwinn
- Swedish Neuroscience Institute, Seattle,WA 98122, USA
| | | | | | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104, USA
| | - Andrew Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA 98104, USA
| | - Boaz P Levi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA 98104, USA
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Daniel Silbergeld
- Department of Neurological Surgery, University of Washington, Seattle, WA 98104, USA
| | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Amy Torkelson
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kimberly Smith
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
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