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Kuzma A, Valladares O, Greenfest-Allen E, Nicaretta H, Kirsch M, Ren Y, Katanic Z, White H, Wilk A, Bass L, Brettschneider J, Carter L, Cifello J, Chuang WH, Clark K, Gangadharan P, Haut J, Ho PC, Horng W, Iqbal T, Jin Y, Keskinen P, Lerro Rose A, Moon MK, Manuel J, Qu L, Robbins F, Saravanan N, Sha J, Tate S, Zhao Y, Cantwell L, Gardner J, Chou SY, Tzeng JY, Bush W, Naj A, Kuksa P, Lee WP, Leung YY, Schellenberg G, Wang LS. NIAGADS: A Comprehensive National Data Repository for Alzheimer's Disease and Related Dementia Genetics and Genomics Research. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.10.07.24315029. [PMID: 39417134 PMCID: PMC11483014 DOI: 10.1101/2024.10.07.24315029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
NIAGADS is the National Institute on Aging (NIA) designated national data repository for human genetics research on Alzheimer's Disease and related dementia (ADRD). NIAGADS maintains a high-quality data collection for ADRD genetic/genomic research and supports genetics data production and analysis. NIAGADS hosts whole genome and exome sequence data from the Alzheimer's Disease Sequencing Project (ADSP) and other genotype/phenotype data, encompassing 209,000 samples. NIAGADS shares these data with hundreds of research groups around the world via the Data Sharing Service, a FISMA moderate compliant cloud-based platform that fully supports the NIH Genome Data Sharing Policy. NIAGADS Open Access consists of multiple knowledge bases with genome-wide association summary statistics and rich annotations on the biological significance of genetic variants and genes across the human genome. NIAGADS stands as a keystone in promoting collaborations to advance the understanding and treatment of Alzheimer's disease.
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Moore A, Ritchie MD. Cross-phenotype associations between Alzheimer's Disease and its comorbidities may provide clues to progression. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:623-631. [PMID: 38827078 PMCID: PMC11141840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease worldwide, with one in nine people over the age of 65 living with the disease in 2023. In this study, we used a phenome wide association study (PheWAS) approach to identify cross-phenotype between previously identified genetic associations for AD and electronic health record (EHR) diagnoses from the UK Biobank (UKBB) (n=361,194 of European ancestry) and the eMERGE Network (n=105,108 of diverse ancestry). Based on 497 previously identified AD-associated variants from the Alzheimer's Disease Variant Portal (ADVP), we found significant associations primarily in immune and cardiac related diseases in our PheWAS. Replicating variants have widespread impacts on immune genes in diverse tissue types. This study demonstrates the potential of using the PheWAS strategy to improve our understanding of AD progression as well as identify potential drug repurposing opportunities for new treatment and disease prevention strategies.
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Affiliation(s)
- Anni Moore
- Genomics and Computational Biology Group, University of Pennsylvania, Philadelphia, PA
| | - Marylyn D Ritchie
- Genomics and Computational Biology Group, University of Pennsylvania, Philadelphia, PA
- Institute of Biomedical Informatics, University of Pennsylvania, Philadelphia, PA
- Division of Informatics, DBEI, Perelman School of Medicine., University of Pennsylvania, Philadelphia, PA
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3
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Gouveia Roque C, Phatnani H, Hengst U. The broken Alzheimer's disease genome. CELL GENOMICS 2024; 4:100555. [PMID: 38697121 PMCID: PMC11099344 DOI: 10.1016/j.xgen.2024.100555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/25/2024] [Accepted: 04/07/2024] [Indexed: 05/04/2024]
Abstract
The complex pathobiology of late-onset Alzheimer's disease (AD) poses significant challenges to therapeutic and preventative interventions. Despite these difficulties, genomics and related disciplines are allowing fundamental mechanistic insights to emerge with clarity, particularly with the introduction of high-resolution sequencing technologies. After all, the disrupted processes at the interface between DNA and gene expression, which we call the broken AD genome, offer detailed quantitative evidence unrestrained by preconceived notions about the disease. In addition to highlighting biological pathways beyond the classical pathology hallmarks, these advances have revitalized drug discovery efforts and are driving improvements in clinical tools. We review genetic, epigenomic, and gene expression findings related to AD pathogenesis and explore how their integration enables a better understanding of the multicellular imbalances contributing to this heterogeneous condition. The frontiers opening on the back of these research milestones promise a future of AD care that is both more personalized and predictive.
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Affiliation(s)
- Cláudio Gouveia Roque
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY 10013, USA; The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
| | - Hemali Phatnani
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY 10013, USA; Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University, New York, NY 10032, USA
| | - Ulrich Hengst
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Department of Pathology & Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
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Haq I, Ngo JC, Roy N, Pan RL, Nawsheen N, Chiu R, Zhang Y, Fujita M, Soni RK, Wu X, Bennett DA, Menon V, Olah M, Sher F. An integrated toolkit for human microglia functional genomics. Stem Cell Res Ther 2024; 15:104. [PMID: 38600587 PMCID: PMC11005142 DOI: 10.1186/s13287-024-03700-9] [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: 09/28/2023] [Accepted: 03/19/2024] [Indexed: 04/12/2024] Open
Abstract
BACKGROUND Microglia, the brain's resident immune cells, play vital roles in brain development, and disorders like Alzheimer's disease (AD). Human iPSC-derived microglia (iMG) provide a promising model to study these processes. However, existing iMG generation protocols face challenges, such as prolonged differentiation time, lack of detailed characterization, and limited gene function investigation via CRISPR-Cas9. METHODS Our integrated toolkit for in-vitro microglia functional genomics optimizes iPSC differentiation into iMG through a streamlined two-step, 20-day process, producing iMG with a normal karyotype. We confirmed the iMG's authenticity and quality through single-cell RNA sequencing, chromatin accessibility profiles (ATAC-Seq), proteomics and functional tests. The toolkit also incorporates a drug-dependent CRISPR-ON/OFF system for temporally controlled gene expression. Further, we facilitate the use of multi-omic data by providing online searchable platform that compares new iMG profiles to human primary microglia: https://sherlab.shinyapps.io/IPSC-derived-Microglia/ . RESULTS Our method generates iMG that closely align with human primary microglia in terms of transcriptomic, proteomic, and chromatin accessibility profiles. Functionally, these iMG exhibit Ca2 + transients, cytokine driven migration, immune responses to inflammatory signals, and active phagocytosis of CNS related substrates including synaptosomes, amyloid beta and myelin. Significantly, the toolkit facilitates repeated iMG harvesting, essential for large-scale experiments like CRISPR-Cas9 screens. The standalone ATAC-Seq profiles of our iMG closely resemble primary microglia, positioning them as ideal tools to study AD-associated single nucleotide variants (SNV) especially in the genome regulatory regions. CONCLUSIONS Our advanced two-step protocol rapidly and efficiently produces authentic iMG. With features like the CRISPR-ON/OFF system and a comprehensive multi-omic data platform, our toolkit equips researchers for robust microglial functional genomic studies. By facilitating detailed SNV investigation and offering a sustainable cell harvest mechanism, the toolkit heralds significant progress in neurodegenerative disease drug research and therapeutic advancement.
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Affiliation(s)
- Imdadul Haq
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Jason C Ngo
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Nainika Roy
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Richard L Pan
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
- Department of Physiology and Cellular Biophysics, Columbia University Medical Center, New York, NY, USA
| | - Nadiya Nawsheen
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Rebecca Chiu
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
- Neuroimmunology Core, Center for Translational & Computational Neuroimmunology, Division of Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Ya Zhang
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
- Neuroimmunology Core, Center for Translational & Computational Neuroimmunology, Division of Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Masashi Fujita
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Rajesh K Soni
- Proteomics Core, Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA
| | - Xuebing Wu
- Department of Medicine, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Vilas Menon
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Marta Olah
- Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Falak Sher
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA.
- Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA.
- Department of Neurology, Columbia University Medical Center, New York, NY, USA.
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Noori A, Jayakumar R, Moturi V, Li Z, Liu R, Serrano-Pozo A, Hyman BT, Das S. Alzheimer DataLENS: An Open Data Analytics Portal for Alzheimer's Disease Research. J Alzheimers Dis 2024; 99:S397-S407. [PMID: 38306039 DOI: 10.3233/jad-230884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Background Recent Alzheimer's disease (AD) discoveries are increasingly based on studies from a variety of omics technologies on large cohorts. Currently, there is no easily accessible resource for neuroscientists to browse, query, and visualize these complex datasets in a harmonized manner. Objective Create an online portal of public omics datasets for AD research. Methods We developed Alzheimer DataLENS, a web-based portal, using the R Shiny platform to query and visualize publicly available transcriptomics and genetics studies of AD on human cohorts. To ensure consistent representation of AD findings, all datasets were processed through a uniform bioinformatics pipeline. Results Alzheimer DataLENS currently houses 2 single-nucleus RNA sequencing datasets, over 30 bulk RNA sequencing datasets from 19 brain regions and 3 cohorts, and 2 genome-wide association studies (GWAS). Available visualizations for single-nucleus data include bubble plots, heatmaps, and UMAP plots; for bulk expression data include box plots and heatmaps; for pathways include protein-protein interaction network plots; and for GWAS results include Manhattan plots. Alzheimer DataLENS also links to two other knowledge resources: the AD Progression Atlas and the Astrocyte Atlas. Conclusions Alzheimer DataLENS is a valuable resource for investigators to quickly and systematically explore omics datasets and is freely accessible at https://alzdatalens.partners.org.
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Affiliation(s)
- Ayush Noori
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Vaishnavi Moturi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Zhaozhi Li
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Rongxin Liu
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Alberto Serrano-Pozo
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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Jiang Y, MacNeil LT. Simple model systems reveal conserved mechanisms of Alzheimer's disease and related tauopathies. Mol Neurodegener 2023; 18:82. [PMID: 37950311 PMCID: PMC10638731 DOI: 10.1186/s13024-023-00664-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 10/04/2023] [Indexed: 11/12/2023] Open
Abstract
The lack of effective therapies that slow the progression of Alzheimer's disease (AD) and related tauopathies highlights the need for a more comprehensive understanding of the fundamental cellular mechanisms underlying these diseases. Model organisms, including yeast, worms, and flies, provide simple systems with which to investigate the mechanisms of disease. The evolutionary conservation of cellular pathways regulating proteostasis and stress response in these organisms facilitates the study of genetic factors that contribute to, or protect against, neurodegeneration. Here, we review genetic modifiers of neurodegeneration and related cellular pathways identified in the budding yeast Saccharomyces cerevisiae, the nematode Caenorhabditis elegans, and the fruit fly Drosophila melanogaster, focusing on models of AD and related tauopathies. We further address the potential of simple model systems to better understand the fundamental mechanisms that lead to AD and other neurodegenerative disorders.
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Affiliation(s)
- Yuwei Jiang
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Canada
| | - Lesley T MacNeil
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Canada.
- Farncombe Family Digestive Health Research Institute, McMaster University, Hamilton, Canada.
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, 1280 Main St W, Hamilton, ON, L8S 4K1, Canada.
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Moore A, Ritchie MD. Cross-phenotype associations between Alzheimer's Disease and its comorbidities may provide clues to progression. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.06.23297993. [PMID: 37986758 PMCID: PMC10659497 DOI: 10.1101/2023.11.06.23297993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease worldwide, with one in nine people over the age of 65 living with the disease in 2023. In this study, we used a phenome wide association study (PheWAS) approach to identify cross-phenotype associations between previously identified genetic AD and for electronic health record (EHR) diagnoses from the UK Biobank (UKBB) (n=361,194 of European ancestry) and the eMERGE Network (n=105,108 of diverse ancestry). Based on 497 previously identified AD-associated variants from the Alzheimer's Disease Variant Portal (ADVP), we found significant associations primarily in immune and cardiac related diseases in our PheWAS. Replicating variants have widespread impacts on immune genes in diverse tissue types. This study demonstrates the potential of using the PheWAS strategy to improve our understanding of AD progression as well as identify potential drug repurposing opportunities for new treatment and disease prevention strategies.
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Affiliation(s)
- Anni Moore
- Genomics and Computational Biology Group, University of Pennsylvania, Philadelphia, PA
| | - Marylyn D Ritchie
- Genomics and Computational Biology Group, University of Pennsylvania, Philadelphia, PA
- Institute of Biomedical Informatics, University of Pennsylvania, Philadelphia, PA; Division of Informatics, DBEI, Perelman School of Medicine., University of Pennsylvania, Philadelphia, PA
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8
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Kuksa PP, Greenfest-Allen E, Cifello J, Ionita M, Wang H, Nicaretta H, Cheng PL, Lee WP, Wang LS, Leung YY. Scalable approaches for functional analyses of whole-genome sequencing non-coding variants. Hum Mol Genet 2022; 31:R62-R72. [PMID: 35943817 PMCID: PMC9585666 DOI: 10.1093/hmg/ddac191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/04/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
Non-coding genetic variants outside of protein-coding genome regions play an important role in genetic and epigenetic regulation. It has become increasingly important to understand their roles, as non-coding variants often make up the majority of top findings of genome-wide association studies (GWAS). In addition, the growing popularity of disease-specific whole-genome sequencing (WGS) efforts expands the library of and offers unique opportunities for investigating both common and rare non-coding variants, which are typically not detected in more limited GWAS approaches. However, the sheer size and breadth of WGS data introduce additional challenges to predicting functional impacts in terms of data analysis and interpretation. This review focuses on the recent approaches developed for efficient, at-scale annotation and prioritization of non-coding variants uncovered in WGS analyses. In particular, we review the latest scalable annotation tools, databases and functional genomic resources for interpreting the variant findings from WGS based on both experimental data and in silico predictive annotations. We also review machine learning-based predictive models for variant scoring and prioritization. We conclude with a discussion of future research directions which will enhance the data and tools necessary for the effective functional analyses of variants identified by WGS to improve our understanding of disease etiology.
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Affiliation(s)
- Pavel P Kuksa
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Emily Greenfest-Allen
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jeffrey Cifello
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matei Ionita
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hui Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Heather Nicaretta
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Po-Liang Cheng
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Wan-Ping Lee
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yuk Yee Leung
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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