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Carey CE, Shafee R, Wedow R, Elliott A, Palmer DS, Compitello J, Kanai M, Abbott L, Schultz P, Karczewski KJ, Bryant SC, Cusick CM, Churchhouse C, Howrigan DP, King D, Davey Smith G, Neale BM, Walters RK, Robinson EB. Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation. Nat Hum Behav 2024:10.1038/s41562-024-01909-5. [PMID: 38965376 DOI: 10.1038/s41562-024-01909-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 05/14/2024] [Indexed: 07/06/2024]
Abstract
Data within biobanks capture broad yet detailed indices of human variation, but biobank-wide insights can be difficult to extract due to complexity and scale. Here, using large-scale factor analysis, we distill hundreds of variables (diagnoses, assessments and survey items) into 35 latent constructs, using data from unrelated individuals with predominantly estimated European genetic ancestry in UK Biobank. These factors recapitulate known disease classifications, disentangle elements of socioeconomic status, highlight the relevance of psychiatric constructs to health and improve measurement of pro-health behaviours. We go on to demonstrate the power of this approach to clarify genetic signal, enhance discovery and identify associations between underlying phenotypic structure and health outcomes. In building a deeper understanding of ways in which constructs such as socioeconomic status, trauma, or physical activity are structured in the dataset, we emphasize the importance of considering the interwoven nature of the human phenome when evaluating public health patterns.
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Affiliation(s)
- Caitlin E Carey
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
| | - Rebecca Shafee
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, USA
| | - Robbee Wedow
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Sociology, Purdue University, West Lafayette, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- AnalytiXIN, Indianapolis, IN, USA
- Center on Aging and the Life Course, Purdue University, West Lafayette, IN, USA
- Department of Statistics, Purdue University, West Lafayette, IN, USA
| | - Amanda Elliott
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Duncan S Palmer
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Nuffield Department of Population Health, Medical Sciences Division University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - John Compitello
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Masahiro Kanai
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Liam Abbott
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick Schultz
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Konrad J Karczewski
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Samuel C Bryant
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Caroline M Cusick
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Claire Churchhouse
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel P Howrigan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel King
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - George Davey Smith
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Benjamin M Neale
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Raymond K Walters
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Elise B Robinson
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
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2
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Wu Q, Zhang Y, Huang X, Ma T, Hong LE, Kochunov P, Chen S. A multivariate to multivariate approach for voxel-wise genome-wide association analysis. Stat Med 2024. [PMID: 38922949 DOI: 10.1002/sim.10101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 03/02/2024] [Accepted: 04/24/2024] [Indexed: 06/28/2024]
Abstract
The joint analysis of imaging-genetics data facilitates the systematic investigation of genetic effects on brain structures and functions with spatial specificity. We focus on voxel-wise genome-wide association analysis, which may involve trillions of single nucleotide polymorphism (SNP)-voxel pairs. We attempt to identify underlying organized association patterns of SNP-voxel pairs and understand the polygenic and pleiotropic networks on brain imaging traits. We propose a bi-clique graph structure (ie, a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern. Next, we develop computational strategies to detect latent SNP-voxel bi-cliques and an inference model for statistical testing. We further provide theoretical results to guarantee the accuracy of our computational algorithms and statistical inference. We validate our method by extensive simulation studies, and then apply it to the whole genome genetic and voxel-level white matter integrity data collected from 1052 participants of the human connectome project. The results demonstrate multiple genetic loci influencing white matter integrity measures on splenium and genu of the corpus callosum.
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Affiliation(s)
- Qiong Wu
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yuan Zhang
- Department of Statistics, Ohio State University, Columbus, Ohio, USA
| | - Xiaoqi Huang
- Department of Mathematics, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, USA
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - L Elliot Hong
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Peter Kochunov
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland, Baltimore, Maryland, USA
- The University of Maryland Institute for Health Computing, University of Maryland, North Bethesda, USA
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3
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Yang X, Sullivan PF, Li B, Fan Z, Ding D, Shu J, Guo Y, Paschou P, Bao J, Shen L, Ritchie MD, Nave G, Platt ML, Li T, Zhu H, Zhao B. Multi-organ imaging-derived polygenic indexes for brain and body health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.04.18.23288769. [PMID: 38883759 PMCID: PMC11177904 DOI: 10.1101/2023.04.18.23288769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.
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Affiliation(s)
- Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Patrick F. Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxuan Li
- UCLA Samueli School of Engineering, Los Angeles, CA 90095, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dezheng Ding
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yuxin Guo
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marylyn D. Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Gideon Nave
- Marketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael L. Platt
- Marketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
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4
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Deng Q, Song C, Lin S. An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics. Eur J Hum Genet 2024; 32:681-690. [PMID: 37237036 PMCID: PMC11153499 DOI: 10.1038/s41431-023-01389-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 05/01/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. In contrast to individual-level data, GWAS summary statistics are usually publicly available, and thus methods using only summary statistics have greater usage. Although many methods have been developed for joint analysis of multiple traits using summary statistics, there are many issues, including inconsistent performance, computational inefficiency, and numerical problems when considering lots of traits. To address these challenges, we propose a multi-trait adaptive Fisher method for summary statistics (MTAFS), a computationally efficient method with robust power performance. We applied MTAFS to two sets of brain imaging derived phenotypes (IDPs) from the UK Biobank, including a set of 58 Volumetric IDPs and a set of 212 Area IDPs. Through annotation analysis, the underlying genes of the SNPs identified by MTAFS were found to exhibit higher expression and are significantly enriched in brain-related tissues. Together with results from a simulation study, MTAFS shows its advantage over existing multi-trait methods, with robust performance across a range of underlying settings. It controls type 1 error well and can efficiently handle a large number of traits.
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Affiliation(s)
- Qiaolan Deng
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
- Department of Statistics, College of Arts and Sciences, The Ohio State University, Columbus, OH, USA
| | - Chi Song
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Shili Lin
- Department of Statistics, College of Arts and Sciences, The Ohio State University, Columbus, OH, USA.
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5
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Le Grand Q, Tsuchida A, Koch A, Imtiaz MA, Aziz NA, Vigneron C, Zago L, Lathrop M, Dubrac A, Couffinhal T, Crivello F, Matthews PM, Mishra A, Breteler MMB, Tzourio C, Debette S. Diffusion imaging genomics provides novel insight into early mechanisms of cerebral small vessel disease. Mol Psychiatry 2024:10.1038/s41380-024-02604-7. [PMID: 38811690 DOI: 10.1038/s41380-024-02604-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 05/06/2024] [Accepted: 05/09/2024] [Indexed: 05/31/2024]
Abstract
Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Genetic risk loci for white matter hyperintensities (WMH), the most common MRI-marker of cSVD in older age, were recently shown to be significantly associated with white matter (WM) microstructure on diffusion tensor imaging (signal-based) in young adults. To provide new insights into these early changes in WM microstructure and their relation with cSVD, we sought to explore the genetic underpinnings of cutting-edge tissue-based diffusion imaging markers across the adult lifespan. We conducted a genome-wide association study of neurite orientation dispersion and density imaging (NODDI) markers in young adults (i-Share study: N = 1 758, (mean[range]) 22.1[18-35] years), with follow-up in young middle-aged (Rhineland Study: N = 714, 35.2[30-40] years) and late middle-aged to older individuals (UK Biobank: N = 33 224, 64.3[45-82] years). We identified 21 loci associated with NODDI markers across brain regions in young adults. The most robust association, replicated in both follow-up cohorts, was with Neurite Density Index (NDI) at chr5q14.3, a known WMH locus in VCAN. Two additional loci were replicated in UK Biobank, at chr17q21.2 with NDI, and chr19q13.12 with Orientation Dispersion Index (ODI). Transcriptome-wide association studies showed associations of STAT3 expression in arterial and adipose tissue (chr17q21.2) with NDI, and of several genes at chr19q13.12 with ODI. Genetic susceptibility to larger WMH volume, but not to vascular risk factors, was significantly associated with decreased NDI in young adults, especially in regions known to harbor WMH in older age. Individually, seven of 25 known WMH risk loci were associated with NDI in young adults. In conclusion, we identified multiple novel genetic risk loci associated with NODDI markers, particularly NDI, in early adulthood. These point to possible early-life mechanisms underlying cSVD and to processes involving remyelination, neurodevelopment and neurodegeneration, with a potential for novel approaches to prevention.
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Affiliation(s)
- Quentin Le Grand
- University of Bordeaux, INSERM, Bordeaux Population Health research center, UMR1219, F-33000, Bordeaux, France
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Ami Tsuchida
- University of Bordeaux, INSERM, Bordeaux Population Health research center, UMR1219, F-33000, Bordeaux, France
- University of Bordeaux, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
- CNRS, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
- CEA, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
| | - Alexandra Koch
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Mohammed-Aslam Imtiaz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - N Ahmad Aziz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Chloé Vigneron
- University of Bordeaux, INSERM, Bordeaux Population Health research center, UMR1219, F-33000, Bordeaux, France
| | - Laure Zago
- University of Bordeaux, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
- CNRS, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
- CEA, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
| | - Mark Lathrop
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada; Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, QC, H3A 0G1, Canada
| | - Alexandre Dubrac
- Centre de Recherche, CHU Sainte-Justine, Montréal, QC, Canada
- Département de Pathologie et Biologie Cellulaire, Université de Montréal, Montréal, QC, Canada
- Département d'Ophtalmologie, Université de Montréal, Montréal, QC, Canada
| | - Thierry Couffinhal
- University of Bordeaux, INSERM, Biologie des maladies cardiovasculaires, U1034, F-33600, Pessac, France
| | - Fabrice Crivello
- University of Bordeaux, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
- CNRS, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
- CEA, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
| | - Paul M Matthews
- UK Dementia Research Institute and Department of Brain Sciences, Imperial College, London, UK
| | - Aniket Mishra
- University of Bordeaux, INSERM, Bordeaux Population Health research center, UMR1219, F-33000, Bordeaux, France
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Christophe Tzourio
- University of Bordeaux, INSERM, Bordeaux Population Health research center, UMR1219, F-33000, Bordeaux, France
- Bordeaux University Hospital, Department of Medical Informatics, F-33000, Bordeaux, France
| | - Stéphanie Debette
- University of Bordeaux, INSERM, Bordeaux Population Health research center, UMR1219, F-33000, Bordeaux, France.
- Bordeaux University Hospital, Department of Neurology, Institute for Neurodegenerative Diseases, F-33000, Bordeaux, France.
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6
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Ma W, Geng Y, Liu Y, Pan H, Wang Q, Zhang Y, Wang L. The mechanisms of white matter injury and immune system crosstalk in promoting the progression of Parkinson's disease: a narrative review. Front Aging Neurosci 2024; 16:1345918. [PMID: 38863783 PMCID: PMC11165104 DOI: 10.3389/fnagi.2024.1345918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 05/15/2024] [Indexed: 06/13/2024] Open
Abstract
Parkinson's disease (PD) is neurodegenerative disease in middle-aged and elderly people with some pathological mechanisms including immune disorder, neuroinflammation, white matter injury and abnormal aggregation of alpha-synuclein, etc. New research suggests that white matter injury may be important in the development of PD, but how inflammation, the immune system, and white matter damage interact to harm dopamine neurons is not yet understood. Therefore, it is particularly important to delve into the crosstalk between immune cells in the central and peripheral nervous system based on the study of white matter damage in PD. This crosstalk could not only exacerbate the pathological process of PD but may also reveal new therapeutic targets. By understanding how immune cells penetrate through the blood-brain barrier and activate inflammatory responses within the central nervous system, we can better grasp the impact of structural destruction of white matter in PD and explore how this process can be modulated to mitigate or combat disease progression. Microglia, astrocytes, oligodendrocytes and peripheral immune cells (especially T cells) play a central role in its pathological process where these immune cells produce and respond to pro-inflammatory cytokines such as tumor necrosis factor (TNF-α), interleukin-1β(IL-1β) and interleukin-6(IL-6), and white matter injury causes microglia to become pro-inflammatory and release inflammatory mediators, which attract more immune cells to the damaged area, increasing the inflammatory response. Moreover, white matter damage also causes dysfunction of blood-brain barrier, allows peripheral immune cells and inflammatory factors to invade the brain further, and enhances microglia activation forming a vicious circle that intensifies neuroinflammation. And these factors collectively promote the neuroinflammatory environment and neurodegeneration changes of PD. Overall, these findings not only deepen our understanding of the complexity of PD, but also provide new targets for the development of therapeutic strategies focused on inflammation and immune regulation mechanisms. In summary, this review provided the theoretical basis for clarifying the pathogenesis of PD, summarized the association between white matter damage and the immune cells in the central and peripheral nervous systems, and then emphasized their potential specific mechanisms of achieving crosstalk with further aggravating the pathological process of PD.
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Affiliation(s)
- Wen Ma
- Graduate School of Education, Shandong Sport University, Jinan, Shandong, China
| | - Yifan Geng
- Xuzhou Clinical School, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Youhan Liu
- Graduate School of Education, Shandong Sport University, Jinan, Shandong, China
| | - Huixin Pan
- Graduate School of Education, Shandong Sport University, Jinan, Shandong, China
| | - Qinglu Wang
- Graduate School of Education, Shandong Sport University, Jinan, Shandong, China
| | - Yaohua Zhang
- Key Laboratory of Biomedical Engineering & Technology of Shandong High School, Qilu Medical University, Zibo, China
| | - Liping Wang
- Graduate School of Education, Shandong Sport University, Jinan, Shandong, China
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7
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Joo YY, Lee E, Kim BG, Kim G, Seo J, Cha J. Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595444. [PMID: 38826224 PMCID: PMC11142157 DOI: 10.1101/2024.05.22.595444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The human brain undergoes structural and functional changes during childhood, a critical period in cognitive and behavioral development. Understanding the genetic architecture of the brain development in children can offer valuable insights into the development of the brain, cognition, and behaviors. Here, we integrated brain imaging-genetic-phenotype data from over 8,600 preadolescent children of diverse ethnic backgrounds using multivariate statistical techniques. We found a low-to-moderate level of SNP-based heritability in most IDPs, which is lower compared to the adult brain. Using sparse generalized canonical correlation analysis (SGCCA), we identified several covariation patterns among genome-wide polygenic scores (GPSs) of 29 traits, 7 different modalities of brain imaging-derived phenotypes (IDPs), and 266 cognitive and psychological phenotype data. In structural MRI, significant positive associations were observed between total grey matter volume, left ventral diencephalon volume, surface area of right accumbens and the GPSs of cognition-related traits. Conversely, negative associations were found with the GPSs of ADHD, depression and neuroticism. Additionally, we identified a significant positive association between educational attainment GPS and regional brain activation during the N-back task. The BMI GPS showed a positive association with fractional anisotropy (FA) of connectivity between the cerebellum cortex and amygdala in diffusion MRI, while the GPSs for educational attainment and cannabis use were negatively associated with the same IDPs. Our GPS-based prediction models revealed substantial genetic contributions to cognitive variability, while the genetic basis for many mental and behavioral phenotypes remained elusive. This study delivers a comprehensive map of the relationships between genetic profiles, neuroanatomical diversity, and the spectrum of cognitive and behavioral traits in preadolescence.
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Affiliation(s)
- Yoonjung Yoonie Joo
- Department of Psychology, Seoul National University
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea
- Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea
| | - Eunji Lee
- Department of Psychology, Seoul National University
| | - Bo-Gyeom Kim
- Department of Psychology, Seoul National University
| | - Gakyung Kim
- Department of Brain and Cognitive Sciences, Seoul National University
| | - Jungwoo Seo
- Department of Brain and Cognitive Sciences, Seoul National University
| | - Jiook Cha
- Department of Psychology, Seoul National University
- Department of Brain and Cognitive Sciences, Seoul National University
- Institute of Psychological Science, Seoul National University, Seoul, South Korea
- Graduate School of Artificial Intelligence, Seoul National University, Seoul, South Korea
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Skampardoni I, Nasrallah IM, Abdulkadir A, Wen J, Melhem R, Mamourian E, Erus G, Doshi J, Singh A, Yang Z, Cui Y, Hwang G, Ren Z, Pomponio R, Srinivasan D, Govindarajan ST, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Yaffe K, Völzke H, Ferrucci L, Benzinger TL, Ezzati A, Shinohara RT, Fan Y, Resnick SM, Habes M, Wolk D, Shou H, Nikita K, Davatzikos C. Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals. JAMA Psychiatry 2024; 81:456-467. [PMID: 38353984 PMCID: PMC10867779 DOI: 10.1001/jamapsychiatry.2023.5599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 11/29/2023] [Indexed: 02/17/2024]
Abstract
Importance Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures Individuals WODCI at baseline scan. Main Outcomes and Measures Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. Results In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.
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Affiliation(s)
- Ioanna Skampardoni
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Ilya M. Nasrallah
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Ahmed Abdulkadir
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Junhao Wen
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Randa Melhem
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Elizabeth Mamourian
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Guray Erus
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Jimit Doshi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Ashish Singh
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zhijian Yang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Yuhan Cui
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Gyujoon Hwang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zheng Ren
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Raymond Pomponio
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Dhivya Srinivasan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | | | - Paraskevi Parmpi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Thomas R. Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St Louis, St Louis, Missouri
| | - Mark A. Espeland
- Sticht Centre for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison
| | - John C. Morris
- Knight Alzheimer Disease Research Centre, Washington University in St Louis, St Louis, Missouri
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Ali Ezzati
- Department of Neurology, University of California, Irvine
| | - Russell T. Shinohara
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Yong Fan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Mohamad Habes
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - David Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - Haochang Shou
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Konstantina Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Christos Davatzikos
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
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9
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Patel K, Xie Z, Yuan H, Islam SMS, Xie Y, He W, Zhang W, Gottlieb A, Chen H, Giancardo L, Knaack A, Fletcher E, Fornage M, Ji S, Zhi D. Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging. Commun Biol 2024; 7:414. [PMID: 38580839 PMCID: PMC10997628 DOI: 10.1038/s42003-024-06096-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/22/2024] [Indexed: 04/07/2024] Open
Abstract
Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.
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Affiliation(s)
- Khush Patel
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Ziqian Xie
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Hao Yuan
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | | | - Yaochen Xie
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Wei He
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Wanheng Zhang
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Assaf Gottlieb
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Han Chen
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Luca Giancardo
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Alexander Knaack
- Department of Neurology and Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, 95618, USA
| | - Evan Fletcher
- Department of Neurology and Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, 95618, USA
| | - Myriam Fornage
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
- McGovern Medical School, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Shuiwang Ji
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Degui Zhi
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
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10
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Wainberg M, Forde NJ, Mansour S, Kerrebijn I, Medland SE, Hawco C, Tripathy SJ. Genetic architecture of the structural connectome. Nat Commun 2024; 15:1962. [PMID: 38438384 PMCID: PMC10912129 DOI: 10.1038/s41467-024-46023-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/12/2024] [Indexed: 03/06/2024] Open
Abstract
Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.
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Affiliation(s)
- Michael Wainberg
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
| | - Natalie J Forde
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Salim Mansour
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Isabel Kerrebijn
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Colin Hawco
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | - Shreejoy J Tripathy
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
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11
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Goltermann O, Alagöz G, Molz B, Fisher SE. Neuroimaging genomics as a window into the evolution of human sulcal organization. Cereb Cortex 2024; 34:bhae078. [PMID: 38466113 PMCID: PMC10926775 DOI: 10.1093/cercor/bhae078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/03/2024] [Accepted: 02/04/2024] [Indexed: 03/12/2024] Open
Abstract
Primate brain evolution has involved prominent expansions of the cerebral cortex, with largest effects observed in the human lineage. Such expansions were accompanied by fine-grained anatomical alterations, including increased cortical folding. However, the molecular bases of evolutionary alterations in human sulcal organization are not yet well understood. Here, we integrated data from recently completed large-scale neuroimaging genetic analyses with annotations of the human genome relevant to various periods and events in our evolutionary history. These analyses identified single-nucleotide polymorphism (SNP) heritability enrichments in fetal brain human-gained enhancer (HGE) elements for a number of sulcal structures, including the central sulcus, which is implicated in human hand dexterity. We zeroed in on a genomic region that harbors DNA variants associated with left central sulcus shape, an HGE element, and genetic loci involved in neurogenesis including ZIC4, to illustrate the value of this approach for probing the complex factors contributing to human sulcal evolution.
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Affiliation(s)
- Ole Goltermann
- Max Planck School of Cognition, Stephanstrasse 1a, 04103 Leipzig, Germany
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands
- Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Gökberk Alagöz
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands
| | - Barbara Molz
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition & Behaviour, Radboud University, Thomas van Aquinostraat 4, 6525 GD Nijmegen, The Netherlands
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12
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Li J, Long Z, Sheng W, Du L, Qiu J, Chen H, Liao W. Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes. Biol Psychiatry 2024; 95:414-425. [PMID: 37573006 DOI: 10.1016/j.biopsych.2023.08.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied. METHODS Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes. RESULTS We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies. CONCLUSIONS Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.
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Affiliation(s)
- Jiao Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Zhiliang Long
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Wei Sheng
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Lian Du
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, P.R. China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Wei Liao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.
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13
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Aldridge CM, Braun R, Lohse K, de Havenon A, Cole JW, Cramer SC, Lindgren AG, Keene KL, Hsu FC, Worrall BB. Genome-Wide Association Studies of 3 Distinct Recovery Phenotypes in Mild Ischemic Stroke. Neurology 2024; 102:e208011. [PMID: 38181310 PMCID: PMC11023036 DOI: 10.1212/wnl.0000000000208011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 09/27/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Stroke genetic research has made substantial progress in the past decade. Its recovery application, however, remains behind, in part due to its reliance on the modified Rankin Scale (mRS) score as a measure of poststroke outcome. The mRS does not map well to biological processes because numerous psychosocial factors drive much of what the mRS captures. Second, the mRS contains multiple disparate biological events into a single measure further limiting its use for biological discovery. This led us to investigate the effect of distinct stroke recovery phenotypes on genetic variation associations with Genome-Wide Association Studies (GWASs) by repurposing the NIH Stroke Scale (NIHSS) and its subscores. METHODS In the Vitamin Intervention for Stroke Prevention cohort, we estimated changes in cognition, motor, and global impairments over 2 years using specific measures. We included genotyped participants with a total NIHSS score greater than zero at randomization and excluded those with recurrent stroke during the trial. A GWAS linear mixed-effects model predicted score changes, with participant as a random effect, and included initial score, age, sex, treatment group, and the first 5 ancestry principal components. RESULTS In total, 1,270 participants (64% male) were included with a median NIHSS score of 2 (interquartile range [IQR] 1-3) and median age 68 (IQR 59-75) years. At randomization, 20% had cognitive deficits (NIHSS Cog-4 score >0) and 70% had ≥1 motor deficits (impairment score >1). At 2 years, these percentages improved to 7.2% with cognitive deficits and 30% with motor deficits. GWAS identified novel suggestive gene-impairment associations (p < 5e-6) for cognition (CAMK2D, EVX2, LINC0143, PTPRM, SGMS1, and SMAD2), motor (ACBD6, KDM4B, MARK4, PTPRS, ROBO1, and ROBO2), and global (MSR1 and ROBO2) impairments. DISCUSSION Defining domain-specific stroke recovery phenotypes and using longitudinal clinical trial designs can help detect novel genes associated with chronic recovery. These data support the use of granular endpoints to identify genetic associations related to stroke recovery.
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Affiliation(s)
- Chad M Aldridge
- From the Department of Neurology (C.M.A., B.B.W.), University of Virginia, Charlottesville; Department of Neurology (R.B., J.W.C.), University of Maryland, Baltimore; Program in Physical Therapy (K.L.), Washington University; Department of Neurology (K.L.), Washington University, St. Louis, MO; Department of Neurology (A.H.), Center for Brain and Mind Health, Yale University, New Haven, CT; Department of Neurology (S.C.C.), University of California Los Angeles; California Rehabilitation Institute (S.C.C.), Los Angeles; Department of Clinical Sciences Lund, Neurology (A.G.L.), Lund University; Department of Neurology (A.G.L.), Skane University Hospital, Sweden; Department of Public Health Sciences (K.L.K., B.B.W.); Center for Health Equity and Precision Public Health (K.L.K.), University of Virginia, Charlottesville; and Department of Biostatistics (F.-C.H.), School of Medicine, Wake Forest University, Winston-Salem, NC
| | - Robynne Braun
- From the Department of Neurology (C.M.A., B.B.W.), University of Virginia, Charlottesville; Department of Neurology (R.B., J.W.C.), University of Maryland, Baltimore; Program in Physical Therapy (K.L.), Washington University; Department of Neurology (K.L.), Washington University, St. Louis, MO; Department of Neurology (A.H.), Center for Brain and Mind Health, Yale University, New Haven, CT; Department of Neurology (S.C.C.), University of California Los Angeles; California Rehabilitation Institute (S.C.C.), Los Angeles; Department of Clinical Sciences Lund, Neurology (A.G.L.), Lund University; Department of Neurology (A.G.L.), Skane University Hospital, Sweden; Department of Public Health Sciences (K.L.K., B.B.W.); Center for Health Equity and Precision Public Health (K.L.K.), University of Virginia, Charlottesville; and Department of Biostatistics (F.-C.H.), School of Medicine, Wake Forest University, Winston-Salem, NC
| | - Keith Lohse
- From the Department of Neurology (C.M.A., B.B.W.), University of Virginia, Charlottesville; Department of Neurology (R.B., J.W.C.), University of Maryland, Baltimore; Program in Physical Therapy (K.L.), Washington University; Department of Neurology (K.L.), Washington University, St. Louis, MO; Department of Neurology (A.H.), Center for Brain and Mind Health, Yale University, New Haven, CT; Department of Neurology (S.C.C.), University of California Los Angeles; California Rehabilitation Institute (S.C.C.), Los Angeles; Department of Clinical Sciences Lund, Neurology (A.G.L.), Lund University; Department of Neurology (A.G.L.), Skane University Hospital, Sweden; Department of Public Health Sciences (K.L.K., B.B.W.); Center for Health Equity and Precision Public Health (K.L.K.), University of Virginia, Charlottesville; and Department of Biostatistics (F.-C.H.), School of Medicine, Wake Forest University, Winston-Salem, NC
| | - Adam de Havenon
- From the Department of Neurology (C.M.A., B.B.W.), University of Virginia, Charlottesville; Department of Neurology (R.B., J.W.C.), University of Maryland, Baltimore; Program in Physical Therapy (K.L.), Washington University; Department of Neurology (K.L.), Washington University, St. Louis, MO; Department of Neurology (A.H.), Center for Brain and Mind Health, Yale University, New Haven, CT; Department of Neurology (S.C.C.), University of California Los Angeles; California Rehabilitation Institute (S.C.C.), Los Angeles; Department of Clinical Sciences Lund, Neurology (A.G.L.), Lund University; Department of Neurology (A.G.L.), Skane University Hospital, Sweden; Department of Public Health Sciences (K.L.K., B.B.W.); Center for Health Equity and Precision Public Health (K.L.K.), University of Virginia, Charlottesville; and Department of Biostatistics (F.-C.H.), School of Medicine, Wake Forest University, Winston-Salem, NC
| | - John W Cole
- From the Department of Neurology (C.M.A., B.B.W.), University of Virginia, Charlottesville; Department of Neurology (R.B., J.W.C.), University of Maryland, Baltimore; Program in Physical Therapy (K.L.), Washington University; Department of Neurology (K.L.), Washington University, St. Louis, MO; Department of Neurology (A.H.), Center for Brain and Mind Health, Yale University, New Haven, CT; Department of Neurology (S.C.C.), University of California Los Angeles; California Rehabilitation Institute (S.C.C.), Los Angeles; Department of Clinical Sciences Lund, Neurology (A.G.L.), Lund University; Department of Neurology (A.G.L.), Skane University Hospital, Sweden; Department of Public Health Sciences (K.L.K., B.B.W.); Center for Health Equity and Precision Public Health (K.L.K.), University of Virginia, Charlottesville; and Department of Biostatistics (F.-C.H.), School of Medicine, Wake Forest University, Winston-Salem, NC
| | - Steven C Cramer
- From the Department of Neurology (C.M.A., B.B.W.), University of Virginia, Charlottesville; Department of Neurology (R.B., J.W.C.), University of Maryland, Baltimore; Program in Physical Therapy (K.L.), Washington University; Department of Neurology (K.L.), Washington University, St. Louis, MO; Department of Neurology (A.H.), Center for Brain and Mind Health, Yale University, New Haven, CT; Department of Neurology (S.C.C.), University of California Los Angeles; California Rehabilitation Institute (S.C.C.), Los Angeles; Department of Clinical Sciences Lund, Neurology (A.G.L.), Lund University; Department of Neurology (A.G.L.), Skane University Hospital, Sweden; Department of Public Health Sciences (K.L.K., B.B.W.); Center for Health Equity and Precision Public Health (K.L.K.), University of Virginia, Charlottesville; and Department of Biostatistics (F.-C.H.), School of Medicine, Wake Forest University, Winston-Salem, NC
| | - Arne G Lindgren
- From the Department of Neurology (C.M.A., B.B.W.), University of Virginia, Charlottesville; Department of Neurology (R.B., J.W.C.), University of Maryland, Baltimore; Program in Physical Therapy (K.L.), Washington University; Department of Neurology (K.L.), Washington University, St. Louis, MO; Department of Neurology (A.H.), Center for Brain and Mind Health, Yale University, New Haven, CT; Department of Neurology (S.C.C.), University of California Los Angeles; California Rehabilitation Institute (S.C.C.), Los Angeles; Department of Clinical Sciences Lund, Neurology (A.G.L.), Lund University; Department of Neurology (A.G.L.), Skane University Hospital, Sweden; Department of Public Health Sciences (K.L.K., B.B.W.); Center for Health Equity and Precision Public Health (K.L.K.), University of Virginia, Charlottesville; and Department of Biostatistics (F.-C.H.), School of Medicine, Wake Forest University, Winston-Salem, NC
| | - Keith L Keene
- From the Department of Neurology (C.M.A., B.B.W.), University of Virginia, Charlottesville; Department of Neurology (R.B., J.W.C.), University of Maryland, Baltimore; Program in Physical Therapy (K.L.), Washington University; Department of Neurology (K.L.), Washington University, St. Louis, MO; Department of Neurology (A.H.), Center for Brain and Mind Health, Yale University, New Haven, CT; Department of Neurology (S.C.C.), University of California Los Angeles; California Rehabilitation Institute (S.C.C.), Los Angeles; Department of Clinical Sciences Lund, Neurology (A.G.L.), Lund University; Department of Neurology (A.G.L.), Skane University Hospital, Sweden; Department of Public Health Sciences (K.L.K., B.B.W.); Center for Health Equity and Precision Public Health (K.L.K.), University of Virginia, Charlottesville; and Department of Biostatistics (F.-C.H.), School of Medicine, Wake Forest University, Winston-Salem, NC
| | - Fang-Chi Hsu
- From the Department of Neurology (C.M.A., B.B.W.), University of Virginia, Charlottesville; Department of Neurology (R.B., J.W.C.), University of Maryland, Baltimore; Program in Physical Therapy (K.L.), Washington University; Department of Neurology (K.L.), Washington University, St. Louis, MO; Department of Neurology (A.H.), Center for Brain and Mind Health, Yale University, New Haven, CT; Department of Neurology (S.C.C.), University of California Los Angeles; California Rehabilitation Institute (S.C.C.), Los Angeles; Department of Clinical Sciences Lund, Neurology (A.G.L.), Lund University; Department of Neurology (A.G.L.), Skane University Hospital, Sweden; Department of Public Health Sciences (K.L.K., B.B.W.); Center for Health Equity and Precision Public Health (K.L.K.), University of Virginia, Charlottesville; and Department of Biostatistics (F.-C.H.), School of Medicine, Wake Forest University, Winston-Salem, NC
| | - Bradford B Worrall
- From the Department of Neurology (C.M.A., B.B.W.), University of Virginia, Charlottesville; Department of Neurology (R.B., J.W.C.), University of Maryland, Baltimore; Program in Physical Therapy (K.L.), Washington University; Department of Neurology (K.L.), Washington University, St. Louis, MO; Department of Neurology (A.H.), Center for Brain and Mind Health, Yale University, New Haven, CT; Department of Neurology (S.C.C.), University of California Los Angeles; California Rehabilitation Institute (S.C.C.), Los Angeles; Department of Clinical Sciences Lund, Neurology (A.G.L.), Lund University; Department of Neurology (A.G.L.), Skane University Hospital, Sweden; Department of Public Health Sciences (K.L.K., B.B.W.); Center for Health Equity and Precision Public Health (K.L.K.), University of Virginia, Charlottesville; and Department of Biostatistics (F.-C.H.), School of Medicine, Wake Forest University, Winston-Salem, NC
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14
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Wang S, Li T, Zhao B, Dai W, Yao Y, Li C, Li T, Zhu H, Zhang H. Identification and validation of supervariants reveal novel loci associated with human white matter microstructure. Genome Res 2024; 34:20-33. [PMID: 38190638 PMCID: PMC10904010 DOI: 10.1101/gr.277905.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 12/05/2023] [Indexed: 01/10/2024]
Abstract
As an essential part of the central nervous system, white matter coordinates communications between different brain regions and is related to a wide range of neurodegenerative and neuropsychiatric disorders. Previous genome-wide association studies (GWASs) have uncovered loci associated with white matter microstructure. However, GWASs suffer from limited reproducibility and difficulties in detecting multi-single-nucleotide polymorphism (multi-SNP) and epistatic effects. In this study, we adopt the concept of supervariants, a combination of alleles in multiple loci, to account for potential multi-SNP effects. We perform supervariant identification and validation to identify loci associated with 22 white matter fractional anisotropy phenotypes derived from diffusion tensor imaging. To increase reproducibility, we use United Kingdom (UK) Biobank White British (n = 30,842) data for discovery and internal validation, and UK Biobank White but non-British (n = 1927) data, Europeans from the Adolescent Brain Cognitive Development study (n = 4399) data, and Europeans from the Human Connectome Project (n = 319) data for external validation. We identify 23 novel loci on the discovery set that have not been reported in the previous GWASs on white matter microstructure. Among them, three supervariants on genomic regions 5q35.1, 8p21.2, and 19q13.32 have P-values lower than 0.05 in the meta-analysis of the three independent validation data sets. These supervariants contain genetic variants located in genes that have been related to brain structures, cognitive functions, and neuropsychiatric diseases. Our findings provide a better understanding of the genetic architecture underlying white matter microstructure.
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Affiliation(s)
- Shiying Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Ting Li
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104-1686, USA
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Yisha Yao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Cai Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Heping Zhang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA;
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15
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Blöchl M, Schaare HL, Kumral D, Gaebler M, Nestler S, Villringer A. Vascular risk factors, white matter microstructure, and depressive symptoms: a longitudinal analysis in the UK Biobank. Psychol Med 2024; 54:125-135. [PMID: 37016768 DOI: 10.1017/s0033291723000697] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
BACKGROUND Cumulative burden from vascular risk factors (VRFs) has been associated with an increased risk of depressive symptoms in mid- and later life. It has been hypothesised that this association arises because VRFs disconnect fronto-subcortical white matter tracts involved in mood regulation, which puts older adults at higher risk of developing depressive symptoms. However, evidence for the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms from longitudinal studies is scarce. METHODS This preregistered study analysed longitudinal data from 6,964 middle-aged and older adults from the UK Biobank who participated in consecutive assessments of VRFs, brain imaging, and depressive symptoms. Using mediation modelling, we directly tested to what extend white matter microstructure mediates the longitudinal association between VRF burden and depressive symptoms. RESULTS VRF burden showed a small association with depressive symptoms at follow-up. However, there was no evidence that fractional anisotropy (FA) of white matter tracts mediated this association. Additional analyses also yielded no mediating effects using alternative operationalisations of VRF burden, mean diffusivity (MD) of single tracts, or overall average of tract-based white matter microstructure (global FA, global MD, white matter hyperintensity volume). CONCLUSIONS Our results lend no support to the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms, while highlighting the relevance of using longitudinal data to directly test pathways linking vascular and mental health.
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Affiliation(s)
- Maria Blöchl
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- International Max Planck Research School: Neuroscience of Communication: Structure, Function, and Plasticity, Leipzig, Germany
- Department of Psychology, University of Münster, Münster, Germany
| | - H Lina Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour) Research Centre Jülich, Germany
| | - Deniz Kumral
- Institute of Psychology, Neuropsychology, University of Freiburg, Freiburg, Germany
- Clinical Psychology and Psychotherapy Unit, Institute of Psychology, University of Freiburg, Freiburg, Germany
| | - Michael Gaebler
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Faculty of Philosophy, Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, MindBrainBody Institute
- Max Planck Dahlem Campus of Cognition, Berlin, Germany
| | - Steffen Nestler
- Department of Psychology, University of Münster, Münster, Germany
| | - Arno Villringer
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University Clinic Leipzig, Leipzig, Germany
- Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
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16
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Feng G, Chen R, Zhao R, Li Y, Ma L, Wang Y, Men W, Gao J, Tan S, Cheng J, He Y, Qin S, Dong Q, Tao S, Shu N. Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture. Commun Biol 2023; 6:1257. [PMID: 38087047 PMCID: PMC10716168 DOI: 10.1038/s42003-023-05647-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rui Zhao
- College of Life Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Gene Resource and Molecular Development, Beijing, China
| | - Yuanyuan Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jiahong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
- BABRI Centre, Beijing Normal University, Beijing, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
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17
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Yang A, Yang YT, Zhao XM. An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset. PLoS Genet 2023; 19:e1011112. [PMID: 38150468 PMCID: PMC10775988 DOI: 10.1371/journal.pgen.1011112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 01/09/2024] [Accepted: 12/12/2023] [Indexed: 12/29/2023] Open
Abstract
Mendelian randomization (MR) is an effective approach for revealing causal risk factors that underpin complex traits and diseases. While MR has been more widely applied under two-sample settings, it is more promising to be used in one single large cohort given the rise of biobank-scale datasets that simultaneously contain genotype data, brain imaging data, and matched complex traits from the same individual. However, most existing multivariable MR methods have been developed for two-sample setting or a small number of exposures. In this study, we introduce a one-sample multivariable MR method based on partial least squares and Lasso regression (MR-PL). MR-PL is capable of considering the correlation among exposures (e.g., brain imaging features) when the number of exposures is extremely upscaled, while also correcting for winner's curse bias. We performed extensive and systematic simulations, and demonstrated the robustness and reliability of our method. Comprehensive simulations confirmed that MR-PL can generate more precise causal estimates with lower false positive rates than alternative approaches. Finally, we applied MR-PL to the datasets from UK Biobank to reveal the causal effects of 36 white matter tracts on 180 complex traits, and showed putative white matter tracts that are implicated in smoking, blood vascular function-related traits, and eating behaviors.
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Affiliation(s)
- Anyi Yang
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
| | - Yucheng T. Yang
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
| | - Xing-Ming Zhao
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, People’s Republic of China
- International Human Phenome Institutes (Shanghai), Shanghai, People’s Republic of China
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18
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Stauffer EM, Bethlehem RAI, Dorfschmidt L, Won H, Warrier V, Bullmore ET. The genetic relationships between brain structure and schizophrenia. Nat Commun 2023; 14:7820. [PMID: 38016951 PMCID: PMC10684873 DOI: 10.1038/s41467-023-43567-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023] Open
Abstract
Genetic risks for schizophrenia are theoretically mediated by genetic effects on brain structure but it has been unclear which genes are associated with both schizophrenia and cortical phenotypes. We accessed genome-wide association studies (GWAS) of schizophrenia (N = 69,369 cases; 236,642 controls), and of three magnetic resonance imaging (MRI) metrics (surface area, cortical thickness, neurite density index) measured at 180 cortical areas (N = 36,843, UK Biobank). Using Hi-C-coupled MAGMA, 61 genes were significantly associated with both schizophrenia and one or more MRI metrics. Whole genome analysis with partial least squares demonstrated significant genetic covariation between schizophrenia and area or thickness of most cortical regions. Genetic similarity between cortical areas was strongly coupled to their phenotypic covariance, and genetic covariation between schizophrenia and brain phenotypes was strongest in the hubs of structural covariance networks. Pleiotropically associated genes were enriched for neurodevelopmental processes and positionally concentrated in chromosomes 3p21, 17q21 and 11p11. Mendelian randomization analysis indicated that genetically determined variation in a posterior cingulate cortical area could be causal for schizophrenia. Parallel analyses of GWAS on bipolar disorder, Alzheimer's disease and height showed that pleiotropic association with MRI metrics was stronger for schizophrenia compared to other disorders.
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Affiliation(s)
| | - Richard A I Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Lena Dorfschmidt
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Hyejung Won
- Department of Genetics and the Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK
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19
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Parker N, Cheng W, Hindley GFL, Parekh P, Shadrin AA, Maximov II, Smeland OB, Djurovic S, Dale AM, Westlye LT, Frei O, Andreassen OA. Psychiatric disorders and brain white matter exhibit genetic overlap implicating developmental and neural cell biology. Mol Psychiatry 2023; 28:4924-4932. [PMID: 37759039 DOI: 10.1038/s41380-023-02264-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023]
Abstract
Improved understanding of the shared genetic architecture between psychiatric disorders and brain white matter may provide mechanistic insights for observed phenotypic associations. Our objective is to characterize the shared genetic architecture of bipolar disorder (BD), major depression (MD), and schizophrenia (SZ) with white matter fractional anisotropy (FA) and identify shared genetic loci to uncover biological underpinnings. We used genome-wide association study (GWAS) summary statistics for BD (n = 413,466), MD (n = 420,359), SZ (n = 320,404), and white matter FA (n = 33,292) to uncover the genetic architecture (i.e., polygenicity and discoverability) of each phenotype and their genetic overlap (i.e., genetic correlations, overlapping trait-influencing variants, and shared loci). This revealed that BD, MD, and SZ are at least 7-times more polygenic and less genetically discoverable than average FA. Even in the presence of weak genetic correlations (range = -0.05 to -0.09), average FA shared an estimated 42.5%, 43.0%, and 90.7% of trait-influencing variants as well as 12, 4, and 28 shared loci with BD, MD, and SZ, respectively. Shared variants were mapped to genes and tested for enrichment among gene-sets which implicated neurodevelopmental expression, neural cell types, myelin, and cell adhesion molecules. For BD and SZ, case vs control tract-level differences in FA associated with genetic correlations between those same tracts and the respective disorder (rBD = 0.83, p = 4.99e-7 and rSZ = 0.65, p = 5.79e-4). Genetic overlap at the tract-level was consistent with average FA results. Overall, these findings suggest a genetic basis for the involvement of brain white matter aberrations in the pathophysiology of psychiatric disorders.
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Affiliation(s)
- Nadine Parker
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Weiqiu Cheng
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F L Hindley
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Ivan I Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Olav B Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Anders M Dale
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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20
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Xue H, Xu X, Yan Z, Cheng J, Zhang L, Zhu W, Cui G, Zhang Q, Qiu S, Yao Z, Qin W, Liu F, Liang M, Fu J, Xu Q, Xu J, Xie Y, Zhang P, Li W, Wang C, Shen W, Zhang X, Xu K, Zuo XN, Ye Z, Yu Y, Xian J, Yu C. Genome-wide association study of hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation in Chinese Han population. iScience 2023; 26:108005. [PMID: 37822511 PMCID: PMC10562876 DOI: 10.1016/j.isci.2023.108005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/29/2023] [Accepted: 09/18/2023] [Indexed: 10/13/2023] Open
Abstract
Correlation between blood-oxygen-level-dependent (BOLD) and cerebral blood flow (CBF) has been used as an index of neurovascular coupling. Hippocampal BOLD-CBF correlation is associated with neurocognition, and the reduced correlation is associated with neuropsychiatric disorders. We conducted the first genome-wide association study of the hippocampal BOLD-CBF correlation in 4,832 Chinese Han subjects. The hippocampal BOLD-CBF correlation had an estimated heritability of 16.2-23.9% and showed reliable genome-wide significant association with a locus at 3q28, in which many variants have been linked to neuroimaging and cerebrospinal fluid markers of Alzheimer's disease. Gene-based association analyses showed four significant genes (GMNC, CRTC2, DENND4B, and GATAD2B) and revealed enrichment for mast cell calcium mobilization, microglial cell proliferation, and ubiquitin-related proteolysis pathways that regulate different cellular components of the neurovascular unit. This is the first unbiased identification of the association of hippocampal BOLD-CBF correlation, providing fresh insights into the genetic architecture of hippocampal neurovascular coupling.
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Affiliation(s)
- Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou 325027, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Guangbin Cui
- Functional and Molecular Imaging Key Lab of Shaanxi Province & Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an 710038, China
| | - Quan Zhang
- Department of Radiology, Characteristic Medical Center of Chinese People’s Armed Police Force, Tianjin 300162, China
| | - Shijun Qiu
- Department of Medical Imaging, the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou 510405, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin 300203, China
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jiayuan Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Peng Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Wei Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, Tianjin 300192, China
| | - Xiaochu Zhang
- Division of Life Science and Medicine, University of Science & Technology of China, Hefei 230027, China
| | - Kai Xu
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, China
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center at IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
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21
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Anwar MY, Graff M, Highland HM, Smit R, Wang Z, Buchanan VL, Young KL, Kenny EE, Fernandez-Rhodes L, Liu S, Assimes T, Garcia DO, Daeeun K, Gignoux CR, Justice AE, Haiman CA, Buyske S, Peters U, Loos RJF, Kooperberg C, North KE. Assessing efficiency of fine-mapping obesity-associated variants through leveraging ancestry architecture and functional annotation using PAGE and UKBB cohorts. Hum Genet 2023; 142:1477-1489. [PMID: 37658231 DOI: 10.1007/s00439-023-02593-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 08/10/2023] [Indexed: 09/03/2023]
Abstract
Inadequate representation of non-European ancestry populations in genome-wide association studies (GWAS) has limited opportunities to isolate functional variants. Fine-mapping in multi-ancestry populations should improve the efficiency of prioritizing variants for functional interrogation. To evaluate this hypothesis, we leveraged ancestry architecture to perform comparative GWAS and fine-mapping of obesity-related phenotypes in European ancestry populations from the UK Biobank (UKBB) and multi-ancestry samples from the Population Architecture for Genetic Epidemiology (PAGE) consortium with comparable sample sizes. In the investigated regions with genome-wide significant associations for obesity-related traits, fine-mapping in our ancestrally diverse sample led to 95% and 99% credible sets (CS) with fewer variants than in the European ancestry sample. Lead fine-mapped variants in PAGE regions had higher average coding scores, and higher average posterior probabilities for causality compared to UKBB. Importantly, 99% CS in PAGE loci contained strong expression quantitative trait loci (eQTLs) in adipose tissues or harbored more variants in tighter linkage disequilibrium (LD) with eQTLs. Leveraging ancestrally diverse populations with heterogeneous ancestry architectures, coupled with functional annotation, increased fine-mapping efficiency and performance, and reduced the set of candidate variants for consideration for future functional studies. Significant overlap in genetic causal variants across populations suggests generalizability of genetic mechanisms underpinning obesity-related traits across populations.
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Affiliation(s)
- Mohammad Yaser Anwar
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Heather M Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Roelof Smit
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Victoria L Buchanan
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Kristin L Young
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Eimear E Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lindsay Fernandez-Rhodes
- Department of Biobehavioral Health, College of Health and Human Development, Pennsylvania State University, University Park, PA, 16802, USA
| | - Simin Liu
- Department of Epidemiology and Center for Global Cardiometabolic Health, School of Public Health, Brown University, Providence, RI, 02903, USA
| | - Themistocles Assimes
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - David O Garcia
- Department of Health Promotion Sciences, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85724, USA
| | - Kim Daeeun
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health, Danville, PA, 17822, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Steve Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ, 08854, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
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22
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Ye Z, Mo C, Liu S, Gao S, Feng L, Zhao B, Canida T, Wu YC, Hatch KS, Ma Y, Mitchell BD, Hong L, Kochunov P, Chen C, Zhao B, Chen S, Ma T. Deciphering the causal relationship between blood pressure and regional white matter integrity: A two-sample Mendelian randomization study. J Neurosci Res 2023; 101:1471-1483. [PMID: 37330925 PMCID: PMC10444533 DOI: 10.1002/jnr.25205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 04/30/2023] [Accepted: 05/10/2023] [Indexed: 06/20/2023]
Abstract
Elevated arterial blood pressure (BP) is a common risk factor for cerebrovascular and cardiovascular diseases, but no causal relationship has been established between BP and cerebral white matter (WM) integrity. In this study, we performed a two-sample Mendelian randomization (MR) analysis with individual-level data by defining two nonoverlapping sets of European ancestry individuals (genetics-exposure set: N = 203,111; mean age = 56.71 years, genetics-outcome set: N = 16,156; mean age = 54.61 years) from UK Biobank to evaluate the causal effects of BP on regional WM integrity, measured by fractional anisotropy of diffusion tensor imaging. Two BP traits: systolic and diastolic blood pressure were used as exposures. Genetic variant was carefully selected as instrumental variable (IV) under the MR analysis assumptions. We existing large-scale genome-wide association study summary data for validation. The main method used was a generalized version of inverse-variance weight method while other MR methods were also applied for consistent findings. Two additional MR analyses were performed to exclude the possibility of reverse causality. We found significantly negative causal effects (FDR-adjusted p < .05; every 10 mmHg increase in BP leads to a decrease in FA value by .4% ~ 2%) of BP traits on a union set of 17 WM tracts, including brain regions related to cognitive function and memory. Our study extended the previous findings of association to causation for regional WM integrity, providing insights into the pathological processes of elevated BP that might chronically alter the brain microstructure in different regions.
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Affiliation(s)
- Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Chen Mo
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Li Feng
- Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, Maryland, United States of America
| | - Boao Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Travis Canida
- Department of Mathematics, The college of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, Maryland, United States of America
| | - Yu-Chia Wu
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Kathryn S Hatch
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Braxton D. Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - L.Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Bingxin Zhao
- Department of Statistics, Purdue University, West Lafayette, Indiana, United States of America
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
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23
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Pretzsch CM, Ecker C. Structural neuroimaging phenotypes and associated molecular and genomic underpinnings in autism: a review. Front Neurosci 2023; 17:1172779. [PMID: 37457001 PMCID: PMC10347684 DOI: 10.3389/fnins.2023.1172779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/09/2023] [Indexed: 07/18/2023] Open
Abstract
Autism has been associated with differences in the developmental trajectories of multiple neuroanatomical features, including cortical thickness, surface area, cortical volume, measures of gyrification, and the gray-white matter tissue contrast. These neuroimaging features have been proposed as intermediate phenotypes on the gradient from genomic variation to behavioral symptoms. Hence, examining what these proxy markers represent, i.e., disentangling their associated molecular and genomic underpinnings, could provide crucial insights into the etiology and pathophysiology of autism. In line with this, an increasing number of studies are exploring the association between neuroanatomical, cellular/molecular, and (epi)genetic variation in autism, both indirectly and directly in vivo and across age. In this review, we aim to summarize the existing literature in autism (and neurotypicals) to chart a putative pathway from (i) imaging-derived neuroanatomical cortical phenotypes to (ii) underlying (neuropathological) biological processes, and (iii) associated genomic variation.
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Affiliation(s)
- Charlotte M. Pretzsch
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
| | - Christine Ecker
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
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24
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Siedlinski M, Carnevale L, Xu X, Carnevale D, Evangelou E, Caulfield MJ, Maffia P, Wardlaw J, Samani NJ, Tomaszewski M, Lembo G, Holmes MV, Guzik TJ. Genetic analyses identify brain structures related to cognitive impairment associated with elevated blood pressure. Eur Heart J 2023; 44:2114-2125. [PMID: 36972688 PMCID: PMC10281555 DOI: 10.1093/eurheartj/ehad101] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/07/2023] [Accepted: 02/13/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND AND AIMS Observational studies have linked elevated blood pressure (BP) to impaired cognitive function. However, the functional and structural changes in the brain that mediate the relationship between BP elevation and cognitive impairment remain unknown. Using observational and genetic data from large consortia, this study aimed to identify brain structures potentially associated with BP values and cognitive function. METHODS AND RESULTS Data on BP were integrated with 3935 brain magnetic resonance imaging-derived phenotypes (IDPs) and cognitive function defined by fluid intelligence score. Observational analyses were performed in the UK Biobank and a prospective validation cohort. Mendelian randomisation (MR) analyses used genetic data derived from the UK Biobank, International Consortium for Blood Pressure, and COGENT consortium. Mendelian randomisation analysis identified a potentially adverse causal effect of higher systolic BP on cognitive function [-0.044 standard deviation (SD); 95% confidence interval (CI) -0.066, -0.021] with the MR estimate strengthening (-0.087 SD; 95% CI -0.132, -0.042), when further adjusted for diastolic BP. Mendelian randomisation analysis found 242, 168, and 68 IDPs showing significant (false discovery rate P < 0.05) association with systolic BP, diastolic BP, and pulse pressure, respectively. Most of these IDPs were inversely associated with cognitive function in observational analysis in the UK Biobank and showed concordant effects in the validation cohort. Mendelian randomisation analysis identified relationships between cognitive function and the nine of the systolic BP-associated IDPs, including the anterior thalamic radiation, anterior corona radiata, or external capsule. CONCLUSION Complementary MR and observational analyses identify brain structures associated with BP, which may be responsible for the adverse effects of hypertension on cognitive performance.
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Affiliation(s)
- Mateusz Siedlinski
- Department of Internal Medicine, Jagiellonian University Medical College, ul. Skarbowa 1, 31-121 Krakow, Poland
- Centre for Cardiovascular Sciences, Queen’s Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- Center for Medical Genomics OMICRON, Jagiellonian University Medical College, ul. Kopernika 7c, 31-034 Kraków, Poland
| | - Lorenzo Carnevale
- Department of Angiocardioneurology and Translational Medicine, I.R.C.C.S. INM Neuromed, Via Atinense, 18, 86077 Pozzilli, Italy
| | - Xiaoguang Xu
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, 46 Grafton Street, Manchester M13 9NT, UK
| | - Daniela Carnevale
- Department of Angiocardioneurology and Translational Medicine, I.R.C.C.S. INM Neuromed, Via Atinense, 18, 86077 Pozzilli, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Viale Regina Elena, 291 - 00161 Roma, Italy
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, University Campus, University of Ioannina, P.O. Box: 1186, 451 10, Ioannina, Greece
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, University Campus GR -451 15, Ioannina, Greece
| | - Mark J Caulfield
- William Harvey Research Institute, NIHR Biomedical Research Centre at Barts, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Pasquale Maffia
- School of Infection & Immunity, College of Medical, Veterinary and Life Sciences, University of Glasgow, University Avenue, Glasgow G12 8QQ, UK
- Department of Pharmacy, School of Medicine and Surgery, University of Naples Federico II, Via Domenico Montesano 49, 80131 Napoli, Italy
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, UK Dementia Research Institute, University of Edinburgh, 49 Little France Crescent, Edinburgh EH16 4SB, UK
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, University Road, Leicester LE1 7RH, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, 46 Grafton Street, Manchester M13 9NT, UK
- Division of Medicine, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Oxford Road, Manchester M13 9WL, UK
| | - Giuseppe Lembo
- Department of Angiocardioneurology and Translational Medicine, I.R.C.C.S. INM Neuromed, Via Atinense, 18, 86077 Pozzilli, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Viale Regina Elena, 291 - 00161 Roma, Italy
| | - Michael V Holmes
- Bristol Medical School, Population Health Sciences, University of Bristol, Queens Road, Bristol BS8 1QU, UK
- Medical Research Council, Integrative Epidemiology Unit, University of Bristol, Queens Road, Bristol BS8 1QU, UK
| | - Tomasz J Guzik
- Department of Internal Medicine, Jagiellonian University Medical College, ul. Skarbowa 1, 31-121 Krakow, Poland
- Centre for Cardiovascular Sciences, Queen’s Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- Center for Medical Genomics OMICRON, Jagiellonian University Medical College, ul. Kopernika 7c, 31-034 Kraków, Poland
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25
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Korbmacher M, Gurholt TP, de Lange AMG, van der Meer D, Beck D, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants. Front Psychol 2023; 14:1117732. [PMID: 37359862 PMCID: PMC10288151 DOI: 10.3389/fpsyg.2023.1117732] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/27/2023] [Indexed: 06/28/2023] Open
Abstract
Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Tiril P. Gurholt
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Ann-Marie G. de Lange
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Dani Beck
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Arvid Lundervold
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
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26
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Cruchaga C, Western D, Timsina J, Wang L, Wang C, Yang C, Ali M, Beric A, Gorijala P, Kohlfeld P, Budde J, Levey A, Morris J, Perrin R, Ruiz A, Marquié M, Boada M, de Rojas I, Rutledge J, Oh H, Wilson E, Guen YL, Alvarez I, Aguilar M, Greicius M, Pastor P, Pulford D, Ibanez L, Wyss-Coray T, Sung YJ, Phillips B. Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and informs causal proteins for Alzheimer's disease. RESEARCH SQUARE 2023:rs.3.rs-2814616. [PMID: 37333337 PMCID: PMC10275048 DOI: 10.21203/rs.3.rs-2814616/v1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The integration of quantitative trait loci (QTL) with disease genome-wide association studies (GWAS) has proven successful at prioritizing candidate genes at disease-associated loci. QTL mapping has mainly been focused on multi-tissue expression QTL or plasma protein QTL (pQTL). Here we generated the largest-to-date cerebrospinal fluid (CSF) pQTL atlas by analyzing 7,028 proteins in 3,107 samples. We identified 3,373 independent study-wide associations for 1,961 proteins, including 2,448 novel pQTLs of which 1,585 are unique to CSF, demonstrating unique genetic regulation of the CSF proteome. In addition to the established chr6p22.2-21.32 HLA region, we identified pleiotropic regions on chr3q28 near OSTN and chr19q13.32 near APOE that were enriched for neuron-specificity and neurological development. We also integrated this pQTL atlas with the latest Alzheimer's disease (AD) GWAS through PWAS, colocalization and Mendelian Randomization and identified 42 putative causal proteins for AD, 15 of which have drugs available. Finally, we developed a proteomics-based risk score for AD that outperforms genetics-based polygenic risk scores. These findings will be instrumental to further understand the biology and identify causal and druggable proteins for brain and neurological traits.
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Affiliation(s)
| | - Dan Western
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Lihua Wang
- Washington University School of Medicine
| | | | | | | | | | | | - Patsy Kohlfeld
- Washington University School of Medicine, St Louis, MO, USA
| | | | | | | | | | | | | | - Mercè Boada
- Memory Clinic of Fundaciò ACE, Catalan Institute of Applied Neurosciences
| | | | | | | | | | | | - Ignacio Alvarez
- Fundació Docència i Recerca Mútua Terrassa, Terrassa, Barcelona, Spain
| | | | | | - Pau Pastor
- University Hospital Germans Trias i Pujol
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Genç E, Metzen D, Fraenz C, Schlüter C, Voelkle MC, Arning L, Streit F, Nguyen HP, Güntürkün O, Ocklenburg S, Kumsta R. Structural architecture and brain network efficiency link polygenic scores to intelligence. Hum Brain Mapp 2023; 44:3359-3376. [PMID: 37013679 PMCID: PMC10171514 DOI: 10.1002/hbm.26286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 02/15/2023] [Accepted: 03/01/2023] [Indexed: 04/05/2023] Open
Abstract
Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.
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Affiliation(s)
- Erhan Genç
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Dorothea Metzen
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Christoph Fraenz
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Caroline Schlüter
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Manuel C Voelkle
- Psychological Research Methods Department of Psychology, Humboldt University, Berlin, Germany
| | - Larissa Arning
- Department of Human Genetics, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Fabian Streit
- Department Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Huu Phuc Nguyen
- Department of Human Genetics, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Onur Güntürkün
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Sebastian Ocklenburg
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- Department of Psychology, Medical School Hamburg, Hamburg, Germany
- ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Hamburg, Germany
| | - Robert Kumsta
- Genetic Psychology, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- Department of Behavioural and Cognitive Sciences, Laboratory for Stress and Gene-Environment Interplay, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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28
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Ou YN, Ge YJ, Wu BS, Zhang Y, Jiang YC, Kuo K, Yang L, Tan L, Feng JF, Cheng W, Yu JT. The genetic architecture of fornix white matter microstructure and their involvement in neuropsychiatric disorders. Transl Psychiatry 2023; 13:180. [PMID: 37236919 DOI: 10.1038/s41398-023-02475-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 05/03/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
The fornix is a white matter bundle located in the center of the hippocampaldiencephalic limbic circuit that controls memory and executive functions, yet its genetic architectures and involvement in brain disorders remain largely unknown. We carried out a genome-wide association analysis of 30,832 UK Biobank individuals of the six fornix diffusion magnetic resonance imaging (dMRI) traits. The post-GWAS analysis allowed us to identify causal genetic variants in phenotypes at the single nucleotide polymorphisms (SNP), locus, and gene levels, as well as genetic overlap with brain health-related traits. We further generalized our GWAS in adolescent brain cognitive development (ABCD) cohort. The GWAS identified 63 independent significant variants within 20 genomic loci associated (P < 8.33 × 10-9) with the six fornix dMRI traits. Geminin coiled-coil domain containing (GMNC) and NUAK family SNF1-like kinase 1 (NUAK1) gene were highlighted, which were found in UKB and replicated in ABCD. The heritability of the six traits ranged from 10% to 27%. Gene mapping strategies identified 213 genes, where 11 were supported by all of four methods. Gene-based analyses revealed pathways relating to cell development and differentiation, with astrocytes found to be significantly enriched. Pleiotropy analyses with eight neurological and psychiatric disorders revealed shared variants, especially with schizophrenia under the conjFDR threshold of 0.05. These findings advance our understanding of the complex genetic architectures of fornix and their relevance in neurological and psychiatric disorders.
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Affiliation(s)
- Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Bang-Sheng Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yi Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yu-Chao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China.
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Chen SJ, Wu BS, Ge YJ, Chen SD, Ou YN, Dong Q, Feng J, Cheng W, Yu JT. The genetic architecture of the corpus callosum and its genetic overlap with common neuropsychiatric diseases. J Affect Disord 2023; 335:418-430. [PMID: 37164063 DOI: 10.1016/j.jad.2023.05.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/25/2023] [Accepted: 05/02/2023] [Indexed: 05/12/2023]
Abstract
BACKGROUND The corpus callosum (CC) is the main structure transferring information between the cerebral hemispheres. Although previous large-scale genome-wide association study (GWAS) has illustrated the genetic architecture of white matter integrity of CC, CC volume is less stressed. METHODS Using MRI data from 33,861 individuals in UK Biobank, we conducted univariate and multivariate GWAS for CC fractional anisotropy (FA) and volume with PLINK 2.0 and MOSTest. All discovered SNPs in the multivariate framework were functionally annotated in FUMA v1.3.8. In the meanwhile, a series of gene property analyses was conducted simultaneously. In addition, we estimated genetic relationship between CC metrics and other neuropsychiatric traits and diseases. RESULTS We identified a total of 36 and 82 significant genomic loci for CC FA and volume (P < 5 × 10-8). And 53 and 27 genes were respectively mapped by four mapping strategies. For CC volume, gene-set analysis revealed pathways mainly relating to cell migration; cell-type analysis found the top enrichment in neuroglia while for CC FA in GABAergic neurons. Furthermore, we found a lot of genetic overlap and shared loci between CC FA and volume and common neuropsychiatric diseases. DISCUSSION Collectively, this study helps to better understand the genetic architecture of whole CC and CC subregions. However, the way to divide CC FA and volume in our study restricts the interpretations of our results. Future work will be needed to pay attention to the genetic structure of white matter volume, and an appropriate division of CC may help to better understand CC structure.
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Affiliation(s)
- Si-Jia Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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30
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Reitz C, Pericak-Vance MA, Foroud T, Mayeux R. A global view of the genetic basis of Alzheimer disease. Nat Rev Neurol 2023; 19:261-277. [PMID: 37024647 PMCID: PMC10686263 DOI: 10.1038/s41582-023-00789-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/21/2023] [Indexed: 04/08/2023]
Abstract
The risk of Alzheimer disease (AD) increases with age, family history and informative genetic variants. Sadly, there is still no cure or means of prevention. As in other complex diseases, uncovering genetic causes of AD could identify underlying pathological mechanisms and lead to potential treatments. Rare, autosomal dominant forms of AD occur in middle age as a result of highly penetrant genetic mutations, but the most common form of AD occurs later in life. Large-scale, genome-wide analyses indicate that 70 or more genes or loci contribute to AD. One of the major factors limiting progress is that most genetic data have been obtained from non-Hispanic white individuals in Europe and North America, preventing the development of personalized approaches to AD in individuals of other ethnicities. Fortunately, emerging genetic data from other regions - including Africa, Asia, India and South America - are now providing information on the disease from a broader range of ethnicities. Here, we summarize the current knowledge on AD genetics in populations across the world. We predominantly focus on replicated genetic discoveries but also include studies in ethnic groups where replication might not be feasible. We attempt to identify gaps that need to be addressed to achieve a complete picture of the genetic and molecular factors that drive AD in individuals across the globe.
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Affiliation(s)
- Christiane Reitz
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA
- The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA
- Department of Neurology, Columbia University, New York, NY, USA
- Department of Epidemiology, Columbia University, New York, NY, USA
| | - Margaret A Pericak-Vance
- The John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- National Centralized Repository for Alzheimer's Disease and Related Dementias, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Richard Mayeux
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA.
- The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA.
- Department of Neurology, Columbia University, New York, NY, USA.
- Department of Epidemiology, Columbia University, New York, NY, USA.
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31
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de Araujo Tavares ME, Cupertino RB, Bandeira CE, da Silva BS, Vitola ES, Salgado CAI, Dos Santos Soares R, Picon FA, Rohde LA, Rovaris DL, Grevet EH, Bau CHD. Refining patterns of MEF2C effects in white matter microstructure and psychiatric features. J Neural Transm (Vienna) 2023; 130:697-706. [PMID: 37002331 DOI: 10.1007/s00702-023-02626-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/20/2023] [Indexed: 04/03/2023]
Abstract
Several GWAS reported Myocyte Enhancer Factor 2 C (MEF2C) gene associations with white matter microstructure and psychiatric disorders, and MEF2C involvement in pathways related to neuronal development suggests a common biological factor underlying these phenotypes. We aim to refine the MEF2C effects in the brain relying on an integrated analysis of white matter and psychiatric phenotypes in an extensively characterized sample. This study included 870 Brazilian adults (47% from an attention-deficit/hyperactivity disorder outpatient clinic) assessed through standardized psychiatric interviews, 139 of which underwent a magnetic resonance imaging scan. We evaluated variants in the MEF2C region using two approaches: 1) a gene-wide analysis, which uses the sum of polymorphism effects, and 2) SNP analyses, restricted to the independent variants within the gene. The outcomes included psychiatric phenotypes and fractional anisotropy for brain images. Results: The gene-wide analyses pointed to a nominal association between MEF2C and the Temporal Portion of the Superior Longitudinal Fasciculus (SLFTEMP). The SNP analysis identified four independent variants significantly associated with SLFTEMP and one (rs4218438) with Substance Use Disorder. Our findings showing specific associations of MEF2C variants with temporal-frontal circuitry components may help to elucidate how the MEF2C gene underlies a broad range of psychiatric phenotypes since these regions are relevant to executive and cognitive functions.
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Affiliation(s)
- Maria Eduarda de Araujo Tavares
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, RS, 91501-970, Brazil
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | | | - Cibele Edom Bandeira
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, RS, 91501-970, Brazil
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Bruna Santos da Silva
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, RS, 91501-970, Brazil
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Eduardo Schneider Vitola
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Carlos Alberto Iglesias Salgado
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Robson Dos Santos Soares
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, RS, 91501-970, Brazil
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Felipe Almeida Picon
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Luis Augusto Rohde
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
- National Institute of Developmental Psychiatry, São Paulo, Brazil
| | - Diego Luiz Rovaris
- Department of Physiology and Biophysics, Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
| | - Eugenio Horacio Grevet
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Claiton Henrique Dotto Bau
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, RS, 91501-970, Brazil.
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil.
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil.
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Wu BS, Ge YJ, Zhang W, Chen SD, Xiang ST, Zhang YR, Ou YN, Jiang YC, Tan L, Cheng W, Suckling J, Feng JF, Yu JT, Mao Y. Genome-wide association study of cerebellar white matter microstructure and genetic overlap with common brain disorders. Neuroimage 2023; 269:119928. [PMID: 36740028 DOI: 10.1016/j.neuroimage.2023.119928] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/12/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The cerebellum is recognized as being involved in neurocognitive and motor functions with communication with extra-cerebellar regions relying on the white matter integrity of the cerebellar peduncles. However, the genetic determinants of cerebellar white matter integrity remain largely unknown. METHODS We conducted a genome-wide association analysis of cerebellar white matter microstructure using diffusion tensor imaging data from 25,415 individuals from UK Biobank. The integrity of cerebellar white matter microstructure was measured as fractional anisotropy (FA) and mean diffusivity (MD). Identification of independent genomic loci, functional annotation, and tissue and cell-type analysis were conducted with FUMA. The linkage disequilibrium score regression (LDSC) was used to calculate genetic correlations between cerebellar white matter microstructure and regional brain volumes and brain-related traits. Furthermore, the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was employed to identify the shared genetic basis between cerebellar white matter microstructure and common brain disorders. RESULTS We identified 11 genetic loci (P < 8.3 × 10-9) and 86 genes associated with cerebellar white matter microstructure. Further functional enrichment analysis implicated the involvement of GABAergic neurons and cholinergic pathways. Significant polygenetic overlap between cerebellar white matter tracts and their anatomically connected or adjacent brain regions was detected. In addition, we report the overall genetic correlation and specific loci shared between cerebellar white matter microstructural integrity and brain-related traits, including movement, cognitive, psychiatric, and cerebrovascular categories. CONCLUSIONS Collectively, this study represents a step forward in understanding the genetics of cerebellar white matter microstructure and its shared genetic etiology with common brain disorders.
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Affiliation(s)
- Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Tong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yu-Chao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - John Suckling
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Ying Mao
- Department of Neurosurgery and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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Petrican R, Fornito A. Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing. Dev Cogn Neurosci 2023; 60:101229. [PMID: 36947895 PMCID: PMC10041470 DOI: 10.1016/j.dcn.2023.101229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/08/2023] [Accepted: 03/12/2023] [Indexed: 03/18/2023] Open
Abstract
In adulthood, stress exposure and genetic risk heighten psychological vulnerability by accelerating neurobiological senescence. To investigate whether molecular and brain network maturation processes play a similar role in adolescence, we analysed genetic, as well as longitudinal task neuroimaging (inhibitory control, incentive processing) and early life adversity (i.e., material deprivation, violence) data from the Adolescent Brain and Cognitive Development study (N = 980, age range: 9-13 years). Genetic risk was estimated separately for Major Depressive Disorder (MDD) and Alzheimer's Disease (AD), two pathologies linked to stress exposure and allegedly sharing a causal connection (MDD-to-AD). Adversity and genetic risk for MDD/AD jointly predicted functional network segregation patterns suggestive of accelerated (GABA-linked) visual/attentional, but delayed (dopamine [D2]/glutamate [GLU5R]-linked) somatomotor/association system development. A positive relationship between brain maturation and psychopathology emerged only among the less vulnerable adolescents, thereby implying that normatively maladaptive neurodevelopmental alterations could foster adjustment among the more exposed and genetically more stress susceptible youths. Transcriptomic analyses suggested that sensitivity to stress may underpin the joint neurodevelopmental effect of adversity and genetic risk for MDD/AD, in line with the proposed role of negative emotionality as a precursor to AD, likely to account for the alleged causal impact of MDD on dementia onset.
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Affiliation(s)
- Raluca Petrican
- Institute of Population Health, Department of Psychology, University of Liverpool, Bedford Street South, Liverpool L69 7ZA, United Kingdom.
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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34
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de Klein N, Tsai EA, Vochteloo M, Baird D, Huang Y, Chen CY, van Dam S, Oelen R, Deelen P, Bakker OB, El Garwany O, Ouyang Z, Marshall EE, Zavodszky MI, van Rheenen W, Bakker MK, Veldink J, Gaunt TR, Runz H, Franke L, Westra HJ. Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases. Nat Genet 2023; 55:377-388. [PMID: 36823318 PMCID: PMC10011140 DOI: 10.1038/s41588-023-01300-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 01/17/2023] [Indexed: 02/25/2023]
Abstract
Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.
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Affiliation(s)
- Niek de Klein
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Wellcome Sanger Institute, Hinxton, UK
| | - Ellen A Tsai
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Martijn Vochteloo
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Institute for Life Science and Technology, Hanze University of Applied Sciences, Groningen, The Netherlands
- Oncode Institute, Groningen, The Netherlands
| | - Denis Baird
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Yunfeng Huang
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Chia-Yen Chen
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Sipko van Dam
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Ancora Health, Groningen, The Netherlands
| | - Roy Oelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Groningen, The Netherlands
| | - Patrick Deelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Groningen, The Netherlands
| | - Olivier B Bakker
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Wellcome Sanger Institute, Hinxton, UK
| | - Omar El Garwany
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Wellcome Sanger Institute, Hinxton, UK
| | | | - Eric E Marshall
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Maria I Zavodszky
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Wouter van Rheenen
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Mark K Bakker
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jan Veldink
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Heiko Runz
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA.
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Groningen, The Netherlands.
| | - Harm-Jan Westra
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Groningen, The Netherlands.
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35
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Sha Z, Schijven D, Fisher SE, Francks C. Genetic architecture of the white matter connectome of the human brain. SCIENCE ADVANCES 2023; 9:eadd2870. [PMID: 36800424 PMCID: PMC9937579 DOI: 10.1126/sciadv.add2870] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
White matter tracts form the structural basis of large-scale brain networks. We applied brain-wide tractography to diffusion images from 30,810 adults (U.K. Biobank) and found significant heritability for 90 node-level and 851 edge-level network connectivity measures. Multivariate genome-wide association analyses identified 325 genetic loci, of which 80% had not been previously associated with brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia, and neurons. The multivariate association profiles implicated 31 loci in connectivity between core regions of the left-hemisphere language network. Polygenic scores for psychiatric, neurological, and behavioral traits also showed significant multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles. This large-scale mapping study revealed common genetic contributions to variation in the structural connectome of the human brain.
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Affiliation(s)
- Zhiqiang Sha
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Dick Schijven
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Simon E. Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Clyde Francks
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
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36
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Yang Y, Knol MJ, Wang R, Mishra A, Liu D, Luciano M, Teumer A, Armstrong N, Bis JC, Jhun MA, Li S, Adams HHH, Aziz NA, Bastin ME, Bourgey M, Brody JA, Frenzel S, Gottesman RF, Hosten N, Hou L, Kardia SLR, Lohner V, Marquis P, Maniega SM, Satizabal CL, Sorond FA, Valdés Hernández MC, van Duijn CM, Vernooij MW, Wittfeld K, Yang Q, Zhao W, Boerwinkle E, Levy D, Deary IJ, Jiang J, Mather KA, Mosley TH, Psaty BM, Sachdev PS, Smith JA, Sotoodehnia N, DeCarli CS, Breteler MMB, Ikram MA, Grabe HJ, Wardlaw J, Longstreth WT, Launer LJ, Seshadri S, Debette S, Fornage M. Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI. Brain 2023; 146:492-506. [PMID: 35943854 PMCID: PMC9924914 DOI: 10.1093/brain/awac290] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/23/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at ∼450 000 cytosine-phosphate-guanine (CpG) sites in 9732 middle-aged to older adults from 14 community-based studies. Single CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10-8), was associated with F2 expression in blood (P = 6.4 × 10-5) and co-localized with FOLH1 expression in brain (posterior probability = 0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis and multi-omics co-localization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug-repositioning analysis indicated antihyperlipidaemic agents, more specifically peroxisome proliferator-activated receptor-alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood-brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidaemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood-brain barrier disruption.
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Affiliation(s)
- Yunju Yang
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science at Houston, Houston, TX 77030, USA
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Ruiqi Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Aniket Mishra
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, F-33000 Bordeaux, France
| | - Dan Liu
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Michelle Luciano
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald 17475, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald 17475, Germany
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, 15-269, Poland
| | - Nicola Armstrong
- Mathematics and Statistics, Curtin University, 6845 Perth, Australia
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 02115, USA
| | - Min A Jhun
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA
| | - Shuo Li
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Hieab H H Adams
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Nasir Ahmad Aziz
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurology, Faculty of Medicine, University of Bonn, 53127 Bonn, Germany
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Mathieu Bourgey
- Canadian Centre for Computational Genomics, McGill University, Montréal, Quebec, Canada H3A 0G1
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, Quebec, Canada H3A 0G1
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 02115, USA
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald 17475, Germany
| | - Rebecca F Gottesman
- Stroke Branch, National Institutes of Neurological Disorders and Stroke, Bethesda, MD 20814, USA
| | - Norbert Hosten
- Department of Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA
| | - Valerie Lohner
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Pascale Marquis
- Canadian Centre for Computational Genomics, McGill University, Montréal, Quebec, Canada H3A 0G1
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, Quebec, Canada H3A 0G1
| | - Susana Muñoz Maniega
- Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases and Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA
- The Framingham Heart Study, Framingham, MA 01701, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02115, USA
| | - Farzaneh A Sorond
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Maria C Valdés Hernández
- Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
- Nuffield Department of Population Health, Oxford University, Oxford, OX3 7LF, UK
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald 17475, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, 17475 Rostock, Germany
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
- The Framingham Heart Study, Framingham, MA 01701, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science at Houston, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel Levy
- The Framingham Heart Study, Framingham, MA 01701, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
- Neuroscience Research Australia, Sydney, NSW 2031, Australia
| | - Thomas H Mosley
- The Memory Impairment Neurodegenerative Dementia (MIND) Research Center, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 02115, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98104, USA
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
- Neuropsychiatric Institute, The Prince of Wales Hospital, University of New South Wales, Randwick, NSW 2031, Australia
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 02115, USA
| | - Charles S DeCarli
- Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA 95816, USA
| | - Monique M B Breteler
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53127 Bonn, Germany
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald 17475, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, 17475 Rostock, Germany
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - W T Longstreth
- Department of Epidemiology, University of Washington, Seattle, WA 98104, USA
- Department of Neurology, University of Washington, Seattle, WA 98104, USA
| | - Lenore J Launer
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases and Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA
- The Framingham Heart Study, Framingham, MA 01701, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02115, USA
| | - Stephanie Debette
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, F-33000 Bordeaux, France
- Department of Neurology, Boston University School of Medicine, Boston, MA 02115, USA
- CHU de Bordeaux, Department of Neurology, F-33000 Bordeaux, France
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science at Houston, Houston, TX 77030, USA
- Human Genetics Center, School of Public Health, University of Texas Health Science at Houston, Houston, TX 77030, USA
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Zhao Y, Chang C, Zhang J, Zhang Z. Genetic underpinnings of brain structural connectome for young adults. J Am Stat Assoc 2023; 118:1473-1487. [PMID: 37982009 PMCID: PMC10655950 DOI: 10.1080/01621459.2022.2156349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 11/29/2022] [Indexed: 12/13/2022]
Abstract
With distinct advantages in power over behavioral phenotypes, brain imaging traits have become emerging endophenotypes to dissect molecular contributions to behaviors and neuropsychiatric illnesses. Among different imaging features, brain structural connectivity (i.e., structural connectome) which summarizes the anatomical connections between different brain regions is one of the most cutting edge while under-investigated traits; and the genetic influence on the structural connectome variation remains highly elusive. Relying on a landmark imaging genetics study for young adults, we develop a biologically plausible brain network response shrinkage model to comprehensively characterize the relationship between high dimensional genetic variants and the structural connectome phenotype. Under a unified Bayesian framework, we accommodate the topology of brain network and biological architecture within the genome; and eventually establish a mechanistic mapping between genetic biomarkers and the associated brain sub-network units. An efficient expectation-maximization algorithm is developed to estimate the model and ensure computing feasibility. In the application to the Human Connectome Project Young Adult (HCP-YA) data, we establish the genetic underpinnings which are highly interpretable under functional annotation and brain tissue eQTL analysis, for the brain white matter tracts connecting the hippocampus and two cerebral hemispheres. We also show the superiority of our method in extensive simulations.
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Affiliation(s)
- Yize Zhao
- Department of Biostatistics, Yale University
| | - Changgee Chang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | - Jingwen Zhang
- Department of Biostatistics, Boston University, Boston, MA
| | - Zhengwu Zhang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill
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Maes HHM, Lapato DM, Schmitt JE, Luciana M, Banich MT, Bjork JM, Hewitt JK, Madden PA, Heath AC, Barch DM, Thompson WK, Iacono WG, Neale MC. Genetic and Environmental Variation in Continuous Phenotypes in the ABCD Study®. Behav Genet 2023; 53:1-24. [PMID: 36357558 PMCID: PMC9823057 DOI: 10.1007/s10519-022-10123-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 10/11/2022] [Indexed: 11/12/2022]
Abstract
Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9-10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background.
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Affiliation(s)
- Hermine H. M. Maes
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA ,grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA ,grid.224260.00000 0004 0458 8737Massey Cancer Center, Virginia Commonwealth University, Richmond, VA USA
| | - Dana M. Lapato
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA
| | - J. Eric Schmitt
- grid.25879.310000 0004 1936 8972Departments of Radiology and Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Monica Luciana
- grid.17635.360000000419368657Department of Psychology, University of Minnesota, Minneapolis, USA
| | - Marie T. Banich
- grid.266190.a0000000096214564Department of Psychology and Neuroscience, University of Colorado, Boulder, USA ,grid.266190.a0000000096214564Institute of Cognitive Science, University of Colorado, Boulder, USA
| | - James M. Bjork
- grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA
| | - John K. Hewitt
- grid.266190.a0000000096214564Institute of Cognitive Science, University of Colorado, Boulder, USA ,grid.266190.a0000000096214564Institute for Behavioral Genetics, University of Colorado, Boulder, USA
| | - Pamela A. Madden
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Andrew C. Heath
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Deanna M. Barch
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Wes K. Thompson
- grid.266100.30000 0001 2107 4242Division of Biostatistics and Department of Radiology, Population Neuroscience and Genetics Lab, University of California at San Diego, La Jolla, CA USA
| | - William G. Iacono
- grid.17635.360000000419368657Department of Psychology, University of Minnesota, Minneapolis, USA
| | - Michael C. Neale
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA ,grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA
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Zheng J, Xu M, Walker V, Yuan J, Korologou-Linden R, Robinson J, Huang P, Burgess S, Au Yeung SL, Luo S, Holmes MV, Davey Smith G, Ning G, Wang W, Gaunt TR, Bi Y. Evaluating the efficacy and mechanism of metformin targets on reducing Alzheimer's disease risk in the general population: a Mendelian randomisation study. Diabetologia 2022; 65:1664-1675. [PMID: 35902387 PMCID: PMC9477943 DOI: 10.1007/s00125-022-05743-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 04/08/2022] [Indexed: 02/05/2023]
Abstract
AIMS/HYPOTHESIS Metformin use has been associated with reduced incidence of dementia in diabetic individuals in observational studies. However, the causality between the two in the general population is unclear. This study uses Mendelian randomisation (MR) to investigate the causal effect of metformin targets on Alzheimer's disease and potential causal mechanisms in the brain linking the two. METHODS Genetic proxies for the effects of metformin drug targets were identified as variants in the gene for the corresponding target that associated with HbA1c level (N=344,182) and expression level of the corresponding gene (N≤31,684). The cognitive outcomes were derived from genome-wide association studies comprising 527,138 middle-aged Europeans, including 71,880 with Alzheimer's disease or Alzheimer's disease-by-proxy. MR estimates representing lifelong metformin use on Alzheimer's disease and cognitive function in the general population were generated. Effect of expression level of 22 metformin-related genes in brain cortex (N=6601 donors) on Alzheimer's disease was further estimated. RESULTS Genetically proxied metformin use, equivalent to a 6.75 mmol/mol (1.09%) reduction on HbA1c, was associated with 4% lower odds of Alzheimer's disease (OR 0.96 [95% CI 0.95, 0.98], p=1.06×10-4) in non-diabetic individuals. One metformin target, mitochondrial complex 1 (MCI), showed a robust effect on Alzheimer's disease (OR 0.88, p=4.73×10-4) that was independent of AMP-activated protein kinase. MR of expression in brain cortex tissue showed that decreased MCI-related gene (NDUFA2) expression was associated with lower Alzheimer's disease risk (OR 0.95, p=4.64×10-4) and favourable cognitive function. CONCLUSIONS/INTERPRETATION Metformin use may cause reduced Alzheimer's disease risk in the general population. Mitochondrial function and the NDUFA2 gene are plausible mechanisms of action in dementia protection.
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Affiliation(s)
- Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
| | - Min Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Venexia Walker
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Jinqiu Yuan
- Clinical Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
- Center for Digestive Disease, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
- Guangzhou Women and Children Medical Center, Guangzhou, Guangdong, China
- Division of Epidemiology, the JC School of Public Health & Primary Care, the Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Roxanna Korologou-Linden
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Jamie Robinson
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Peiyuan Huang
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Shiu Lun Au Yeung
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, SAR, China
| | - Shan Luo
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, SAR, China
| | - Michael V Holmes
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- NIHR Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, UK
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
- NIHR Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, UK.
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Makowski C, Wang H, Chen CH. Clinical opportunity awaits at the intersection of genomics and brain imaging. J Psychiatry Neurosci 2022; 47:E293-E298. [PMID: 35948342 PMCID: PMC9377545 DOI: 10.1503/jpn.220075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Affiliation(s)
| | | | - Chi-Hua Chen
- From the Center for Multimodal Imaging and Genetics, Department of Radiology, University of California San Diego, San Diego, Cali., USA
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41
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Liu K, Wu P, Chen B, Cai Y, Yuan R, Zou J. Implicating Causal Brain Magnetic Resonance Imaging in Glaucoma Using Mendelian Randomization. Front Med (Lausanne) 2022; 9:956339. [PMID: 35847794 PMCID: PMC9283577 DOI: 10.3389/fmed.2022.956339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 06/13/2022] [Indexed: 12/31/2022] Open
Abstract
Background Glaucoma is hypothesized to originate in the brain but manifests as an eye disease as it possesses the common features of neurodegeneration diseases. But there is no evidence to demonstrate the primary brain changes in glaucoma patients. In the present study, we have used Mendelian randomization (MR) to understand the causal effect of brain alterations on glaucoma. Methods Our MR study was carried out using summary statistics from genome-wide associations for 110 diffusion tensor imaging (DTI) measurements of white matter (WM) tracts (17,706 individuals), 101 brain region-of-interest (ROI) volumes (19,629 individuals), and glaucoma (8,591 cases, 210,201 control subjects). The causal relationship was evaluated by multiplicative random effects inverse variance weighted (IVW) method and verified by two other MR methods, including MR Egger, weighted median, and extensive sensitivity analyses. Results Genetic liability to fornix fractional anisotropy (FX.FA) (OR = 0.71, 95%CI = 0.56–0.88, P = 2.44 × 10–3), and uncinate fasciculus UNC.FA (OR = 0.65, 95%CI = 0.48–0.88, P = 5.57 × 10–3) was associated with a low risk of glaucoma. Besides, the right ventral diencephalon (OR = 1.72, 95%CI = 1.17–2.52, P = 5.64 × 10–3) and brain stem (OR = 1.35, 95%CI = 1.08–1.69, P = 8.94 × 10–3) were associated with the increased risk of glaucoma. No heterogeneity and pleiotropy were detected. Conclusion Our study suggests that the fornix and uncinate fasciculus degenerations and injures of the right ventral diencephalon and brain stem potentially increase the occurrence of glaucoma and reveal the existence of the brain-eye axis.
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Affiliation(s)
- Kangcheng Liu
- Eye Center of Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Ophthalmology, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Jiangxi Clinical Research Center for Ophthalmic Disease, Jiangxi Research Institute of Ophthalmology and Visual Science, Affiliated Eye Hospital of Nanchang University, Nanchang, China
| | - Pengfei Wu
- Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Bolin Chen
- Eye Center of Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Ophthalmology, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yingjun Cai
- Eye Center of Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Ophthalmology, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Ruolan Yuan
- Eye Center of Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Ophthalmology, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jing Zou
- Eye Center of Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Ophthalmology, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Jing Zou,
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Wang C, Martins-Bach AB, Alfaro-Almagro F, Douaud G, Klein JC, Llera A, Fiscone C, Bowtell R, Elliott LT, Smith SM, Tendler BC, Miller KL. Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging. Nat Neurosci 2022; 25:818-831. [PMID: 35606419 PMCID: PMC9174052 DOI: 10.1038/s41593-022-01074-w] [Citation(s) in RCA: 10] [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] [Received: 08/11/2021] [Accepted: 04/11/2022] [Indexed: 12/17/2022]
Abstract
A key aim in epidemiological neuroscience is identification of markers to assess brain health and monitor therapeutic interventions. Quantitative susceptibility mapping (QSM) is an emerging magnetic resonance imaging technique that measures tissue magnetic susceptibility and has been shown to detect pathological changes in tissue iron, myelin and calcification. We present an open resource of QSM-based imaging measures of multiple brain structures in 35,273 individuals from the UK Biobank prospective epidemiological study. We identify statistically significant associations of 251 phenotypes with magnetic susceptibility that include body iron, disease, diet and alcohol consumption. Genome-wide associations relate magnetic susceptibility to 76 replicating clusters of genetic variants with biological functions involving iron, calcium, myelin and extracellular matrix. These patterns of associations include relationships that are unique to QSM, in particular being complementary to T2* signal decay time measures. These new imaging phenotypes are being integrated into the core UK Biobank measures provided to researchers worldwide, creating the potential to discover new, non-invasive markers of brain health.
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Affiliation(s)
- Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Aurea B Martins-Bach
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Johannes C Klein
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
| | - Alberto Llera
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, the Netherlands
| | - Cristiana Fiscone
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Lloyd T Elliott
- Department of Statistics and Actuarial Science, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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Wang G, Wu W, Xu Y, Yang Z, Xiao B, Long L. Imaging Genetics in Epilepsy: Current Knowledge and New Perspectives. Front Mol Neurosci 2022; 15:891621. [PMID: 35706428 PMCID: PMC9189397 DOI: 10.3389/fnmol.2022.891621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/06/2022] [Indexed: 12/11/2022] Open
Abstract
Epilepsy is a neurological network disease with genetics playing a much greater role than was previously appreciated. Unfortunately, the relationship between genetic basis and imaging phenotype is by no means simple. Imaging genetics integrates multidimensional datasets within a unified framework, providing a unique opportunity to pursue a global vision for epilepsy. This review delineates the current knowledge of underlying genetic mechanisms for brain networks in different epilepsy syndromes, particularly from a neural developmental perspective. Further, endophenotypes and their potential value are discussed. Finally, we highlight current challenges and provide perspectives for the future development of imaging genetics in epilepsy.
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Affiliation(s)
- Ge Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Wenyue Wu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Yuchen Xu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhuanyi Yang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Lili Long
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
- *Correspondence: Lili Long
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Velazquez-Arcelay K, Benton ML, Capra JA. Diverse functions associate with non-coding polymorphisms shared between humans and chimpanzees. BMC Ecol Evol 2022; 22:68. [PMID: 35606693 PMCID: PMC9125839 DOI: 10.1186/s12862-022-02020-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/09/2022] [Indexed: 11/24/2022] Open
Abstract
Background Long-term balancing selection (LTBS) can maintain allelic variation at a locus over millions of years and through speciation events. Variants shared between species in the state of identity-by-descent, hereafter “trans-species polymorphisms”, can result from LTBS, often due to host–pathogen interactions. For instance, the major histocompatibility complex (MHC) locus contains TSPs present across primates. Several hundred candidate LTBS regions have been identified in humans and chimpanzees; however, because many are in non-protein-coding regions of the genome, the functions and potential adaptive roles for most remain unknown. Results We integrated diverse genomic annotations to explore the functions of 60 previously identified regions with multiple shared polymorphisms (SPs) between humans and chimpanzees, including 19 with strong evidence of LTBS. We analyzed genome-wide functional assays, expression quantitative trait loci (eQTL), genome-wide association studies (GWAS), and phenome-wide association studies (PheWAS) for all the regions. We identify functional annotations for 59 regions, including 58 with evidence of gene regulatory function from GTEx or functional genomics data and 19 with evidence of trait association from GWAS or PheWAS. As expected, the SPs associate in humans with many immune system phenotypes, including response to pathogens, but we also find associations with a range of other phenotypes, including body size, alcohol intake, cognitive performance, risk-taking behavior, and urate levels. Conclusions The diversity of traits associated with non-coding regions with multiple SPs support previous hypotheses that functions beyond the immune system are likely subject to LTBS. Furthermore, several of these trait associations provide support and candidate genetic loci for previous hypothesis about behavioral diversity in human and chimpanzee populations, such as the importance of variation in risk sensitivity. Supplementary Information The online version contains supplementary material available at 10.1186/s12862-022-02020-x.
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Fan CC, Loughnan R, Makowski C, Pecheva D, Chen CH, Hagler DJ, Thompson WK, Parker N, van der Meer D, Frei O, Andreassen OA, Dale AM. Multivariate genome-wide association study on tissue-sensitive diffusion metrics highlights pathways that shape the human brain. Nat Commun 2022; 13:2423. [PMID: 35505052 PMCID: PMC9065144 DOI: 10.1038/s41467-022-30110-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 04/12/2022] [Indexed: 11/12/2022] Open
Abstract
The molecular determinants of tissue composition of the human brain remain largely unknown. Recent genome-wide association studies (GWAS) on this topic have had limited success due to methodological constraints. Here, we apply advanced whole-brain analyses on multi-shell diffusion imaging data and multivariate GWAS to two large scale imaging genetic datasets (UK Biobank and the Adolescent Brain Cognitive Development study) to identify and validate genetic association signals. We discover 503 unique genetic loci that have impact on multiple regions of human brain. Among them, more than 79% are validated in either of two large-scale independent imaging datasets. Key molecular pathways involved in axonal growth, astrocyte-mediated neuroinflammation, and synaptogenesis during development are found to significantly impact the measured variations in tissue-specific imaging features. Our results shed new light on the biological determinants of brain tissue composition and their potential overlap with the genetic basis of neuropsychiatric disorders.
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Affiliation(s)
- Chun Chieh Fan
- Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla, CA, USA. .,Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA. .,Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA.
| | - Robert Loughnan
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Carolina Makowski
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Diliana Pecheva
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Chi-Hua Chen
- Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Donald J Hagler
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Wesley K Thompson
- Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Nadine Parker
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Oleksandr Frei
- NORMENT Centre, 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
| | - Ole A Andreassen
- NORMENT Centre, 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, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA.,Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.,Department of Neuroscience, University of California, San Diego, La Jolla, CA, USA
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Homann J, Osburg T, Ohlei O, Dobricic V, Deecke L, Bos I, Vandenberghe R, Gabel S, Scheltens P, Teunissen CE, Engelborghs S, Frisoni G, Blin O, Richardson JC, Bordet R, Lleó A, Alcolea D, Popp J, Clark C, Peyratout G, Martinez-Lage P, Tainta M, Dobson RJB, Legido-Quigley C, Sleegers K, Van Broeckhoven C, Wittig M, Franke A, Lill CM, Blennow K, Zetterberg H, Lovestone S, Streffer J, ten Kate M, Vos SJB, Barkhof F, Visser PJ, Bertram L. Genome-Wide Association Study of Alzheimer's Disease Brain Imaging Biomarkers and Neuropsychological Phenotypes in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Dataset. Front Aging Neurosci 2022; 14:840651. [PMID: 35386118 PMCID: PMC8979334 DOI: 10.3389/fnagi.2022.840651] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/15/2022] [Indexed: 12/24/2022] Open
Abstract
Alzheimer's disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e., case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n = 931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses. As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1 (EH domain binding protein 1; on chromosome 2p15) and CEP112 (centrosomal protein 112; 17q24.1) with delayed recall as well as SMOC2 (SPARC related modular calcium binding 2; 6p27) with immediate recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 (interleukin 1 receptor accessory protein like 1; Xp21.3). While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline.
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Affiliation(s)
- Jan Homann
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Tim Osburg
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Olena Ohlei
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Laura Deecke
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Isabelle Bos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Neurology Service, University Hospital Leuven, Leuven, Belgium
| | - Silvy Gabel
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Charlotte E. Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, Netherlands
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Center for Neurosciences, Universitair Ziekenhuis Brussel and Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Giovanni Frisoni
- Department of Psychiatry, University of Geneva, Geneva, Switzerland
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Olivier Blin
- Institut Neurosciences Timone, AIX Marseille University, Marseille, France
| | - Jill C. Richardson
- Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevenage, United Kingdom
| | - Regis Bordet
- Lille Neuroscience and Cognition, University of Lille, Inserm, CHU Lille, Lille, France
| | - Alberto Lleó
- Memory Unit, Neurology Department, Hospital de Sant Pau, Barcelona and Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Daniel Alcolea
- Memory Unit, Neurology Department, Hospital de Sant Pau, Barcelona and Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Julius Popp
- Department of Geriatric Psychiatry, University Hospital of Psychiatry Zurich, Zurich, Switzerland
- Old Age Psychiatry, Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Christopher Clark
- Department of Geriatric Psychiatry, University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Gwendoline Peyratout
- Old Age Psychiatry, Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Pablo Martinez-Lage
- Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Spain
| | - Mikel Tainta
- Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Spain
| | - Richard J. B. Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Cristina Legido-Quigley
- Steno Diabetes Center, Copenhagen, Denmark
- King’s College London, Institute of Pharmaceutical Sciences, London, United Kingdom
| | - Kristel Sleegers
- Complex Genetics of Alzheimer’s Disease Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Michael Wittig
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Christina M. Lill
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, United Kingdom
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, Queen Square, London, United Kingdom
- UK Dementia Research Institute at University College London, London, United Kingdom
| | - Simon Lovestone
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Johannes Streffer
- Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
- Janssen R&D, LLC. Beerse, Belgium
| | - Mara ten Kate
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Stephanie J. B. Vos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam Neuroscience, Amsterdam, Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, United Kingdom
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
- Department of Psychology, University of Oslo, Oslo, Norway
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Daghals I, Sargurupremraj M, Danning R, Gormley P, Malik R, Amouyel P, Metso T, Pezzini A, Kurth T, Debette S, Chasman D. Migraine, Stroke, and Cervical Arterial Dissection: Shared Genetics for a Triad of Brain Disorders With Vascular Involvement. Neurol Genet 2022; 8:e653. [PMID: 35128049 PMCID: PMC8808356 DOI: 10.1212/nxg.0000000000000653] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/07/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND OBJECTIVES Migraine, stroke, and cervical artery dissection (CeAD) represent a triad of cerebrovascular disorders with pairwise comorbid relationships and vascular involvement. Larger samples and recent advances in methodology invite systematic exploration of their shared genetics. METHODS Genetic analyses leveraged summary statistics from genome-wide association studies of the largest available samples of each disorder, including subtypes of stroke (ischemic stroke, large artery stroke, small vessel stroke, and cardioembolic stroke) and migraine (with aura and without aura). For each pair of disorders, genetic correlation was assessed both on a genome-wide basis and within independent segments across the genome including known specific loci for each disorder. A cross-trait meta-analysis was used to identify novel candidate loci. Finally, potential causality of migraine susceptibility on stroke and CeAD was assessed by Mendelian randomization. RESULTS Among all pairs of disorders, genome-wide genetic correlation was observed only between CeAD and migraine, particularly MO. Local genetic correlations were more extensive between migraine and CeAD than those between migraine and stroke or CeAD and stroke and revealed evidence for novel CeAD associations at rs6693567 (ADAMTSL4/ECM1), rs11187838 (PLCE1), and rs7940646 (MRVI1) while strengthening prior subthreshold evidence at rs9486725 (FHL5) and rs650724 (LRP1). At known migraine loci, novel associations with stroke had concordant risk alleles for small vessel stroke at rs191602009 (CARF) and for cardioembolic stroke at rs55884259 (NKX2-5). Known migraine loci also revealed novel associations but with opposite risk alleles for all stroke, ischemic stroke, and small vessel stroke at rs55928386 (HTRA1), for large artery stroke at rs11172113 (LRP1), and for all stroke and ischemic stroke at rs1535791 and rs4942561 (both LRCH1), respectively. rs182923402 (near PTCH1) was a novel concordant locus for migraine and cardioembolic stroke. Mendelian randomization supported potential causal influences of migraine on CeAD (odds ratio [95% confidence interval] per doubling migraine prevalence = 1.69 [1.24-2.3], p = 0.0009) with concordant risk, but with opposite risk on large artery stroke (0.86 [0.76-0.96], p = 0.0067). DISCUSSION The findings emphasize shared genetic risk between migraine and CeAD while identifying loci with likely vascular function in migraine and shared but opposite genetic risk between migraine and stroke subtypes, and a central role of LRP1 in all 3 cerebrovascular disorders.
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Affiliation(s)
| | | | - Rebecca Danning
- From the Harvard Medical School (I.D., D.C.), Boston, MA; Division of Preventive Medicine (I.D., R.D., D.C.), Brigham and Women's Hospital, Boston, MA; University of Bordeaux (M.S., S.D.), Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, France; Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (M.S.), University of Texas Health, San Antonio; Massachusetts General Hospital (P.G.), Boston; Institute for Stroke and Dementia Research (R.M.), Klinikum der Universität München, Ludwig-Maximilians-University, Germany; LabEx DISTALZ-U1167 (P.A.), RID-AGE-Risk Factors and Molecular Determinants of Aging-Related Diseases, University of Lille; Inserm U1167 (P.A.), Lille; Centre Hospitalier Universitaire Lille (P.A.); Institut Pasteur de Lille (P.A.), France; Department of Neurology (T.M.), Helsinki University Central Hospital, Finland; Department of Clinical and Experimental Sciences (A.P.), Neurology Clinic, Brescia University Hospital, Italy; Institute of Public Health (T.K.), Charité—Universitätsmedizin Berlin, Germany; and Department of Neurology (S.D.), CHU de Bordeaux, France
| | - Padhraig Gormley
- From the Harvard Medical School (I.D., D.C.), Boston, MA; Division of Preventive Medicine (I.D., R.D., D.C.), Brigham and Women's Hospital, Boston, MA; University of Bordeaux (M.S., S.D.), Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, France; Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (M.S.), University of Texas Health, San Antonio; Massachusetts General Hospital (P.G.), Boston; Institute for Stroke and Dementia Research (R.M.), Klinikum der Universität München, Ludwig-Maximilians-University, Germany; LabEx DISTALZ-U1167 (P.A.), RID-AGE-Risk Factors and Molecular Determinants of Aging-Related Diseases, University of Lille; Inserm U1167 (P.A.), Lille; Centre Hospitalier Universitaire Lille (P.A.); Institut Pasteur de Lille (P.A.), France; Department of Neurology (T.M.), Helsinki University Central Hospital, Finland; Department of Clinical and Experimental Sciences (A.P.), Neurology Clinic, Brescia University Hospital, Italy; Institute of Public Health (T.K.), Charité—Universitätsmedizin Berlin, Germany; and Department of Neurology (S.D.), CHU de Bordeaux, France
| | - Rainer Malik
- From the Harvard Medical School (I.D., D.C.), Boston, MA; Division of Preventive Medicine (I.D., R.D., D.C.), Brigham and Women's Hospital, Boston, MA; University of Bordeaux (M.S., S.D.), Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, France; Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (M.S.), University of Texas Health, San Antonio; Massachusetts General Hospital (P.G.), Boston; Institute for Stroke and Dementia Research (R.M.), Klinikum der Universität München, Ludwig-Maximilians-University, Germany; LabEx DISTALZ-U1167 (P.A.), RID-AGE-Risk Factors and Molecular Determinants of Aging-Related Diseases, University of Lille; Inserm U1167 (P.A.), Lille; Centre Hospitalier Universitaire Lille (P.A.); Institut Pasteur de Lille (P.A.), France; Department of Neurology (T.M.), Helsinki University Central Hospital, Finland; Department of Clinical and Experimental Sciences (A.P.), Neurology Clinic, Brescia University Hospital, Italy; Institute of Public Health (T.K.), Charité—Universitätsmedizin Berlin, Germany; and Department of Neurology (S.D.), CHU de Bordeaux, France
| | - Philippe Amouyel
- From the Harvard Medical School (I.D., D.C.), Boston, MA; Division of Preventive Medicine (I.D., R.D., D.C.), Brigham and Women's Hospital, Boston, MA; University of Bordeaux (M.S., S.D.), Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, France; Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (M.S.), University of Texas Health, San Antonio; Massachusetts General Hospital (P.G.), Boston; Institute for Stroke and Dementia Research (R.M.), Klinikum der Universität München, Ludwig-Maximilians-University, Germany; LabEx DISTALZ-U1167 (P.A.), RID-AGE-Risk Factors and Molecular Determinants of Aging-Related Diseases, University of Lille; Inserm U1167 (P.A.), Lille; Centre Hospitalier Universitaire Lille (P.A.); Institut Pasteur de Lille (P.A.), France; Department of Neurology (T.M.), Helsinki University Central Hospital, Finland; Department of Clinical and Experimental Sciences (A.P.), Neurology Clinic, Brescia University Hospital, Italy; Institute of Public Health (T.K.), Charité—Universitätsmedizin Berlin, Germany; and Department of Neurology (S.D.), CHU de Bordeaux, France
| | - Tiina Metso
- From the Harvard Medical School (I.D., D.C.), Boston, MA; Division of Preventive Medicine (I.D., R.D., D.C.), Brigham and Women's Hospital, Boston, MA; University of Bordeaux (M.S., S.D.), Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, France; Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (M.S.), University of Texas Health, San Antonio; Massachusetts General Hospital (P.G.), Boston; Institute for Stroke and Dementia Research (R.M.), Klinikum der Universität München, Ludwig-Maximilians-University, Germany; LabEx DISTALZ-U1167 (P.A.), RID-AGE-Risk Factors and Molecular Determinants of Aging-Related Diseases, University of Lille; Inserm U1167 (P.A.), Lille; Centre Hospitalier Universitaire Lille (P.A.); Institut Pasteur de Lille (P.A.), France; Department of Neurology (T.M.), Helsinki University Central Hospital, Finland; Department of Clinical and Experimental Sciences (A.P.), Neurology Clinic, Brescia University Hospital, Italy; Institute of Public Health (T.K.), Charité—Universitätsmedizin Berlin, Germany; and Department of Neurology (S.D.), CHU de Bordeaux, France
| | - Alessandro Pezzini
- From the Harvard Medical School (I.D., D.C.), Boston, MA; Division of Preventive Medicine (I.D., R.D., D.C.), Brigham and Women's Hospital, Boston, MA; University of Bordeaux (M.S., S.D.), Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, France; Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (M.S.), University of Texas Health, San Antonio; Massachusetts General Hospital (P.G.), Boston; Institute for Stroke and Dementia Research (R.M.), Klinikum der Universität München, Ludwig-Maximilians-University, Germany; LabEx DISTALZ-U1167 (P.A.), RID-AGE-Risk Factors and Molecular Determinants of Aging-Related Diseases, University of Lille; Inserm U1167 (P.A.), Lille; Centre Hospitalier Universitaire Lille (P.A.); Institut Pasteur de Lille (P.A.), France; Department of Neurology (T.M.), Helsinki University Central Hospital, Finland; Department of Clinical and Experimental Sciences (A.P.), Neurology Clinic, Brescia University Hospital, Italy; Institute of Public Health (T.K.), Charité—Universitätsmedizin Berlin, Germany; and Department of Neurology (S.D.), CHU de Bordeaux, France
| | - Tobias Kurth
- From the Harvard Medical School (I.D., D.C.), Boston, MA; Division of Preventive Medicine (I.D., R.D., D.C.), Brigham and Women's Hospital, Boston, MA; University of Bordeaux (M.S., S.D.), Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, France; Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (M.S.), University of Texas Health, San Antonio; Massachusetts General Hospital (P.G.), Boston; Institute for Stroke and Dementia Research (R.M.), Klinikum der Universität München, Ludwig-Maximilians-University, Germany; LabEx DISTALZ-U1167 (P.A.), RID-AGE-Risk Factors and Molecular Determinants of Aging-Related Diseases, University of Lille; Inserm U1167 (P.A.), Lille; Centre Hospitalier Universitaire Lille (P.A.); Institut Pasteur de Lille (P.A.), France; Department of Neurology (T.M.), Helsinki University Central Hospital, Finland; Department of Clinical and Experimental Sciences (A.P.), Neurology Clinic, Brescia University Hospital, Italy; Institute of Public Health (T.K.), Charité—Universitätsmedizin Berlin, Germany; and Department of Neurology (S.D.), CHU de Bordeaux, France
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48
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Mo C, Ye Z, Ke H, Lu T, Canida T, Liu S, Wu Q, Zhao Z, Ma Y, Elliot Hong L, Kochunov P, Ma T, Chen S. A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2022; 27:73-84. [PMID: 34890138 PMCID: PMC8669774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The advent of simultaneously collected imaging-genetics data in large study cohorts provides an unprecedented opportunity to assess the causal effect of brain imaging traits on externally measured experimental results (e.g., cognitive tests) by treating genetic variants as instrumental variables. However, classic Mendelian Randomization methods are limited when handling high-throughput imaging traits as exposures to identify causal effects. We propose a new Mendelian Randomization framework to jointly select instrumental variables and imaging exposures, and then estimate the causal effect of multivariable imaging data on the outcome. We validate the proposed method with extensive data analyses and compare it with existing methods. We further apply our method to evaluate the causal effect of white matter microstructure integrity (WM) on cognitive function. The findings suggest that our method achieved better performance regarding sensitivity, bias, and false discovery rate compared to individually assessing the causal effect of a single exposure and jointly assessing the causal effect of multiple exposures without dimension reduction. Our application results indicated that WM measures across different tracts have a joint causal effect that significantly impacts the cognitive function among the participants from the UK Biobank.
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Affiliation(s)
- Chen Mo
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
| | - Hongjie Ke
- Department of Mathematics, University of Maryland, College Park, Maryland 20740, United States of America
| | - Tong Lu
- Department of Mathematics, University of Maryland, College Park, Maryland 20740, United States of America
| | - Travis Canida
- Department of Mathematics, University of Maryland, College Park, Maryland 20740, United States of America
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250353, China
| | - Qiong Wu
- Department of Mathematics, University of Maryland, College Park, Maryland 20740, United States of America
| | - Zhiwei Zhao
- Department of Mathematics, University of Maryland, College Park, Maryland 20740, United States of America
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland 20740, United States of America
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
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49
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Deary IJ, Cox SR, Hill WD. Genetic variation, brain, and intelligence differences. Mol Psychiatry 2022; 27:335-353. [PMID: 33531661 PMCID: PMC8960418 DOI: 10.1038/s41380-021-01027-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 12/28/2020] [Accepted: 01/11/2021] [Indexed: 01/30/2023]
Abstract
Individual differences in human intelligence, as assessed using cognitive test scores, have a well-replicated, hierarchical phenotypic covariance structure. They are substantially stable across the life course, and are predictive of educational, social, and health outcomes. From this solid phenotypic foundation and importance for life, comes an interest in the environmental, social, and genetic aetiologies of intelligence, and in the foundations of intelligence differences in brain structure and functioning. Here, we summarise and critique the last 10 years or so of molecular genetic (DNA-based) research on intelligence, including the discovery of genetic loci associated with intelligence, DNA-based heritability, and intelligence's genetic correlations with other traits. We summarise new brain imaging-intelligence findings, including whole-brain associations and grey and white matter associations. We summarise regional brain imaging associations with intelligence and interpret these with respect to theoretical accounts. We address research that combines genetics and brain imaging in studying intelligence differences. There are new, though modest, associations in all these areas, and mechanistic accounts are lacking. We attempt to identify growing points that might contribute toward a more integrated 'systems biology' account of some of the between-individual differences in intelligence.
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Affiliation(s)
- Ian J. Deary
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ UK
| | - Simon R. Cox
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ UK
| | - W. David Hill
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ UK
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50
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Farrell SF, Campos AI, Kho PF, de Zoete RMJ, Sterling M, Rentería ME, Ngo TT, Cuéllar-Partida G. Genetic basis to structural grey matter associations with chronic pain. Brain 2021; 144:3611-3622. [PMID: 34907416 DOI: 10.1093/brain/awab334] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/06/2021] [Accepted: 08/12/2021] [Indexed: 01/26/2023] Open
Abstract
Structural neuroimaging studies of individuals with chronic pain conditions have often observed decreased regional grey matter at a phenotypic level. However, it is not known if this association can be attributed to genetic factors. Here we employed a novel integrative data-driven and hypothesis-testing approach to determine whether there is a genetic basis to grey matter morphology differences in chronic pain. Using publicly available genome-wide association study summary statistics for regional chronic pain conditions (n = 196 963) and structural neuroimaging measures (n = 19 629-34 000), we applied bivariate linkage disequilibrium-score regression and latent causal variable analyses to determine the genetic correlations (rG) and genetic causal proportion (GCP) between these complex traits, respectively. Five a priori brain regions (i.e. prefrontal cortex, cingulate cortex, insula, thalamus and superior temporal gyrus) were selected based on systematic reviews of grey matter morphology studies in chronic pain. Across this evidence-based selection of five brain regions, 10 significant negative genetic correlations (out of 369) were found (false discovery rate < 5%), suggesting a shared genetic basis to both reduced regional grey matter morphology and the presence of chronic pain. Specifically, negative genetic correlations were observed between reduced insula grey matter morphology and chronic pain in the abdomen (mean insula cortical thickness), hips (left insula volume) and neck/shoulders (left and right insula volume). Similarly, a shared genetic basis was found for reduced posterior cingulate cortex volume in chronic pain of the hip (left and right posterior cingulate), neck/shoulder (left posterior cingulate) and chronic pain at any site (left posterior cingulate); and for reduced pars triangularis volume in chronic neck/shoulder (left pars triangularis) and widespread pain (right pars triangularis). Across these negative genetic correlations, a significant genetic causal proportion was only found between mean insula thickness and chronic abdominal pain [rG (standard error, SE) = -0.25 (0.08), P = 1.06 × 10-3; GCP (SE) = -0.69 (0.20), P = 4.96 × 10-4]. This finding suggests that the genes underlying reduced cortical thickness of the insula causally contribute to an increased risk of chronic abdominal pain. Altogether, these results provide independent corroborating evidence for observational reports of decreased grey matter of particular brain regions in chronic pain. Further, we show for the first time that this association is mediated (in part) by genetic factors. These novel findings warrant further investigation into the neurogenetic pathways that underlie the development and prolongation of chronic pain conditions.
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Affiliation(s)
- Scott F Farrell
- RECOVER Injury Research Centre, The University of Queensland, Herston, QLD, Australia.,NHMRC Centre for Research Excellence in Road Traffic Injury Recovery, The University of Queensland, Herston, QLD, Australia.,Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia
| | - Adrián I Campos
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, St Lucia, QLD, Australia.,Genetic Epidemiology Laboratory, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Pik-Fang Kho
- Molecular Cancer Epidemiology Laboratory, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.,School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Rutger M J de Zoete
- School of Allied Health Science and Practice, The University of Adelaide, Adelaide, SA, Australia
| | - Michele Sterling
- RECOVER Injury Research Centre, The University of Queensland, Herston, QLD, Australia.,NHMRC Centre for Research Excellence in Road Traffic Injury Recovery, The University of Queensland, Herston, QLD, Australia
| | - Miguel E Rentería
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, St Lucia, QLD, Australia.,Genetic Epidemiology Laboratory, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Trung Thanh Ngo
- Diamantina Institute, The University of Queensland and Translational Research Institute, Woolloongabba, QLD, Australia
| | - Gabriel Cuéllar-Partida
- Diamantina Institute, The University of Queensland and Translational Research Institute, Woolloongabba, QLD, Australia
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