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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 2024:10.1038/s41588-024-01766-y. [PMID: 38811844 DOI: 10.1038/s41588-024-01766-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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Jiang Z, Sullivan PF, Li T, Zhao B, Wang X, Luo T, Huang S, Guan PY, Chen J, Yang Y, Stein JL, Li Y, Liu D, Sun L, Zhu H. The pivotal role of the X-chromosome in the genetic architecture of the human brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.08.30.23294848. [PMID: 37693466 PMCID: PMC10491353 DOI: 10.1101/2023.08.30.23294848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Genes on the X-chromosome are extensively expressed in the human brain. However, little is known for the X-chromosome's impact on the brain anatomy, microstructure, and functional network. We examined 1,045 complex brain imaging traits from 38,529 participants in the UK Biobank. We unveiled potential autosome-X-chromosome interactions, while proposing an atlas outlining dosage compensation (DC) for brain imaging traits. Through extensive association studies, we identified 72 genome-wide significant trait-locus pairs (including 29 new associations) that share genetic architectures with brain-related disorders, notably schizophrenia. Furthermore, we discovered unique sex-specific associations and assessed variations in genetic effects between sexes. Our research offers critical insights into the X-chromosome's role in the human brain, underscoring its contribution to the differences observed in brain structure and functionality between sexes.
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Lv Y, Cheng X, Dong Q. SGLT1 and SGLT2 inhibition, circulating metabolites, and cerebral small vessel disease: a mediation Mendelian Randomization study. Cardiovasc Diabetol 2024; 23:157. [PMID: 38715111 PMCID: PMC11077823 DOI: 10.1186/s12933-024-02255-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Sodium-glucose cotransporter 2 (SGLT2) and SGLT1 inhibitors may have additional beneficial metabolic effects on circulating metabolites beyond glucose regulation, which could contribute to a reduction in the burden of cerebral small vessel disease (CSVD). Accordingly, we used Mendelian Randomization (MR) to examine the role of circulating metabolites in mediating SGLT2 and SGLT1 inhibition in CSVD. METHODS Genetic instruments for SGLT1/2 inhibition were identified as genetic variants, which were both associated with the expression of encoding genes of SGLT1/2 inhibitors and glycated hemoglobin A1c (HbA1c) level. A two-sample two-step MR was used to determine the causal effects of SGLT1/2 inhibition on CSVD manifestations and the mediating effects of 1400 circulating metabolites linking SGLT1/2 inhibition with CSVD manifestations. RESULTS A lower risk of deep cerebral microbleeds (CMBs) and small vessel stroke (SVS) was linked to genetically predicted SGLT2 inhibition. Better white matter structure integrity was also achieved, as evidenced by decreased mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), as well as lower deep (DWMH) and periventrivular white matter hyperintensity (PWMH) volume. Inhibiting SGLT2 could also lessen the incidence of severe enlarged perivascular spaces (EPVS) located at white matter, basal ganglia (BG) and hippocampus (HIP). SGLT1 inhibition could preserve white matter integrity, shown as decreased MD of white matter and DWMH volume. The effect of SGLT2 inhibition on SVS and MD of white matter through the concentration of 4-acetamidobutanoate and the cholesterol to oleoyl-linoleoyl-glycerol (18:1 to 18:2) ratio, with a mediated proportion of 30.3% and 35.5% of the total effect, respectively. CONCLUSIONS SGLT2 and SGLT1 inhibition play protective roles in CSVD development. The SGLT2 inhibition could lower the risk of SVS and improve the integrity of white matter microstructure via modulating the level of 4-acetamidobutanoate and cholesterol metabolism. Further mechanistic and clinical studies research are needed to validate our findings.
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Affiliation(s)
- Yanchen Lv
- Department of Neurology, National Center for Neurological Disorders, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
- , 12 Wulumuqi Zhong Road, 200040, Shanghai, P. R. China.
| | - Xin Cheng
- Department of Neurology, National Center for Neurological Disorders, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology, National Center for Neurological Disorders, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
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4
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Ma S, Xu F, Fu Y, Zheng JS. Investigation of the influence of plasma proteome on brain structure: a Mendelian randomization study. J Genet Genomics 2024:S1673-8527(24)00073-0. [PMID: 38608937 DOI: 10.1016/j.jgg.2024.03.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/28/2024] [Accepted: 03/31/2024] [Indexed: 04/14/2024]
Affiliation(s)
- Shengyi Ma
- Fudan University, Shanghai 200433, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310030, China; Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Fengzhe Xu
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310030, China; Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Yuanqing Fu
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310030, China; Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
| | - Ju-Sheng Zheng
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310030, China; Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
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Wen J, Tian YE, Skampardoni I, Yang Z, Cui Y, Anagnostakis F, Mamourian E, Zhao B, Toga AW, Zaleskey A, Davatzikos C. The Genetic Architecture of Biological Age in Nine Human Organ Systems. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.06.08.23291168. [PMID: 37398441 PMCID: PMC10312870 DOI: 10.1101/2023.06.08.23291168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized the genetic architecture of the biological age gap (BAG) across nine human organ systems in 377,028 individuals of European ancestry from the UK Biobank. We discovered 393 genomic loci-BAG pairs (P-value<5×10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal systems. We observed BAG-organ specificity and inter-organ connections. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system while exerting pleiotropic effects on traits linked to multiple organ systems. A gene-drug-disease network confirmed the involvement of the metabolic BAG-associated genes in drugs targeting various metabolic disorders. Genetic correlation analyses supported Cheverud's Conjecture1 - the genetic correlation between BAGs mirrors their phenotypic correlation. A causal network revealed potential causal effects linking chronic diseases (e.g., Alzheimer's disease), body weight, and sleep duration to the BAG of multiple organ systems. Our findings shed light on promising therapeutic interventions to enhance human organ health within a complex multi-organ network, including lifestyle modifications and potential drug repositioning strategies for treating chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Ye Ella Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Filippos Anagnostakis
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Andrew Zaleskey
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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Pan W, Shan Y, Li C, Huang S, Li T, Li Y, Zhu H. FPLS-DC: functional partial least squares through distance covariance for imaging genetics. Bioinformatics 2024; 40:btae173. [PMID: 38552322 PMCID: PMC11034987 DOI: 10.1093/bioinformatics/btae173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 02/28/2024] [Accepted: 03/27/2024] [Indexed: 04/24/2024] Open
Abstract
MOTIVATION Imaging genetics integrates imaging and genetic techniques to examine how genetic variations influence the function and structure of organs like the brain or heart, providing insights into their impact on behavior and disease phenotypes. The use of organ-wide imaging endophenotypes has increasingly been used to identify potential genes associated with complex disorders. However, analyzing organ-wide imaging data alongside genetic data presents two significant challenges: high dimensionality and complex relationships. To address these challenges, we propose a novel, nonlinear inference framework designed to partially mitigate these issues. RESULTS We propose a functional partial least squares through distance covariance (FPLS-DC) framework for efficient genome wide analyses of imaging phenotypes. It consists of two components. The first component utilizes the FPLS-derived base functions to reduce image dimensionality while screening genetic markers. The second component maximizes the distance correlation between genetic markers and projected imaging data, which is a linear combination of the FPLS-basis functions, using simulated annealing algorithm. In addition, we proposed an iterative FPLS-DC method based on FPLS-DC framework, which effectively overcomes the influence of inter-gene correlation on inference analysis. We efficiently approximate the null distribution of test statistics using a gamma approximation. Compared to existing methods, FPLS-DC offers computational and statistical efficiency for handling large-scale imaging genetics. In real-world applications, our method successfully detected genetic variants associated with the hippocampus, demonstrating its value as a statistical toolbox for imaging genetic studies. AVAILABILITY AND IMPLEMENTATION The FPLS-DC method we propose opens up new research avenues and offers valuable insights for analyzing functional and high-dimensional data. In addition, it serves as a useful tool for scientific analysis in practical applications within the field of imaging genetics research. The R package FPLS-DC is available in Github: https://github.com/BIG-S2/FPLSDC.
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Affiliation(s)
- Wenliang Pan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Yue Shan
- Departments of Biostatistics, Statistics, Genetics, and Computer Science and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Chuang Li
- Department of Statistical Science, School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Shuai Huang
- Departments of Biostatistics, Statistics, Genetics, and Computer Science and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Tengfei Li
- Departments of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Yun Li
- Departments of Biostatistics, Statistics, Genetics, and Computer Science and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Departments of Biostatistics, Statistics, Genetics, and Computer Science and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA
- Departments of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA
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Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The genetic architecture of multimodal human brain age. Nat Commun 2024; 15:2604. [PMID: 38521789 PMCID: PMC10960798 DOI: 10.1038/s41467-024-46796-6] [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: 05/13/2023] [Accepted: 03/06/2024] [Indexed: 03/25/2024] Open
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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8
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Tian X, Wang Y, Wang S, Zhao Y, Zhao Y. Bayesian mixed model inference for genetic association under related samples with brain network phenotype. Biostatistics 2024:kxae008. [PMID: 38494649 DOI: 10.1093/biostatistics/kxae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 01/22/2024] [Accepted: 02/19/2024] [Indexed: 03/19/2024] Open
Abstract
Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.
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Affiliation(s)
- Xinyuan Tian
- Department of Biostatistics, Yale University, 60 College St, New Haven, CT 06520, United States
| | - Yiting Wang
- Department of Biostatistics, Yale University, 60 College St, New Haven, CT 06520, United States
| | - Selena Wang
- Department of Biostatistics, Yale University, 60 College St, New Haven, CT 06520, United States
| | - Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University, 410W. 10th St, Indianapolis, IN 46202, United States
| | - Yize Zhao
- Department of Biostatistics, Yale University, 60 College St, New Haven, CT 06520, United States
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Li Y, Xue F, Li B, Yang Y, Fan Z, Shu J, Yang X, Wang X, Lin J, Copana C, Zhao B. Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.28.538585. [PMID: 38559152 PMCID: PMC10979906 DOI: 10.1101/2023.04.28.538585] [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] [Indexed: 04/04/2024]
Abstract
As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summary statistics for thousands of phenotypes can be both logistically challenging and computationally demanding. In this paper, we introduce BIGA (https://bigagwas.org/), a website that aims to offer unified data analysis pipelines and processed data resources for cross-trait genetic architecture analyses using GWAS summary statistics. We have developed a framework to implement statistical genetics tools on a cloud computing platform, combined with extensive curated GWAS data resources. Through BIGA, users can upload data, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights.
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Affiliation(s)
- Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Fei Xue
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Yilin Yang
- Department of Computer and Information Science and Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT 06511, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
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10
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Oh EY, Han KM, Kim A, Kang Y, Tae WS, Han MR, Ham BJ. Integration of whole-exome sequencing and structural neuroimaging analysis in major depressive disorder: a joint study. Transl Psychiatry 2024; 14:141. [PMID: 38461185 PMCID: PMC10924915 DOI: 10.1038/s41398-024-02849-4] [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: 09/15/2023] [Revised: 02/07/2024] [Accepted: 02/22/2024] [Indexed: 03/11/2024] Open
Abstract
Major depressive disorder (MDD) is a common mental illness worldwide and is triggered by an intricate interplay between environmental and genetic factors. Although there are several studies on common variants in MDD, studies on rare variants are relatively limited. In addition, few studies have examined the genetic contributions to neurostructural alterations in MDD using whole-exome sequencing (WES). We performed WES in 367 patients with MDD and 161 healthy controls (HCs) to detect germline and copy number variations in the Korean population. Gene-based rare variants were analyzed to investigate the association between the genes and individuals, followed by neuroimaging-genetic analysis to explore the neural mechanisms underlying the genetic impact in 234 patients with MDD and 135 HCs using diffusion tensor imaging data. We identified 40 MDD-related genes and observed 95 recurrent regions of copy number variations. We also discovered a novel gene, FRMPD3, carrying rare variants that influence MDD. In addition, the single nucleotide polymorphism rs771995197 in the MUC6 gene was significantly associated with the integrity of widespread white matter tracts. Moreover, we identified 918 rare exonic missense variants in genes associated with MDD susceptibility. We postulate that rare variants of FRMPD3 may contribute significantly to MDD, with a mild penetration effect.
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Affiliation(s)
- Eun-Young Oh
- 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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11
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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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12
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Shukla R. Two Distinct Biotypes in Major Depression Unveiled. Biol Psychiatry 2024; 95:382-384. [PMID: 38325914 DOI: 10.1016/j.biopsych.2023.11.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 02/09/2024]
Affiliation(s)
- Rammohan Shukla
- Department of Zoology and Physiology, University of Wyoming, Laramie, Wyoming.
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13
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Li J, Long Z, Sheng W, Du L, Qiu J, Chen H, Liao W. Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes. Biol Psychiatry 2024; 95:414-425. [PMID: 37573006 DOI: 10.1016/j.biopsych.2023.08.003] [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: 03/14/2023] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied. METHODS Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes. RESULTS We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies. CONCLUSIONS Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.
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Affiliation(s)
- Jiao Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Zhiliang Long
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Wei Sheng
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Lian Du
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, P.R. China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Wei Liao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.
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14
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Huang XL, Gao J, Wang YM, Zhu F, Qin J, Yao QN, Zhang XB, Sun HY. Neuropathological characteristics of abnormal white matter functional signaling in adolescents with major depression. World J Psychiatry 2024; 14:276-286. [PMID: 38464765 PMCID: PMC10921285 DOI: 10.5498/wjp.v14.i2.276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 12/13/2023] [Accepted: 01/08/2024] [Indexed: 02/06/2024] Open
Abstract
BACKGROUND Major depression disorder (MDD) constitutes a significant mental health concern. Epidemiological surveys indicate that the lifetime prevalence of depression in adolescents is much higher than that in adults, with a corresponding increased risk of suicide. In studying brain dysfunction associated with MDD in adole-scents, research on brain white matter (WM) is sparse. Some researchers even mistakenly regard the signals generated by the WM as noise points. In fact, studies have shown that WM exhibits similar blood oxygen level-dependent signal fluctuations. The alterations in WM signals and their relationship with disease severity in adolescents with MDD remain unclear. AIM To explore potential abnormalities in WM functional signals in adolescents with MDD. METHODS This study involved 48 adolescent patients with MDD and 31 healthy controls (HC). All participants were assessed using the Patient Health Questionnaire-9 Scale and the mini international neuropsychiatric interview (MINI) suicide inventory. In addition, a Siemens Skyra 3.0T magnetic resonance scanner was used to obtain the subjects' image data. The DPABI software was utilized to calculate the WM signal of the fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity, followed by a two-sample t-test between the MDD and HC groups. Independent component analysis (ICA) was also used to evaluate the WM functional signal. Pearson's correlation was performed to assess the relationship between statistical test results and clinical scales. RESULTS Compared to HC, individuals with MDD demonstrated a decrease in the fALFF of WM in the corpus callosum body, left posterior limb of the internal capsule, right superior corona radiata, and bilateral posterior corona radiata [P < 0.001, family-wise error (FWE) voxel correction]. The regional homogeneity of WM increased in the right posterior limb of internal capsule and left superior corona radiata, and decreased in the left superior longitudinal fasciculus (P < 0.001, FWE voxel correction). The ICA results of WM overlapped with those of regional homo-geneity. The fALFF of WM signal in the left posterior limb of the internal capsule was negatively correlated with the MINI suicide scale (P = 0.026, r = -0.32), and the right posterior corona radiata was also negatively correlated with the MINI suicide scale (P = 0.047, r = -0.288). CONCLUSION Adolescents with MDD involves changes in WM functional signals, and these differences in brain regions may increase the risk of suicide.
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Affiliation(s)
- Xin-Lin Huang
- Imaging and Nuclear Medicine, Jiamusi University, Jiamusi 154000, Heilongjiang Province, China
| | - Ju Gao
- Department of Psychiatry, The Affiliated Guangji Hospital of Soochow University, Suzhou 215137, Jiangsu Province, China
| | - Yong-Ming Wang
- School of Biology & Basic Medical Sciences, Medical College of Soochow University, Suzhou 215137, Jiangsu Province, China
| | - Feng Zhu
- Department of Psychiatry, The Affiliated Guangji Hospital of Soochow University, Suzhou 215137, Jiangsu Province, China
| | - Jing Qin
- Department of Radiology, Shanghai Anting Hospital, Shanghai 20000, China
| | - Qian-Nan Yao
- Imaging and Nuclear Medicine, Jiamusi University, Jiamusi 154000, Heilongjiang Province, China
| | - Xiao-Bin Zhang
- Department of Psychiatry, The Affiliated Guangji Hospital of Soochow University, Suzhou 215137, Jiangsu Province, China
| | - Hong-Yan Sun
- Department of Radiology, The Affiliated Guangji Hospital of Soochow University, Suzhou 215137, Jiangsu Province, China
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15
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Wang S, Li T, Zhao B, Dai W, Yao Y, Li C, Li T, Zhu H, Zhang H. Identification and validation of supervariants reveal novel loci associated with human white matter microstructure. Genome Res 2024; 34:20-33. [PMID: 38190638 PMCID: PMC10904010 DOI: 10.1101/gr.277905.123] [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: 03/18/2023] [Accepted: 12/05/2023] [Indexed: 01/10/2024]
Abstract
As an essential part of the central nervous system, white matter coordinates communications between different brain regions and is related to a wide range of neurodegenerative and neuropsychiatric disorders. Previous genome-wide association studies (GWASs) have uncovered loci associated with white matter microstructure. However, GWASs suffer from limited reproducibility and difficulties in detecting multi-single-nucleotide polymorphism (multi-SNP) and epistatic effects. In this study, we adopt the concept of supervariants, a combination of alleles in multiple loci, to account for potential multi-SNP effects. We perform supervariant identification and validation to identify loci associated with 22 white matter fractional anisotropy phenotypes derived from diffusion tensor imaging. To increase reproducibility, we use United Kingdom (UK) Biobank White British (n = 30,842) data for discovery and internal validation, and UK Biobank White but non-British (n = 1927) data, Europeans from the Adolescent Brain Cognitive Development study (n = 4399) data, and Europeans from the Human Connectome Project (n = 319) data for external validation. We identify 23 novel loci on the discovery set that have not been reported in the previous GWASs on white matter microstructure. Among them, three supervariants on genomic regions 5q35.1, 8p21.2, and 19q13.32 have P-values lower than 0.05 in the meta-analysis of the three independent validation data sets. These supervariants contain genetic variants located in genes that have been related to brain structures, cognitive functions, and neuropsychiatric diseases. Our findings provide a better understanding of the genetic architecture underlying white matter microstructure.
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Affiliation(s)
- Shiying Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Ting Li
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104-1686, USA
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Yisha Yao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Cai Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Heping Zhang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA;
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16
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Shan Y, Huang C, Li Y, Zhu H. Merging or ensembling: integrative analysis in multiple neuroimaging studies. Biometrics 2024; 80:ujae003. [PMID: 38465984 PMCID: PMC10926268 DOI: 10.1093/biomtc/ujae003] [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: 12/02/2022] [Revised: 11/27/2023] [Accepted: 01/10/2024] [Indexed: 03/12/2024]
Abstract
The aim of this paper is to systematically investigate merging and ensembling methods for spatially varying coefficient mixed effects models (SVCMEM) in order to carry out integrative learning of neuroimaging data obtained from multiple biomedical studies. The "merged" approach involves training a single learning model using a comprehensive dataset that encompasses information from all the studies. Conversely, the "ensemble" approach involves creating a weighted average of distinct learning models, each developed from an individual study. We systematically investigate the prediction accuracy of the merged and ensemble learners under the presence of different degrees of interstudy heterogeneity. Additionally, we establish asymptotic guidelines for making strategic decisions about when to employ either of these models in different scenarios, along with deriving optimal weights for the ensemble learner. To validate our theoretical results, we perform extensive simulation studies. The proposed methodology is also applied to 3 large-scale neuroimaging studies.
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Affiliation(s)
- Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Chao Huang
- Department of Statistics, Florida State University, Tallahassee, FL 32306, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
- Department of Statistics, Florida State University, Tallahassee, FL 32306, United States
- Department of Statistics & Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
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17
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Chen J, Li T, Zhao B, Chen H, Yuan C, Garden GA, Wu G, Zhu H. The interaction effects of age, APOE and common environmental risk factors on human brain structure. Cereb Cortex 2024; 34:bhad472. [PMID: 38112569 PMCID: PMC10793588 DOI: 10.1093/cercor/bhad472] [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: 05/04/2023] [Revised: 10/09/2023] [Accepted: 11/06/2023] [Indexed: 12/21/2023] Open
Abstract
Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.
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Affiliation(s)
- Jie Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
| | - Tengfei Li
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Bingxin Zhao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, 265 South 37th Street, 3rd & 4th Floors, Philadelphia, PA 19104-1686, United States
| | - Hui Chen
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
| | - Changzheng Yuan
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
- Department of Nutrition, Harvard T H Chan School of Public Health, 665 Huntington Avenue Boston, MA, 02115, United States
| | - Gwenn A Garden
- Department of Neurology, School of Medicine, University of North Carolina at Chapel Hill, 170 Manning Drive Chapel Hill, NC 27599-7025, United States
| | - Guorong Wu
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Dr, Chapel Hill, NC 27599, United States
- Carolina Institute for Developmental Disabilities, 101 Renee Lynne Ct, Carrboro, NC 27510, United States
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- Departments of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27514, United States
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18
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Yang ZC, Yin CD, Yeh FC, Xue BW, Song XY, Li G, Sun SJ, Deng ZH, Hou ZG, Xie J. Exploring MGMT methylation-driven structural connectivity changes in insular gliomas: a tractography and graph theoretical analysis. J Neurooncol 2024; 166:155-165. [PMID: 38150062 DOI: 10.1007/s11060-023-04539-5] [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: 11/10/2023] [Accepted: 12/11/2023] [Indexed: 12/28/2023]
Abstract
OBJECTIVES This study aims to explore the relationship between the methylation levels of the O-6-methylguanine-DNA methyltransferase (MGMT) promoter and the structural connectivity in insular gliomas across hemispheres. METHODS We analyzed 32 left and 29 right insular glioma cases and 50 healthy controls, using differential tractography, correlational tractography, and graph theoretical analysis to investigate the correlation between structural connectivity and the methylation level. RESULTS The differential tractography results revealed that in left insular glioma, the volume of affected inferior fronto-occipital fasciculus (IFOF, p = 0.019) significantly correlated with methylation levels. Correlational tractography results showed that the quantitative anisotropy (QA) value of peritumoral fiber tracts also exhibited a significant correlation with methylation levels (FDR < 0.05). On the other hand, in right insular glioma, anterior internal part of the reticular tract, IFOF, and thalamic radiation showed a significant correlation with methylation levels but at a different correlation direction from the left side (FDR < 0.05). The graph theoretical analysis showed that in the left insular gliomas, only the radius of graph was significantly lower in methylated MGMT group than unmethylated group (p = 0.047). No significant correlations between global properties and methylation levels were observed in insular gliomas on both sides. CONCLUSION Our findings highlight a significant, hemisphere-specific correlation between MGMT promoter methylation and structural connectivity in insular gliomas. This study provides new insights into the genetic influence on glioma pathology, which could inform targeted therapeutic strategies.
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Affiliation(s)
- Zuo-Cheng Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 of South 4th Ring Road, Fengtai District, Beijing, China
| | - Chuan-Dong Yin
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 of South 4th Ring Road, Fengtai District, Beijing, China
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bo-Wen Xue
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 of South 4th Ring Road, Fengtai District, Beijing, China
| | - Xin-Yu Song
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 of South 4th Ring Road, Fengtai District, Beijing, China
| | - Gen Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 of South 4th Ring Road, Fengtai District, Beijing, China
| | - Sheng-Jun Sun
- Neuroimaging Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zheng-Hai Deng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 of South 4th Ring Road, Fengtai District, Beijing, China
| | - Zong-Gang Hou
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 of South 4th Ring Road, Fengtai District, Beijing, China.
| | - Jian Xie
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 of South 4th Ring Road, Fengtai District, Beijing, China.
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19
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Blöchl M, Schaare HL, Kumral D, Gaebler M, Nestler S, Villringer A. Vascular risk factors, white matter microstructure, and depressive symptoms: a longitudinal analysis in the UK Biobank. Psychol Med 2024; 54:125-135. [PMID: 37016768 DOI: 10.1017/s0033291723000697] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
BACKGROUND Cumulative burden from vascular risk factors (VRFs) has been associated with an increased risk of depressive symptoms in mid- and later life. It has been hypothesised that this association arises because VRFs disconnect fronto-subcortical white matter tracts involved in mood regulation, which puts older adults at higher risk of developing depressive symptoms. However, evidence for the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms from longitudinal studies is scarce. METHODS This preregistered study analysed longitudinal data from 6,964 middle-aged and older adults from the UK Biobank who participated in consecutive assessments of VRFs, brain imaging, and depressive symptoms. Using mediation modelling, we directly tested to what extend white matter microstructure mediates the longitudinal association between VRF burden and depressive symptoms. RESULTS VRF burden showed a small association with depressive symptoms at follow-up. However, there was no evidence that fractional anisotropy (FA) of white matter tracts mediated this association. Additional analyses also yielded no mediating effects using alternative operationalisations of VRF burden, mean diffusivity (MD) of single tracts, or overall average of tract-based white matter microstructure (global FA, global MD, white matter hyperintensity volume). CONCLUSIONS Our results lend no support to the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms, while highlighting the relevance of using longitudinal data to directly test pathways linking vascular and mental health.
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Affiliation(s)
- Maria Blöchl
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- International Max Planck Research School: Neuroscience of Communication: Structure, Function, and Plasticity, Leipzig, Germany
- Department of Psychology, University of Münster, Münster, Germany
| | - H Lina Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour) Research Centre Jülich, Germany
| | - Deniz Kumral
- Institute of Psychology, Neuropsychology, University of Freiburg, Freiburg, Germany
- Clinical Psychology and Psychotherapy Unit, Institute of Psychology, University of Freiburg, Freiburg, Germany
| | - Michael Gaebler
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Faculty of Philosophy, Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, MindBrainBody Institute
- Max Planck Dahlem Campus of Cognition, Berlin, Germany
| | - Steffen Nestler
- Department of Psychology, University of Münster, Münster, Germany
| | - Arno Villringer
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University Clinic Leipzig, Leipzig, Germany
- Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
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20
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Feng G, Chen R, Zhao R, Li Y, Ma L, Wang Y, Men W, Gao J, Tan S, Cheng J, He Y, Qin S, Dong Q, Tao S, Shu N. Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture. Commun Biol 2023; 6:1257. [PMID: 38087047 PMCID: PMC10716168 DOI: 10.1038/s42003-023-05647-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rui Zhao
- College of Life Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Gene Resource and Molecular Development, Beijing, China
| | - Yuanyuan Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jiahong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
- BABRI Centre, Beijing Normal University, Beijing, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
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21
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Yang A, Yang YT, Zhao XM. An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset. PLoS Genet 2023; 19:e1011112. [PMID: 38150468 PMCID: PMC10775988 DOI: 10.1371/journal.pgen.1011112] [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: 06/08/2023] [Revised: 01/09/2024] [Accepted: 12/12/2023] [Indexed: 12/29/2023] Open
Abstract
Mendelian randomization (MR) is an effective approach for revealing causal risk factors that underpin complex traits and diseases. While MR has been more widely applied under two-sample settings, it is more promising to be used in one single large cohort given the rise of biobank-scale datasets that simultaneously contain genotype data, brain imaging data, and matched complex traits from the same individual. However, most existing multivariable MR methods have been developed for two-sample setting or a small number of exposures. In this study, we introduce a one-sample multivariable MR method based on partial least squares and Lasso regression (MR-PL). MR-PL is capable of considering the correlation among exposures (e.g., brain imaging features) when the number of exposures is extremely upscaled, while also correcting for winner's curse bias. We performed extensive and systematic simulations, and demonstrated the robustness and reliability of our method. Comprehensive simulations confirmed that MR-PL can generate more precise causal estimates with lower false positive rates than alternative approaches. Finally, we applied MR-PL to the datasets from UK Biobank to reveal the causal effects of 36 white matter tracts on 180 complex traits, and showed putative white matter tracts that are implicated in smoking, blood vascular function-related traits, and eating behaviors.
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Affiliation(s)
- Anyi Yang
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
| | - Yucheng T. Yang
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
| | - Xing-Ming Zhao
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, People’s Republic of China
- International Human Phenome Institutes (Shanghai), Shanghai, People’s Republic of China
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22
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Parker N, Cheng W, Hindley GFL, Parekh P, Shadrin AA, Maximov II, Smeland OB, Djurovic S, Dale AM, Westlye LT, Frei O, Andreassen OA. Psychiatric disorders and brain white matter exhibit genetic overlap implicating developmental and neural cell biology. Mol Psychiatry 2023; 28:4924-4932. [PMID: 37759039 DOI: 10.1038/s41380-023-02264-z] [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: 05/18/2023] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023]
Abstract
Improved understanding of the shared genetic architecture between psychiatric disorders and brain white matter may provide mechanistic insights for observed phenotypic associations. Our objective is to characterize the shared genetic architecture of bipolar disorder (BD), major depression (MD), and schizophrenia (SZ) with white matter fractional anisotropy (FA) and identify shared genetic loci to uncover biological underpinnings. We used genome-wide association study (GWAS) summary statistics for BD (n = 413,466), MD (n = 420,359), SZ (n = 320,404), and white matter FA (n = 33,292) to uncover the genetic architecture (i.e., polygenicity and discoverability) of each phenotype and their genetic overlap (i.e., genetic correlations, overlapping trait-influencing variants, and shared loci). This revealed that BD, MD, and SZ are at least 7-times more polygenic and less genetically discoverable than average FA. Even in the presence of weak genetic correlations (range = -0.05 to -0.09), average FA shared an estimated 42.5%, 43.0%, and 90.7% of trait-influencing variants as well as 12, 4, and 28 shared loci with BD, MD, and SZ, respectively. Shared variants were mapped to genes and tested for enrichment among gene-sets which implicated neurodevelopmental expression, neural cell types, myelin, and cell adhesion molecules. For BD and SZ, case vs control tract-level differences in FA associated with genetic correlations between those same tracts and the respective disorder (rBD = 0.83, p = 4.99e-7 and rSZ = 0.65, p = 5.79e-4). Genetic overlap at the tract-level was consistent with average FA results. Overall, these findings suggest a genetic basis for the involvement of brain white matter aberrations in the pathophysiology of psychiatric disorders.
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Affiliation(s)
- Nadine Parker
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Weiqiu Cheng
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F L Hindley
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Ivan I Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Olav B Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, 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
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, 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
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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23
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Wang D, Fan Q, Xiao X, He H, Yang Y, Li Y. Structural Fingerprinting of the Frontal Aslant Tract: Predicting Cognitive Control Capacity and Obsessive-Compulsive Symptoms. J Neurosci 2023; 43:7016-7027. [PMID: 37696666 PMCID: PMC10586535 DOI: 10.1523/jneurosci.0628-23.2023] [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: 04/04/2023] [Revised: 06/29/2023] [Accepted: 08/18/2023] [Indexed: 09/13/2023] Open
Abstract
White matter of the human brain is influenced by common genetic variations and shaped by neural activity-dependent experiences. Variations in microstructure of cerebral white matter across individuals and even across fiber tracts might underlie differences in cognitive capacity and vulnerabilities to mental disorders. The frontoparietal and cingulo-opercular networks of the brain constitute the central system supporting cognitive functions, and functional connectivity of these networks has been used to distinguish individuals known as "functional fingerprinting." The frontal aslant tract (FAT) that passes through the two networks has been implicated in executive functions. However, whether FAT can be used as a "structural fingerprint" to distinguish individuals and predict an individual's cognitive function and dysfunction is unknown. Here we investigated the fingerprinting property of FAT microstructural profiles using three independent diffusion MRI datasets with repeated scans on human participants including both females and males. We found that diffusion and geometric profiles of FAT can be used to distinguish individuals with a high accuracy. Next, we demonstrated that fractional anisotropy in different FAT segments predicted distinct cognitive functions, including working memory, inhibitory control, and relational reasoning. Finally, we assessed the contribution of altered FAT microstructural profiles to cognitive dysfunction in unmedicated patients with obsessive-compulsive disorders. We found that the altered microstructure in FAT was associated with the severity of obsessive-compulsive symptoms. Collectively, our findings suggest that the microstructural profiles of FAT can identify individuals with a high accuracy and may serve as an imaging marker for predicting an individual's cognitive capacity and disease severity.SIGNIFICANCE STATEMENT The frontoparietal network and cingulo-opercular network of the brain constitute a dual-network architecture for human cognitive functions, and functional connectivity of these two networks can be used as a "functional fingerprint" to distinguish individuals. However, the structural underpinnings of these networks subserving individual heterogeneities in their functional connectivity and cognitive ability remain unknown. We show here that the frontal aslant tract (FAT) that passes through the two networks distinguishes individuals with a high accuracy. Further, we demonstrate that the diffusion profiles of FAT predict distinct cognitive functions in healthy subjects and are associated with the clinical symptoms in patients with obsessive-compulsive disorders. Our findings suggest that the FAT may serve as a unique structural fingerprint underlying individual cognitive capability.
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Affiliation(s)
- Danni Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland 21224
| | - Qing Fan
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, People's Republic of China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, People's Republic of China
| | - Xiang Xiao
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland 21224
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou 310027, People's Republic of China
- School of Physics, Zhejiang University, Hangzhou 310027, People's Republic of China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland 21224
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
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Carozza S, Akarca D, Astle D. The adaptive stochasticity hypothesis: Modeling equifinality, multifinality, and adaptation to adversity. Proc Natl Acad Sci U S A 2023; 120:e2307508120. [PMID: 37816058 PMCID: PMC10589678 DOI: 10.1073/pnas.2307508120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 08/25/2023] [Indexed: 10/12/2023] Open
Abstract
Neural phenotypes are the result of probabilistic developmental processes. This means that stochasticity is an intrinsic aspect of the brain as it self-organizes over a protracted period. In other words, while both genomic and environmental factors shape the developing nervous system, another significant-though often neglected-contributor is the randomness introduced by probability distributions. Using generative modeling of brain networks, we provide a framework for probing the contribution of stochasticity to neurodevelopmental diversity. To mimic the prenatal scaffold of brain structure set by activity-independent mechanisms, we start our simulations from the medio-posterior neonatal rich club (Developing Human Connectome Project, n = 630). From this initial starting point, models implementing Hebbian-like wiring processes generate variable yet consistently plausible brain network topologies. By analyzing repeated runs of the generative process (>107 simulations), we identify critical determinants and effects of stochasticity. Namely, we find that stochastic variation has a greater impact on brain organization when networks develop under weaker constraints. This heightened stochasticity makes brain networks more robust to random and targeted attacks, but more often results in non-normative phenotypic outcomes. To test our framework empirically, we evaluated whether stochasticity varies according to the experience of early-life deprivation using a cohort of neurodiverse children (Centre for Attention, Learning and Memory; n = 357). We show that low-socioeconomic status predicts more stochastic brain wiring. We conclude that stochasticity may be an unappreciated contributor to relevant developmental outcomes and make specific predictions for future research.
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Affiliation(s)
- Sofia Carozza
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, CambridgeCB2 7EF, United Kingdom
- Department of Neurology, Harvard Medical School, Boston, MA02115
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA02115
| | - Danyal Akarca
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, CambridgeCB2 7EF, United Kingdom
| | - Duncan Astle
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, CambridgeCB2 7EF, United Kingdom
- Department of Psychiatry, University of Cambridge, CambridgeCB2 0SZ, United Kingdom
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25
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Bao J, Wen J, Wen Z, Yang S, Cui Y, Yang Z, Erus G, Saykin AJ, Long Q, Davatzikos C, Shen L. Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer's disease. Neuroimage 2023; 280:120346. [PMID: 37634885 PMCID: PMC10552907 DOI: 10.1016/j.neuroimage.2023.120346] [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: 02/16/2023] [Revised: 06/19/2023] [Accepted: 08/22/2023] [Indexed: 08/29/2023] Open
Abstract
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD.
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Affiliation(s)
- Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA 90292, USA
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yuhan Cui
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Zhijian Yang
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
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26
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Zhu K, Bendl J, Rahman S, Vicari JM, Coleman C, Clarence T, Latouche O, Tsankova NM, Li A, Brennand KJ, Lee D, Yuan GC, Fullard JF, Roussos P. Multi-omic profiling of the developing human cerebral cortex at the single-cell level. SCIENCE ADVANCES 2023; 9:eadg3754. [PMID: 37824614 PMCID: PMC10569714 DOI: 10.1126/sciadv.adg3754] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/01/2023] [Indexed: 10/14/2023]
Abstract
The cellular complexity of the human brain is established via dynamic changes in gene expression throughout development that is mediated, in part, by the spatiotemporal activity of cis-regulatory elements (CREs). We simultaneously profiled gene expression and chromatin accessibility in 45,549 cortical nuclei across six broad developmental time points from fetus to adult. We identified cell type-specific domains in which chromatin accessibility is highly correlated with gene expression. Differentiation pseudotime trajectory analysis indicates that chromatin accessibility at CREs precedes transcription and that dynamic changes in chromatin structure play a critical role in neuronal lineage commitment. In addition, we mapped cell type-specific and temporally specific genetic loci implicated in neuropsychiatric traits, including schizophrenia and bipolar disorder. Together, our results describe the complex regulation of cell composition at critical stages in lineage determination and shed light on the impact of spatiotemporal alterations in gene expression on neuropsychiatric disease.
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Affiliation(s)
- Kaiyi Zhu
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Samir Rahman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - James M. Vicari
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Claire Coleman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Tereza Clarence
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ovaun Latouche
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Nadejda M. Tsankova
- Department of Pathology and Laboratory Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Aiqun Li
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kristen J. Brennand
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John F. Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
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27
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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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28
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Morey R, Zheng Y, Sun D, Garrett M, Gasperi M, Maihofer A, Baird CL, Grasby K, Huggins A, Haswell C, Thompson P, Medland S, Gustavson D, Panizzon M, Kremen W, Nievergelt C, Ashley-Koch A, Logue L. Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture. RESEARCH SQUARE 2023:rs.3.rs-3253035. [PMID: 37886496 PMCID: PMC10602057 DOI: 10.21203/rs.3.rs-3253035/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for the 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs. The multivariate GWASs of these GIBNs identified 74 genome-wide significant (GWS) loci (p<5×10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed with attention deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), and insomnia, indicating genetic predisposition to a larger SA in the specific GIBN is associated with lower genetic risk of these disorders. CT GIBNs displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping the cortex into genetically informed networks.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Paul Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, California, USA
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29
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Lin S, Zhang H, Qi M, Cooper DN, Yang Y, Yang Y, Zhao H. Inferring the genetic relationship between brain imaging-derived phenotypes and risk of complex diseases by Mendelian randomization and genome-wide colocalization. Neuroimage 2023; 279:120325. [PMID: 37579999 DOI: 10.1016/j.neuroimage.2023.120325] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/09/2023] [Accepted: 08/11/2023] [Indexed: 08/16/2023] Open
Abstract
Observational studies consistently disclose brain imaging-derived phenotypes (IDPs) as critical markers for early diagnosis of both brain disorders and cardiovascular diseases. However, it remains unclear about the shared genetic landscape between brain IDPs and the risk of brain disorders and cardiovascular diseases, restricting the applications of potential diagnostic techniques through brain IDPs. Here, we reported genetic correlations and putative causal relationships between 921 brain IDPs, 20 brain disorders and six cardiovascular diseases by leveraging their large-scale genome-wide association study (GWAS) summary statistics. Applications of Mendelian randomization (MR) identified significant putative causal effects of multiple region-specific brain IDPs in relation to the increased risks for amyotrophic lateral sclerosis (ALS), major depressive disorder (MDD), autism spectrum disorder (ASD) and schizophrenia (SCZ). We also found brain IDPs specifically from temporal lobe as a putatively causal consequence of hypertension. The genome-wide colocalization analysis identified three genomic regions in which MDD, ASD and SCZ colocalized with the brain IDPs, and two novel SNPs to be associated with ASD, SCZ, and multiple brain IDPs. Furthermore, we identified a list of candidate genes involved in the shared genetics underlying pairs of brain IDPs and MDD, ASD, SCZ, ALS and hypertension. Our results provide novel insights into the genetic relationships between brain disorders and cardiovascular diseases and brain IDP, which may server as clues for using brain IDPs to predict risks of diseases.
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Affiliation(s)
- Siying Lin
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China
| | - Haoyang Zhang
- School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China
| | - Mengling Qi
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, United Kingdom
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China.
| | - Yuanhao Yang
- Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia.
| | - Huiying Zhao
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
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30
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Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The Genetic Architecture of Multimodal Human Brain Age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.13.536818. [PMID: 37333190 PMCID: PMC10274645 DOI: 10.1101/2023.04.13.536818] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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31
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Yao Y, Charkraborty D, Zhang L, Shen X, Pan W. Deep causal feature extraction and inference with neuroimaging genetic data. Stat Med 2023; 42:3665-3684. [PMID: 37336556 DOI: 10.1002/sim.9824] [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: 11/09/2022] [Revised: 04/04/2023] [Accepted: 05/29/2023] [Indexed: 06/21/2023]
Abstract
Alzheimer's disease (AD) is a severe public health issue in the world. Magnetic Resonance Imaging (MRI) offers a way to study brain differences between AD patients and healthy individuals through feature extraction and comparison. However, in most previous works, the extracted features were not aimed to be causal, hindering biological understanding and interpretation. In order to extract causal features, we propose using instrumental variable (IV) regression with genetic variants as IVs. Specifically, we propose Deep Feature Extraction via Instrumental Variable Regression (DeepFEIVR), which uses a nonlinear neural network to extract causal features from three-dimensional neuroimages to predict an outcome (eg, AD status in our application) while maintaining a linear relationship between the extracted features and IVs. DeepFEIVR not only can handle high dimensional individual-level data for model building, but also is applicable to GWAS summary data to test associations of the extracted features with the outcome in subsequent analysis. In addition, we propose an extension of DeepFEIVR, called DeepFEIVR-CA, for covariate adjustment (CA). We apply DeepFEIVR and DeepFEIVR-CA to the Alzheimer's Disease Neuroimaging Initiative (ADNI) individual-level data as training data for model building, then apply to the UK Biobank neuroimaging and the International Genomics of Alzheimer's Project (IGAP) AD GWAS summary data, showcasing how the extracted causal features are related to AD and various brain endophenotypes.
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Affiliation(s)
- Yuchen Yao
- School of Statistics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Dipnil Charkraborty
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Lin Zhang
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
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32
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Zhao B, Li T, Li Y, Fan Z, Xiong D, Wang X, Gao M, Smith SM, Zhu H. An atlas of trait associations with resting-state and task-evoked human brain functional organizations in the UK Biobank. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-23. [PMID: 38770197 PMCID: PMC11105703 DOI: 10.1162/imag_a_00015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Functional magnetic resonance imaging (fMRI) has been widely used to identify brain regions linked to critical functions, such as language and vision, and to detect tumors, strokes, brain injuries, and diseases. It is now known that large sample sizes are necessary for fMRI studies to detect small effect sizes and produce reproducible results. Here we report a systematic association analysis of 647 traits with imaging features extracted from resting-state and task-evoked fMRI data of more than 40,000 UK Biobank participants. We used a parcellation-based approach to generate 64,620 functional connectivity measures to reveal fine-grained details about cerebral cortex functional organizations. The difference between functional organizations at rest and during task was examined, and we have prioritized important brain regions and networks associated with a variety of human traits and clinical outcomes. For example, depression was most strongly associated with decreased connectivity in the somatomotor network. We have made our results publicly available and developed a browser framework to facilitate the exploration of brain function-trait association results (http://fmriatlas.org/).
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
- These authors contributed equally to this work
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- These authors contributed equally to this work
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Di Xiong
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mufeng Gao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Hongtu Zhu
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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33
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Warrier V, Stauffer EM, Huang QQ, Wigdor EM, Slob EAW, Seidlitz J, Ronan L, Valk SL, Mallard TT, Grotzinger AD, Romero-Garcia R, Baron-Cohen S, Geschwind DH, Lancaster MA, Murray GK, Gandal MJ, Alexander-Bloch A, Won H, Martin HC, Bullmore ET, Bethlehem RAI. Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes. Nat Genet 2023; 55:1483-1493. [PMID: 37592024 PMCID: PMC10600728 DOI: 10.1038/s41588-023-01475-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 07/13/2023] [Indexed: 08/19/2023]
Abstract
Our understanding of the genetics of the human cerebral cortex is limited both in terms of the diversity and the anatomical granularity of brain structural phenotypes. Here we conducted a genome-wide association meta-analysis of 13 structural and diffusion magnetic resonance imaging-derived cortical phenotypes, measured globally and at 180 bilaterally averaged regions in 36,663 individuals and identified 4,349 experiment-wide significant loci. These phenotypes include cortical thickness, surface area, gray matter volume, measures of folding, neurite density and water diffusion. We identified four genetic latent structures and causal relationships between surface area and some measures of cortical folding. These latent structures partly relate to different underlying gene expression trajectories during development and are enriched for different cell types. We also identified differential enrichment for neurodevelopmental and constrained genes and demonstrate that common genetic variants associated with cortical expansion are associated with cephalic disorders. Finally, we identified complex interphenotype and inter-regional genetic relationships among the 13 phenotypes, reflecting the developmental differences among them. Together, these analyses identify distinct genetic organizational principles of the cortex and their correlates with neurodevelopment.
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Affiliation(s)
- Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Psychology, University of Cambridge, Cambridge, UK.
| | | | | | | | - Eric A W Slob
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Lisa Ronan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sofie L Valk
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Andrew D Grotzinger
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Rafael Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Dpto. de Fisiología Médica y Biofísica, Seville, Spain
| | - Simon Baron-Cohen
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Daniel H Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Program in Neurogenetics, Department of Neurology, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Jane and TerrySemel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Institute of Precision Health, University of California, Los Angeles, CA, USA
| | - Madeline A Lancaster
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge, UK
| | - Graham K Murray
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Trust, Cambridge, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Michael J Gandal
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Aaron Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Hyejung Won
- Department of Genetics and the Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Trust, Cambridge, UK
| | - Richard A I Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Psychology, University of Cambridge, Cambridge, UK.
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Zhu H, Li T, Zhao B. Statistical Learning Methods for Neuroimaging Data Analysis with Applications. Annu Rev Biomed Data Sci 2023; 6:73-104. [PMID: 37127052 DOI: 10.1146/annurev-biodatasci-020722-100353] [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] [Indexed: 05/03/2023]
Abstract
The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.
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Affiliation(s)
- Hongtu Zhu
- Department of Biostatistics, Department of Statistics, Department of Genetics, and Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA;
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Tengfei Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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35
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Ng S, Duffau H. Brain Plasticity Profiling as a Key Support to Therapeutic Decision-Making in Low-Grade Glioma Oncological Strategies. Cancers (Basel) 2023; 15:3698. [PMID: 37509359 PMCID: PMC10378506 DOI: 10.3390/cancers15143698] [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: 06/27/2023] [Revised: 07/13/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
The ability of neural circuits to compensate for damage to the central nervous system is called postlesional plasticity. In diffuse low-grade gliomas (LGGs), a crosstalk between the brain and the tumor activates modulations of plasticity, as well as tumor proliferation and migration, by means of paracrine and electrical intercommunications. Such adaptative mechanisms have a major impact on the benefits and risks of oncological treatments but are still disregarded by current neuro-oncological guidelines. In this review, the authors first aimed to highlight clinical, radiological, and oncological markers that robustly reflect the plasticity potentials and limitations in LGG patients, including the location of the tumor and the degree of critical white matter tract infiltration, the velocity of tumor expansion, and the reactional changes of neuropsychological performances over time. Second, the interactions between the potential/limitations of cerebral plasticity and the efficacy/tolerance of treatment options (i.e., surgery, chemotherapy, and radiotherapy) are reviewed. Finally, a longitudinal and multimodal treatment approach accounting for the evolutive profiles of brain plasticity is proposed. Such an approach integrates personalized predictive models of plasticity potentials with a step-by-step therapeutic decision making and supports onco-functional balanced strategies in patients with LGG, with the ultimate aim of optimizing overall survival and quality of life.
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Affiliation(s)
- Sam Ng
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, 34295 Montpellier, France
- Institute of Functional Genomics, University of Montpellier, Centre National de le Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale 1191, 34094 Montpellier, France
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, 34295 Montpellier, France
- Institute of Functional Genomics, University of Montpellier, Centre National de le Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale 1191, 34094 Montpellier, France
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Ciochetti NP, Lugli-Moraes B, da Silva BS, Rovaris DL. Genome-wide association studies: utility and limitations for research in physiology. J Physiol 2023; 601:2771-2799. [PMID: 37208942 PMCID: PMC10527550 DOI: 10.1113/jp284241] [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: 01/31/2023] [Accepted: 05/10/2023] [Indexed: 05/21/2023] Open
Abstract
Physiological systems are subject to interindividual variation encoded by genetics. Genome-wide association studies (GWAS) operate by surveying thousands of genetic variants from a substantial number of individuals and assessing their association to a trait of interest, be it a physiological variable, a molecular phenotype (e.g. gene expression), or even a disease or condition. Through a myriad of methods, GWAS downstream analyses then explore the functional consequences of each variant and attempt to ascertain a causal relationship to the phenotype of interest, as well as to delve into its links to other traits. This type of investigation allows mechanistic insights into physiological functions, pathological disturbances and shared biological processes between traits (i.e. pleiotropy). An exciting example is the discovery of a new thyroid hormone transporter (SLC17A4) and hormone metabolising enzyme (AADAT) from a GWAS on free thyroxine levels. Therefore, GWAS have substantially contributed with insights into physiology and have been shown to be useful in unveiling the genetic control underlying complex traits and pathological conditions; they will continue to do so with global collaborations and advances in genotyping technology. Finally, the increasing number of trans-ancestry GWAS and initiatives to include ancestry diversity in genomics will boost the power for discoveries, making them also applicable to non-European populations.
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Affiliation(s)
- Nicolas Pereira Ciochetti
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
| | - Beatriz Lugli-Moraes
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
| | - Bruna Santos da Silva
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil
| | - Diego Luiz Rovaris
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
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Zhao B, Li T, Fan Z, Yang Y, Shu J, Yang X, Wang X, Luo T, Tang J, Xiong D, Wu Z, Li B, Chen J, Shan Y, Tomlinson C, Zhu Z, Li Y, Stein JL, Zhu H. Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images. Science 2023; 380:abn6598. [PMID: 37262162 DOI: 10.1126/science.abn6598] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/11/2023] [Indexed: 06/03/2023]
Abstract
Cardiovascular health interacts with cognitive and mental health in complex ways, yet little is known about the phenotypic and genetic links of heart-brain systems. We quantified heart-brain connections using multiorgan magnetic resonance imaging (MRI) data from more than 40,000 subjects. Heart MRI traits displayed numerous association patterns with brain gray matter morphometry, white matter microstructure, and functional networks. We identified 80 associated genomic loci (P < 6.09 × 10-10) for heart MRI traits, which shared genetic influences with cardiovascular and brain diseases. Genetic correlations were observed between heart MRI traits and brain-related traits and disorders. Mendelian randomization suggests that heart conditions may causally contribute to brain disorders. Our results advance a multiorgan perspective on human health by revealing heart-brain connections and shared genetic influences.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jiarui Tang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Di Xiong
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Jie Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Chalmer Tomlinson
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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38
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Fabo T, Khavari P. Functional characterization of human genomic variation linked to polygenic diseases. Trends Genet 2023; 39:462-490. [PMID: 36997428 PMCID: PMC11025698 DOI: 10.1016/j.tig.2023.02.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/30/2023]
Abstract
The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.
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Affiliation(s)
- Tania Fabo
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Graduate Program in Genetics, Stanford University, Stanford, CA, USA; Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Paul Khavari
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Graduate Program in Genetics, Stanford University, Stanford, CA, USA; Stanford University School of Medicine, Stanford University, Stanford, CA, USA; Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA.
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39
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Genç E, Metzen D, Fraenz C, Schlüter C, Voelkle MC, Arning L, Streit F, Nguyen HP, Güntürkün O, Ocklenburg S, Kumsta R. Structural architecture and brain network efficiency link polygenic scores to intelligence. Hum Brain Mapp 2023; 44:3359-3376. [PMID: 37013679 PMCID: PMC10171514 DOI: 10.1002/hbm.26286] [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: 07/27/2022] [Revised: 02/15/2023] [Accepted: 03/01/2023] [Indexed: 04/05/2023] Open
Abstract
Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.
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Affiliation(s)
- Erhan Genç
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Dorothea Metzen
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Christoph Fraenz
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Caroline Schlüter
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Manuel C Voelkle
- Psychological Research Methods Department of Psychology, Humboldt University, Berlin, Germany
| | - Larissa Arning
- Department of Human Genetics, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Fabian Streit
- Department Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Huu Phuc Nguyen
- Department of Human Genetics, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Onur Güntürkün
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Sebastian Ocklenburg
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- Department of Psychology, Medical School Hamburg, Hamburg, Germany
- ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Hamburg, Germany
| | - Robert Kumsta
- Genetic Psychology, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- Department of Behavioural and Cognitive Sciences, Laboratory for Stress and Gene-Environment Interplay, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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40
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Chen SJ, Wu BS, Ge YJ, Chen SD, Ou YN, Dong Q, Feng J, Cheng W, Yu JT. The genetic architecture of the corpus callosum and its genetic overlap with common neuropsychiatric diseases. J Affect Disord 2023; 335:418-430. [PMID: 37164063 DOI: 10.1016/j.jad.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/25/2023] [Accepted: 05/02/2023] [Indexed: 05/12/2023]
Abstract
BACKGROUND The corpus callosum (CC) is the main structure transferring information between the cerebral hemispheres. Although previous large-scale genome-wide association study (GWAS) has illustrated the genetic architecture of white matter integrity of CC, CC volume is less stressed. METHODS Using MRI data from 33,861 individuals in UK Biobank, we conducted univariate and multivariate GWAS for CC fractional anisotropy (FA) and volume with PLINK 2.0 and MOSTest. All discovered SNPs in the multivariate framework were functionally annotated in FUMA v1.3.8. In the meanwhile, a series of gene property analyses was conducted simultaneously. In addition, we estimated genetic relationship between CC metrics and other neuropsychiatric traits and diseases. RESULTS We identified a total of 36 and 82 significant genomic loci for CC FA and volume (P < 5 × 10-8). And 53 and 27 genes were respectively mapped by four mapping strategies. For CC volume, gene-set analysis revealed pathways mainly relating to cell migration; cell-type analysis found the top enrichment in neuroglia while for CC FA in GABAergic neurons. Furthermore, we found a lot of genetic overlap and shared loci between CC FA and volume and common neuropsychiatric diseases. DISCUSSION Collectively, this study helps to better understand the genetic architecture of whole CC and CC subregions. However, the way to divide CC FA and volume in our study restricts the interpretations of our results. Future work will be needed to pay attention to the genetic structure of white matter volume, and an appropriate division of CC may help to better understand CC structure.
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Affiliation(s)
- Si-Jia 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
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - 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-Nan Ou
- 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
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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41
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Seyedsalehi A, Warrier V, Bethlehem RAI, Perry BI, Burgess S, Murray GK. Educational attainment, structural brain reserve and Alzheimer's disease: a Mendelian randomization analysis. Brain 2023; 146:2059-2074. [PMID: 36310536 PMCID: PMC10151197 DOI: 10.1093/brain/awac392] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 09/01/2022] [Accepted: 09/19/2022] [Indexed: 11/13/2022] Open
Abstract
Higher educational attainment is observationally associated with lower risk of Alzheimer's disease. However, the biological mechanisms underpinning this association remain unclear. The protective effect of education on Alzheimer's disease may be mediated via increased brain reserve. We used two-sample Mendelian randomization to explore putative causal relationships between educational attainment, structural brain reserve as proxied by MRI phenotypes and Alzheimer's disease. Summary statistics were obtained from genome-wide association studies of educational attainment (n = 1 131 881), late-onset Alzheimer's disease (35 274 cases, 59 163 controls) and 15 measures of grey or white matter macro- or micro-structure derived from structural or diffusion MRI (nmax = 33 211). We conducted univariable Mendelian randomization analyses to investigate bidirectional associations between (i) educational attainment and Alzheimer's disease; (ii) educational attainment and imaging-derived phenotypes; and (iii) imaging-derived phenotypes and Alzheimer's disease. Multivariable Mendelian randomization was used to assess whether brain structure phenotypes mediated the effect of education on Alzheimer's disease risk. Genetically proxied educational attainment was inversely associated with Alzheimer's disease (odds ratio per standard deviation increase in genetically predicted years of schooling = 0.70, 95% confidence interval 0.60, 0.80). There were positive associations between genetically predicted educational attainment and four cortical metrics (standard deviation units change in imaging phenotype per one standard deviation increase in genetically predicted years of schooling): surface area 0.30 (95% confidence interval 0.20, 0.40); volume 0.29 (95% confidence interval 0.20, 0.37); intrinsic curvature 0.18 (95% confidence interval 0.11, 0.25); local gyrification index 0.21 (95% confidence interval 0.11, 0.31)]; and inverse associations with cortical intracellular volume fraction [-0.09 (95% confidence interval -0.15, -0.03)] and white matter hyperintensities volume [-0.14 (95% confidence interval -0.23, -0.05)]. Genetically proxied levels of surface area, cortical volume and intrinsic curvature were positively associated with educational attainment [standard deviation units change in years of schooling per one standard deviation increase in respective genetically predicted imaging phenotype: 0.13 (95% confidence interval 0.10, 0.16); 0.15 (95% confidence interval 0.11, 0.19) and 0.12 (95% confidence interval 0.04, 0.19)]. We found no evidence of associations between genetically predicted imaging-derived phenotypes and Alzheimer's disease. The inverse association of genetically predicted educational attainment with Alzheimer's disease did not attenuate after adjusting for imaging-derived phenotypes in multivariable analyses. Our results provide support for a protective causal effect of educational attainment on Alzheimer's disease risk, as well as potential bidirectional causal relationships between education and brain macro- and micro-structure. However, we did not find evidence that these structural markers affect risk of Alzheimer's disease. The protective effect of education on Alzheimer's disease may be mediated via other measures of brain reserve not included in the present study, or by alternative mechanisms.
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Affiliation(s)
- Aida Seyedsalehi
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford OX3 7JX, UK
| | - Varun Warrier
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Richard A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Benjamin I Perry
- Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- CAMEO, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB4 1PX, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0BB, UK
| | - Graham K Murray
- Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- CAMEO, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB4 1PX, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane 4072, Australia
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42
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Reitz C, Pericak-Vance MA, Foroud T, Mayeux R. A global view of the genetic basis of Alzheimer disease. Nat Rev Neurol 2023; 19:261-277. [PMID: 37024647 PMCID: PMC10686263 DOI: 10.1038/s41582-023-00789-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/21/2023] [Indexed: 04/08/2023]
Abstract
The risk of Alzheimer disease (AD) increases with age, family history and informative genetic variants. Sadly, there is still no cure or means of prevention. As in other complex diseases, uncovering genetic causes of AD could identify underlying pathological mechanisms and lead to potential treatments. Rare, autosomal dominant forms of AD occur in middle age as a result of highly penetrant genetic mutations, but the most common form of AD occurs later in life. Large-scale, genome-wide analyses indicate that 70 or more genes or loci contribute to AD. One of the major factors limiting progress is that most genetic data have been obtained from non-Hispanic white individuals in Europe and North America, preventing the development of personalized approaches to AD in individuals of other ethnicities. Fortunately, emerging genetic data from other regions - including Africa, Asia, India and South America - are now providing information on the disease from a broader range of ethnicities. Here, we summarize the current knowledge on AD genetics in populations across the world. We predominantly focus on replicated genetic discoveries but also include studies in ethnic groups where replication might not be feasible. We attempt to identify gaps that need to be addressed to achieve a complete picture of the genetic and molecular factors that drive AD in individuals across the globe.
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Affiliation(s)
- Christiane Reitz
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA
- The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA
- Department of Neurology, Columbia University, New York, NY, USA
- Department of Epidemiology, Columbia University, New York, NY, USA
| | - Margaret A Pericak-Vance
- The John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- National Centralized Repository for Alzheimer's Disease and Related Dementias, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Richard Mayeux
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA.
- The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA.
- Department of Neurology, Columbia University, New York, NY, USA.
- Department of Epidemiology, Columbia University, New York, NY, USA.
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43
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de Araujo Tavares ME, Cupertino RB, Bandeira CE, da Silva BS, Vitola ES, Salgado CAI, Dos Santos Soares R, Picon FA, Rohde LA, Rovaris DL, Grevet EH, Bau CHD. Refining patterns of MEF2C effects in white matter microstructure and psychiatric features. J Neural Transm (Vienna) 2023; 130:697-706. [PMID: 37002331 DOI: 10.1007/s00702-023-02626-5] [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: 10/27/2022] [Accepted: 03/20/2023] [Indexed: 04/03/2023]
Abstract
Several GWAS reported Myocyte Enhancer Factor 2 C (MEF2C) gene associations with white matter microstructure and psychiatric disorders, and MEF2C involvement in pathways related to neuronal development suggests a common biological factor underlying these phenotypes. We aim to refine the MEF2C effects in the brain relying on an integrated analysis of white matter and psychiatric phenotypes in an extensively characterized sample. This study included 870 Brazilian adults (47% from an attention-deficit/hyperactivity disorder outpatient clinic) assessed through standardized psychiatric interviews, 139 of which underwent a magnetic resonance imaging scan. We evaluated variants in the MEF2C region using two approaches: 1) a gene-wide analysis, which uses the sum of polymorphism effects, and 2) SNP analyses, restricted to the independent variants within the gene. The outcomes included psychiatric phenotypes and fractional anisotropy for brain images. Results: The gene-wide analyses pointed to a nominal association between MEF2C and the Temporal Portion of the Superior Longitudinal Fasciculus (SLFTEMP). The SNP analysis identified four independent variants significantly associated with SLFTEMP and one (rs4218438) with Substance Use Disorder. Our findings showing specific associations of MEF2C variants with temporal-frontal circuitry components may help to elucidate how the MEF2C gene underlies a broad range of psychiatric phenotypes since these regions are relevant to executive and cognitive functions.
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Affiliation(s)
- Maria Eduarda de Araujo Tavares
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, RS, 91501-970, Brazil
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | | | - Cibele Edom Bandeira
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, RS, 91501-970, Brazil
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Bruna Santos da Silva
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, RS, 91501-970, Brazil
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Eduardo Schneider Vitola
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Carlos Alberto Iglesias Salgado
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Robson Dos Santos Soares
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, RS, 91501-970, Brazil
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Felipe Almeida Picon
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Luis Augusto Rohde
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
- National Institute of Developmental Psychiatry, São Paulo, Brazil
| | - Diego Luiz Rovaris
- Department of Physiology and Biophysics, Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
| | - Eugenio Horacio Grevet
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Claiton Henrique Dotto Bau
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, RS, 91501-970, Brazil.
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil.
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil.
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Trofimova O, Latypova A, DiDomenicantonio G, Lutti A, de Lange AMG, Kliegel M, Stringhini S, Marques-Vidal P, Vaucher J, Vollenweider P, Strippoli MPF, Preisig M, Kherif F, Draganski B. Topography of associations between cardiovascular risk factors and myelin loss in the ageing human brain. Commun Biol 2023; 6:392. [PMID: 37037939 PMCID: PMC10086032 DOI: 10.1038/s42003-023-04741-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/21/2023] [Indexed: 04/12/2023] Open
Abstract
Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration.
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Affiliation(s)
- Olga Trofimova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Giulia DiDomenicantonio
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ann-Marie G de Lange
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Matthias Kliegel
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Silvia Stringhini
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Julien Vaucher
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Marie-Pierre F Strippoli
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martin Preisig
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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45
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Wu BS, Ge YJ, Zhang W, Chen SD, Xiang ST, Zhang YR, Ou YN, Jiang YC, Tan L, Cheng W, Suckling J, Feng JF, Yu JT, Mao Y. Genome-wide association study of cerebellar white matter microstructure and genetic overlap with common brain disorders. Neuroimage 2023; 269:119928. [PMID: 36740028 DOI: 10.1016/j.neuroimage.2023.119928] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/12/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The cerebellum is recognized as being involved in neurocognitive and motor functions with communication with extra-cerebellar regions relying on the white matter integrity of the cerebellar peduncles. However, the genetic determinants of cerebellar white matter integrity remain largely unknown. METHODS We conducted a genome-wide association analysis of cerebellar white matter microstructure using diffusion tensor imaging data from 25,415 individuals from UK Biobank. The integrity of cerebellar white matter microstructure was measured as fractional anisotropy (FA) and mean diffusivity (MD). Identification of independent genomic loci, functional annotation, and tissue and cell-type analysis were conducted with FUMA. The linkage disequilibrium score regression (LDSC) was used to calculate genetic correlations between cerebellar white matter microstructure and regional brain volumes and brain-related traits. Furthermore, the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was employed to identify the shared genetic basis between cerebellar white matter microstructure and common brain disorders. RESULTS We identified 11 genetic loci (P < 8.3 × 10-9) and 86 genes associated with cerebellar white matter microstructure. Further functional enrichment analysis implicated the involvement of GABAergic neurons and cholinergic pathways. Significant polygenetic overlap between cerebellar white matter tracts and their anatomically connected or adjacent brain regions was detected. In addition, we report the overall genetic correlation and specific loci shared between cerebellar white matter microstructural integrity and brain-related traits, including movement, cognitive, psychiatric, and cerebrovascular categories. CONCLUSIONS Collectively, this study represents a step forward in understanding the genetics of cerebellar white matter microstructure and its shared genetic etiology with common brain disorders.
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Affiliation(s)
- Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Tong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yu-Chao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - John Suckling
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Ying Mao
- Department of Neurosurgery and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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Wang R, Bashyam V, Yang Z, Yu F, Tassopoulou V, Chintapalli SS, Skampardoni I, Sreepada LP, Sahoo D, Nikita K, Abdulkadir A, Wen J, Davatzikos C. Applications of generative adversarial networks in neuroimaging and clinical neuroscience. Neuroimage 2023; 269:119898. [PMID: 36702211 PMCID: PMC9992336 DOI: 10.1016/j.neuroimage.2023.119898] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/16/2022] [Accepted: 01/21/2023] [Indexed: 01/25/2023] Open
Abstract
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.
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Affiliation(s)
- Rongguang Wang
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA.
| | - Vishnu Bashyam
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Zhijian Yang
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Fanyang Yu
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Vasiliki Tassopoulou
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Sai Spandana Chintapalli
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Ioanna Skampardoni
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA; School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Lasya P Sreepada
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Dushyant Sahoo
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Konstantina Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Ahmed Abdulkadir
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA; Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Junhao Wen
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Christos Davatzikos
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
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47
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Duperron MG, Knol MJ, Le Grand Q, Evans TE, Mishra A, Tsuchida A, Roshchupkin G, Konuma T, Trégouët DA, Romero JR, Frenzel S, Luciano M, Hofer E, Bourgey M, Dueker ND, Delgado P, Hilal S, Tankard RM, Dubost F, Shin J, Saba Y, Armstrong NJ, Bordes C, Bastin ME, Beiser A, Brodaty H, Bülow R, Carrera C, Chen C, Cheng CY, Deary IJ, Gampawar PG, Himali JJ, Jiang J, Kawaguchi T, Li S, Macalli M, Marquis P, Morris Z, Muñoz Maniega S, Miyamoto S, Okawa M, Paradise M, Parva P, Rundek T, Sargurupremraj M, Schilling S, Setoh K, Soukarieh O, Tabara Y, Teumer A, Thalamuthu A, Trollor JN, Valdés Hernández MC, Vernooij MW, Völker U, Wittfeld K, Wong TY, Wright MJ, Zhang J, Zhao W, Zhu YC, Schmidt H, Sachdev PS, Wen W, Yoshida K, Joutel A, Satizabal CL, Sacco RL, Bourque G, Lathrop M, Paus T, Fernandez-Cadenas I, Yang Q, Mazoyer B, Boutinaud P, Okada Y, Grabe HJ, Mather KA, Schmidt R, Joliot M, Ikram MA, Matsuda F, Tzourio C, Wardlaw JM, Seshadri S, Adams HHH, Debette S. Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease. Nat Med 2023; 29:950-962. [PMID: 37069360 PMCID: PMC10115645 DOI: 10.1038/s41591-023-02268-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 02/15/2023] [Indexed: 04/19/2023]
Abstract
Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.
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Affiliation(s)
- Marie-Gabrielle Duperron
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
- Department of Neurology, Institute of Neurodegenerative Diseases, Bordeaux University Hospital, Bordeaux, France
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Quentin Le Grand
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Tavia E Evans
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Aniket Mishra
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Ami Tsuchida
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
- Groupe d'Imagerie Neurofonctionelle - Institut des maladies neurodégénératives (GIN-IMN), UMR 5293, University of Bordeaux, CNRS, CEA, Bordeaux, France
| | - Gennady Roshchupkin
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Takahiro Konuma
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - David-Alexandre Trégouët
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Jose Rafael Romero
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | | | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Mathieu Bourgey
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Quebec, Canada
- Canadian Centre for Computational Genomics, McGill University, Montreal, Quebec, Canada
| | - Nicole D Dueker
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA
| | - Pilar Delgado
- Institut de Recerca Vall d'hebron, Neurovascular Research Lab, Universitat Autònoma de Barcelona, Barcelona, Spain
- Hospital Universitari Vall d'Hebron, Neurology Department, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Saima Hilal
- Memory Aging and Cognition Center, National University Health System, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Rick M Tankard
- Department of Mathematics and Statistics, Curtin University, Perth, Western Australia, Australia
| | - Florian Dubost
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Jean Shin
- The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Yasaman Saba
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
- Institute for Molecular Biology & Biochemistry, Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Graz, Austria
| | - Nicola J Armstrong
- Department of Mathematics and Statistics, Curtin University, Perth, Western Australia, Australia
| | - Constance Bordes
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Alexa Beiser
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
- Dementia Collaborative Research Centre Assessment and Better Care, UNSW, Sydney, New South Wales, Australia
| | - Robin Bülow
- Institute for Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Caty Carrera
- Stroke Pharmacogenomics and Genetics Group, Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain
| | - Christopher Chen
- Memory Aging and Cognition Center, National University Health System, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Center for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
| | - Ian J Deary
- School of Psychology, University of Edinburgh, Edinburgh, UK
| | - Piyush G Gampawar
- Institute for Molecular Biology & Biochemistry, Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Graz, Austria
| | - Jayandra J Himali
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, USA
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shuo Li
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Melissa Macalli
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Pascale Marquis
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Quebec, Canada
- Canadian Centre for Computational Genomics, McGill University, Montreal, Quebec, Canada
| | - Zoe Morris
- Neuroimaging, Department of Clinical Neurosciences, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Susana Muñoz Maniega
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, UK
| | | | - Masakazu Okawa
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Matthew Paradise
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Pedram Parva
- The Framingham Heart Study, Framingham, MA, USA
- Radiology Department, Boston University School of Medicine, Boston, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Tatjana Rundek
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
- Evelyn F. McKnight Brain Institute, Department of Neurology, University of Miami, Miami, FL, USA
| | | | - Sabrina Schilling
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Kazuya Setoh
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Graduate School of Public Health, Shizuoka Graduate University of Public Health, Shizuoka, Japan
| | - Omar Soukarieh
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Graduate School of Public Health, Shizuoka Graduate University of Public Health, Shizuoka, Japan
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Julian N Trollor
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
- Department of Developmental Disability Neuropsychiatry, UNSW, Sydney, New South Wales, Australia
| | - Maria C Valdés Hernández
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Row Fogo Centre for Research into Ageing and the Brain, University of Edinburgh, Edinburgh, UK
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Junyi Zhang
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Wanting Zhao
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- The Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Yi-Cheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Helena Schmidt
- Institute for Molecular Biology & Biochemistry, Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Graz, Austria
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
- Neuropsychiatric Institute, the Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Kazumichi Yoshida
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Anne Joutel
- Institut de Psychiatrie et Neurosciences de Paris, Université Paris Cité, Inserm, France
| | - Claudia L Satizabal
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, USA
| | - Ralph L Sacco
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
- Evelyn F. McKnight Brain Institute, Department of Neurology, University of Miami, Miami, FL, USA
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA
- Department of Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Guillaume Bourque
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Quebec, Canada
- Canadian Centre for Computational Genomics, McGill University, Montreal, Quebec, Canada
| | - Mark Lathrop
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Quebec, Canada
| | - Tomas Paus
- University of Montreal, Faculty of Medicine, Departments of Psychiatry and Neuroscience, Montreal, Quebec, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
- Centre Hospitalier Universitaire Sainte Justine, Montreal, Quebec, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Israel Fernandez-Cadenas
- Stroke Pharmacogenomics and Genetics Group, Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain
- Stroke Pharmacogenomics and Genetics Group, Fundació per la Docència i la Recerca Mutua Terrassa, Terrassa, Spain
| | - Qiong Yang
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Bernard Mazoyer
- Groupe d'Imagerie Neurofonctionelle - Institut des maladies neurodégénératives (GIN-IMN), UMR 5293, University of Bordeaux, CNRS, CEA, Bordeaux, France
- Bordeaux University Hospital, Bordeaux, France
| | | | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Karen A Mather
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
- Neuroscience Research Australia, Sydney, New South Wales, Australia
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Marc Joliot
- Groupe d'Imagerie Neurofonctionelle - Institut des maladies neurodégénératives (GIN-IMN), UMR 5293, University of Bordeaux, CNRS, CEA, Bordeaux, France
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Christophe Tzourio
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
- Department of Medical Informatics, Bordeaux University Hospital, Bordeaux, France
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, UK
- Row Fogo Centre for Research into Ageing and the Brain, University of Edinburgh, Edinburgh, UK
| | - Sudha Seshadri
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, USA
| | - Hieab H H Adams
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
| | - Stéphanie Debette
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France.
- Department of Neurology, Institute of Neurodegenerative Diseases, Bordeaux University Hospital, Bordeaux, France.
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48
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Petrican R, Fornito A. Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing. Dev Cogn Neurosci 2023; 60:101229. [PMID: 36947895 PMCID: PMC10041470 DOI: 10.1016/j.dcn.2023.101229] [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: 12/08/2022] [Revised: 03/08/2023] [Accepted: 03/12/2023] [Indexed: 03/18/2023] Open
Abstract
In adulthood, stress exposure and genetic risk heighten psychological vulnerability by accelerating neurobiological senescence. To investigate whether molecular and brain network maturation processes play a similar role in adolescence, we analysed genetic, as well as longitudinal task neuroimaging (inhibitory control, incentive processing) and early life adversity (i.e., material deprivation, violence) data from the Adolescent Brain and Cognitive Development study (N = 980, age range: 9-13 years). Genetic risk was estimated separately for Major Depressive Disorder (MDD) and Alzheimer's Disease (AD), two pathologies linked to stress exposure and allegedly sharing a causal connection (MDD-to-AD). Adversity and genetic risk for MDD/AD jointly predicted functional network segregation patterns suggestive of accelerated (GABA-linked) visual/attentional, but delayed (dopamine [D2]/glutamate [GLU5R]-linked) somatomotor/association system development. A positive relationship between brain maturation and psychopathology emerged only among the less vulnerable adolescents, thereby implying that normatively maladaptive neurodevelopmental alterations could foster adjustment among the more exposed and genetically more stress susceptible youths. Transcriptomic analyses suggested that sensitivity to stress may underpin the joint neurodevelopmental effect of adversity and genetic risk for MDD/AD, in line with the proposed role of negative emotionality as a precursor to AD, likely to account for the alleged causal impact of MDD on dementia onset.
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Affiliation(s)
- Raluca Petrican
- Institute of Population Health, Department of Psychology, University of Liverpool, Bedford Street South, Liverpool L69 7ZA, United Kingdom.
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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49
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Chakraborty D, Zhuang Z, Xue H, Fiecas MB, Shen X, Pan W. Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer's Disease. Genes (Basel) 2023; 14:626. [PMID: 36980898 PMCID: PMC10047952 DOI: 10.3390/genes14030626] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 02/27/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
The prognosis and treatment of patients suffering from Alzheimer's disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly, in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular software; this, however, may cause the loss of important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple, divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in genome-wide association studies (GWASs) to identify associated genetic variants. When we applied this method to ADNI data, we identified several associated SNPs that have been previously shown to be related to several neurodegenerative/mental disorders, such as AD, depression, and schizophrenia.
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Affiliation(s)
- Dipnil Chakraborty
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Zhong Zhuang
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Haoran Xue
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mark B. Fiecas
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Xiatong Shen
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
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50
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Zhao B, Li Y, Fan Z, Wu Z, Shu J, Yang X, Yang Y, Wang X, Li B, Wang X, Copana C, Yang Y, Lin J, Li Y, Stein JL, O'Brien JM, Li T, Zhu H. Eye-brain connections revealed by multimodal retinal and brain imaging genetics in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.16.23286035. [PMID: 36824893 PMCID: PMC9949187 DOI: 10.1101/2023.02.16.23286035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
As an anatomical extension of the brain, the retina of the eye is synaptically connected to the visual cortex, establishing physiological connections between the eye and the brain. Despite the unique opportunity retinal structures offer for assessing brain disorders, less is known about their relationship to brain structure and function. Here we present a systematic cross-organ genetic architecture analysis of eye-brain connections using retina and brain imaging endophenotypes. Novel phenotypic and genetic links were identified between retinal imaging biomarkers and brain structure and function measures derived from multimodal magnetic resonance imaging (MRI), many of which were involved in the visual pathways, including the primary visual cortex. In 65 genomic regions, retinal imaging biomarkers shared genetic influences with brain diseases and complex traits, 18 showing more genetic overlaps with brain MRI traits. Mendelian randomization suggests that retinal structures have bidirectional genetic causal links with neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, cross-organ imaging genetics reveals a genetic basis for eye-brain connections, suggesting that the retinal images can elucidate genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yilin Yang
- Department of Computer and Information Science and Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT 06511, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joan M. O'Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Diseases, PA, 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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