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Yang B, Hu S, Jiang Y, Xu L, Shu S, Zhang H. Advancements in Single-Cell RNA Sequencing Research for Neurological Diseases. Mol Neurobiol 2024:10.1007/s12035-024-04126-3. [PMID: 38564138 DOI: 10.1007/s12035-024-04126-3] [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: 11/29/2023] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
Neurological diseases are a major cause of the global burden of disease. Although the mechanisms of the occurrence and development of neurological diseases are not fully clear, most of them are associated with cells mediating neuroinflammation. Yet medications and other therapeutic options to improve treatment are still very limited. Single-cell RNA sequencing (scRNA-seq), as a delightfully potent breakthrough technology, not only identifies various cell types and response states but also uncovers cell-specific gene expression changes, gene regulatory networks, intercellular communication, and cellular movement trajectories, among others, in different cell types. In this review, we describe the technology of scRNA-seq in detail and discuss and summarize the application of scRNA-seq in exploring neurological diseases, elaborating the corresponding specific mechanisms of the diseases as well as providing a reliable basis for new therapeutic approaches. Finally, we affirm that scRNA-seq promotes the development of the neuroscience field and enables us to have a deeper cellular understanding of neurological diseases in the future, which provides strong support for the treatment of neurological diseases and the improvement of patients' prognosis.
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Affiliation(s)
- Bingjie Yang
- Department of Neurology, The Fourth Clinical School of Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Shuqi Hu
- Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Neurology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Yiru Jiang
- Department of Neurology, The Fourth Clinical School of Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Lei Xu
- Department of Neurology, Zhejiang Rongjun Hospital, Jiaxing, Zhejiang, China
| | - Song Shu
- Department of Neurology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Hao Zhang
- Department of Neurology, The Fourth Clinical School of Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
- Department of Neurology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China.
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2
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Huang AY, Zhou Z, Talukdar M, Miller MB, Chhouk B, Enyenihi L, Rosen I, Stronge E, Zhao B, Kim D, Choi J, Khoshkhoo S, Kim J, Ganz J, Travaglini K, Gabitto M, Hodge R, Kaplan E, Lein E, De Jager PL, Bennett DA, Lee EA, Walsh CA. Somatic cancer driver mutations are enriched and associated with inflammatory states in Alzheimer's disease microglia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.03.574078. [PMID: 38260600 PMCID: PMC10802273 DOI: 10.1101/2024.01.03.574078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Alzheimer's disease (AD) is an age-associated neurodegenerative disorder characterized by progressive neuronal loss and pathological accumulation of the misfolded proteins amyloid-β and tau1,2. Neuroinflammation mediated by microglia and brain-resident macrophages plays a crucial role in AD pathogenesis1-5, though the mechanisms by which age, genes, and other risk factors interact remain largely unknown. Somatic mutations accumulate with age and lead to clonal expansion of many cell types, contributing to cancer and many non-cancer diseases6,7. Here we studied somatic mutation in normal aged and AD brains by three orthogonal methods and in three independent AD cohorts. Analysis of bulk RNA sequencing data from 866 samples from different brain regions revealed significantly higher (~two-fold) overall burdens of somatic single-nucleotide variants (sSNVs) in AD brains compared to age-matched controls. Molecular-barcoded deep (>1000X) gene panel sequencing of 311 prefrontal cortex samples showed enrichment of sSNVs and somatic insertions and deletions (sIndels) in cancer driver genes in AD brain compared to control, with recurrent, and often multiple, mutations in genes implicated in clonal hematopoiesis (CH)8,9. Pathogenic sSNVs were enriched in CSF1R+ microglia of AD brains, and the high proportion of microglia (up to 40%) carrying some sSNVs in cancer driver genes suggests mutation-driven microglial clonal expansion (MiCE). Analysis of single-nucleus RNA sequencing (snRNAseq) from temporal neocortex of 62 additional AD cases and controls exhibited nominally increased mosaic chromosomal alterations (mCAs) associated with CH10,11. Microglia carrying mCA showed upregulated pro-inflammatory genes, resembling the transcriptomic features of disease-associated microglia (DAM) in AD. Our results suggest that somatic driver mutations in microglia are common with normal aging but further enriched in AD brain, driving MiCE with inflammatory and DAM signatures. Our findings provide the first insights into microglial clonal dynamics in AD and identify potential new approaches to AD diagnosis and therapy.
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Affiliation(s)
- August Yue Huang
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zinan Zhou
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Maya Talukdar
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard-MIT MD/PhD Program, Boston, MA, USA
| | - Michael B. Miller
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Neuropathology, Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Brian Chhouk
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
| | - Liz Enyenihi
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard-MIT MD/PhD Program, Boston, MA, USA
| | - Ila Rosen
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
| | - Edward Stronge
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard-MIT MD/PhD Program, Boston, MA, USA
| | - Boxun Zhao
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dachan Kim
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Department of Otorhinolaryngology, Severance Hospital, Yonsei University Health System, Yonsei University College of Medicine, Seoul, South Korea
| | - Jaejoon Choi
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sattar Khoshkhoo
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Junho Kim
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Sciences, Sungkyunkwan University, Suwon, South Korea
| | - Javier Ganz
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | - Eitan Kaplan
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Philip L. De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical College, Chicago, IL, USA
| | - Eunjung Alice Lee
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christopher A. Walsh
- Division of Genetics and Genomics and Manton Center for Orphan Diseases, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Boston, MA USA
- Departments of Neurology, Harvard Medical School, Boston, MA, USA
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3
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Morgan SL, Naderi P, Koler K, Pita-Juarez Y, Prokopenko D, Vlachos IS, Tanzi RE, Bertram L, Hide WA. Most Pathways Can Be Related to the Pathogenesis of Alzheimer’s Disease. Front Aging Neurosci 2022; 14:846902. [PMID: 35813951 PMCID: PMC9263183 DOI: 10.3389/fnagi.2022.846902] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 04/11/2022] [Indexed: 11/18/2022] Open
Abstract
Alzheimer’s disease (AD) is a complex neurodegenerative disorder. The relative contribution of the numerous underlying functional mechanisms is poorly understood. To comprehensively understand the context and distribution of pathways that contribute to AD, we performed text-mining to generate an exhaustive, systematic assessment of the breadth and diversity of biological pathways within a corpus of 206,324 dementia publication abstracts. A total of 91% (325/335) of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways have publications containing an association via at least 5 studies, while 63% of pathway terms have at least 50 studies providing a clear association with AD. Despite major technological advances, the same set of top-ranked pathways have been consistently related to AD for 30 years, including AD, immune system, metabolic pathways, cholinergic synapse, long-term depression, proteasome, diabetes, cancer, and chemokine signaling. AD pathways studied appear biased: animal model and human subject studies prioritize different AD pathways. Surprisingly, human genetic discoveries and drug targeting are not enriched in the most frequently studied pathways. Our findings suggest that not only is this disorder incredibly complex, but that its functional reach is also nearly global. As a consequence of our study, research results can now be assessed in the context of the wider AD literature, supporting the design of drug therapies that target a broader range of mechanisms. The results of this study can be explored at www.adpathways.org.
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Affiliation(s)
- Sarah L. Morgan
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Blizard Institute, Department of Neuroscience, Surgery and Trauma, Queen Mary University of London, London, United Kingdom
| | - Pourya Naderi
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Katjuša Koler
- Department of Computer Science, The University of Sheffield, Sheffield, United Kingdom
| | - Yered Pita-Juarez
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Dmitry Prokopenko
- Harvard Medical School, Boston, MA, United States
- Genetics and Aging Research Unit, The Henry and Allison McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Ioannis S. Vlachos
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Rudolph E. Tanzi
- Harvard Medical School, Boston, MA, United States
- Genetics and Aging Research Unit, The Henry and Allison McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Winston A. Hide
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- *Correspondence: Winston A. Hide,
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4
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Gąsiorowski K, Brokos JB, Sochocka M, Ochnik M, Chojdak-Łukasiewicz J, Zajączkowska K, Fułek M, Leszek J. Current and Near-Future Treatment of Alzheimer's Disease. Curr Neuropharmacol 2022; 20:1144-1157. [PMID: 34856906 PMCID: PMC9886829 DOI: 10.2174/1570159x19666211202124239] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/19/2021] [Accepted: 11/26/2021] [Indexed: 11/22/2022] Open
Abstract
Recent findings have improved our understanding of the multifactorial nature of AD. While in early asymptomatic stages of AD, increased amyloid-β synthesis and tau hyperphosphorylation play a key role, while in the latter stages of the disease, numerous dysfunctions of homeostatic mechanisms in neurons, glial cells, and cerebrovascular endothelium determine the rate of progression of clinical symptoms. The main driving forces of advanced neurodegeneration include increased inflammatory reactions in neurons and glial cells, oxidative stress, deficiencies in neurotrophic growth and regenerative capacity of neurons, brain insulin resistance with disturbed metabolism in neurons, or reduction of the activity of the Wnt-β catenin pathway, which should integrate the homeostatic mechanisms of brain tissue. In order to more effectively inhibit the progress of neurodegeneration, combination therapies consisting of drugs that rectify several above-mentioned dysfunctions should be used. It should be noted that many widely-used drugs from various pharmacological groups, "in addition" to the main therapeutic indications, have a beneficial effect on neurodegeneration and may be introduced into clinical practice in combination therapy of AD. There is hope that complex treatment will effectively inhibit the progression of AD and turn it into a slowly progressing chronic disease. Moreover, as the mechanisms of bidirectional communication between the brain and microbiota are better understood, it is expected that these pathways will be harnessed to provide novel methods to enhance health and treat AD.
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Affiliation(s)
| | | | - Marta Sochocka
- Laboratory of Virology, Department of Immunology of Infectious Diseases, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland
| | - Michał Ochnik
- Laboratory of Virology, Department of Immunology of Infectious Diseases, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland
| | | | | | - Michał Fułek
- Department of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology, Wrocław Medical University, Wrocław, Poland
| | - Jerzy Leszek
- Department of Psychiatry, Wrocław Medical University, Wrocław, Poland,Address correspondence to this author at the Department of Psychiatry, Wrocław Medical University, 10 Ludwika Pasteura Str., 50-367 Wrocław, Poland; Tel:+48603880572; E-mail:
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5
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Magusali N, Graham AC, Piers TM, Panichnantakul P, Yaman U, Shoai M, Reynolds RH, Botia JA, Brookes KJ, Guetta-Baranes T, Bellou E, Bayram S, Sokolova D, Ryten M, Sala Frigerio C, Escott-Price V, Morgan K, Pocock JM, Hardy J, Salih DA. A genetic link between risk for Alzheimer's disease and severe COVID-19 outcomes via the OAS1 gene. Brain 2021; 144:3727-3741. [PMID: 34619763 PMCID: PMC8500089 DOI: 10.1093/brain/awab337] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 07/19/2021] [Accepted: 08/02/2021] [Indexed: 01/12/2023] Open
Abstract
Recently, we reported oligoadenylate synthetase 1 (OAS1) contributed to the risk of Alzheimer's disease, by its enrichment in transcriptional networks expressed by microglia. However, the function of OAS1 within microglia was not known. Using genotyping from 1313 individuals with sporadic Alzheimer's disease and 1234 control individuals, we confirm the OAS1 variant, rs1131454, is associated with increased risk for Alzheimer's disease. The same OAS1 locus has been recently associated with severe coronavirus disease 2019 (COVID-19) outcomes, linking risk for both diseases. The single nucleotide polymorphisms rs1131454(A) and rs4766676(T) are associated with Alzheimer's disease, and rs10735079(A) and rs6489867(T) are associated with severe COVID-19, where the risk alleles are linked with decreased OAS1 expression. Analysing single-cell RNA-sequencing data of myeloid cells from Alzheimer's disease and COVID-19 patients, we identify co-expression networks containing interferon (IFN)-responsive genes, including OAS1, which are significantly upregulated with age and both diseases. In human induced pluripotent stem cell-derived microglia with lowered OAS1 expression, we show exaggerated production of TNF-α with IFN-γ stimulation, indicating OAS1 is required to limit the pro-inflammatory response of myeloid cells. Collectively, our data support a link between genetic risk for Alzheimer's disease and susceptibility to critical illness with COVID-19 centred on OAS1, a finding with potential implications for future treatments of Alzheimer's disease and COVID-19, and development of biomarkers to track disease progression.
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Affiliation(s)
- Naciye Magusali
- UK Dementia Research Institute at UCL, Gower Street, London WC1E 6BT, UK
| | - Andrew C Graham
- UK Dementia Research Institute at UCL, Gower Street, London WC1E 6BT, UK
| | - Thomas M Piers
- Department of Neuroinflammation, Queen Square Institute of Neurology, UCL, London WC1N 1PJ, UK
| | | | - Umran Yaman
- UK Dementia Research Institute at UCL, Gower Street, London WC1E 6BT, UK
| | - Maryam Shoai
- UK Dementia Research Institute at UCL, Gower Street, London WC1E 6BT, UK
- Department of Neurodegenerative Diseases, Queen Square Institute of Neurology, UCL, London WC1N 1PJ, UK
| | - Regina H Reynolds
- Department of Neurodegenerative Diseases, Queen Square Institute of Neurology, UCL, London WC1N 1PJ, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL, London WC1N 1EH, UK
- Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, UCL, London WC1N 1EH, UK
| | - Juan A Botia
- Department of Neurodegenerative Diseases, Queen Square Institute of Neurology, UCL, London WC1N 1PJ, UK
- Department of Information and Communications Engineering, Universidad de Murcia, 30100 Murcia, Spain
| | - Keeley J Brookes
- Biosciences, School of Science and Technology, Nottingham Trent University, Nottingham NG8 11NS, UK
| | - Tamar Guetta-Baranes
- Genetics, School of Life Sciences, Life Sciences Building, University Park, University of Nottingham, Nottingham NG7 2RD, UK
| | - Eftychia Bellou
- Dementia Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff CF24 4HQ, UK
| | - Sevinc Bayram
- Hitachi Rail Europe Ltd, New Ludgate, London EC4M 7HX, UK
| | - Dimitra Sokolova
- UK Dementia Research Institute at UCL, Gower Street, London WC1E 6BT, UK
| | - Mina Ryten
- Department of Neurodegenerative Diseases, Queen Square Institute of Neurology, UCL, London WC1N 1PJ, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL, London WC1N 1EH, UK
- Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, UCL, London WC1N 1EH, UK
| | | | - Valentina Escott-Price
- Dementia Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff CF24 4HQ, UK
| | - Kevin Morgan
- Genetics, School of Life Sciences, Life Sciences Building, University Park, University of Nottingham, Nottingham NG7 2RD, UK
| | - Jennifer M Pocock
- Department of Neuroinflammation, Queen Square Institute of Neurology, UCL, London WC1N 1PJ, UK
| | - John Hardy
- UK Dementia Research Institute at UCL, Gower Street, London WC1E 6BT, UK
- Department of Neurodegenerative Diseases, Queen Square Institute of Neurology, UCL, London WC1N 1PJ, UK
| | - Dervis A Salih
- UK Dementia Research Institute at UCL, Gower Street, London WC1E 6BT, UK
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6
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Rowe TW, Katzourou IK, Stevenson-Hoare JO, Bracher-Smith MR, Ivanov DK, Escott-Price V. Machine learning for the life-time risk prediction of Alzheimer's disease: a systematic review. Brain Commun 2021; 3:fcab246. [PMID: 34805994 PMCID: PMC8598986 DOI: 10.1093/braincomms/fcab246] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 06/30/2021] [Accepted: 07/19/2021] [Indexed: 12/23/2022] Open
Abstract
Alzheimer’s disease is a neurodegenerative disorder and the most common form of dementia. Early diagnosis may assist interventions to delay onset and reduce the progression rate of the disease. We systematically reviewed the use of machine learning algorithms for predicting Alzheimer’s disease using single nucleotide polymorphisms and instances where these were combined with other types of data. We evaluated the ability of machine learning models to distinguish between controls and cases, while also assessing their implementation and potential biases. Articles published between December 2009 and June 2020 were collected using Scopus, PubMed and Google Scholar. These were systematically screened for inclusion leading to a final set of 12 publications. Eighty-five per cent of the included studies used the Alzheimer's Disease Neuroimaging Initiative dataset. In studies which reported area under the curve, discrimination varied (0.49–0.97). However, more than half of the included manuscripts used other forms of measurement, such as accuracy, sensitivity and specificity. Model calibration statistics were also found to be reported inconsistently across all studies. The most frequent limitation in the assessed studies was sample size, with the total number of participants often numbering less than a thousand, whilst the number of predictors usually ran into the many thousands. In addition, key steps in model implementation and validation were often not performed or unreported, making it difficult to assess the capability of machine learning models.
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Affiliation(s)
- Thomas W Rowe
- UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | | | | | - Matthew R Bracher-Smith
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff CF24 4HQ, UK
| | - Dobril K Ivanov
- UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Valentina Escott-Price
- UK Dementia Research Institute, Cardiff University, Cardiff, UK.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff CF24 4HQ, UK
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7
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Kondratyev NV, Alfimova MV, Golov AK, Golimbet VE. Bench Research Informed by GWAS Results. Cells 2021; 10:3184. [PMID: 34831407 PMCID: PMC8623533 DOI: 10.3390/cells10113184] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/11/2021] [Accepted: 11/11/2021] [Indexed: 12/15/2022] Open
Abstract
Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually 'highly polygenic'. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise 'wet biologists' with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.
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Affiliation(s)
| | | | - Arkadiy K. Golov
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
- Institute of Gene Biology, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Vera E. Golimbet
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
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