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Hirsch F, Bumanglag Â, Zhang Y, Wohlschlaeger A. Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis. Neuroimage Clin 2024; 43:103657. [PMID: 39208481 DOI: 10.1016/j.nicl.2024.103657] [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: 05/31/2024] [Revised: 08/05/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state functional magnetic resonance imaging (rs-fMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains. METHODS rs-fMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron emission tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients. RESULTS Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta-power. Exploratory analyses revealed a close statistical relationship between LEN and positive symptom severity in patients. CONCLUSION Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.
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Affiliation(s)
- Fabian Hirsch
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany.
| | - Ângelo Bumanglag
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany
| | - Yifei Zhang
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany
| | - Afra Wohlschlaeger
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany
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Bao J, Lee BN, Wen J, Kim M, Mu S, Yang S, Davatzikos C, Long Q, Ritchie MD, Shen L. Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes. Annu Rev Biomed Data Sci 2024; 7:391-418. [PMID: 38848574 DOI: 10.1146/annurev-biodatasci-102423-121021] [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: 06/09/2024]
Abstract
Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than $250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.
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Affiliation(s)
- Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| | - Brian N Lee
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| | - 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
| | - Mansu Kim
- AI Graduate School, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Shizhuo Mu
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
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Hirsch F, Bumanglag Â, Zhang Y, Wohlschlaeger A. Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.07.24306932. [PMID: 38766002 PMCID: PMC11100938 DOI: 10.1101/2024.05.07.24306932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Background Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state fMRI (rfMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains. Methods rfMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron-Emission Tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients. Results Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta power. Exploratory analyses revealed a close statistical relationship between LEN and positive PSD symptoms. Conclusion Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs. CRediT Authorship Contribution Statement Fabian Hirsch: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization; Ângelo Bumanglag: Methodology, Software, Formal analysis, Writing - Review & Editing; Yifei Zhang: Methodology, Software, Formal analysis, Writing - Review & Editing; Afra Wohlschlaeger: Methodology, Writing - Review & Editing, Supervision, Project administration.
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Zhang R, Chen L, Oliver LD, Voineskos AN, Park JY. SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites. Hum Brain Mapp 2024; 45:e26692. [PMID: 38712767 PMCID: PMC11075170 DOI: 10.1002/hbm.26692] [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: 12/13/2023] [Revised: 03/27/2024] [Accepted: 04/08/2024] [Indexed: 05/08/2024] Open
Abstract
In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter-scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter-scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex-level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called spatial autocorrelation normalization (SAN) that preserves homogeneous covariance vertex-level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner-invariant and scanner-specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.
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Affiliation(s)
- Rongqian Zhang
- Department of Statistical SciencesUniversity of TorontoTorontoOntarioCanada
| | - Linxi Chen
- Department of Statistical SciencesUniversity of TorontoTorontoOntarioCanada
| | | | - Aristotle N. Voineskos
- Centre for Addiction and Mental HealthTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
| | - Jun Young Park
- Department of Statistical SciencesUniversity of TorontoTorontoOntarioCanada
- Department of PsychologyUniversity of TorontoTorontoOntarioCanada
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Blokland G, Maleki N, Jovicich J, Mesholam-Gately R, DeLisi L, Turner J, Shenton M, Voineskos A, Kahn R, Roffman J, Holt D, Ehrlich S, Kikinis Z, Dazzan P, Murray R, Lee J, Sim K, Lam M, de Zwarte S, Walton E, Kelly S, Picchioni M, Bramon E, Makris N, David A, Mondelli V, Reinders A, Oykhman E, Morris D, Gill M, Corvin A, Cahn W, Ho N, Liu J, Gollub R, Manoach D, Calhoun V, Sponheim S, Buka S, Cherkerzian S, Thermenos H, Dickie E, Ciufolini S, Reis Marques T, Crossley N, Purcell S, Smoller J, van Haren N, Toulopoulou T, Donohoe G, Goldstein J, Keshavan M, Petryshen T, del Re E. MIR137 polygenic risk for schizophrenia and ephrin-regulated pathway: Role in lateral ventricles and corpus callosum volume. Int J Clin Health Psychol 2024; 24:100458. [PMID: 38623146 PMCID: PMC11017057 DOI: 10.1016/j.ijchp.2024.100458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 04/02/2024] [Indexed: 04/17/2024] Open
Abstract
Background/Objective. Enlarged lateral ventricle (LV) volume and decreased volume in the corpus callosum (CC) are hallmarks of schizophrenia (SZ). We previously showed an inverse correlation between LV and CC volumes in SZ, with global functioning decreasing with increased LV volume. This study investigates the relationship between LV volume, CC abnormalities, and the microRNA MIR137 and its regulated genes in SZ, because of MIR137's essential role in neurodevelopment. Methods. Participants were 1224 SZ probands and 1466 unaffected controls from the GENUS Consortium. Brain MRI scans, genotype, and clinical data were harmonized across cohorts and employed in the analyses. Results. Increased LV volumes and decreased CC central, mid-anterior, and mid-posterior volumes were observed in SZ probands. The MIR137-regulated ephrin pathway was significantly associated with CC:LV ratio, explaining a significant proportion (3.42 %) of CC:LV variance, and more than for LV and CC separately. Other pathways explained variance in either CC or LV, but not both. CC:LV ratio was also positively correlated with Global Assessment of Functioning, supporting previous subsample findings. SNP-based heritability estimates were higher for CC central:LV ratio (0.79) compared to CC or LV separately. Discussion. Our results indicate that the CC:LV ratio is highly heritable, influenced in part by variation in the MIR137-regulated ephrin pathway. Findings suggest that the CC:LV ratio may be a risk indicator in SZ that correlates with global functioning.
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Affiliation(s)
- G.A.M. Blokland
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health, Medicine, and Life Sciences, Maastricht University, Netherlands
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - N. Maleki
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - J. Jovicich
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
| | - R.I. Mesholam-Gately
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Massachusetts Mental Health Center Public Psychiatry Division, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - L.E. DeLisi
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry, Cambridge Health Alliance, Cambridge, MA, United States
| | - J.A. Turner
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, United States
| | - M.E. Shenton
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry, Veterans Affairs Boston Healthcare System, Brockton, MA, United States
| | - A.N. Voineskos
- Kimel Family Translational Imaging Genetics Laboratory, Department of Psychiatry, Research Imaging Centre, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry and Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - R.S. Kahn
- Brain Centre Rudolf Magnus, Department of Psychiatry, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - J.L. Roffman
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - D.J. Holt
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - S. Ehrlich
- Division of Psychological & Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Z. Kikinis
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States
| | - P. Dazzan
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - R.M. Murray
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - J. Lee
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | - K. Sim
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | - M. Lam
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Institute of Mental Health, Woodbridge Hospital, Singapore
- Analytical & Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
| | - S.M.C. de Zwarte
- Brain Centre Rudolf Magnus, Department of Psychiatry, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - E. Walton
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - S. Kelly
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland
- Laboratory of NeuroImaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - M.M. Picchioni
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - E. Bramon
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Mental Health Neuroscience Research Department, UCL Division of Psychiatry, University College London, United Kingdom
| | - N. Makris
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Department of Neurology, Harvard Medical School, Boston, MA, United States
| | - A.S. David
- Division of Psychiatry, University College London, London, United Kingdom
| | - V. Mondelli
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - A.A.T.S. Reinders
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - E. Oykhman
- Massachusetts Mental Health Center Public Psychiatry Division, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - D.W. Morris
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and Cognitive Genomics (NICOG) Centre and NCBES Galway Neuroscience Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - M. Gill
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland
| | - A.P. Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland
| | - W. Cahn
- Brain Centre Rudolf Magnus, Department of Psychiatry, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - N. Ho
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | - J. Liu
- Genome Institute, Singapore
| | - R.L. Gollub
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - D.S. Manoach
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - V.D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - S.R. Sponheim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States
| | - S.L. Buka
- Department of Epidemiology, Brown University, Providence, RI, United States
| | - S. Cherkerzian
- Department of Medicine, Division of Women's Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - H.W. Thermenos
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Massachusetts Mental Health Center Public Psychiatry Division, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - E.W. Dickie
- Kimel Family Translational Imaging Genetics Laboratory, Department of Psychiatry, Research Imaging Centre, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - S. Ciufolini
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - T. Reis Marques
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - N.A. Crossley
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - S.M. Purcell
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States
- Division of Psychiatric Genomics, Departments of Psychiatry and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - J.W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - N.E.M. van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre, Rotterdam, The Netherlands
- Department of Psychiatry, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - T. Toulopoulou
- Department of Psychology & National Magnetic Resonance Research Center (UMRAM), Aysel Sabuncu Brain Research Centre (ASBAM), Bilkent University, Ankara, Turkey
- Department of Psychiatry, Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - G. Donohoe
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and Cognitive Genomics (NICOG) Centre and NCBES Galway Neuroscience Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - J.M. Goldstein
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Department of Medicine, Division of Women's Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States
| | - M.S. Keshavan
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Massachusetts Mental Health Center Public Psychiatry Division, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
- University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - T.L. Petryshen
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - E.C. del Re
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Massachusetts Mental Health Center Public Psychiatry Division, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry, Veterans Affairs Boston Healthcare System, Brockton, MA, United States
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Zhang R, Chen L, Oliver LD, Voineskos AN, Park JY. SAN: mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.04.569619. [PMID: 38105933 PMCID: PMC10723364 DOI: 10.1101/2023.12.04.569619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter-scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter-scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex-level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called SAN (Spatial Autocorrelation Normalization) that preserves homogeneous covariance vertex-level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner-invariant and scanner-specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.
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Affiliation(s)
- Rongqian Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Linxi Chen
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | | | - Aristotle N. Voineskos
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Jun Young Park
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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7
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Matkovič A, Anticevic A, Murray JD, Repovš G. Static and dynamic fMRI-derived functional connectomes represent largely similar information. Netw Neurosci 2023; 7:1266-1301. [PMID: 38144686 PMCID: PMC10631791 DOI: 10.1162/netn_a_00325] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/06/2023] [Indexed: 12/26/2023] Open
Abstract
Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.
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Affiliation(s)
- Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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8
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Pan R, Dickie EW, Hawco C, Reid N, Voineskos AN, Park JY. Spatial-extent inference for testing variance components in reliability and heritability studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.19.537270. [PMID: 37131799 PMCID: PMC10153210 DOI: 10.1101/2023.04.19.537270] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Clusterwise inference is a popular approach in neuroimaging to increase sensitivity, but most existing methods are currently restricted to the General Linear Model (GLM) for testing mean parameters. Statistical methods for testing variance components, which are critical in neuroimaging studies that involve estimation of narrow-sense heritability or test-retest reliability, are underdeveloped due to methodological and computational challenges, which would potentially lead to low power. We propose a fast and powerful test for variance components called CLEAN-V (CLEAN for testing Variance components). CLEAN-V models the global spatial dependence structure of imaging data and computes a locally powerful variance component test statistic by data-adaptively pooling neighborhood information. Correction for multiple comparisons is achieved by permutations to control family-wise error rate (FWER). Through analysis of task-fMRI data from the Human Connectome Project across five tasks and comprehensive data-driven simulations, we show that CLEAN-V outperforms existing methods in detecting test-retest reliability and narrow-sense heritability with significantly improved power, with the detected areas aligning with activation maps. The computational efficiency of CLEAN-V also speaks of its practical utility, and it is available as an R package.
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Affiliation(s)
- Ruyi Pan
- Department of Statistical Sciences, University of Toronto, Toronto, ON, M5G 1Z5, Canada
- The Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
| | - Erin W. Dickie
- The Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, M5T 1R8, Canada
| | - Colin Hawco
- The Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, M5T 1R8, Canada
| | - Nancy Reid
- Department of Statistical Sciences, University of Toronto, Toronto, ON, M5G 1Z5, Canada
| | - Aristotle N. Voineskos
- The Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, M5T 1R8, Canada
| | - Jun Young Park
- Department of Statistical Sciences, University of Toronto, Toronto, ON, M5G 1Z5, Canada
- Department of Psychology, University of Toronto, Toronto, ON, M5G 1Z5, Canada
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9
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Matkovič A, Anticevic A, Murray JD, Repovš G. Static and dynamic functional connectomes represent largely similar information. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.24.525348. [PMID: 36747845 PMCID: PMC9900764 DOI: 10.1101/2023.01.24.525348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Functional connectivity (FC) of blood-oxygen-level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate) and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.
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Affiliation(s)
- Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, United States
- Interdepartmental Neuroscience Program, Yale University, New Haven, United States
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, United States
- Interdepartmental Neuroscience Program, Yale University, New Haven, United States
- Department of Psychiatry, Yale University, New Haven, United States
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana
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10
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Smith DM, Loughnan R, Friedman NP, Parekh P, Frei O, Thompson WK, Andreassen OA, Neale M, Jernigan TL, Dale AM. Heritability Estimation of Cognitive Phenotypes in the ABCD Study ® Using Mixed Models. Behav Genet 2023; 53:169-188. [PMID: 37024669 PMCID: PMC10154273 DOI: 10.1007/s10519-023-10141-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 03/15/2023] [Indexed: 04/08/2023]
Abstract
Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study® sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study® sample.
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Affiliation(s)
- Diana M Smith
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, USA.
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA.
- Center for Multimodal Imaging and Genetics, San Diego School of Medicine, University of California, La Jolla, CA, USA.
| | - Robert Loughnan
- Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla, CA, USA
| | - Naomi P Friedman
- Institute for Behavioral Genetics, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, 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
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Wesley K Thompson
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Michael Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Terry L Jernigan
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, San Diego School of Medicine, University of California, La Jolla, CA, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Department of Neuroscience, University of California, San Diego School of Medicine, La Jolla, CA, USA
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11
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Bianco MG, Duggento A, Nigro S, Conti A, Toschi N, Passamonti L. Heritability of human "directed" functional connectome. Brain Behav 2023; 13:e2839. [PMID: 36989125 PMCID: PMC10175995 DOI: 10.1002/brb3.2839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 10/03/2022] [Accepted: 11/15/2022] [Indexed: 03/30/2023] Open
Abstract
INTRODUCTION The functional connectivity patterns in the brain are highly heritable; however, it is unclear how genetic factors influence the directionality of such "information flows." Studying the "directionality" of the brain functional connectivity and assessing how heritability modulates it can improve our understanding of the human connectome. METHODS Here, we investigated the heritability of "directed" functional connections using a state-space formulation of Granger causality (GC), in conjunction with blind deconvolution methods accounting for local variability in the hemodynamic response function. Such GC implementation is ideal to explore the directionality of functional interactions across a large number of networks. Resting-state functional magnetic resonance imaging data were drawn from the Human Connectome Project (total n = 898 participants). To add robustness to our findings, the dataset was randomly split into a "discovery" and a "replication" sample (each with n = 449 participants). The two cohorts were carefully matched in terms of demographic variables and other confounding factors (e.g., education). The effect of shared environment was also modeled. RESULTS The parieto- and prefronto-cerebellar, parieto-prefrontal, and posterior-cingulate to hippocampus connections showed the highest and most replicable heritability effects with little influence by shared environment. In contrast, shared environmental factors significantly affected the visuo-parietal and sensory-motor directed connectivity. CONCLUSION We suggest a robust role of heritability in influencing the directed connectivity of some cortico-subcortical circuits implicated in cognition. Further studies, for example using task-based fMRI and GC, are warranted to confirm the asymmetric effects of genetic factors on the functional connectivity within cognitive networks and their role in supporting executive functions and learning.
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Affiliation(s)
- Maria Giovanna Bianco
- Neuroscience Research Center, Department of Medical and Surgical Sciences, "Magna Graecia" University of Catanzaro, Italy
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University "Tor Vergata", Rome, Italy
| | - Salvatore Nigro
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari 'Aldo Moro, "Pia Fondazione Cardinale G. Panico", Tricase, Italy
| | - Allegra Conti
- Department of Biomedicine and Prevention, University "Tor Vergata", Rome, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University "Tor Vergata", Rome, Italy
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, Boston, MA, 02129, USA
| | - Luca Passamonti
- Institute of Bioimaging and Molecular Physiology, National Research Council, Milan, Italy
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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12
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Tang M, Wang T, Zhang X. A review of SNP heritability estimation methods. Brief Bioinform 2022; 23:6548385. [PMID: 35289357 DOI: 10.1093/bib/bbac067] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/23/2022] [Accepted: 02/08/2022] [Indexed: 11/13/2022] Open
Abstract
Over the past decade, statistical methods have been developed to estimate single nucleotide polymorphism (SNP) heritability, which measures the proportion of phenotypic variance explained by all measured SNPs in the data. Estimates of SNP heritability measure the degree to which the available genetic variants influence phenotypes and improve our understanding of the genetic architecture of complex phenotypes. In this article, we review the recently developed and commonly used SNP heritability estimation methods for continuous and binary phenotypes from the perspective of model assumptions and parameter optimization. We primarily focus on their capacity to handle multiple phenotypes and longitudinal measurements, their ability for SNP heritability partition and their use of individual-level data versus summary statistics. State-of-the-art statistical methods that are scalable to the UK Biobank dataset are also elucidated in detail.
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Affiliation(s)
- Mingsheng Tang
- Division of Health Statistics, School of Public Health, Shanxi Medical University, No.56 Xin jian South Road, 030001, Shanxi, China
| | - Tong Wang
- Division of Health Statistics, School of Public Health, Shanxi Medical University, No.56 Xin jian South Road, 030001, Shanxi, China
| | - Xuefen Zhang
- Social Medicine, School of Public Health, Shanxi Medical University, No.56 Xin jian South Road, 030001, Shanxi, China
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13
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Lin Z, Seal S, Basu S. Estimating SNP heritability in presence of population substructure in biobank-scale datasets. Genetics 2022; 220:iyac015. [PMID: 35106569 PMCID: PMC8982037 DOI: 10.1093/genetics/iyac015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 01/04/2022] [Indexed: 02/04/2023] Open
Abstract
Single nucleotide polymorphism heritability of a trait is measured as the proportion of total variance explained by the additive effects of genome-wide single nucleotide polymorphisms. Linear mixed models are routinely used to estimate single nucleotide polymorphism heritability for many complex traits, which requires estimation of a genetic relationship matrix among individuals. Heritability is usually estimated by the restricted maximum likelihood or method of moments approaches such as Haseman-Elston regression. The common practice of accounting for such population substructure is to adjust for the top few principal components of the genetic relationship matrix as covariates in the linear mixed model. This can get computationally very intensive on large biobank-scale datasets. Here, we propose a method of moments approach for estimating single nucleotide polymorphism heritability in presence of population substructure. Our proposed method is computationally scalable on biobank datasets and gives an asymptotically unbiased estimate of heritability in presence of discrete substructures. It introduces the adjustments for population stratification in a second-order estimating equation. It allows these substructures to vary in their single nucleotide polymorphism allele frequencies and in their trait distributions (means and variances) while the heritability is assumed to be the same across these substructures. Through extensive simulation studies and the application on 7 quantitative traits in the UK Biobank cohort, we demonstrate that our proposed method performs well in the presence of population substructure and much more computationally efficient than existing approaches.
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Affiliation(s)
- Zhaotong Lin
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Souvik Seal
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80217, USA
| | - Saonli Basu
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
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14
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Zhao Y, Zhao X, Kim M, Bao J, Shen L. A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12905:678-687. [PMID: 35299630 PMCID: PMC8922551 DOI: 10.1007/978-3-030-87240-3_65] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Heritability analysis is an important research topic in brain imaging genetics. Its primary motivation is to identify highly heritable imaging quantitative traits (QTs) for subsequent in-depth imaging genetic analyses. Most existing studies perform heritability analyses on regional imaging QTs using predefined brain parcellation schemes such as the AAL atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion is largely deteriorate with inner partition noise and signal dilution. To bridge the gap, we propose a new semi-parametric Bayesian heritability estimation model to construct highly heritable imaging QTs. Our method leverages the aggregate of genetic signals to imaging QT construction by developing a new brain parcellation driven by voxel-level heritability. To ensure biological plausibility and clinical interpretability of the resulting brain heritability parcellations, hierarchical sparsity and smoothness, coupled with structural connectivity of the brain, are properly imposed on genetic effects to induce spatial contiguity of heritable imaging QTs. Using the ADNI imaging genetic data, we demonstrate the strength of our proposed method, in comparison with the standard GCTA method, in identifying highly heritable and biologically meaningful new imaging QTs.
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Affiliation(s)
- Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, NJ, USA
| | - Xiwen Zhao
- Department of Biostatistics, Yale University School of Public Health, NJ, USA
| | - Mansu Kim
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
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15
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Biton A, Traut N, Poline JB, Aribisala BS, Bastin ME, Bülow R, Cox SR, Deary IJ, Fukunaga M, Grabe HJ, Hagenaars S, Hashimoto R, Kikuchi M, Muñoz Maniega S, Nauck M, Royle NA, Teumer A, Valdés Hernández M, Völker U, Wardlaw JM, Wittfeld K, Yamamori H, Bourgeron T, Toro R. Polygenic Architecture of Human Neuroanatomical Diversity. Cereb Cortex 2021; 30:2307-2320. [PMID: 32109272 DOI: 10.1093/cercor/bhz241] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 01/15/2023] Open
Abstract
We analyzed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26 000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r ~ 0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes (~51%) captured ~1.5 times more genetic variance than the rest, and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF <5% captured <one fourth of the genetic variance. We also observed extensive pleiotropy across regions, with an average genetic correlation of rG ~ 0.45. Genetic correlations were similar to phenotypic and environmental correlations; however, genetic correlations were often larger than phenotypic correlations for the left/right volumes of the same region. The heritability of differences in left/right volumes was generally not statistically significant, suggesting an important influence of environmental causes in the variability of brain asymmetry. Our code is available athttps://github.com/neuroanatomy/genomic-architecture.
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Affiliation(s)
- Anne Biton
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, UMR 3571, CNRS, Université Paris Diderot, Paris 75015, France.,Hub de Bioinformatique et Biostatistique-Département Biologie Computationnelle, Institut Pasteur, USR 3756 CNRS, Paris 75015, France
| | - Nicolas Traut
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, UMR 3571, CNRS, Université Paris Diderot, Paris 75015, France
| | - Jean-Baptiste Poline
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Benjamin S Aribisala
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK.,Department of Computer Science, Lagos State University, Lagos, 102101, Nigeria
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK.,Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Robin Bülow
- The Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, 17489, Germany
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan.,Department of Physiological Sciences, School of Life Sciences, The Graduate University for Advanced Studies (SOKENDAI), Hayama, 240-0193, Japan
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, 17485, Germany.,German Centre of Neurodegenerative Diseases (DZNE) Site Greifswald/Rostock, Greifswald, 17489, Germany
| | - Saskia Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,The Social Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, 187-0031, Japan
| | - Masataka Kikuchi
- Department of Genome Informatics, Graduate School of Medicine, Osaka University, Osaka, 565-0871, Japan
| | - Susana Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK.,Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, 17475, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, 17475, Germany
| | - Natalie A Royle
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK.,Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, 17475, Germany
| | - Maria Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK.,Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Uwe Völker
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, 17475, Germany.,Department of Functional Genomics, Interfaculty Institute of Genetics and Functional Genomics, University Greifswald, Greifswald, 17475, Germany
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK.,Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, 17485, Germany.,German Centre of Neurodegenerative Diseases (DZNE) Site Greifswald/Rostock, Greifswald, 17489, Germany
| | - Hidenaga Yamamori
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, 565-0871, Japan
| | | | - Thomas Bourgeron
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, UMR 3571, CNRS, Université Paris Diderot, Paris 75015, France
| | - Roberto Toro
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, UMR 3571, CNRS, Université Paris Diderot, Paris 75015, France.,Center for Research and Interdisciplinarity (CRI), Université Paris Descartes, Paris, 75004, France
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16
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Gong M, Liu P, Sciurba FC, Stojanov P, Tao D, Tseng GC, Zhang K, Batmanghelich K. Unpaired data empowers association tests. Bioinformatics 2021; 37:785-792. [PMID: 33070196 PMCID: PMC8098021 DOI: 10.1093/bioinformatics/btaa886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 09/07/2020] [Accepted: 10/05/2020] [Indexed: 11/25/2022] Open
Abstract
Motivation There is growing interest in the biomedical research community to incorporate retrospective data, available in healthcare systems, to shed light on associations between different biomarkers. Understanding the association between various types of biomedical data, such as genetic, blood biomarkers, imaging, etc. can provide a holistic understanding of human diseases. To formally test a hypothesized association between two types of data in Electronic Health Records (EHRs), one requires a substantial sample size with both data modalities to achieve a reasonable power. Current association test methods only allow using data from individuals who have both data modalities. Hence, researchers cannot take advantage of much larger EHR samples that includes individuals with at least one of the data types, which limits the power of the association test. Results We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and to improve the power of the association test. We study the properties of the new test theoretically and empirically, through a series of simulations and by applying our method on real studies in the context of Chronic Obstructive Pulmonary Disease. We are able to identify an association between the high-dimensional characterization of Computed Tomography chest images and several blood biomarkers as well as the expression of dozens of genes involved in the immune system. Availability and implementation Code is available on https://github.com/batmanlab/Semi-paired-Association-Test. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mingming Gong
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA.,Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213, USA.,School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Peng Liu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
| | - Frank C Sciurba
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
| | - Petar Stojanov
- Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Dacheng Tao
- Australia School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia
| | - George C Tseng
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
| | - Kun Zhang
- Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Kayhan Batmanghelich
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
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17
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Ma SX, Lim SB. Single-Cell RNA Sequencing in Parkinson's Disease. Biomedicines 2021; 9:368. [PMID: 33916045 PMCID: PMC8066089 DOI: 10.3390/biomedicines9040368] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/28/2021] [Accepted: 03/30/2021] [Indexed: 02/07/2023] Open
Abstract
Single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) technologies have enhanced the understanding of the molecular pathogenesis of neurodegenerative disorders, including Parkinson's disease (PD). Nonetheless, their application in PD has been limited due mainly to the technical challenges resulting from the scarcity of postmortem brain tissue and low quality associated with RNA degradation. Despite such challenges, recent advances in animals and human in vitro models that recapitulate features of PD along with sequencing assays have fueled studies aiming to obtain an unbiased and global view of cellular composition and phenotype of PD at the single-cell resolution. Here, we reviewed recent sc/snRNA-seq efforts that have successfully characterized diverse cell-type populations and identified cell type-specific disease associations in PD. We also examined how these studies have employed computational and analytical tools to analyze and interpret the rich information derived from sc/snRNA-seq. Finally, we highlighted important limitations and emerging technologies for addressing key technical challenges currently limiting the integration of new findings into clinical practice.
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Affiliation(s)
- Shi-Xun Ma
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
| | - Su Bin Lim
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon 16499, Korea
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18
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Anderson KM, Ge T, Kong R, Patrick LM, Spreng RN, Sabuncu MR, Yeo BTT, Holmes AJ. Heritability of individualized cortical network topography. Proc Natl Acad Sci U S A 2021; 118:e2016271118. [PMID: 33622790 PMCID: PMC7936334 DOI: 10.1073/pnas.2016271118] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n = 1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2 : M = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex (h2 : M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional estimation of heritability (h2-multi; M = 0.14, SD = 0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.
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Affiliation(s)
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114
- Stanley Center for Psychiatric Research, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114
| | - Ru Kong
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, National University of Singapore, Singapore 119077
- Department of Electrical and Computer Engineering, Centre for Translational Magnetic Resonance Research, National University of Singapore, Singapore 119077
- N.1 Institute for Health, National University of Singapore, Singapore 119077
- Institute for Digital Medicine, National University of Singapore, Singapore 119077
| | | | - R Nathan Spreng
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC H3A 0G4, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC H3A 0G4, Canada
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14850
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, National University of Singapore, Singapore 119077
- Department of Electrical and Computer Engineering, Centre for Translational Magnetic Resonance Research, National University of Singapore, Singapore 119077
- N.1 Institute for Health, National University of Singapore, Singapore 119077
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- National University of Singapore Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore 119077
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT 06520
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114
- Department of Psychiatry, Yale University, New Haven, CT 06520
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19
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Zhao B, Ibrahim JG, Li Y, Li T, Wang Y, Shan Y, Zhu Z, Zhou F, Zhang J, Huang C, Liao H, Yang L, Thompson PM, Zhu H. Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies. Cereb Cortex 2020; 29:2904-2914. [PMID: 30010813 DOI: 10.1093/cercor/bhy157] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 06/11/2018] [Indexed: 12/20/2022] Open
Abstract
Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (P ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population and common genetic variants can explain up to 80% of their variabilities (median heritability 34.8%). We observed omnigenic impact across the genome and examined the enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks, exhibit a symmetric pattern across left and right hemispheres, and are consistent in females and males (correlation = 0.638). We repeated the main analysis in Alzheimer's Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, which demonstrated that more stable estimates can be obtained from the UK Biobank.
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Affiliation(s)
- Bingxin Zhao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joseph G Ibrahim
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tengfei Li
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yue Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Fan Zhou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jingwen Zhang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Chao Huang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Huiling Liao
- Department of Statistics, Texas A&M University, College Station, TX, USA
| | - Liuqing Yang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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20
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Zhao Y, Li T, Zhu H. Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes. Biostatistics 2020; 23:467-484. [PMID: 32948880 PMCID: PMC9308456 DOI: 10.1093/biostatistics/kxaa035] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 07/15/2020] [Accepted: 08/11/2020] [Indexed: 12/24/2022] Open
Abstract
Heritability analysis plays a central role in quantitative genetics to describe genetic contribution to human complex traits and prioritize downstream analyses under large-scale phenotypes. Existing works largely focus on modeling single phenotype and currently available multivariate phenotypic methods often suffer from scaling and interpretation. In this article, motivated by understanding how genetic underpinning impacts human brain variation, we develop an integrative Bayesian heritability analysis to jointly estimate heritabilities for high-dimensional neuroimaging traits. To induce sparsity and incorporate brain anatomical configuration, we impose hierarchical selection among both regional and local measurements based on brain structural network and voxel dependence. We also use a nonparametric Dirichlet process mixture model to realize grouping among single nucleotide polymorphism-associated phenotypic variations, providing biological plausibility. Through extensive simulations, we show the proposed method outperforms existing ones in heritability estimation and heritable traits selection under various scenarios. We finally apply the method to two large-scale imaging genetics datasets: the Alzheimer's Disease Neuroimaging Initiative and United Kingdom Biobank and show biologically meaningful results.
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Affiliation(s)
- Yize Zhao
- Department of Biostatistics, Yale University, 300 George Street, New Haven, CT 06511, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, 101 Manning Dr, Chapel Hill, NC 27514, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27514, USA
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21
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Ge T, Chen CY, Doyle AE, Vettermann R, Tuominen LJ, Holt DJ, Sabuncu MR, Smoller JW. The Shared Genetic Basis of Educational Attainment and Cerebral Cortical Morphology. Cereb Cortex 2020; 29:3471-3481. [PMID: 30272126 PMCID: PMC6644848 DOI: 10.1093/cercor/bhy216] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2018] [Revised: 07/20/2018] [Indexed: 01/03/2023] Open
Abstract
Individual differences in educational attainment are linked to differences in intelligence, and predict important social, economic, and health outcomes. Previous studies have found common genetic factors that influence educational achievement, cognitive performance and total brain volume (i.e., brain size). Here, in a large sample of participants from the UK Biobank, we investigate the shared genetic basis between educational attainment and fine-grained cerebral cortical morphological features, and associate this genetic variation with a related aspect of cognitive ability. Importantly, we execute novel statistical methods that enable high-dimensional genetic correlation analysis, and compute high-resolution surface maps for the genetic correlations between educational attainment and vertex-wise morphological measurements. We conduct secondary analyses, using the UK Biobank verbal-numerical reasoning score, to confirm that variation in educational attainment that is genetically correlated with cortical morphology is related to differences in cognitive performance. Our analyses relate the genetic overlap between cognitive ability and cortical thickness measurements to bilateral primary motor cortex as well as predominantly left superior temporal cortex and proximal regions. These findings extend our understanding of the neurobiology that connects genetic variation to individual differences in educational attainment and cognitive performance.
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Affiliation(s)
- Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chia-Yen Chen
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alysa E Doyle
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Richard Vettermann
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Lauri J Tuominen
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Daphne J Holt
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering and Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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22
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Alexander-Bloch AF, Raznahan A, Vandekar SN, Seidlitz J, Lu Z, Mathias SR, Knowles E, Mollon J, Rodrigue A, Curran JE, Görring HHH, Satterthwaite TD, Gur RE, Bassett DS, Hoftman GD, Pearlson G, Shinohara RT, Liu S, Fox PT, Almasy L, Blangero J, Glahn DC. Imaging local genetic influences on cortical folding. Proc Natl Acad Sci U S A 2020; 117:7430-7436. [PMID: 32170019 PMCID: PMC7132284 DOI: 10.1073/pnas.1912064117] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Recent progress in deciphering mechanisms of human brain cortical folding leave unexplained whether spatially patterned genetic influences contribute to this folding. High-resolution in vivo brain MRI can be used to estimate genetic correlations (covariability due to shared genetic factors) in interregional cortical thickness, and biomechanical studies predict an influence of cortical thickness on folding patterns. However, progress has been hampered because shared genetic influences related to folding patterns likely operate at a scale that is much more local (<1 cm) than that addressed in prior imaging studies. Here, we develop methodological approaches to examine local genetic influences on cortical thickness and apply these methods to two large, independent samples. We find that such influences are markedly heterogeneous in strength, and in some cortical areas are notably stronger in specific orientations relative to gyri or sulci. The overall, phenotypic local correlation has a significant basis in shared genetic factors and is highly symmetric between left and right cortical hemispheres. Furthermore, the degree of local cortical folding relates systematically with the strength of local correlations, which tends to be higher in gyral crests and lower in sulcal fundi. The relationship between folding and local correlations is stronger in primary sensorimotor areas and weaker in association areas such as prefrontal cortex, consistent with reduced genetic constraints on the structural topology of association cortex. Collectively, our results suggest that patterned genetic influences on cortical thickness, measurable at the scale of in vivo MRI, may be a causal factor in the development of cortical folding.
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Affiliation(s)
- Aaron F Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104;
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
| | | | - Simon N Vandekar
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37203
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
| | - Zhixin Lu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
| | - Samuel R Mathias
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Emma Knowles
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Josephine Mollon
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Amanda Rodrigue
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Joanne E Curran
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
| | - Harald H H Görring
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
| | | | - Raquel E Gur
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle S Bassett
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104
| | - Gil D Hoftman
- Department of Psychiatry, University of California, Los Angeles, CA 90095
| | - Godfrey Pearlson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford, CT 06102
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Siyuan Liu
- National Institute of Mental Health, Bethesda, MD 20814
| | - Peter T Fox
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford, CT 06102
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23
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Shen L, Thompson PM. Brain Imaging Genomics: Integrated Analysis and Machine Learning. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:125-162. [PMID: 31902950 PMCID: PMC6941751 DOI: 10.1109/jproc.2019.2947272] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.
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Affiliation(s)
- Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90232, USA
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24
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Fan CC, Smeland OB, Schork AJ, Chen CH, Holland D, Lo MT, Sundar VS, Frei O, Jernigan TL, Andreassen OA, Dale AM. Beyond heritability: improving discoverability in imaging genetics. Hum Mol Genet 2019. [PMID: 29522091 DOI: 10.1093/hmg/ddy082] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Structural neuroimaging measures based on magnetic resonance imaging have been at the forefront of imaging genetics. Global efforts to ensure homogeneity of measurements across study sites have enabled large-scale imaging genetic projects, accumulating nearly 50K samples for genome-wide association studies (GWAS). However, not many novel genetic variants have been identified by these GWAS, despite the high heritability of structural neuroimaging measures. Here, we discuss the limitations of using heritability as a guidance for assessing statistical power of GWAS, and highlight the importance of discoverability-which is the power to detect genetic variants for a given phenotype depending on its unique genomic architecture and GWAS sample size. Further, we present newly developed methods that boost genetic discovery in imaging genetics. By redefining imaging measures independent of traditional anatomical conventions, it is possible to improve discoverability, enabling identification of more genetic effects. Moreover, by leveraging enrichment priors from genomic annotations and independent GWAS of pleiotropic traits, we can better characterize effect size distributions, and identify reliable and replicable loci associated with structural neuroimaging measures. Statistical tools leveraging novel insights into the genetic discoverability of human traits, promises to accelerate the identification of genetic underpinnings underlying brain structural variation.
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Affiliation(s)
- Chun Chieh Fan
- Center for Multimodal Imaging and Genetics, School of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Olav B Smeland
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Andrew J Schork
- Institute for Biological Psychiatry, Mental Health Center Sct. Hans, Capital Region of Denmark, Denmark
| | - Chi-Hua Chen
- Center for Multimodal Imaging and Genetics, School of Medicine, University of California San Diego, La Jolla, CA 92093, USA.,Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Dominic Holland
- Department of Neurosciences, School of Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Min-Tzu Lo
- Center for Multimodal Imaging and Genetics, School of Medicine, University of California San Diego, La Jolla, CA 92093, USA.,Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - V S Sundar
- Center for Multimodal Imaging and Genetics, School of Medicine, University of California San Diego, La Jolla, CA 92093, USA.,Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Oleksandr Frei
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Terry L Jernigan
- Center for Human Development, University of California San Diego, La Jolla, CA 92093, USA
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, School of Medicine, University of California San Diego, La Jolla, CA 92093, USA.,Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA 92037, USA.,Department of Neurosciences, School of Medicine, University of California San Diego, La Jolla, CA 92037, USA
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25
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Ong ML, Tuan TA, Poh J, Teh AL, Chen L, Pan H, MacIsaac JL, Kobor MS, Chong YS, Kwek K, Saw SM, Godfrey KM, Gluckman PD, Fortier MV, Karnani N, Meaney MJ, Qiu A, Holbrook JD. Neonatal amygdalae and hippocampi are influenced by genotype and prenatal environment, and reflected in the neonatal DNA methylome. GENES BRAIN AND BEHAVIOR 2019; 18:e12576. [PMID: 31020763 DOI: 10.1111/gbb.12576] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 03/01/2019] [Accepted: 04/13/2019] [Indexed: 12/28/2022]
Abstract
The amygdala and hippocampus undergo rapid development in early life. The relative contribution of genetic and environmental factors to the establishment of their developmental trajectories has yet to be examined. We performed imaging on neonates and examined how the observed variation in volume and microstructure of the amygdala and hippocampus varied by genotype, and compared with prenatal maternal mental health and socioeconomic status. Gene × Environment models outcompeted models containing genotype or environment only to best explain the majority of measures but some, especially of the amygdaloid microstructure, were best explained by genotype only. Models including DNA methylation measured in the neonate umbilical cords outcompeted the Gene and Gene × Environment models for the majority of amygdaloid measures and minority of hippocampal measures. This study identified brain region-specific gene networks associated with individual differences in fetal brain development. In particular, genetic and epigenetic variation within CUX1 was highlighted.
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Affiliation(s)
- Mei-Lyn Ong
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
| | - Ta A Tuan
- Department of Biomedical Engineering, Clinical Imaging research Centre, National University of Singapore, Singapore
| | - Joann Poh
- Department of Biomedical Engineering, Clinical Imaging research Centre, National University of Singapore, Singapore
| | - Ai L Teh
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
| | - Li Chen
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
| | - Hong Pan
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore.,School of Computer Engineering, Nanyang Technological University (NTU), Singapore
| | - Julia L MacIsaac
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Michael S Kobor
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Yap S Chong
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore
| | - Kenneth Kwek
- KK Women's and Children's Hospital, Duke National University of Singapore, Singapore
| | - Seang M Saw
- Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Unit and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Peter D Gluckman
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore.,Centre for Human Evolution, Adaptation and disease, Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Marielle V Fortier
- KK Women's and Children's Hospital, Duke National University of Singapore, Singapore
| | - Neerja Karnani
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
| | - Michael J Meaney
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore.,Ludmer Centre for Neuroinformatics and Mental Health, Sackler Program for Epigenetics & Psychobiology at McGill University, Douglas University Mental Health Institute, McGill University, Montreal, Canada
| | - Anqi Qiu
- Department of Biomedical Engineering, Clinical Imaging research Centre, National University of Singapore, Singapore.,Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
| | - Joanna D Holbrook
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
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26
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Li X, Wu D, Cui Y, Liu B, Walter H, Schumann G, Li C, Jiang T. Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies. BMC Bioinformatics 2019; 20:219. [PMID: 31039742 PMCID: PMC6492418 DOI: 10.1186/s12859-019-2792-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 04/02/2019] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability. RESULTS In this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy. CONCLUSIONS The proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era.
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Affiliation(s)
- Xin Li
- School of Mathematical Sciences, Zhejiang University, 38 Zheda Road, Hangzhou, 310027 China
| | - Dongya Wu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing, 100049 China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
| | - Bing Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Chong Li
- School of Mathematical Sciences, Zhejiang University, 38 Zheda Road, Hangzhou, 310027 China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, 4 Section 2 North Jianshe Road, Chengdu, 610054 China
- The Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072 Australia
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing, 100049 China
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27
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Chen X, Formisano E, Blokland GAM, Strike LT, McMahon KL, de Zubicaray GI, Thompson PM, Wright MJ, Winkler AM, Ge T, Nichols TE. Accelerated estimation and permutation inference for ACE modeling. Hum Brain Mapp 2019; 40:3488-3507. [PMID: 31037793 PMCID: PMC6680147 DOI: 10.1002/hbm.24611] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 04/15/2019] [Accepted: 04/17/2019] [Indexed: 11/30/2022] Open
Abstract
There are a wealth of tools for fitting linear models at each location in the brain in neuroimaging analysis, and a wealth of genetic tools for estimating heritability for a small number of phenotypes. But there remains a need for computationally efficient neuroimaging genetic tools that can conduct analyses at the brain‐wide scale. Here we present a simple method for heritability estimation on twins that replaces a variance component model‐which requires iterative optimisation‐with a (noniterative) linear regression model, by transforming data to squared twin‐pair differences. We demonstrate that the method has comparable bias, mean squared error, false positive risk, and power to best practice maximum‐likelihood‐based methods, while requiring a small fraction of the computation time. Combined with permutation, we call this approach “Accelerated Permutation Inference for the ACE Model (APACE)” where ACE refers to the additive genetic (A) effects, and common (C), and unique (E) environmental influences on the trait. We show how the use of spatial statistics like cluster size can dramatically improve power, and illustrate the method on a heritability analysis of an fMRI working memory dataset.
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Affiliation(s)
- Xu Chen
- Department of Statistics, University of Warwick, Coventry, UK.,Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.,Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands.,Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Elia Formisano
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.,Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Gabriëlla A M Blokland
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Lachlan T Strike
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Katie L McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Greig I de Zubicaray
- Faculty of Health and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Paul M Thompson
- Imaging Genetics Center, University of Southern California, Los Angeles, California
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Anderson M Winkler
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.,Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Thomas E Nichols
- Department of Statistics, University of Warwick, Coventry, UK.,Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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28
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Schweiger R, Fisher E, Weissbrod O, Rahmani E, Müller-Nurasyid M, Kunze S, Gieger C, Waldenberger M, Rosset S, Halperin E. Detecting heritable phenotypes without a model using fast permutation testing for heritability and set-tests. Nat Commun 2018; 9:4919. [PMID: 30464216 PMCID: PMC6249264 DOI: 10.1038/s41467-018-07276-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 10/26/2018] [Indexed: 01/08/2023] Open
Abstract
Testing for association between a set of genetic markers and a phenotype is a fundamental task in genetic studies. Standard approaches for heritability and set testing strongly rely on parametric models that make specific assumptions regarding phenotypic variability. Here, we show that resulting p-values may be inflated by up to 15 orders of magnitude, in a heritability study of methylation measurements, and in a heritability and expression quantitative trait loci analysis of gene expression profiles. We propose FEATHER, a method for fast permutation-based testing of marker sets and of heritability, which properly controls for false-positive results. FEATHER eliminated 47% of methylation sites found to be heritable by the parametric test, suggesting a substantial inflation of false-positive findings by alternative methods. Our approach can rapidly identify heritable phenotypes out of millions of phenotypes acquired via high-throughput technologies, does not suffer from model misspecification and is highly efficient. Standard approaches for heritability and set testing in statistical genetics rely on parametric models that might not hold in reality and give inflated p-values. Here, the authors develop a fast method for permutation-based testing of marker sets and of heritability that does not suffer from model misspecification.
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Affiliation(s)
- Regev Schweiger
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 6997801, Israel.
| | - Eyal Fisher
- School of Mathematical Sciences, Department of Statistics, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA
| | - Elior Rahmani
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, 85764, Germany.,Department of Medicine I, Ludwig-Maximilians-Universität, Munich, 80539, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, 80636, Germany
| | - Sonja Kunze
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany.,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Christian Gieger
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany.,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Melanie Waldenberger
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, 80636, Germany.,Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany.,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Saharon Rosset
- School of Mathematical Sciences, Department of Statistics, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Eran Halperin
- Los Angeles, University of California Los Angeles, Los Angeles, 90095, CA, USA.,Department of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, 90095, CA, USA
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29
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Smoller JW. The use of electronic health records for psychiatric phenotyping and genomics. Am J Med Genet B Neuropsychiatr Genet 2018; 177:601-612. [PMID: 28557243 PMCID: PMC6440216 DOI: 10.1002/ajmg.b.32548] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2017] [Accepted: 04/20/2017] [Indexed: 12/22/2022]
Abstract
The widespread adoption of electronic health record (EHRs) in healthcare systems has created a vast and continuously growing resource of clinical data and provides new opportunities for population-based research. In particular, the linking of EHRs to biospecimens and genomic data in biobanks may help address what has become a rate-limiting study for genetic research: the need for large sample sizes. The principal roadblock to capitalizing on these resources is the need to establish the validity of phenotypes extracted from the EHR. For psychiatric genetic research, this represents a particular challenge given that diagnosis is based on patient reports and clinician observations that may not be well-captured in billing codes or narrative records. This review addresses the opportunities and pitfalls in EHR-based phenotyping with a focus on their application to psychiatric genetic research. A growing number of studies have demonstrated that diagnostic algorithms with high positive predictive value can be derived from EHRs, especially when structured data are supplemented by text mining approaches. Such algorithms enable semi-automated phenotyping for large-scale case-control studies. In addition, the scale and scope of EHR databases have been used successfully to identify phenotypic subgroups and derive algorithms for longitudinal risk prediction. EHR-based genomics are particularly well-suited to rapid look-up replication of putative risk genes, studies of pleiotropy (phenomewide association studies or PheWAS), investigations of genetic networks and overlap across the phenome, and pharmacogenomic research. EHR phenotyping has been relatively under-utilized in psychiatric genomic research but may become a key component of efforts to advance precision psychiatry.
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Affiliation(s)
- Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
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30
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Grimm O, Kittel-Schneider S, Reif A. Recent developments in the genetics of attention-deficit hyperactivity disorder. Psychiatry Clin Neurosci 2018; 72:654-672. [PMID: 29722101 DOI: 10.1111/pcn.12673] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/01/2018] [Indexed: 12/19/2022]
Abstract
Attention-deficit hyperactivity disorder (ADHD) is a developmental psychiatric disorder that affects children and adults. ADHD is one of the psychiatric disorders with the strongest genetic basis according to familial, twin, and single nucleotide polymorphisms (SNP)-based epidemiological studies. In this review, we provide an update of recent insights into the genetic basis of ADHD. We discuss recent progress from genome-wide association studies (GWAS) looking at common variants as well as rare copy number variations. New analysis of gene groups, so-called functional ontologies, provide some insight into the gene networks afflicted, pointing to the role of neurodevelopmentally expressed gene networks. Bioinformatic methods, such as functional enrichment analysis and protein-protein network analysis, are used to highlight biological processes of likely relevance to the etiology of ADHD. Additionally, copy number variations seem to map on important pathways implicated in synaptic signaling and neurodevelopment. While some candidate gene associations of, for example, neurotransmitter receptors and signaling, have been replicated, they do not seem to explain significant variance in recent GWAS. We discuss insights from recent case-control SNP-GWAS that have presented the first whole-genome significant SNP in ADHD.
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Affiliation(s)
- Oliver Grimm
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Sarah Kittel-Schneider
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
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31
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Blokland GAM, del Re EC, Mesholam-Gately RI, Jovicich J, Trampush JW, Keshavan MS, DeLisi LE, Walters JTR, Turner JA, Malhotra AK, Lencz T, Shenton ME, Voineskos AN, Rujescu D, Giegling I, Kahn RS, Roffman JL, Holt DJ, Ehrlich S, Kikinis Z, Dazzan P, Murray RM, Di Forti M, Lee J, Sim K, Lam M, Wolthusen RPF, de Zwarte SMC, Walton E, Cosgrove D, Kelly S, Maleki N, Osiecki L, Picchioni MM, Bramon E, Russo M, David AS, Mondelli V, Reinders AATS, Falcone MA, Hartmann AM, Konte B, Morris DW, Gill M, Corvin AP, Cahn W, Ho NF, Liu JJ, Keefe RSE, Gollub RL, Manoach DS, Calhoun VD, Schulz SC, Sponheim SR, Goff DC, Buka SL, Cherkerzian S, Thermenos HW, Kubicki M, Nestor PG, Dickie EW, Vassos E, Ciufolini S, Marques TR, Crossley NA, Purcell SM, Smoller JW, van Haren NEM, Toulopoulou T, Donohoe G, Goldstein JM, Seidman LJ, McCarley RW, Petryshen TL. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) consortium: A collaborative cognitive and neuroimaging genetics project. Schizophr Res 2018; 195:306-317. [PMID: 28982554 PMCID: PMC5882601 DOI: 10.1016/j.schres.2017.09.024] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 09/15/2017] [Accepted: 09/20/2017] [Indexed: 01/02/2023]
Abstract
BACKGROUND Schizophrenia has a large genetic component, and the pathways from genes to illness manifestation are beginning to be identified. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium aims to clarify the role of genetic variation in brain abnormalities underlying schizophrenia. This article describes the GENUS Consortium sample collection. METHODS We identified existing samples collected for schizophrenia studies consisting of patients, controls, and/or individuals at familial high-risk (FHR) for schizophrenia. Samples had single nucleotide polymorphism (SNP) array data or genomic DNA, clinical and demographic data, and neuropsychological and/or brain magnetic resonance imaging (MRI) data. Data were subjected to quality control procedures at a central site. RESULTS Sixteen research groups contributed data from 5199 psychosis patients, 4877 controls, and 725 FHR individuals. All participants have relevant demographic data and all patients have relevant clinical data. The sex ratio is 56.5% male and 43.5% female. Significant differences exist between diagnostic groups for premorbid and current IQ (both p<1×10-10). Data from a diversity of neuropsychological tests are available for 92% of participants, and 30% have structural MRI scans (half also have diffusion-weighted MRI scans). SNP data are available for 76% of participants. The ancestry composition is 70% European, 20% East Asian, 7% African, and 3% other. CONCLUSIONS The Consortium is investigating the genetic contribution to brain phenotypes in a schizophrenia sample collection of >10,000 participants. The breadth of data across clinical, genetic, neuropsychological, and MRI modalities provides an important opportunity for elucidating the genetic basis of neural processes underlying schizophrenia.
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Affiliation(s)
- Gabriëlla A. M. Blokland
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic
Medicine, Massachusetts General Hospital, Boston, MA, United States,Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Stanley Center for Psychiatric Research, Broad Institute of MIT and
Harvard, Cambridge, MA, United States
| | - Elisabetta C. del Re
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Department of Psychiatry, Veterans Affairs Boston Healthcare System,
Brockton, MA, United States,Psychiatry Neuroimaging Laboratory, Department of Psychiatry,
Brigham and Women’s Hospital, Boston, MA, United States
| | - Raquelle I. Mesholam-Gately
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Massachusetts Mental Health Center Public Psychiatry Division, Beth
Israel Deaconess Medical Center, Boston, MA, United States
| | - Jorge Jovicich
- Center for Mind/Brain Sciences (CiMEC), University of Trento,
Trento, Italy
| | - Joey W. Trampush
- Center for Psychiatric Neuroscience, The Feinstein Institute for
Medical Research, Division of Northwell Health, Manhasset, NY, United States;
Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell
Health, Glen Oaks, NY, United States; Hofstra Northwell School of Medicine,
Departments of Psychiatry and Molecular Medicine, Hempstead, NY, United States,BrainWorkup, LLC, Los Angeles, CA, United States
| | - Matcheri S. Keshavan
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Massachusetts Mental Health Center Public Psychiatry Division, Beth
Israel Deaconess Medical Center, Boston, MA, United States,University of Pittsburgh Medical Center, Pittsburgh, PA, United
States
| | - Lynn E. DeLisi
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Department of Psychiatry, Veterans Affairs Boston Healthcare System,
Brockton, MA, United States
| | - James T. R. Walters
- Department of Psychological Medicine, Cardiff University, Cardiff,
United Kingdom
| | - Jessica A. Turner
- The Mind Research Network, Albuquerque, NM, United States,Department of Psychology and Neuroscience Institute, Georgia State
University, GA, United States
| | - Anil K. Malhotra
- Center for Psychiatric Neuroscience, The Feinstein Institute for
Medical Research, Division of Northwell Health, Manhasset, NY, United States;
Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell
Health, Glen Oaks, NY, United States; Hofstra Northwell School of Medicine,
Departments of Psychiatry and Molecular Medicine, Hempstead, NY, United States
| | - Todd Lencz
- Center for Psychiatric Neuroscience, The Feinstein Institute for
Medical Research, Division of Northwell Health, Manhasset, NY, United States;
Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell
Health, Glen Oaks, NY, United States; Hofstra Northwell School of Medicine,
Departments of Psychiatry and Molecular Medicine, Hempstead, NY, United States
| | - Martha E. Shenton
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Department of Psychiatry, Veterans Affairs Boston Healthcare System,
Brockton, MA, United States,Psychiatry Neuroimaging Laboratory, Department of Psychiatry,
Brigham and Women’s Hospital, Boston, MA, United States,Department of Radiology, Brigham and Women’s Hospital,
Harvard Medical School, Boston, MA, United States
| | - Aristotle N. Voineskos
- Kimel Family Translational Imaging Genetics Laboratory, Research
Imaging Centre, Campbell Family Mental Health Institute, Centre for Addiction and
Mental Health, Department of Psychiatry, Faculty of Medicine, University of Toronto,
Toronto, ON, Canada,Department of Psychiatry and Institute of Medical Science,
University of Toronto, Toronto, ON, Canada
| | - Dan Rujescu
- Department of Psychiatry, Psychotherapy and Psychosomatics,
University of Halle-Wittenberg, Halle an der Saale, Germany,Department of Psychiatry, Ludwig Maximilians University, Munich,
Germany
| | - Ina Giegling
- Department of Psychiatry, Psychotherapy and Psychosomatics,
University of Halle-Wittenberg, Halle an der Saale, Germany
| | - René S. Kahn
- Brain Centre Rudolf Magnus, Department of Psychiatry, University
Medical Centre Utrecht, Utrecht, The Netherlands
| | - Joshua L. Roffman
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States
| | - Daphne J. Holt
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States
| | - Stefan Ehrlich
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States,Division of Psychological & Social Medicine and Developmental
Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden,
Germany
| | - Zora Kikinis
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Psychiatry Neuroimaging Laboratory, Department of Psychiatry,
Brigham and Women’s Hospital, Boston, MA, United States
| | - Paola Dazzan
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Robin M. Murray
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Marta Di Forti
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Jimmy Lee
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | - Kang Sim
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | - Max Lam
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | - Rick P. F. Wolthusen
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States,Division of Psychological & Social Medicine and Developmental
Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden,
Germany
| | - Sonja M. C. de Zwarte
- Brain Centre Rudolf Magnus, Department of Psychiatry, University
Medical Centre Utrecht, Utrecht, The Netherlands
| | - Esther Walton
- Division of Psychological & Social Medicine and Developmental
Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden,
Germany
| | - Donna Cosgrove
- The Cognitive Genetics and Cognitive Therapy Group, Department of
Psychology, National University of Ireland, Galway, Ireland
| | - Sinead Kelly
- Neuropsychiatric Genetics Research Group, Department of Psychiatry,
Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland; Trinity
College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland,Laboratory of NeuroImaging, Keck School of Medicine, University of
Southern California, Los Angeles, CA, United States
| | - Nasim Maleki
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States
| | - Lisa Osiecki
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic
Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Marco M. Picchioni
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Elvira Bramon
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom,Mental Health Neuroscience Research Department, UCL Division of
Psychiatry, University College London, United Kingdom
| | - Manuela Russo
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Anthony S. David
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Valeria Mondelli
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Antje A. T. S. Reinders
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - M. Aurora Falcone
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Annette M. Hartmann
- Department of Psychiatry, Psychotherapy and Psychosomatics,
University of Halle-Wittenberg, Halle an der Saale, Germany
| | - Bettina Konte
- Department of Psychiatry, Psychotherapy and Psychosomatics,
University of Halle-Wittenberg, Halle an der Saale, Germany
| | - Derek W. Morris
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and
Cognitive Genomics (NICOG) Centre and NCBES Galway Neuroscience Centre, School of
Psychology and Discipline of Biochemistry, National University of Ireland, Galway,
Ireland
| | - Michael Gill
- Neuropsychiatric Genetics Research Group, Department of Psychiatry,
Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland; Trinity
College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Aiden P. Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry,
Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland; Trinity
College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Wiepke Cahn
- Brain Centre Rudolf Magnus, Department of Psychiatry, University
Medical Centre Utrecht, Utrecht, The Netherlands
| | - New Fei Ho
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | | | - Richard S. E. Keefe
- Department of Psychiatry and Behavioral Sciences, Duke University
Medical Center, Durham, NC, United States
| | - Randy L. Gollub
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States
| | - Dara S. Manoach
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, United States,Department of Electrical and Computer Engineering, University of
New Mexico, Albuquerque, NM, United States
| | - S. Charles Schulz
- Department of Psychiatry, University of Minnesota, Minneapolis, MN,
United States
| | - Scott R. Sponheim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN,
United States
| | - Donald C. Goff
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Nathan S. Kline Institute for Psychiatric Research, Department of
Psychiatry, New York University Langone Medical Center, New York, NY, United
States
| | - Stephen L. Buka
- Department of Epidemiology, Brown University, Providence, RI,
United States
| | - Sara Cherkerzian
- Department of Medicine, Division of Women’s Health, Brigham
and Women’s Hospital, Harvard Medical School, Boston, MA, United
States
| | - Heidi W. Thermenos
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Massachusetts Mental Health Center Public Psychiatry Division, Beth
Israel Deaconess Medical Center, Boston, MA, United States
| | - Marek Kubicki
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Psychiatry Neuroimaging Laboratory, Department of Psychiatry,
Brigham and Women’s Hospital, Boston, MA, United States,Department of Radiology, Brigham and Women’s Hospital,
Harvard Medical School, Boston, MA, United States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States
| | - Paul G. Nestor
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Department of Psychiatry, Veterans Affairs Boston Healthcare System,
Brockton, MA, United States,Laboratory of Applied Neuropsychology, University of Massachusetts,
Boston, MA, United States
| | - Erin W. Dickie
- Kimel Family Translational Imaging Genetics Laboratory, Research
Imaging Centre, Campbell Family Mental Health Institute, Centre for Addiction and
Mental Health, Department of Psychiatry, Faculty of Medicine, University of Toronto,
Toronto, ON, Canada
| | - Evangelos Vassos
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Simone Ciufolini
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Tiago Reis Marques
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Nicolas A. Crossley
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Shaun M. Purcell
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Stanley Center for Psychiatric Research, Broad Institute of MIT and
Harvard, Cambridge, MA, United States,Department of Psychiatry, Brigham and Women’s Hospital,
Boston, MA, United States,Division of Psychiatric Genomics, Departments of Psychiatry and
Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York,
NY, United States
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic
Medicine, Massachusetts General Hospital, Boston, MA, United States,Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Stanley Center for Psychiatric Research, Broad Institute of MIT and
Harvard, Cambridge, MA, United States
| | - Neeltje E. M. van Haren
- Brain Centre Rudolf Magnus, Department of Psychiatry, University
Medical Centre Utrecht, Utrecht, The Netherlands
| | - Timothea Toulopoulou
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,Department of Psychology, Bilkent University, Bilkent, Ankara,
Turkey,Department of Psychology, The University of Hong Kong, Pokfulam,
Hong Kong, SAR, China
| | - Gary Donohoe
- Neuropsychiatric Genetics Research Group, Department of Psychiatry,
Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland; Trinity
College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland,Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and
Cognitive Genomics (NICOG) Centre and NCBES Galway Neuroscience Centre, School of
Psychology and Discipline of Biochemistry, National University of Ireland, Galway,
Ireland
| | - Jill M. Goldstein
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Department of Medicine, Division of Women’s Health, Brigham
and Women’s Hospital, Harvard Medical School, Boston, MA, United
States,Department of Psychiatry, Brigham and Women’s Hospital,
Boston, MA, United States
| | - Larry J. Seidman
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Massachusetts Mental Health Center Public Psychiatry Division, Beth
Israel Deaconess Medical Center, Boston, MA, United States
| | - Robert W. McCarley
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Department of Psychiatry, Veterans Affairs Boston Healthcare System,
Brockton, MA, United States
| | - Tracey L. Petryshen
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic
Medicine, Massachusetts General Hospital, Boston, MA, United States,Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Stanley Center for Psychiatric Research, Broad Institute of MIT and
Harvard, Cambridge, MA, United States
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32
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Verma SS, Ritchie MD. Another Round of "Clue" to Uncover the Mystery of Complex Traits. Genes (Basel) 2018; 9:E61. [PMID: 29370075 PMCID: PMC5852557 DOI: 10.3390/genes9020061] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 12/19/2017] [Accepted: 01/15/2018] [Indexed: 12/13/2022] Open
Abstract
A plethora of genetic association analyses have identified several genetic risk loci. Technological and statistical advancements have now led to the identification of not only common genetic variants, but also low-frequency variants, structural variants, and environmental factors, as well as multi-omics variations that affect the phenotypic variance of complex traits in a population, thus referred to as complex trait architecture. The concept of heritability, or the proportion of phenotypic variance due to genetic inheritance, has been studied for several decades, but its application is mainly in addressing the narrow sense heritability (or additive genetic component) from Genome-Wide Association Studies (GWAS). In this commentary, we reflect on our perspective on the complexity of understanding heritability for human traits in comparison to model organisms, highlighting another round of clues beyond GWAS and an alternative approach, investigating these clues comprehensively to help in elucidating the genetic architecture of complex traits.
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Affiliation(s)
- Shefali Setia Verma
- The Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Marylyn D Ritchie
- The Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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33
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Sample composition alters associations between age and brain structure. Nat Commun 2017; 8:874. [PMID: 29026076 PMCID: PMC5638928 DOI: 10.1038/s41467-017-00908-7] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 08/01/2017] [Indexed: 11/24/2022] Open
Abstract
Despite calls to incorporate population science into neuroimaging research, most studies recruit small, non-representative samples. Here, we examine whether sample composition influences age-related variation in global measurements of gray matter volume, thickness, and surface area. We apply sample weights to structural brain imaging data from a community-based sample of children aged 3–18 (N = 1162) to create a “weighted sample” that approximates the distribution of socioeconomic status, race/ethnicity, and sex in the U.S. Census. We compare associations between age and brain structure in this weighted sample to estimates from the original sample with no sample weights applied (i.e., unweighted). Compared to the unweighted sample, we observe earlier maturation of cortical and sub-cortical structures, and patterns of brain maturation that better reflect known developmental trajectories in the weighted sample. Our empirical demonstration of bias introduced by non-representative sampling in this neuroimaging cohort suggests that sample composition may influence understanding of fundamental neural processes. The influence of sample composition on human neuroimaging results is unknown. Here, the authors weight a large, community-based sample to better reflect the US population and describe how applying these sample weights changes conclusions about age-related variation in brain structure.
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34
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RL-SKAT: An Exact and Efficient Score Test for Heritability and Set Tests. Genetics 2017; 207:1275-1283. [PMID: 29025915 DOI: 10.1534/genetics.117.300395] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 09/24/2017] [Indexed: 11/18/2022] Open
Abstract
Testing for the existence of variance components in linear mixed models is a fundamental task in many applicative fields. In statistical genetics, the score test has recently become instrumental in the task of testing an association between a set of genetic markers and a phenotype. With few markers, this amounts to set-based variance component tests, which attempt to increase power in association studies by aggregating weak individual effects. When the entire genome is considered, it allows testing for the heritability of a phenotype, defined as the proportion of phenotypic variance explained by genetics. In the popular score-based Sequence Kernel Association Test (SKAT) method, the assumed distribution of the score test statistic is uncalibrated in small samples, with a correction being computationally expensive. This may cause severe inflation or deflation of P-values, even when the null hypothesis is true. Here, we characterize the conditions under which this discrepancy holds, and show it may occur also in large real datasets, such as a dataset from the Wellcome Trust Case Control Consortium 2 (n = 13,950) study, and, in particular, when the individuals in the sample are unrelated. In these cases, the SKAT approximation tends to be highly overconservative and therefore underpowered. To address this limitation, we suggest an efficient method to calculate exact P-values for the score test in the case of a single variance component and a continuous response vector, which can speed up the analysis by orders of magnitude. Our results enable fast and accurate application of the score test in heritability and in set-based association tests. Our method is available in http://github.com/cozygene/RL-SKAT.
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35
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Huang C, Thompson P, Wang Y, Yu Y, Zhang J, Kong D, Colen RR, Knickmeyer RC, Zhu H. FGWAS: Functional genome wide association analysis. Neuroimage 2017; 159:107-121. [PMID: 28735012 PMCID: PMC5984052 DOI: 10.1016/j.neuroimage.2017.07.030] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 07/12/2017] [Accepted: 07/14/2017] [Indexed: 12/11/2022] Open
Abstract
Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.
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Affiliation(s)
- Chao Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Yang Yu
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jingwen Zhang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dehan Kong
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Rivka R Colen
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rebecca C Knickmeyer
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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36
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Wigmore EM, Clarke TK, Howard DM, Adams MJ, Hall LS, Zeng Y, Gibson J, Davies G, Fernandez-Pujals AM, Thomson PA, Hayward C, Smith BH, Hocking LJ, Padmanabhan S, Deary IJ, Porteous DJ, Nicodemus KK, McIntosh AM. Do regional brain volumes and major depressive disorder share genetic architecture? A study of Generation Scotland (n=19 762), UK Biobank (n=24 048) and the English Longitudinal Study of Ageing (n=5766). Transl Psychiatry 2017; 7:e1205. [PMID: 28809859 PMCID: PMC5611720 DOI: 10.1038/tp.2017.148] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Revised: 05/08/2017] [Accepted: 06/07/2017] [Indexed: 12/23/2022] Open
Abstract
Major depressive disorder (MDD) is a heritable and highly debilitating condition. It is commonly associated with subcortical volumetric abnormalities, the most replicated of these being reduced hippocampal volume. Using the most recent published data from Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium's genome-wide association study of regional brain volume, we sought to test whether there is shared genetic architecture between seven subcortical brain volumes and intracranial volume (ICV) and MDD. We explored this using linkage disequilibrium score regression, polygenic risk scoring (PRS) techniques, Mendelian randomisation (MR) analysis and BUHMBOX. Utilising summary statistics from ENIGMA and Psychiatric Genomics Consortium, we demonstrated that hippocampal volume was positively genetically correlated with MDD (rG=0.46, P=0.02), although this did not survive multiple comparison testing. None of the other six brain regions studied were genetically correlated and amygdala volume heritability was too low for analysis. Using PRS analysis, no regional volumetric PRS demonstrated a significant association with MDD or recurrent MDD. MR analysis in hippocampal volume and MDD identified no causal association, however, BUHMBOX analysis identified genetic subgrouping in GS:SFHS MDD cases only (P=0.00281). In this study, we provide some evidence that hippocampal volume and MDD may share genetic architecture in a subgroup of individuals, albeit the genetic correlation did not survive multiple testing correction and genetic subgroup heterogeneity was not replicated. In contrast, we found no evidence to support a shared genetic architecture between MDD and other regional subcortical volumes or ICV.
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Affiliation(s)
- E M Wigmore
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK,Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh EH10 5HF, UK. E-mail:
| | - T-K Clarke
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - D M Howard
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - M J Adams
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - L S Hall
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Y Zeng
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - J Gibson
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - G Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - A M Fernandez-Pujals
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - P A Thomson
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK,Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - C Hayward
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - B H Smith
- Division of Population Health Sciences, University of Dundee, Dundee, UK
| | - L J Hocking
- Division of Applied Medicine, University of Aberdeen, Aberdeen, UK
| | - S Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - I J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - D J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - K K Nicodemus
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK,Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - A M McIntosh
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
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37
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Heritability analysis with repeat measurements and its application to resting-state functional connectivity. Proc Natl Acad Sci U S A 2017; 114:5521-5526. [PMID: 28484032 DOI: 10.1073/pnas.1700765114] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Heritability, defined as the proportion of phenotypic variation attributable to genetic variation, provides important information about the genetic basis of a trait. Existing heritability analysis methods do not discriminate between stable effects (e.g., due to the subject's unique environment) and transient effects, such as measurement error. This can lead to misleading assessments, particularly when comparing the heritability of traits that exhibit different levels of reliability. Here, we present a linear mixed effects model to conduct heritability analyses that explicitly accounts for intrasubject fluctuations (e.g., due to measurement noise or biological transients) using repeat measurements. We apply the proposed strategy to the analysis of resting-state fMRI measurements-a prototypic data modality that exhibits variable levels of test-retest reliability across space. Our results reveal that the stable components of functional connectivity within and across well-established large-scale brain networks can be considerably heritable. Furthermore, we demonstrate that dissociating intra- and intersubject variation can reveal genetic influence on a phenotype that is not fully captured by conventional heritability analyses.
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38
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Freytag V, Carrillo-Roa T, Milnik A, Sämann PG, Vukojevic V, Coynel D, Demougin P, Egli T, Gschwind L, Jessen F, Loos E, Maier W, Riedel-Heller SG, Scherer M, Vogler C, Wagner M, Binder EB, de Quervain DJF, Papassotiropoulos A. A peripheral epigenetic signature of immune system genes is linked to neocortical thickness and memory. Nat Commun 2017; 8:15193. [PMID: 28443631 PMCID: PMC5414038 DOI: 10.1038/ncomms15193] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 03/08/2017] [Indexed: 01/01/2023] Open
Abstract
Increasing age is tightly linked to decreased thickness of the human neocortex. The biological mechanisms that mediate this effect are hitherto unknown. The DNA methylome, as part of the epigenome, contributes significantly to age-related phenotypic changes. Here, we identify an epigenetic signature that is associated with cortical thickness (P=3.86 × 10−8) and memory performance in 533 healthy young adults. The epigenetic effect on cortical thickness was replicated in a sample comprising 596 participants with major depressive disorder and healthy controls. The epigenetic signature mediates partially the effect of age on cortical thickness (P<0.001). A multilocus genetic score reflecting genetic variability of this signature is associated with memory performance (P=0.0003) in 3,346 young and elderly healthy adults. The genomic location of the contributing methylation sites points to the involvement of specific immune system genes. The decomposition of blood methylome-wide patterns bears considerable potential for the study of brain-related traits. Cortical thickness has high heritability estimates and is known to be influenced by genetic factors. Here, Freytag and colleagues show that DNA methylation patterns of peripheral blood monocytes are also correlated with cortical thickness and memory performance in human.
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Affiliation(s)
- Virginie Freytag
- Division of Molecular Neuroscience, Department of Psychology, University of Basel, CH-4055 Basel, Switzerland.,Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, CH-4055 Basel, Switzerland
| | - Tania Carrillo-Roa
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, D-80804 Munich, Germany
| | - Annette Milnik
- Division of Molecular Neuroscience, Department of Psychology, University of Basel, CH-4055 Basel, Switzerland.,Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, CH-4055 Basel, Switzerland.,Psychiatric University Clinics, University of Basel, CH-4055 Basel, Switzerland
| | - Philipp G Sämann
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, D-80804 Munich, Germany
| | - Vanja Vukojevic
- Division of Molecular Neuroscience, Department of Psychology, University of Basel, CH-4055 Basel, Switzerland.,Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, CH-4055 Basel, Switzerland.,Department Biozentrum, Life Sciences Training Facility, University of Basel, CH-4056 Basel, Switzerland
| | - David Coynel
- Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, CH-4055 Basel, Switzerland.,Division of Cognitive Neuroscience, Department of Psychology, University of Basel, CH-4055 Basel, Switzerland
| | - Philippe Demougin
- Division of Molecular Neuroscience, Department of Psychology, University of Basel, CH-4055 Basel, Switzerland.,Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, CH-4055 Basel, Switzerland.,Department Biozentrum, Life Sciences Training Facility, University of Basel, CH-4056 Basel, Switzerland
| | - Tobias Egli
- Division of Molecular Neuroscience, Department of Psychology, University of Basel, CH-4055 Basel, Switzerland.,Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, CH-4055 Basel, Switzerland
| | - Leo Gschwind
- Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, CH-4055 Basel, Switzerland.,Division of Cognitive Neuroscience, Department of Psychology, University of Basel, CH-4055 Basel, Switzerland
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), D-53175 Bonn, Germany.,Department of Psychiatry, University of Cologne, Medical Faculty, D-50924 Cologne, Germany
| | - Eva Loos
- Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, CH-4055 Basel, Switzerland.,Division of Cognitive Neuroscience, Department of Psychology, University of Basel, CH-4055 Basel, Switzerland
| | - Wolfgang Maier
- German Center for Neurodegenerative Diseases (DZNE), D-53175 Bonn, Germany.,Department of Psychiatry, University of Bonn, D-53105 Bonn, Germany
| | - Steffi G Riedel-Heller
- Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, D-04103 Leipzig, Germany
| | - Martin Scherer
- Center for Psychosocial Medicine, Department of Primary Medical Care, University Medical Center Hamburg-Eppendorf, D-20246 Hamburg, Germany
| | - Christian Vogler
- Division of Molecular Neuroscience, Department of Psychology, University of Basel, CH-4055 Basel, Switzerland.,Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, CH-4055 Basel, Switzerland.,Psychiatric University Clinics, University of Basel, CH-4055 Basel, Switzerland
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), D-53175 Bonn, Germany.,Department of Psychiatry, University of Bonn, D-53105 Bonn, Germany
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, D-80804 Munich, Germany.,Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia 30322, USA
| | - Dominique J-F de Quervain
- Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, CH-4055 Basel, Switzerland.,Psychiatric University Clinics, University of Basel, CH-4055 Basel, Switzerland.,Division of Cognitive Neuroscience, Department of Psychology, University of Basel, CH-4055 Basel, Switzerland
| | - Andreas Papassotiropoulos
- Division of Molecular Neuroscience, Department of Psychology, University of Basel, CH-4055 Basel, Switzerland.,Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, CH-4055 Basel, Switzerland.,Psychiatric University Clinics, University of Basel, CH-4055 Basel, Switzerland.,Department Biozentrum, Life Sciences Training Facility, University of Basel, CH-4056 Basel, Switzerland
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Ge T, Chen CY, Neale BM, Sabuncu MR, Smoller JW. Phenome-wide heritability analysis of the UK Biobank. PLoS Genet 2017; 13:e1006711. [PMID: 28388634 PMCID: PMC5400281 DOI: 10.1371/journal.pgen.1006711] [Citation(s) in RCA: 144] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 04/21/2017] [Accepted: 03/22/2017] [Indexed: 11/18/2022] Open
Abstract
Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient heritability estimation method that can handle large sample sizes, and report the SNP heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting heritability. Heritability of a trait refers to the proportion of phenotypic variation that is due to genetic variation among individuals. It provides important information about the genetic basis of complex traits and indicates whether a phenotype is an appropriate target for more specific statistical and molecular genetic analyses. Recent studies have leveraged the increasingly ubiquitous genome-wide data and documented the heritability attributable to common genetic variation captured by genotyping microarrays for a wide range of human traits. However, heritability is not a fixed property of a phenotype and can vary with population-specific differences in the genetic background and environmental variation. Here, using a computationally and memory efficient heritability estimation method, we report the heritability for a large number of traits derived from the large-scale, population-based UK Biobank, and, for the first time, demonstrate the moderating effect of three major demographic variables (age, sex and socioeconomic status) on heritability estimates derived from genome-wide common genetic variation. Our study represents the first comprehensive heritability analysis across the phenotypic spectrum in the UK Biobank.
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Affiliation(s)
- Tian Ge
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital / Harvard Medical School, Charlestown, MA, United States of America
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
- * E-mail: (TG); (JWS)
| | - Chia-Yen Chen
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America
| | - Benjamin M. Neale
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America
| | - Mert R. Sabuncu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital / Harvard Medical School, Charlestown, MA, United States of America
- School of Electrical and Computer Engineering and Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States of America
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
- * E-mail: (TG); (JWS)
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40
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Romme IAC, de Reus MA, Ophoff RA, Kahn RS, van den Heuvel MP. Connectome Disconnectivity and Cortical Gene Expression in Patients With Schizophrenia. Biol Psychiatry 2017; 81:495-502. [PMID: 27720199 DOI: 10.1016/j.biopsych.2016.07.012] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Revised: 07/16/2016] [Accepted: 07/18/2016] [Indexed: 12/13/2022]
Abstract
BACKGROUND Genome-wide association studies have identified several common risk loci for schizophrenia (SCZ). In parallel, neuroimaging studies have shown consistent findings of widespread white matter disconnectivity in patients with SCZ. METHODS We examined the role of genes in brain connectivity in patients with SCZ by combining transcriptional profiles of 43 SCZ risk genes identified by the recent genome-wide association study of the Schizophrenia Working Group of the Psychiatric Genomics Consortium with data on macroscale connectivity reductions in patients with SCZ. Expression profiles of 43 Psychiatric Genomics Consortium SCZ risk genes were extracted from the Allen Human Brain Atlas, and their average profile across the cortex was correlated to the pattern of cortical disconnectivity as derived from diffusion-weighted magnetic resonance imaging data of patients with SCZ (n = 48) and matched healthy controls (n = 43). RESULTS The expression profile of SCZ risk genes across cortical regions was significantly correlated with the regional macroscale disconnectivity (r = .588; p = .017). In addition, effects were found to be potentially specific to SCZ, with transcriptional profiles not related to cortical disconnectivity in patients with bipolar I disorder (diffusion-weighted magnetic resonance imaging data; 216 patients, 144 controls). Further examination of correlations across all 20,737 genes present in the Allen Human Brain Atlas showed the set of top 100 strongest correlating genes to display significant enrichment for the disorder, potentially identifying new genes involved in the pathophysiology of SCZ. CONCLUSIONS Our results suggest that under disease conditions, cortical areas with pronounced expression of risk genes implicated in SCZ form central areas for white matter disconnectivity.
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Affiliation(s)
- Ingrid A C Romme
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marcel A de Reus
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Roel A Ophoff
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands; Center for Neurobehavioral Genetics and Department of Human Genetics , University of California Los Angeles, Los Angeles, California
| | - René S Kahn
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martijn P van den Heuvel
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands.
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41
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ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide. Neuroimage 2017; 145:389-408. [PMID: 26658930 PMCID: PMC4893347 DOI: 10.1016/j.neuroimage.2015.11.057] [Citation(s) in RCA: 125] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Revised: 10/16/2015] [Accepted: 11/23/2015] [Indexed: 11/22/2022] Open
Abstract
In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) - a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date - of schizophrenia and major depression - ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens - now numbering over 30,000 MRI scans - have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants - and genetic variants in general - may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures - from tens of thousands of people - that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.
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42
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Iacono WG, Malone SM, Vrieze SI. Endophenotype best practices. Int J Psychophysiol 2017; 111:115-144. [PMID: 27473600 PMCID: PMC5219856 DOI: 10.1016/j.ijpsycho.2016.07.516] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 07/21/2016] [Accepted: 07/24/2016] [Indexed: 01/19/2023]
Abstract
This review examines the current state of electrophysiological endophenotype research and recommends best practices that are based on knowledge gleaned from the last decade of molecular genetic research with complex traits. Endophenotype research is being oversold for its potential to help discover psychopathology relevant genes using the types of small samples feasible for electrophysiological research. This is largely because the genetic architecture of endophenotypes appears to be very much like that of behavioral traits and disorders: they are complex, influenced by many variants (e.g., tens of thousands) within many genes, each contributing a very small effect. Out of over 40 electrophysiological endophenotypes covered by our review, only resting heart, a measure that has received scant advocacy as an endophenotype, emerges as an electrophysiological variable with verified associations with molecular genetic variants. To move the field forward, investigations designed to discover novel variants associated with endophenotypes will need extremely large samples best obtained by forming consortia and sharing data obtained from genome wide arrays. In addition, endophenotype research can benefit from successful molecular genetic studies of psychopathology by examining the degree to which these verified psychopathology-relevant variants are also associated with an endophenotype, and by using knowledge about the functional significance of these variants to generate new endophenotypes. Even without molecular genetic associations, endophenotypes still have value in studying the development of disorders in unaffected individuals at high genetic risk, constructing animal models, and gaining insight into neural mechanisms that are relevant to clinical disorder.
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43
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Roshchupkin GV, Gutman BA, Vernooij MW, Jahanshad N, Martin NG, Hofman A, McMahon KL, van der Lee SJ, van Duijn CM, de Zubicaray GI, Uitterlinden AG, Wright MJ, Niessen WJ, Thompson PM, Ikram MA, Adams HHH. Heritability of the shape of subcortical brain structures in the general population. Nat Commun 2016; 7:13738. [PMID: 27976715 PMCID: PMC5172387 DOI: 10.1038/ncomms13738] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 10/28/2016] [Indexed: 11/24/2022] Open
Abstract
The volumes of subcortical brain structures are highly heritable, but genetic underpinnings of their shape remain relatively obscure. Here we determine the relative contribution of genetic factors to individual variation in the shape of seven bilateral subcortical structures: the nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen and thalamus. In 3,686 unrelated individuals aged between 45 and 98 years, brain magnetic resonance imaging and genotyping was performed. The maximal heritability of shape varies from 32.7 to 53.3% across the subcortical structures. Genetic contributions to shape extend beyond influences on intracranial volume and the gross volume of the respective structure. The regional variance in heritability was related to the reliability of the measurements, but could not be accounted for by technical factors only. These findings could be replicated in an independent sample of 1,040 twins. Differences in genetic contributions within a single region reveal the value of refined brain maps to appreciate the genetic complexity of brain structures.
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Affiliation(s)
- Gennady V. Roshchupkin
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Medical Informatics, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Boris A. Gutman
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del ReyLos Angeles, California 90292, USA
| | - Meike W. Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del ReyLos Angeles, California 90292, USA
| | - Nicholas G. Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Albert Hofman
- Department of Epidemiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Katie L. McMahon
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Queensland 4072, Australia
| | | | - Cornelia M. van Duijn
- Department of Epidemiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Translational Epidemiology, Faculty Science, Leiden University, Leiden, 2333 CC, The Netherlands
| | - Greig I. de Zubicaray
- Faculty of Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland 4059, Australia
| | | | - Margaret J. Wright
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Queensland 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Wiro J. Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Medical Informatics, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Faculty of Applied Sciences, Delft University of Technology, Delft 2628 CJ, The Netherlands
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del ReyLos Angeles, California 90292, USA
| | - M. Arfan Ikram
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Neurology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Hieab H. H. Adams
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
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44
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Lee PH, Baker JT, Holmes AJ, Jahanshad N, Ge T, Jung JY, Cruz Y, Manoach DS, Hibar DP, Faskowitz J, McMahon KL, de Zubicaray GI, Martin NG, Wright MJ, Öngür D, Buckner R, Roffman J, Thompson PM, Smoller JW. Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia. Mol Psychiatry 2016; 21:1680-1689. [PMID: 27725656 PMCID: PMC5144575 DOI: 10.1038/mp.2016.164] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 07/14/2016] [Accepted: 08/11/2016] [Indexed: 01/18/2023]
Abstract
Schizophrenia is a devastating neurodevelopmental disorder with a complex genetic etiology. Widespread cortical gray matter loss has been observed in patients and prodromal samples. However, it remains unresolved whether schizophrenia-associated cortical structure variations arise due to disease etiology or secondary to the illness. Here we address this question using a partitioning-based heritability analysis of genome-wide single-nucleotide polymorphism (SNP) and neuroimaging data from 1750 healthy individuals. We find that schizophrenia-associated genetic variants explain a significantly enriched proportion of trait heritability in eight brain phenotypes (false discovery rate=10%). In particular, intracranial volume and left superior frontal gyrus thickness exhibit significant and robust associations with schizophrenia genetic risk under varying SNP selection conditions. Cross-disorder comparison suggests that the neurogenetic architecture of schizophrenia-associated brain regions is, at least in part, shared with other psychiatric disorders. Our study highlights key neuroanatomical correlates of schizophrenia genetic risk in the general population. These may provide fundamental insights into the complex pathophysiology of the illness, and a potential link to neurocognitive deficits shaping the disorder.
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Affiliation(s)
- P H Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - J T Baker
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Schizophrenia and Bipolar Disorder Program, Psychotic Disorders Division, McLean Hospital, Belmont, MA, USA
| | - A J Holmes
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - N Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - T Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
| | - J-Y Jung
- Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, CA, USA
| | - Y Cruz
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Harvard Graduate School of Education, Cambridge, MA, USA
| | - D S Manoach
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
| | - D P Hibar
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - J Faskowitz
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - K L McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - G I de Zubicaray
- Faculty of Health and Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - N G Martin
- Queensland Institute of Medical Research (QIMR) Berghofer, Brisbane, QLD, Australia
| | - M J Wright
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - D Öngür
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Schizophrenia and Bipolar Disorder Program, Psychotic Disorders Division, McLean Hospital, Belmont, MA, USA
| | - R Buckner
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - J Roffman
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Schizophrenia Clinical and Research Program, Massachusetts General Hospital, Boston, MA, USA
| | - P M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - J W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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45
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Multidimensional heritability analysis of neuroanatomical shape. Nat Commun 2016; 7:13291. [PMID: 27845344 PMCID: PMC5116071 DOI: 10.1038/ncomms13291] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 09/21/2016] [Indexed: 12/11/2022] Open
Abstract
In the dawning era of large-scale biomedical data, multidimensional phenotype vectors will play an increasing role in examining the genetic underpinnings of brain features, behaviour and disease. For example, shape measurements derived from brain MRI scans are multidimensional geometric descriptions of brain structure and provide an alternate class of phenotypes that remains largely unexplored in genetic studies. Here we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide SNP and MRI data from 1,320 unrelated, young and healthy individuals. We replicate our findings in an extended twin sample from the Human Connectome Project (HCP). Our results demonstrate that neuroanatomical shape can be significantly heritable, above and beyond volume, and can serve as a complementary phenotype to study the genetic determinants and clinical relevance of brain structure.
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46
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Cognitive specialization for verbal vs. spatial ability in men and women: Neural and behavioral correlates. PERSONALITY AND INDIVIDUAL DIFFERENCES 2016. [DOI: 10.1016/j.paid.2016.06.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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47
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Tao C, Nichols TE, Hua X, Ching CRK, Rolls ET, Thompson PM, Feng J. Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications. Neuroimage 2016; 144:35-57. [PMID: 27666385 DOI: 10.1016/j.neuroimage.2016.08.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2015] [Revised: 08/01/2016] [Accepted: 08/14/2016] [Indexed: 11/18/2022] Open
Abstract
We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches.
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Affiliation(s)
- Chenyang Tao
- Centre for Computational Systems Biology and School of Mathematical Sciences, Fudan University, Shanghai, PR China; Department of Computer Science, Warwick University, Coventry, UK
| | | | - Xue Hua
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Interdepartmental Neuroscience Graduate Program, UCLA School of Medicine, Los Angeles, CA, USA
| | - Edmund T Rolls
- Department of Computer Science, Warwick University, Coventry, UK; Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics, and Ophthalmology, USC, Los Angeles, CA, USA
| | - Jianfeng Feng
- Centre for Computational Systems Biology and School of Mathematical Sciences, Fudan University, Shanghai, PR China; Department of Computer Science, Warwick University, Coventry, UK; School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai 200433, PR China.
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48
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Morphometricity as a measure of the neuroanatomical signature of a trait. Proc Natl Acad Sci U S A 2016; 113:E5749-56. [PMID: 27613854 DOI: 10.1073/pnas.1604378113] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer's disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.
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Mormino EC, Sperling RA, Holmes AJ, Buckner RL, De Jager PL, Smoller JW, Sabuncu MR. Polygenic risk of Alzheimer disease is associated with early- and late-life processes. Neurology 2016; 87:481-8. [PMID: 27385740 DOI: 10.1212/wnl.0000000000002922] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 04/22/2016] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To examine associations between aggregate genetic risk and Alzheimer disease (AD) markers in stages preceding the clinical symptoms of dementia using data from 2 large observational cohort studies. METHODS We computed polygenic risk scores (PGRS) using summary statistics from the International Genomics of Alzheimer's Project genome-wide association study of AD. Associations between PGRS and AD markers (cognitive decline, clinical progression, hippocampus volume, and β-amyloid) were assessed within older participants with dementia. Associations between PGRS and hippocampus volume were additionally examined within healthy younger participants (age 18-35 years). RESULTS Within participants without dementia, elevated PGRS was associated with worse memory (p = 0.002) and smaller hippocampus (p = 0.002) at baseline, as well as greater longitudinal cognitive decline (memory: p = 0.0005, executive function: p = 0.01) and clinical progression (p < 0.00001). High PGRS was associated with AD-like levels of β-amyloid burden as measured with florbetapir PET (p = 0.03) but did not reach statistical significance for CSF β-amyloid (p = 0.11). Within the younger group, higher PGRS was associated with smaller hippocampus volume (p = 0.05). This pattern was evident when examining a PGRS that included many loci below the genome-wide association study (GWAS)-level significance threshold (16,123 single nucleotide polymorphisms), but not when PGRS was restricted to GWAS-level significant loci (18 single nucleotide polymorphisms). CONCLUSIONS Effects related to common genetic risk loci distributed throughout the genome are detectable among individuals without dementia. The influence of this genetic risk may begin in early life and make an individual more susceptible to cognitive impairment in late life. Future refinement of polygenic risk scores may help identify individuals at risk for AD dementia.
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Affiliation(s)
- Elizabeth C Mormino
- From the Departments of Neurology (E.C.M., R.A.S.) and Radiology (R.A.S.), Massachusetts General Hospital, Harvard Medical School, Charlestown; Center for Alzheimer Research and Treatment, Department of Neurology (R.A.S.), and Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry (P.L.D.), Brigham and Women's Hospital, Harvard Medical School (P.L.D.), Boston, MA; Department of Psychology (A.J.H.), Yale University, New Haven, CT; Department of Psychiatry (A.J.H.), Massachusetts General Hospital, Harvard Medical School, Boston; Athinoula A. Martinos Center for Biomedical Imaging (A.J.H.) and Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research (J.W.S.), Massachusetts General Hospital, Boston; Department of Psychology and Center for Brain Science (R.L.B.), Harvard University, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (R.L.B., M.R.S.), Massachusetts General Hospital, Charlestown; Program in Medical and Population Genetics (P.L.D.), Broad Institute; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard (J.W.S.); and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge.
| | - Reisa A Sperling
- From the Departments of Neurology (E.C.M., R.A.S.) and Radiology (R.A.S.), Massachusetts General Hospital, Harvard Medical School, Charlestown; Center for Alzheimer Research and Treatment, Department of Neurology (R.A.S.), and Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry (P.L.D.), Brigham and Women's Hospital, Harvard Medical School (P.L.D.), Boston, MA; Department of Psychology (A.J.H.), Yale University, New Haven, CT; Department of Psychiatry (A.J.H.), Massachusetts General Hospital, Harvard Medical School, Boston; Athinoula A. Martinos Center for Biomedical Imaging (A.J.H.) and Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research (J.W.S.), Massachusetts General Hospital, Boston; Department of Psychology and Center for Brain Science (R.L.B.), Harvard University, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (R.L.B., M.R.S.), Massachusetts General Hospital, Charlestown; Program in Medical and Population Genetics (P.L.D.), Broad Institute; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard (J.W.S.); and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge
| | - Avram J Holmes
- From the Departments of Neurology (E.C.M., R.A.S.) and Radiology (R.A.S.), Massachusetts General Hospital, Harvard Medical School, Charlestown; Center for Alzheimer Research and Treatment, Department of Neurology (R.A.S.), and Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry (P.L.D.), Brigham and Women's Hospital, Harvard Medical School (P.L.D.), Boston, MA; Department of Psychology (A.J.H.), Yale University, New Haven, CT; Department of Psychiatry (A.J.H.), Massachusetts General Hospital, Harvard Medical School, Boston; Athinoula A. Martinos Center for Biomedical Imaging (A.J.H.) and Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research (J.W.S.), Massachusetts General Hospital, Boston; Department of Psychology and Center for Brain Science (R.L.B.), Harvard University, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (R.L.B., M.R.S.), Massachusetts General Hospital, Charlestown; Program in Medical and Population Genetics (P.L.D.), Broad Institute; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard (J.W.S.); and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge
| | - Randy L Buckner
- From the Departments of Neurology (E.C.M., R.A.S.) and Radiology (R.A.S.), Massachusetts General Hospital, Harvard Medical School, Charlestown; Center for Alzheimer Research and Treatment, Department of Neurology (R.A.S.), and Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry (P.L.D.), Brigham and Women's Hospital, Harvard Medical School (P.L.D.), Boston, MA; Department of Psychology (A.J.H.), Yale University, New Haven, CT; Department of Psychiatry (A.J.H.), Massachusetts General Hospital, Harvard Medical School, Boston; Athinoula A. Martinos Center for Biomedical Imaging (A.J.H.) and Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research (J.W.S.), Massachusetts General Hospital, Boston; Department of Psychology and Center for Brain Science (R.L.B.), Harvard University, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (R.L.B., M.R.S.), Massachusetts General Hospital, Charlestown; Program in Medical and Population Genetics (P.L.D.), Broad Institute; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard (J.W.S.); and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge
| | - Philip L De Jager
- From the Departments of Neurology (E.C.M., R.A.S.) and Radiology (R.A.S.), Massachusetts General Hospital, Harvard Medical School, Charlestown; Center for Alzheimer Research and Treatment, Department of Neurology (R.A.S.), and Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry (P.L.D.), Brigham and Women's Hospital, Harvard Medical School (P.L.D.), Boston, MA; Department of Psychology (A.J.H.), Yale University, New Haven, CT; Department of Psychiatry (A.J.H.), Massachusetts General Hospital, Harvard Medical School, Boston; Athinoula A. Martinos Center for Biomedical Imaging (A.J.H.) and Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research (J.W.S.), Massachusetts General Hospital, Boston; Department of Psychology and Center for Brain Science (R.L.B.), Harvard University, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (R.L.B., M.R.S.), Massachusetts General Hospital, Charlestown; Program in Medical and Population Genetics (P.L.D.), Broad Institute; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard (J.W.S.); and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge
| | - Jordan W Smoller
- From the Departments of Neurology (E.C.M., R.A.S.) and Radiology (R.A.S.), Massachusetts General Hospital, Harvard Medical School, Charlestown; Center for Alzheimer Research and Treatment, Department of Neurology (R.A.S.), and Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry (P.L.D.), Brigham and Women's Hospital, Harvard Medical School (P.L.D.), Boston, MA; Department of Psychology (A.J.H.), Yale University, New Haven, CT; Department of Psychiatry (A.J.H.), Massachusetts General Hospital, Harvard Medical School, Boston; Athinoula A. Martinos Center for Biomedical Imaging (A.J.H.) and Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research (J.W.S.), Massachusetts General Hospital, Boston; Department of Psychology and Center for Brain Science (R.L.B.), Harvard University, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (R.L.B., M.R.S.), Massachusetts General Hospital, Charlestown; Program in Medical and Population Genetics (P.L.D.), Broad Institute; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard (J.W.S.); and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge
| | - Mert R Sabuncu
- From the Departments of Neurology (E.C.M., R.A.S.) and Radiology (R.A.S.), Massachusetts General Hospital, Harvard Medical School, Charlestown; Center for Alzheimer Research and Treatment, Department of Neurology (R.A.S.), and Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry (P.L.D.), Brigham and Women's Hospital, Harvard Medical School (P.L.D.), Boston, MA; Department of Psychology (A.J.H.), Yale University, New Haven, CT; Department of Psychiatry (A.J.H.), Massachusetts General Hospital, Harvard Medical School, Boston; Athinoula A. Martinos Center for Biomedical Imaging (A.J.H.) and Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research (J.W.S.), Massachusetts General Hospital, Boston; Department of Psychology and Center for Brain Science (R.L.B.), Harvard University, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (R.L.B., M.R.S.), Massachusetts General Hospital, Charlestown; Program in Medical and Population Genetics (P.L.D.), Broad Institute; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard (J.W.S.); and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge
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Dudbridge F. Polygenic Epidemiology. Genet Epidemiol 2016; 40:268-72. [PMID: 27061411 PMCID: PMC4982028 DOI: 10.1002/gepi.21966] [Citation(s) in RCA: 106] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Revised: 02/05/2016] [Accepted: 02/05/2016] [Indexed: 01/05/2023]
Abstract
Much of the genetic basis of complex traits is present on current genotyping products, but the individual variants that affect the traits have largely not been identified. Several traditional problems in genetic epidemiology have recently been addressed by assuming a polygenic basis for disease and treating it as a single entity. Here I briefly review some of these applications, which collectively may be termed polygenic epidemiology. Methodologies in this area include polygenic scoring, linear mixed models, and linkage disequilibrium scoring. They have been used to establish a polygenic effect, estimate genetic correlation between traits, estimate how many variants affect a trait, stratify cases into subphenotypes, predict individual disease risks, and infer causal effects using Mendelian randomization. Polygenic epidemiology will continue to yield useful applications even while much of the specific variation underlying complex traits remains undiscovered.
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Affiliation(s)
- Frank Dudbridge
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom
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