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Vaz A, Salgado A, Patrício P, Pinto L. Patient-derived induced pluripotent stem cells: Tools to advance the understanding and drug discovery in Major Depressive Disorder. Psychiatry Res 2024; 339:116033. [PMID: 38968917 DOI: 10.1016/j.psychres.2024.116033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 06/13/2024] [Indexed: 07/07/2024]
Abstract
Major Depressive Disorder (MDD) is a pleomorphic disease with substantial patterns of symptoms and severity with mensurable deficits in several associated domains. The broad spectrum of phenotypes observed in patients diagnosed with depressive disorders is the reflection of a very complex disease where clusters of biological and external factors (e.g., response/processing of life events, intrapsychic factors) converge and mediate pathogenesis, clinical presentation/phenotypes and trajectory. Patient-derived induced pluripotent stem cells (iPSCs) enable their differentiation into specialised cell types in the central nervous system to explore the pathophysiological substrates of MDD. These models may complement animal models to advance drug discovery and identify therapeutic approaches, such as cell therapy, drug repurposing, and elucidation of drug metabolism, toxicity, and mechanisms of action at the molecular/cellular level, to pave the way for precision psychiatry. Despite the remarkable scientific and clinical progress made over the last few decades, the disease is still poorly understood, the incidence and prevalence continue to increase, and more research is needed to meet clinical demands. This review aims to summarise and provide a critical overview of the research conducted thus far using patient-derived iPSCs for the modelling of psychiatric disorders, with a particular emphasis on MDD.
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Affiliation(s)
- Andreia Vaz
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar 4710-057 Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga, Guimarães, Portugal; Bn'ML, Behavioral and Molecular Lab, Braga, Portugal
| | - António Salgado
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar 4710-057 Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga, Guimarães, Portugal
| | - Patrícia Patrício
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar 4710-057 Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga, Guimarães, Portugal; Bn'ML, Behavioral and Molecular Lab, Braga, Portugal
| | - Luísa Pinto
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar 4710-057 Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga, Guimarães, Portugal; Bn'ML, Behavioral and Molecular Lab, Braga, Portugal.
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2
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Zhou W, Cuomo ASE, Xue A, Kanai M, Chau G, Krishna C, Xavier RJ, MacArthur DG, Powell JE, Daly MJ, Neale BM. Efficient and accurate mixed model association tool for single-cell eQTL analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.15.24307317. [PMID: 38798318 PMCID: PMC11118640 DOI: 10.1101/2024.05.15.24307317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Understanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the "pseudobulk" approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets. Here, we propose SAIGE-QTL, a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level. Additionally, SAIGE-QTL allows for testing the effects of less frequent/rare genetic variation through set-based tests, which is traditionally excluded from eQTL mapping studies. We evaluate the performance of SAIGE-QTL on both real and simulated data and demonstrate the improved power for eQTL mapping over existing pipelines.
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Popp JM, Rhodes K, Jangi R, Li M, Barr K, Tayeb K, Battle A, Gilad Y. Cell-type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592174. [PMID: 38746382 PMCID: PMC11092595 DOI: 10.1101/2024.05.02.592174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Identifying the molecular effects of human genetic variation across cellular contexts is crucial for understanding the mechanisms underlying disease-associated loci, yet many cell-types and developmental stages remain underexplored. Here we harnessed the potential of heterogeneous differentiating cultures ( HDCs ), an in vitro system in which pluripotent cells asynchronously differentiate into a broad spectrum of cell-types. We generated HDCs for 53 human donors and collected single-cell RNA-sequencing data from over 900,000 cells. We identified expression quantitative trait loci in 29 cell-types and characterized regulatory dynamics across diverse differentiation trajectories. This revealed novel regulatory variants for genes involved in key developmental and disease-related processes while replicating known effects from primary tissues, and dynamic regulatory effects associated with a range of complex traits.
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4
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Arthur TD, Nguyen JP, D'Antonio-Chronowska A, Jaureguy J, Silva N, Henson B, Panopoulos AD, Belmonte JCI, D'Antonio M, McVicker G, Frazer KA. Multi-omic QTL mapping in early developmental tissues reveals phenotypic and temporal complexity of regulatory variants underlying GWAS loci. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588874. [PMID: 38645112 PMCID: PMC11030419 DOI: 10.1101/2024.04.10.588874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Most GWAS loci are presumed to affect gene regulation, however, only ∼43% colocalize with expression quantitative trait loci (eQTLs). To address this colocalization gap, we identify eQTLs, chromatin accessibility QTLs (caQTLs), and histone acetylation QTLs (haQTLs) using molecular samples from three early developmental (EDev) tissues. Through colocalization, we annotate 586 GWAS loci for 17 traits by QTL complexity, QTL phenotype, and QTL temporal specificity. We show that GWAS loci are highly enriched for colocalization with complex QTL modules that affect multiple elements (genes and/or peaks). We also demonstrate that caQTLs and haQTLs capture regulatory variations not associated with eQTLs and explain ∼49% of the functionally annotated GWAS loci. Additionally, we show that EDev-unique QTLs are strongly depleted for colocalizing with GWAS loci. By conducting one of the largest multi-omic QTL studies to date, we demonstrate that many GWAS loci exhibit phenotypic complexity and therefore, are missed by traditional eQTL analyses.
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Vochteloo M, Deelen P, Vink B, Tsai EA, Runz H, Andreu-Sánchez S, Fu J, Zhernakova A, Westra HJ, Franke L. PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs. Genome Biol 2024; 25:29. [PMID: 38254182 PMCID: PMC10802033 DOI: 10.1186/s13059-023-03151-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets. These contexts are biologically informative and reproducible, outperforming cell counts or expression-based principal components. Furthermore, we show that RNA quality and cell type proportions interact with thousands of eQTLs. Knowledge of hidden eQTL contexts may aid in the inference of functional mechanisms underlying disease variants.
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Affiliation(s)
- Martijn Vochteloo
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Patrick Deelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Britt Vink
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Institute for Life Science & Technology, Hanze University of Applied Sciences, Groningen, The Netherlands
| | - Ellen A Tsai
- Translational Sciences, Research and Development, Biogen, Cambridge, MA, USA
| | - Heiko Runz
- Translational Sciences, Research and Development, Biogen, Cambridge, MA, USA
| | - Sergio Andreu-Sánchez
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jingyuan Fu
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Harm-Jan Westra
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
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Wang R, Helbig I, Edmondson AC, Lin L, Xing Y. Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis. Brief Bioinform 2023; 24:bbad284. [PMID: 37580177 PMCID: PMC10516351 DOI: 10.1093/bib/bbad284] [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: 03/14/2023] [Revised: 07/10/2023] [Accepted: 07/20/2023] [Indexed: 08/16/2023] Open
Abstract
Genomic variants affecting pre-messenger RNA splicing and its regulation are known to underlie many rare genetic diseases. However, common workflows for genetic diagnosis and clinical variant interpretation frequently overlook splice-altering variants. To better serve patient populations and advance biomedical knowledge, it has become increasingly important to develop and refine approaches for detecting and interpreting pathogenic splicing variants. In this review, we will summarize a few recent developments and challenges in using RNA sequencing technologies for rare disease investigation. Moreover, we will discuss how recent computational splicing prediction tools have emerged as complementary approaches for revealing disease-causing variants underlying splicing defects. We speculate that continuous improvements to sequencing technologies and predictive modeling will not only expand our understanding of splicing regulation but also bring us closer to filling the diagnostic gap for rare disease patients.
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Affiliation(s)
- Robert Wang
- Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ingo Helbig
- The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew C Edmondson
- Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Lan Lin
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Yi Xing
- Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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7
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Smirnov D, Konstantinovskiy N, Prokisch H. Integrative omics approaches to advance rare disease diagnostics. J Inherit Metab Dis 2023; 46:824-838. [PMID: 37553850 DOI: 10.1002/jimd.12663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/10/2023]
Abstract
Over the past decade high-throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease-causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA-sequencing, proteomics, metabolomics and DNA-methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.
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Affiliation(s)
- Dmitrii Smirnov
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
| | - Nikita Konstantinovskiy
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
| | - Holger Prokisch
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
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Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE. Single-cell genomics meets human genetics. Nat Rev Genet 2023; 24:535-549. [PMID: 37085594 PMCID: PMC10784789 DOI: 10.1038/s41576-023-00599-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 04/23/2023]
Abstract
Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.
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Affiliation(s)
- Anna S E Cuomo
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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9
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Solé-Navais P, Flatley C, Steinthorsdottir V, Vaudel M, Juodakis J, Chen J, Laisk T, LaBella AL, Westergaard D, Bacelis J, Brumpton B, Skotte L, Borges MC, Helgeland Ø, Mahajan A, Wielscher M, Lin F, Briggs C, Wang CA, Moen GH, Beaumont RN, Bradfield JP, Abraham A, Thorleifsson G, Gabrielsen ME, Ostrowski SR, Modzelewska D, Nohr EA, Hypponen E, Srivastava A, Talbot O, Allard C, Williams SM, Menon R, Shields BM, Sveinbjornsson G, Xu H, Melbye M, Lowe W, Bouchard L, Oken E, Pedersen OB, Gudbjartsson DF, Erikstrup C, Sørensen E, Lie RT, Teramo K, Hallman M, Juliusdottir T, Hakonarson H, Ullum H, Hattersley AT, Sletner L, Merialdi M, Rifas-Shiman SL, Steingrimsdottir T, Scholtens D, Power C, West J, Nyegaard M, Capra JA, Skogholt AH, Magnus P, Andreassen OA, Thorsteinsdottir U, Grant SFA, Qvigstad E, Pennell CE, Hivert MF, Hayes GM, Jarvelin MR, McCarthy MI, Lawlor DA, Nielsen HS, Mägi R, Rokas A, Hveem K, Stefansson K, Feenstra B, Njolstad P, Muglia LJ, Freathy RM, Johansson S, Zhang G, Jacobsson B. Genetic effects on the timing of parturition and links to fetal birth weight. Nat Genet 2023; 55:559-567. [PMID: 37012456 PMCID: PMC10101852 DOI: 10.1038/s41588-023-01343-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 02/22/2023] [Indexed: 04/05/2023]
Abstract
The timing of parturition is crucial for neonatal survival and infant health. Yet, its genetic basis remains largely unresolved. We present a maternal genome-wide meta-analysis of gestational duration (n = 195,555), identifying 22 associated loci (24 independent variants) and an enrichment in genes differentially expressed during labor. A meta-analysis of preterm delivery (18,797 cases, 260,246 controls) revealed six associated loci and large genetic similarities with gestational duration. Analysis of the parental transmitted and nontransmitted alleles (n = 136,833) shows that 15 of the gestational duration genetic variants act through the maternal genome, whereas 7 act both through the maternal and fetal genomes and 2 act only via the fetal genome. Finally, the maternal effects on gestational duration show signs of antagonistic pleiotropy with the fetal effects on birth weight: maternal alleles that increase gestational duration have negative fetal effects on birth weight. The present study provides insights into the genetic effects on the timing of parturition and the complex maternal-fetal relationship between gestational duration and birth weight.
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Affiliation(s)
- Pol Solé-Navais
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden.
| | - Christopher Flatley
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden
| | | | - Marc Vaudel
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Julius Juodakis
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden
| | - Jing Chen
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Abigail L LaBella
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Methods and Analysis, Statistics Denmark, Copenhagen, Denmark
| | - Jonas Bacelis
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden
| | - Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Line Skotte
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Maria C Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Øyvind Helgeland
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Matthias Wielscher
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Frederick Lin
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Catherine Briggs
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Carol A Wang
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, New South Wales, Australia
- Hunter Medical Research Institute, New Lambton Heights, New South Wales, Australia
| | - Gunn-Helen Moen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, Australia
| | - Robin N Beaumont
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | | | - Abin Abraham
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Maiken E Gabrielsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Dominika Modzelewska
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden
| | - Ellen A Nohr
- Research Unit of Gynecology and Obstetrics, Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Elina Hypponen
- Australian Centre for Precision Health, Uni Clinical & Health Sciences, University of South Australia, Adelaide, Australia
- South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Amit Srivastava
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Human Genetics, Center for the Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Octavious Talbot
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Catherine Allard
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CHUS), Sherbrooke, Québec, Canada
| | - Scott M Williams
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Ramkumar Menon
- Department of Obstetrics and Gynaecology, University of Texas Medical Branch, Galveston, TX, USA
| | - Beverley M Shields
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | | | - Huan Xu
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Human Genetics, Center for the Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Mads Melbye
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - William Lowe
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Luigi Bouchard
- Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada
- Clinical Department of Laboratory Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) du Saguenay-Lac-St-Jean - Hôpital Universitaire de Chicoutimi, Saguenay, Québec, Canada
| | - Emily Oken
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Faculty of Health, University of Aarhus, Aarhus, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Rolv T Lie
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Kari Teramo
- Department of Obstetrics and Gynecology, Helsinki University Central Hospital, Helsinki, Finland
- University of Helsinki, Helsinki, Finland
| | - Mikko Hallman
- PEDEGO Research Unit and Medical Research Center Oulu, University of Oulu, Oulu, Finland
- Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | | | - Hakon Hakonarson
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Line Sletner
- Department of Pediatric and Adolescents Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Mario Merialdi
- Maternal Newborn Health Innovations, PBC, Geneva, Switzerland
| | - Sheryl L Rifas-Shiman
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Thora Steingrimsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Obstetrics and Gynecology, Landspitali - The National University Hospital of Iceland, Reykjavik, Iceland
| | - Denise Scholtens
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Christine Power
- Population, Policy, Practice. Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Jane West
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Mette Nyegaard
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - John A Capra
- Bakar Computational Health Sciences Institute and Department of Epidemiology and Statistics, University of California San Francisco, San Francisco, CA, USA
| | - Anne H Skogholt
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A Andreassen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- NORMENT Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Struan F A Grant
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Spatial and Functional Genomics Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Divisions of Human Genetics and Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elisabeth Qvigstad
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Craig E Pennell
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, New South Wales, Australia
- Hunter Medical Research Institute, New Lambton Heights, New South Wales, Australia
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Geoffrey M Hayes
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter of Oulu, University of Oulu, Linnanmaa, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, UK
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Henriette S Nielsen
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- The Recurrent Pregnancy Loss Unit, The Capital Region, Copenhagen University Hospitals Rigshospitalet & Hvidovre Hospital, Hvidovre, Denmark
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
- Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Kari Stefansson
- deCODE genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Pål Njolstad
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Louis J Muglia
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Human Genetics, Center for the Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Rachel M Freathy
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Stefan Johansson
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Ge Zhang
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Human Genetics, Center for the Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Bo Jacobsson
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden.
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway.
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10
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D'Antonio M, Nguyen JP, Arthur TD, Matsui H, D'Antonio-Chronowska A, Frazer KA. Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease. Nat Commun 2023; 14:1132. [PMID: 36854752 PMCID: PMC9975214 DOI: 10.1038/s41467-023-36638-2] [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: 03/30/2022] [Accepted: 02/10/2023] [Indexed: 03/02/2023] Open
Abstract
The causal variants and genes underlying thousands of cardiac GWAS signals have yet to be identified. Here, we leverage spatiotemporal information on 966 RNA-seq cardiac samples and perform an expression quantitative trait locus (eQTL) analysis detecting eQTLs considering both eGenes and eIsoforms. We identify 2,578 eQTLs associated with a specific developmental stage-, tissue- and/or cell type. Colocalization between eQTL and GWAS signals of five cardiac traits identified variants with high posterior probabilities for being causal in 210 GWAS loci. Pulse pressure GWAS loci are enriched for colocalization with fetal- and smooth muscle- eQTLs; pulse rate with adult- and cardiac muscle- eQTLs; and atrial fibrillation with cardiac muscle- eQTLs. Fine mapping identifies 79 credible sets with five or fewer SNPs, of which 15 were associated with spatiotemporal eQTLs. Our study shows that many cardiac GWAS variants impact traits and disease in a developmental stage-, tissue- and/or cell type-specific fashion.
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Affiliation(s)
- Matteo D'Antonio
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
| | - Jennifer P Nguyen
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Timothy D Arthur
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | | | - Kelly A Frazer
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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11
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Xue A, Yazar S, Neavin D, Powell JE. Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses. Genome Biol 2023; 24:33. [PMID: 36823676 PMCID: PMC9948363 DOI: 10.1186/s13059-023-02873-5] [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: 06/22/2022] [Accepted: 02/13/2023] [Indexed: 02/25/2023] Open
Abstract
Using latent variables in gene expression data can help correct unobserved confounders and increase statistical power for expression quantitative trait Loci (eQTL) detection. The probabilistic estimation of expression residuals (PEER) and principal component analysis (PCA) are widely used methods that can remove unwanted variation and improve eQTL discovery power in bulk RNA-seq analysis. However, their performance has not been evaluated extensively in single-cell eQTL analysis, especially for different cell types. Potential challenges arise due to the structure of single-cell RNA-seq data, including sparsity, skewness, and mean-variance relationship. Here, we show by a series of analyses that PEER and PCA require additional quality control and data transformation steps on the pseudo-bulk matrix to obtain valid latent variables; otherwise, it can result in highly correlated factors (Pearson's correlation r = 0.63 ~ 0.99). Incorporating valid PFs/PCs in the eQTL association model would identify 1.7 ~ 13.3% more eGenes. Sensitivity analysis showed that the pattern of change between the number of eGenes detected and fitted PFs/PCs varied significantly in different cell types. In addition, using highly variable genes to generate latent variables could achieve similar eGenes discovery power as using all genes but save considerable computational resources (~ 6.2-fold faster).
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Affiliation(s)
- Angli Xue
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia. .,School of Biomedical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia.
| | - Seyhan Yazar
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Drew Neavin
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia. .,UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, 2052, Australia.
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12
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Oliva M, Demanelis K, Lu Y, Chernoff M, Jasmine F, Ahsan H, Kibriya MG, Chen LS, Pierce BL. DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits. Nat Genet 2023; 55:112-122. [PMID: 36510025 PMCID: PMC10249665 DOI: 10.1038/s41588-022-01248-z] [Citation(s) in RCA: 53] [Impact Index Per Article: 53.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 10/26/2022] [Indexed: 12/14/2022]
Abstract
Studies of DNA methylation (DNAm) in solid human tissues are relatively scarce; tissue-specific characterization of DNAm is needed to understand its role in gene regulation and its relevance to complex traits. We generated array-based DNAm profiles for 987 human samples from the Genotype-Tissue Expression (GTEx) project, representing 9 tissue types and 424 subjects. We characterized methylome and transcriptome correlations (eQTMs), genetic regulation in cis (mQTLs and eQTLs) across tissues and e/mQTLs links to complex traits. We identified mQTLs for 286,152 CpG sites, many of which (>5%) show tissue specificity, and mQTL colocalizations with 2,254 distinct GWAS hits across 83 traits. For 91% of these loci, a candidate gene link was identified by integration of functional maps, including eQTMs, and/or eQTL colocalization, but only 33% of loci involved an eQTL and mQTL present in the same tissue type. With this DNAm-focused integrative analysis, we contribute to the understanding of molecular regulatory mechanisms in human tissues and their impact on complex traits.
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Affiliation(s)
- Meritxell Oliva
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
| | - Kathryn Demanelis
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yihao Lu
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Meytal Chernoff
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Farzana Jasmine
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Habibul Ahsan
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Comprehensive Cancer Center, University of Chicago, Chicago, IL, USA
- Department of Medicine, University of Chicago, Chicago, IL, USA
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
| | - Muhammad G Kibriya
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
| | - Lin S Chen
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
| | - Brandon L Pierce
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Comprehensive Cancer Center, University of Chicago, Chicago, IL, USA.
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13
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Connally NJ, Nazeen S, Lee D, Shi H, Stamatoyannopoulos J, Chun S, Cotsapas C, Cassa CA, Sunyaev SR. The missing link between genetic association and regulatory function. eLife 2022; 11:74970. [PMID: 36515579 PMCID: PMC9842386 DOI: 10.7554/elife.74970] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/02/2022] [Indexed: 12/15/2022] Open
Abstract
The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis-linked genes. However, despite the availability of gene expression and epigenomic datasets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic expression QTLs, suggesting that better models are needed. The field must confront this deficit and pursue this 'missing regulation.'
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Affiliation(s)
- Noah J Connally
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Sumaiya Nazeen
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Department of Neurology, Harvard Medical SchoolBostonUnited States
| | - Daniel Lee
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Huwenbo Shi
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
- Department of Epidemiology, Harvard T.H. Chan School of Public HealthBostonUnited States
| | | | - Sung Chun
- Division of Pulmonary Medicine, Boston Children’s HospitalBostonUnited States
| | - Chris Cotsapas
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
- Department of Neurology, Yale Medical SchoolNew HavenUnited States
- Department of Genetics, Yale Medical SchoolNew HavenUnited States
| | - Christopher A Cassa
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Shamil R Sunyaev
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
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14
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Tabet D, Parikh V, Mali P, Roth FP, Claussnitzer M. Scalable Functional Assays for the Interpretation of Human Genetic Variation. Annu Rev Genet 2022; 56:441-465. [PMID: 36055970 DOI: 10.1146/annurev-genet-072920-032107] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Scalable sequence-function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.
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Affiliation(s)
- Daniel Tabet
- Donnelly Centre, Department of Molecular Genetics, and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Victoria Parikh
- Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Prashant Mali
- Department of Bioengineering, University of California, San Diego, California, USA
| | - Frederick P Roth
- Donnelly Centre, Department of Molecular Genetics, and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Melina Claussnitzer
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine and Endocrine Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA;
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15
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Shi PA, Luchsinger LL, Greally JM, Delaney CS. Umbilical cord blood: an undervalued and underutilized resource in allogeneic hematopoietic stem cell transplant and novel cell therapy applications. Curr Opin Hematol 2022; 29:317-326. [PMID: 36066376 PMCID: PMC9547826 DOI: 10.1097/moh.0000000000000732] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
PURPOSE OF REVIEW The purpose of this review is to primarily discuss the unwarranted decline in the use of umbilical cord blood (UCB) as a source of donor hematopoietic stem cells (HSC) for hematopoietic cell transplantation (HCT) and the resulting important implications in addressing healthcare inequities, and secondly to highlight the incredible potential of UCB and related birthing tissues for the development of a broad range of therapies to treat human disease including but not limited to oncology, neurologic, cardiac, orthopedic and immunologic conditions. RECENT FINDINGS When current best practices are followed, unrelated donor umbilical cord blood transplant (CBT) can provide superior quality of life-related survival compared to other allogeneic HSC donor sources (sibling, matched or mismatched unrelated, and haploidentical) through decreased risks of relapse and chronic graft vs. host disease. Current best practices include improved UCB donor selection criteria with consideration of higher resolution human leukocyte antigen (HLA) typing and CD34+ cell dose, availability of newer myeloablative but reduced toxicity conditioning regimens, and rigorous supportive care in the early posttransplant period with monitoring for known complications, especially related to viral and other infections that may require intervention. Emerging best practice may include the use of ex vivo expanded single-unit CBT rather than double-unit CBT (dCBT) or 'haplo-cord' transplant, and the incorporation of posttransplant cyclophosphamide as with haploidentical transplant and/or incorporation of novel posttransplant therapies to reduce the risk of relapse, such as NK cell adoptive transfer. Novel, non-HCT uses of UCB and birthing tissue include the production of UCB-derived immune effector cell therapies such as unmodified NK cells, chimeric antigen receptor-natural killer cells and immune T-cell populations, the isolation of mesenchymal stem cells for immune modulatory treatments and derivation of induced pluripotent stem cells haplobanks for regenerative medicine development and population studies to facilitate exploration of drug development through functional genomics. SUMMARY The potential of allogeneic UCB for HCT and novel cell-based therapies is undervalued and underutilized. The inventory of high-quality UCB units available from public cord blood banks (CBB) should be expanding rather than contracting in order to address ongoing healthcare inequities and to maintain a valuable source of cellular starting material for cell and gene therapies and regenerative medicine approaches. The expertise in Good Manufacturing Practice-grade manufacturing provided by CBB should be supported to effectively partner with groups developing UCB for novel cell-based therapies.
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Affiliation(s)
- Patricia A. Shi
- Lindsley F. Kimball Research Institute, New York Blood Center, New York City, NY 10065
| | - Larry L. Luchsinger
- Lindsley F. Kimball Research Institute, New York Blood Center, New York City, NY 10065
| | - John M. Greally
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461
| | - Colleen S. Delaney
- Division of Hematology-Oncology, Seattle Children’s Hospital, Seattle WA; and Department of Pediatrics, University of Washington School of Medicine, Seattle, WA 98195
- Deverra Therapeutics, Inc., Seattle, WA 98102
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16
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Jezkova J, Shaw S, Taverner NV, Williams HJ. Rapid genome sequencing for pediatrics. Hum Mutat 2022; 43:1507-1518. [PMID: 36086948 PMCID: PMC9826377 DOI: 10.1002/humu.24466] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/30/2022] [Accepted: 09/06/2022] [Indexed: 01/11/2023]
Abstract
The advancements made in next-generation sequencing (NGS) technology over the past two decades have transformed our understanding of genetic variation in humans and had a profound impact on our ability to diagnose patients with rare genetic diseases. In this review, we discuss the recently developed application of rapid NGS techniques, used to diagnose pediatric patients with suspected rare diseases who are critically ill. We highlight the challenges associated with performing such clinical diagnostics tests in terms of the laboratory infrastructure, bioinformatic analysis pipelines, and the ethical considerations that need to be addressed. We end by looking at what future developments in this field may look like and how they can be used to augment the genetic data to further improve the diagnostic rates for these high-priority patients.
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Affiliation(s)
- Jana Jezkova
- All Wales Medical Genomics Service, Cardiff and Vale NHS TrustHeath HospitalCardiffUK
| | - Sophie Shaw
- All Wales Medical Genomics Service, Cardiff and Vale NHS TrustHeath HospitalCardiffUK
| | - Nicola V. Taverner
- All Wales Medical Genomics Service, Cardiff and Vale NHS TrustHeath HospitalCardiffUK,Centre for Medical Education, School of MedicineCardiff UniversityHeath ParkCardiffUK
| | - Hywel J. Williams
- Division of Cancer and Genetics, Genetic and Genomic Medicine, School of MedicineCardiff UniversityHeath ParkCardiffUK
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17
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Martinez-Delgado B, Barrero MJ. Epigenomic Approaches for the Diagnosis of Rare Diseases. EPIGENOMES 2022; 6:epigenomes6030021. [PMID: 35997367 PMCID: PMC9397041 DOI: 10.3390/epigenomes6030021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/13/2022] [Accepted: 07/20/2022] [Indexed: 01/27/2023] Open
Abstract
Rare diseases affect more than 300 million people worldwide. Diagnosing rare diseases is a major challenge as they have different causes and etiologies. Careful assessment of clinical symptoms often leads to the testing of the most common genetic alterations that could explain the disease. Patients with negative results for these tests frequently undergo whole exome or genome sequencing, leading to the identification of the molecular cause of the disease in 50% of patients at best. Therefore, a significant proportion of patients remain undiagnosed after sequencing their genome. Recently, approaches based on functional aspects of the genome, including transcriptomics and epigenomics, are beginning to emerge. Here, we will review these approaches, including studies that have successfully provided diagnoses for complex undiagnosed cases.
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Affiliation(s)
- Beatriz Martinez-Delgado
- Molecular Genetics Unit, Institute of Rare Diseases Research (IIER), Spanish National Institute of Health Carlos III (ISCIII), 28220 Madrid, Spain;
- Centro de Investigación Biomédica en Red de Enfermedades Raras, CIBERER U758, 28029 Madrid, Spain
| | - Maria J. Barrero
- Models and Mechanisms Unit, Institute of Rare Diseases Research (IIER), Spanish National Institute of Health Carlos III (ISCIII), 28220 Madrid, Spain
- Correspondence:
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18
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Smail C, Ferraro NM, Hui Q, Durrant MG, Aguirre M, Tanigawa Y, Keever-Keigher MR, Rao AS, Justesen JM, Li X, Gloudemans MJ, Assimes TL, Kooperberg C, Reiner AP, Huang J, O'Donnell CJ, Sun YV, Rivas MA, Montgomery SB. Integration of rare expression outlier-associated variants improves polygenic risk prediction. Am J Hum Genet 2022; 109:1055-1064. [PMID: 35588732 PMCID: PMC9247823 DOI: 10.1016/j.ajhg.2022.04.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 04/25/2022] [Indexed: 11/28/2022] Open
Abstract
Polygenic risk scores (PRSs) quantify the contribution of multiple genetic loci to an individual's likelihood of a complex trait or disease. However, existing PRSs estimate this likelihood with common genetic variants, excluding the impact of rare variants. Here, we report on a method to identify rare variants associated with outlier gene expression and integrate their impact into PRS predictions for body mass index (BMI), obesity, and bariatric surgery. Between the top and bottom 10%, we observed a 20.8% increase in risk for obesity (p = 3 × 10-14), 62.3% increase in risk for severe obesity (p = 1 × 10-6), and median 5.29 years earlier onset for bariatric surgery (p = 0.008), as a function of expression outlier-associated rare variant burden when controlling for common variant PRS. We show that these predictions were more significant than integrating the effects of rare protein-truncating variants (PTVs), observing a mean 19% increase in phenotypic variance explained with expression outlier-associated rare variants when compared with PTVs (p = 2 × 10-15). We replicated these findings by using data from the Million Veteran Program and demonstrated that PRSs across multiple traits and diseases can benefit from the inclusion of expression outlier-associated rare variants identified through population-scale transcriptome sequencing.
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Affiliation(s)
- Craig Smail
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA; Genomic Medicine Center, Children's Mercy Research Institute and Children's Mercy Kansas City, Kansas City, MO, USA.
| | - Nicole M Ferraro
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Qin Hui
- Atlanta VA Health Care System, Decatur, GA, USA; Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Matthew G Durrant
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Matthew Aguirre
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Yosuke Tanigawa
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Marissa R Keever-Keigher
- Genomic Medicine Center, Children's Mercy Research Institute and Children's Mercy Kansas City, Kansas City, MO, USA
| | - Abhiram S Rao
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Johanne M Justesen
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Xin Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Michael J Gloudemans
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Themistocles L Assimes
- Palo Alto VA Health Care System, Palo Alto, CA, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Charles Kooperberg
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | | | - Jie Huang
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Christopher J O'Donnell
- Boston VA Health Care System, Boston, MA, USA; Division of Cardiology, Department of Medicine, Harvard Medical School, Boston, MA, USA; Division of Cardiology, Department of Medicine, Brigham Women's Hospital, Boston, MA, USA
| | - Yan V Sun
- Atlanta VA Health Care System, Decatur, GA, USA; Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Manuel A Rivas
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen B Montgomery
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
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19
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Grishin D, Gusev A. Allelic imbalance of chromatin accessibility in cancer identifies candidate causal risk variants and their mechanisms. Nat Genet 2022; 54:837-849. [PMID: 35697866 PMCID: PMC9886437 DOI: 10.1038/s41588-022-01075-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 04/08/2022] [Indexed: 02/02/2023]
Abstract
While many germline cancer risk variants have been identified through genome-wide association studies (GWAS), the mechanisms by which these variants operate remain largely unknown. Here we used 406 cancer ATAC-Seq samples across 23 cancer types to identify 7,262 germline allele-specific accessibility QTLs (as-aQTLs). Cancer as-aQTLs had stronger enrichment for cancer risk heritability (up to 145 fold) than any other functional annotation across seven cancer GWAS. Most cancer as-aQTLs directly altered transcription factor (TF) motifs and exhibited differential TF binding and gene expression in functional screens. To connect as-aQTLs to putative risk mechanisms, we introduced the regulome-wide associations study (RWAS). RWAS identified genetically associated accessible peaks at >70% of known breast and prostate loci and discovered new risk loci in all examined cancer types. Integrating as-aQTL discovery, motif analysis and RWAS identified candidate causal regulatory elements and their probable upstream regulators. Our work establishes cancer as-aQTLs and RWAS analysis as powerful tools to study the genetic architecture of cancer risk.
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Affiliation(s)
- Dennis Grishin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. .,The Eli and Edythe L. Broad Institute, Cambridge, MA, USA. .,Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA.
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20
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Smirnov D, Schlieben LD, Peymani F, Berutti R, Prokisch H. Guidelines for clinical interpretation of variant pathogenicity using RNA phenotypes. Hum Mutat 2022; 43:1056-1070. [DOI: 10.1002/humu.24416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 05/15/2022] [Accepted: 05/25/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Dmitrii Smirnov
- School of Medicine, Institute of Human GeneticsTechnical University of MunichMunichGermany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Zentrum MünchenNeuherbergGermany
| | - Lea D. Schlieben
- School of Medicine, Institute of Human GeneticsTechnical University of MunichMunichGermany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Zentrum MünchenNeuherbergGermany
| | - Fatemeh Peymani
- School of Medicine, Institute of Human GeneticsTechnical University of MunichMunichGermany
| | - Riccardo Berutti
- School of Medicine, Institute of Human GeneticsTechnical University of MunichMunichGermany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Zentrum MünchenNeuherbergGermany
| | - Holger Prokisch
- School of Medicine, Institute of Human GeneticsTechnical University of MunichMunichGermany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Zentrum MünchenNeuherbergGermany
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21
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Brooks IR, Garrone CM, Kerins C, Kiar CS, Syntaka S, Xu JZ, Spagnoli FM, Watt FM. Functional genomics and the future of iPSCs in disease modeling. Stem Cell Reports 2022; 17:1033-1047. [PMID: 35487213 PMCID: PMC9133703 DOI: 10.1016/j.stemcr.2022.03.019] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 10/28/2022] Open
Abstract
Induced pluripotent stem cells (iPSCs) are valuable in disease modeling because of their potential to expand and differentiate into virtually any cell type and recapitulate key aspects of human biology. Functional genomics are genome-wide studies that aim to discover genotype-phenotype relationships, thereby revealing the impact of human genetic diversity on normal and pathophysiology. In this review, we make the case that human iPSCs (hiPSCs) are a powerful tool for functional genomics, since they provide an in vitro platform for the study of population genetics. We describe cutting-edge tools and strategies now available to researchers, including multi-omics technologies, advances in hiPSC culture techniques, and innovations in drug development. Functional genomics approaches based on hiPSCs hold great promise for advancing drug discovery, disease etiology, and the impact of genetic variation on human biology.
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Affiliation(s)
- Imogen R Brooks
- St John's Institute of Dermatology, King's College London, London, SE1 9RT, UK
| | - Cristina M Garrone
- Centre for Gene Therapy and Regenerative Medicine, King's College London, London, SE1 9RT, UK
| | - Caoimhe Kerins
- Centre for Craniofacial and Regenerative Biology, King's College London, London, SE1 9RT, UK
| | - Cher Shen Kiar
- Peter Gorer Department of Immunobiology, King's College London, London, SE1 9RT, UK
| | - Sofia Syntaka
- Centre for Gene Therapy and Regenerative Medicine, King's College London, London, SE1 9RT, UK
| | - Jessie Z Xu
- Centre for Gene Therapy and Regenerative Medicine, King's College London, London, SE1 9RT, UK
| | - Francesca M Spagnoli
- Centre for Gene Therapy and Regenerative Medicine, King's College London, London, SE1 9RT, UK.
| | - Fiona M Watt
- Centre for Gene Therapy and Regenerative Medicine, King's College London, London, SE1 9RT, UK; Directors' Research Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
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22
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Yépez VA, Gusic M, Kopajtich R, Mertes C, Smith NH, Alston CL, Ban R, Beblo S, Berutti R, Blessing H, Ciara E, Distelmaier F, Freisinger P, Häberle J, Hayflick SJ, Hempel M, Itkis YS, Kishita Y, Klopstock T, Krylova TD, Lamperti C, Lenz D, Makowski C, Mosegaard S, Müller MF, Muñoz-Pujol G, Nadel A, Ohtake A, Okazaki Y, Procopio E, Schwarzmayr T, Smet J, Staufner C, Stenton SL, Strom TM, Terrile C, Tort F, Van Coster R, Vanlander A, Wagner M, Xu M, Fang F, Ghezzi D, Mayr JA, Piekutowska-Abramczuk D, Ribes A, Rötig A, Taylor RW, Wortmann SB, Murayama K, Meitinger T, Gagneur J, Prokisch H. Clinical implementation of RNA sequencing for Mendelian disease diagnostics. Genome Med 2022; 14:38. [PMID: 35379322 PMCID: PMC8981716 DOI: 10.1186/s13073-022-01019-9] [Citation(s) in RCA: 75] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 02/03/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Lack of functional evidence hampers variant interpretation, leaving a large proportion of individuals with a suspected Mendelian disorder without genetic diagnosis after whole genome or whole exome sequencing (WES). Research studies advocate to further sequence transcriptomes to directly and systematically probe gene expression defects. However, collection of additional biopsies and establishment of lab workflows, analytical pipelines, and defined concepts in clinical interpretation of aberrant gene expression are still needed for adopting RNA sequencing (RNA-seq) in routine diagnostics. METHODS We implemented an automated RNA-seq protocol and a computational workflow with which we analyzed skin fibroblasts of 303 individuals with a suspected mitochondrial disease that previously underwent WES. We also assessed through simulations how aberrant expression and mono-allelic expression tests depend on RNA-seq coverage. RESULTS We detected on average 12,500 genes per sample including around 60% of all disease genes-a coverage substantially higher than with whole blood, supporting the use of skin biopsies. We prioritized genes demonstrating aberrant expression, aberrant splicing, or mono-allelic expression. The pipeline required less than 1 week from sample preparation to result reporting and provided a median of eight disease-associated genes per patient for inspection. A genetic diagnosis was established for 16% of the 205 WES-inconclusive cases. Detection of aberrant expression was a major contributor to diagnosis including instances of 50% reduction, which, together with mono-allelic expression, allowed for the diagnosis of dominant disorders caused by haploinsufficiency. Moreover, calling aberrant splicing and variants from RNA-seq data enabled detecting and validating splice-disrupting variants, of which the majority fell outside WES-covered regions. CONCLUSION Together, these results show that streamlined experimental and computational processes can accelerate the implementation of RNA-seq in routine diagnostics.
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Affiliation(s)
- Vicente A. Yépez
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Garching, Germany
- Quantitative Biosciences Munich, Department of Biochemistry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Mirjana Gusic
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Robert Kopajtich
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Christian Mertes
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Nicholas H. Smith
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Charlotte L. Alston
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH UK
- NHS Highly Specialised Services for Rare Mitochondrial Disorders, Royal Victoria Infirmary, Newcastle upon Tyne Hospitals NHS Foundation Trust, Queen Victoria Road, Newcastle upon Tyne, NE1 4LP UK
| | - Rui Ban
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Pediatric Neurology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Skadi Beblo
- Department of Women and Child Health, Hospital for Children and Adolescents, Center for Pediatric Research Leipzig (CPL), Center for Rare Diseases, University Hospitals, University of Leipzig, Leipzig, Germany
| | - Riccardo Berutti
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Holger Blessing
- Department for Inborn Metabolic Diseases, Children’s and Adolescents’ Hospital, University of Erlangen-Nürnberg, Erlangen, Germany
| | - Elżbieta Ciara
- Department of Medical Genetics, Children’s Memorial Health Institute, Warsaw, Poland
| | - Felix Distelmaier
- Department of General Pediatrics, Neonatology and Pediatric Cardiology, Heinrich-Heine-University, Düsseldorf, Germany
| | - Peter Freisinger
- Department of Pediatrics, Klinikum Reutlingen, Reutlingen, Germany
| | - Johannes Häberle
- University Children’s Hospital Zurich and Children’s Research Centre, Zürich, Switzerland
| | - Susan J. Hayflick
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, USA
| | - Maja Hempel
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Yoshihito Kishita
- Diagnostics and Therapeutics of Intractable Diseases, Intractable Disease Research Center, Juntendo University, Graduate School of Medicine, Tokyo, Japan
- Department of Life Science, Faculty of Science and Engineering, Kindai University, Osaka, Japan
| | - Thomas Klopstock
- Department of Neurology, Friedrich-Baur-Institute, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | | | - Costanza Lamperti
- Unit of Medical Genetics and Neurogenetics, Fondazione IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico) Istituto Neurologico Carlo Besta, Milan, Italy
| | - Dominic Lenz
- Division of Neuropediatrics and Pediatric Metabolic Medicine, Center for Pediatric and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Christine Makowski
- Department of Pediatrics, Technical University of Munich, Munich, Germany
| | - Signe Mosegaard
- Research Unit for Molecular Medicine, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Michaela F. Müller
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Gerard Muñoz-Pujol
- Section of Inborn Errors of Metabolism-IBC, Department of Biochemistry and Molecular Genetics, Hospital Clínic, IDIBAPS, CIBERER, Barcelona, Spain
| | - Agnieszka Nadel
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Akira Ohtake
- Department of Pediatrics & Clinical Genomics, Faculty of Medicine, Saitama Medical University, Saitama, Japan
- Center for Intractable Diseases, Saitama Medical University Hospital, Saitama, Japan
| | - Yasushi Okazaki
- Diagnostics and Therapeutics of Intractable Diseases, Intractable Disease Research Center, Juntendo University, Graduate School of Medicine, Tokyo, Japan
| | - Elena Procopio
- Inborn Metabolic and Muscular Disorders Unit, Anna Meyer Children Hospital, Florence, Italy
| | - Thomas Schwarzmayr
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Joél Smet
- Department of Pediatric Neurology and Metabolism, Ghent University Hospital, Ghent, Belgium
| | - Christian Staufner
- Division of Neuropediatrics and Pediatric Metabolic Medicine, Center for Pediatric and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Sarah L. Stenton
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Tim M. Strom
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Caterina Terrile
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Frederic Tort
- Section of Inborn Errors of Metabolism-IBC, Department of Biochemistry and Molecular Genetics, Hospital Clínic, IDIBAPS, CIBERER, Barcelona, Spain
| | - Rudy Van Coster
- Department of Pediatric Neurology and Metabolism, Ghent University Hospital, Ghent, Belgium
| | - Arnaud Vanlander
- Department of Pediatric Neurology and Metabolism, Ghent University Hospital, Ghent, Belgium
| | - Matias Wagner
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Manting Xu
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Pediatric Neurology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Fang Fang
- Department of Pediatric Neurology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Daniele Ghezzi
- Unit of Medical Genetics and Neurogenetics, Fondazione IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico) Istituto Neurologico Carlo Besta, Milan, Italy
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Johannes A. Mayr
- University Children’s Hospital, Paracelsus Medical University Salzburg, Salzburg, Austria
| | | | - Antonia Ribes
- Section of Inborn Errors of Metabolism-IBC, Department of Biochemistry and Molecular Genetics, Hospital Clínic, IDIBAPS, CIBERER, Barcelona, Spain
| | - Agnès Rötig
- Université de Paris, Institut Imagine, INSERM UMR 1163, Paris, France
| | - Robert W. Taylor
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH UK
- NHS Highly Specialised Services for Rare Mitochondrial Disorders, Royal Victoria Infirmary, Newcastle upon Tyne Hospitals NHS Foundation Trust, Queen Victoria Road, Newcastle upon Tyne, NE1 4LP UK
| | - Saskia B. Wortmann
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- University Children’s Hospital, Paracelsus Medical University Salzburg, Salzburg, Austria
- Amalia Children’s Hospital, Radboudumc Nijmegen, Nijmegen, The Netherlands
| | - Kei Murayama
- Department of Metabolism, Chiba Children’s Hospital, Chiba, Japan
| | - Thomas Meitinger
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julien Gagneur
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Garching, Germany
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Holger Prokisch
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Pediatric Neurology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
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23
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Marwaha S, Knowles JW, Ashley EA. A guide for the diagnosis of rare and undiagnosed disease: beyond the exome. Genome Med 2022; 14:23. [PMID: 35220969 PMCID: PMC8883622 DOI: 10.1186/s13073-022-01026-w] [Citation(s) in RCA: 83] [Impact Index Per Article: 41.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 02/10/2022] [Indexed: 02/07/2023] Open
Abstract
AbstractRare diseases affect 30 million people in the USA and more than 300–400 million worldwide, often causing chronic illness, disability, and premature death. Traditional diagnostic techniques rely heavily on heuristic approaches, coupling clinical experience from prior rare disease presentations with the medical literature. A large number of rare disease patients remain undiagnosed for years and many even die without an accurate diagnosis. In recent years, gene panels, microarrays, and exome sequencing have helped to identify the molecular cause of such rare and undiagnosed diseases. These technologies have allowed diagnoses for a sizable proportion (25–35%) of undiagnosed patients, often with actionable findings. However, a large proportion of these patients remain undiagnosed. In this review, we focus on technologies that can be adopted if exome sequencing is unrevealing. We discuss the benefits of sequencing the whole genome and the additional benefit that may be offered by long-read technology, pan-genome reference, transcriptomics, metabolomics, proteomics, and methyl profiling. We highlight computational methods to help identify regionally distant patients with similar phenotypes or similar genetic mutations. Finally, we describe approaches to automate and accelerate genomic analysis. The strategies discussed here are intended to serve as a guide for clinicians and researchers in the next steps when encountering patients with non-diagnostic exomes.
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24
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Montgomery SB, Bernstein JA, Wheeler MT. Toward transcriptomics as a primary tool for rare disease investigation. Cold Spring Harb Mol Case Stud 2022; 8:a006198. [PMID: 35217565 PMCID: PMC8958920 DOI: 10.1101/mcs.a006198] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
In the past 5 years transcriptome or RNA-sequencing (RNA-seq) has steadily emerged as a complementary assay for rare disease diagnosis and discovery. In this perspective, we summarize several recent developments and challenges in the use of RNA-seq for rare disease investigation. Using an accessible patient sample, such as blood, skin, or muscle, RNA-seq enables the assay of expressed RNA transcripts. Analysis of RNA-seq allows the identification of aberrant or outlier gene expression and alternative splicing as functional evidence to support rare disease study and diagnosis. Further, many types of variant effects can be profiled beyond coding variants, as the consequences of noncoding variants that impact gene expression and splicing can be directly observed. This is particularly apparent for structural variants that disproportionately underlie outlier gene expression and for splicing variants in which RNA-seq can both measure aberrant canonical splicing and detect deep intronic effects. However, a major potential limitation of RNA-seq in rare disease investigation is the developmental and cell type specificity of gene expression as a pathogenic variant's effect may be limited to a specific spatiotemporal context and access to a patient's tissue sample from the relevant tissue and timing of disease expression may not be possible. We speculate that as advances in computational methods and emerging experimental techniques overcome both developmental and cell type specificity, there will be broadening use of RNA sequencing and multiomics in rare disease diagnosis and delivery of precision health.
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Affiliation(s)
- Stephen B Montgomery
- Departments of Genetics and Pathology, Stanford University, California 94305, USA
| | - Jonathan A Bernstein
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Matthew T Wheeler
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, California 94304, USA
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25
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Espinosa-Espinosa J, González-Barriga A, López-Castel A, Artero R. Deciphering the Complex Molecular Pathogenesis of Myotonic Dystrophy Type 1 through Omics Studies. Int J Mol Sci 2022; 23:ijms23031441. [PMID: 35163365 PMCID: PMC8836095 DOI: 10.3390/ijms23031441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/24/2022] [Accepted: 01/26/2022] [Indexed: 12/15/2022] Open
Abstract
Omics studies are crucial to improve our understanding of myotonic dystrophy type 1 (DM1), the most common muscular dystrophy in adults. Employing tissue samples and cell lines derived from patients and animal models, omics approaches have revealed the myriad alterations in gene and microRNA expression, alternative splicing, 3′ polyadenylation, CpG methylation, and proteins levels, among others, that contribute to this complex multisystem disease. In addition, omics characterization of drug candidate treatment experiments provides crucial insight into the degree of therapeutic rescue and off-target effects that can be achieved. Finally, several innovative technologies such as single-cell sequencing and artificial intelligence will have a significant impact on future DM1 research.
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Affiliation(s)
- Jorge Espinosa-Espinosa
- University Research Institute for Biotechnology and Biomedicine (BIOTECMED), Universidad de Valencia, 46100 Valencia, Spain; (J.E.-E.); (R.A.)
- Translational Genomics Group, Incliva Biomedical Research Institute, 46010 Valencia, Spain
| | - Anchel González-Barriga
- Centre de Recherche en Myologie, Inserm, Institut de Myologie, Sorbonne Université, 75013 Paris, France;
| | - Arturo López-Castel
- University Research Institute for Biotechnology and Biomedicine (BIOTECMED), Universidad de Valencia, 46100 Valencia, Spain; (J.E.-E.); (R.A.)
- Translational Genomics Group, Incliva Biomedical Research Institute, 46010 Valencia, Spain
- Correspondence: ; Tel.: +34-963543028
| | - Rubén Artero
- University Research Institute for Biotechnology and Biomedicine (BIOTECMED), Universidad de Valencia, 46100 Valencia, Spain; (J.E.-E.); (R.A.)
- Translational Genomics Group, Incliva Biomedical Research Institute, 46010 Valencia, Spain
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26
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D'Antonio M, Nguyen JP, Arthur TD, Matsui H, D'Antonio-Chronowska A, Frazer KA. SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues. Cell Rep 2021; 37:110020. [PMID: 34762851 PMCID: PMC8563343 DOI: 10.1016/j.celrep.2021.110020] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/06/2021] [Accepted: 10/28/2021] [Indexed: 01/08/2023] Open
Abstract
Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types.
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Affiliation(s)
- Matteo D'Antonio
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Jennifer P Nguyen
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Timothy D Arthur
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA; Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA
| | | | | | - Kelly A Frazer
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA.
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27
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Vickers A, Tewary M, Laddach A, Poletti M, Salameti V, Fraternali F, Danovi D, Watt FM. Plating human iPSC lines on micropatterned substrates reveals role for ITGB1 nsSNV in endoderm formation. Stem Cell Reports 2021; 16:2628-2641. [PMID: 34678211 PMCID: PMC8581167 DOI: 10.1016/j.stemcr.2021.09.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 12/03/2022] Open
Abstract
Quantitative analysis of human induced pluripotent stem cell (iPSC) lines from healthy donors is a powerful tool for uncovering the relationship between genetic variants and cellular behavior. We previously identified rare, deleterious non-synonymous single nucleotide variants (nsSNVs) in cell adhesion genes that are associated with outlier iPSC phenotypes in the pluripotent state. Here, we generated micropatterned colonies of iPSCs to test whether nsSNVs influence patterning of radially ordered germ layers. Using a custom-built image analysis pipeline, we quantified the differentiation phenotypes of 13 iPSC lines that harbor nsSNVs in genes related to cell adhesion or germ layer development. All iPSC lines differentiated into the three germ layers; however, there was donor-specific variation in germ layer patterning. We identified one line that presented an outlier phenotype of expanded endodermal differentiation, which was associated with a nsSNV in ITGB1. Our study establishes a platform for investigating the impact of nsSNVs on differentiation.
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Affiliation(s)
- Alice Vickers
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Mukul Tewary
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Anna Laddach
- Randall Centre for Cell and Molecular Biophysics, King's College London, New Hunt's House, Great Maze Pond, London SE1 9RT, UK; Development and Homeostasis of the Nervous System Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Martina Poletti
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK; Quadram Institute, Norwich Research Park, Norwich NR4 7UZ, UK
| | - Vasiliki Salameti
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Franca Fraternali
- Randall Centre for Cell and Molecular Biophysics, King's College London, New Hunt's House, Great Maze Pond, London SE1 9RT, UK
| | - Davide Danovi
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK; bit.bio, Babraham Research Campus, The Dorothy Hodgkin Building, Cambridge CB22 3FH, UK
| | - Fiona M Watt
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK.
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De Los Angeles A, Fernando MB, Hall NAL, Brennand KJ, Harrison PJ, Maher BJ, Weinberger DR, Tunbridge EM. Induced Pluripotent Stem Cells in Psychiatry: An Overview and Critical Perspective. Biol Psychiatry 2021; 90:362-372. [PMID: 34176589 PMCID: PMC8375580 DOI: 10.1016/j.biopsych.2021.04.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 03/16/2021] [Accepted: 04/07/2021] [Indexed: 02/08/2023]
Abstract
A key challenge in psychiatry research is the development of high-fidelity model systems that can be experimentally manipulated to explore and test pathophysiological mechanisms of illness. In this respect, the emerging capacity to derive neural cells and circuits from human induced pluripotent stem cells (iPSCs) has generated significant excitement. This review aims to provide a critical appraisal of the potential for iPSCs in illuminating pathophysiological mechanisms in the context of other available technical approaches. We discuss the selection of iPSC phenotypes relevant to psychiatry, the information that researchers can draw on to help guide these decisions, and how researchers choose between the use of 2-dimensional cultures and the use of more complex 3-dimensional model systems. We discuss the strengths and limitations of current models and the challenges and opportunities that they present. Finally, we discuss the potential of iPSC-based model systems for clarifying the mechanisms underlying genetic risk for psychiatry and the steps that will be needed to ensure that robust and reliable conclusions can be drawn. We argue that while iPSC-based models are ideally placed to study fundamental processes occurring within and between neural cells, they are often less well suited for case-control studies, given issues relating to statistical power and the challenges in identifying which cellular phenotypes are meaningful at the level of the whole individual. Our aim is to highlight the importance of considering the hypotheses of a given study to guide decisions about which, if any, iPSC-based system is most appropriate to address it.
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Affiliation(s)
- Alejandro De Los Angeles
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Michael B Fernando
- Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, New York; Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Nicola A L Hall
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Kristen J Brennand
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Paul J Harrison
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Brady J Maher
- Lieber Institute for Brain Development, Baltimore, Maryland; Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, Maryland; Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Elizabeth M Tunbridge
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom.
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29
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Aygün N, Elwell AL, Liang D, Lafferty MJ, Cheek KE, Courtney KP, Mory J, Hadden-Ford E, Krupa O, de la Torre-Ubieta L, Geschwind DH, Love MI, Stein JL. Brain-trait-associated variants impact cell-type-specific gene regulation during neurogenesis. Am J Hum Genet 2021; 108:1647-1668. [PMID: 34416157 PMCID: PMC8456186 DOI: 10.1016/j.ajhg.2021.07.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 07/23/2021] [Indexed: 12/21/2022] Open
Abstract
Interpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing quantitative trait locus (e/sQTL) analyses is generally performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements active during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map e/sQTLs, and allele-specific expression in cultured cells representing two major developmental stages, primary human neural progenitors (n = 85) and their sorted neuronal progeny (n = 74), identifying numerous loci not detected in either bulk developing cortical wall or adult cortex. Using colocalization and genetic imputation via transcriptome-wide association, we uncover cell-type-specific regulatory mechanisms underlying risk for brain-relevant traits that are active during neocortical differentiation. Specifically, we identified a progenitor-specific eQTL for CENPW co-localized with common variant associations for cortical surface area and educational attainment.
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Affiliation(s)
- Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Angela L Elwell
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dan Liang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Michael J Lafferty
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kerry E Cheek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kenan P Courtney
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jessica Mory
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ellie Hadden-Ford
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Luis de la Torre-Ubieta
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Daniel H Geschwind
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael I Love
- Department of Genetics, 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
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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30
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Willson J. Stem cells root out genetic variants. Nat Rev Genet 2021; 22:265. [PMID: 33785895 DOI: 10.1038/s41576-021-00357-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Cuomo ASE, Alvari G, Azodi CB, McCarthy DJ, Bonder MJ. Optimizing expression quantitative trait locus mapping workflows for single-cell studies. Genome Biol 2021; 22:188. [PMID: 34167583 PMCID: PMC8223300 DOI: 10.1186/s13059-021-02407-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/09/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. RESULTS While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. CONCLUSION We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.
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Affiliation(s)
- Anna S E Cuomo
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.
- Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
| | - Giordano Alvari
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christina B Azodi
- St. Vincent's Institute of Medical Research, Fitzroy, Victoria, Australia
- University of Melbourne, Parkville, Victoria, Australia
| | - Davis J McCarthy
- St. Vincent's Institute of Medical Research, Fitzroy, Victoria, Australia.
- University of Melbourne, Parkville, Victoria, Australia.
| | - Marc Jan Bonder
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
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32
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Findley AS, Monziani A, Richards AL, Rhodes K, Ward MC, Kalita CA, Alazizi A, Pazokitoroudi A, Sankararaman S, Wen X, Lanfear DE, Pique-Regi R, Gilad Y, Luca F. Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions. eLife 2021; 10:e67077. [PMID: 33988505 PMCID: PMC8248987 DOI: 10.7554/elife.67077] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/13/2021] [Indexed: 12/14/2022] Open
Abstract
Genetic effects on gene expression and splicing can be modulated by cellular and environmental factors; yet interactions between genotypes, cell type, and treatment have not been comprehensively studied together. We used an induced pluripotent stem cell system to study multiple cell types derived from the same individuals and exposed them to a large panel of treatments. Cellular responses involved different genes and pathways for gene expression and splicing and were highly variable across contexts. For thousands of genes, we identified variable allelic expression across contexts and characterized different types of gene-environment interactions, many of which are associated with complex traits. Promoter functional and evolutionary features distinguished genes with elevated allelic imbalance mean and variance. On average, half of the genes with dynamic regulatory interactions were missed by large eQTL mapping studies, indicating the importance of exploring multiple treatments to reveal previously unrecognized regulatory loci that may be important for disease.
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Affiliation(s)
- Anthony S Findley
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Alan Monziani
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Allison L Richards
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Katherine Rhodes
- Department of Human Genetics, University of ChicagoChicagoUnited States
| | - Michelle C Ward
- Department of Medicine, University of ChicagoChicagoUnited States
| | - Cynthia A Kalita
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Adnan Alazizi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | | | - Sriram Sankararaman
- Department of Computer Science, UCLALos AngelesUnited States
- Department of Human Genetics, UCLALos AngelesUnited States
- Department of Computational Medicine, UCLALos AngelesUnited States
| | - Xiaoquan Wen
- Department of Biostatistics, University of MichiganAnn ArborUnited States
| | - David E Lanfear
- Center for Individualized and Genomic Medicine Research, Henry Ford HospitalDetroitUnited States
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
| | - Yoav Gilad
- Department of Human Genetics, University of ChicagoChicagoUnited States
- Department of Medicine, University of ChicagoChicagoUnited States
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
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33
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D’Antonio M, Arthur TD, Nguyen JP, Matsui H, D’Antonio-Chronowska A, Frazer KA. Insights into genetic factors contributing to variability in SARS-CoV-2 susceptibility and COVID-19 disease severity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.05.10.21256423. [PMID: 34013287 PMCID: PMC8132261 DOI: 10.1101/2021.05.10.21256423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we applied colocalization to compare summary statistics for 16 GWASs from the COVID-19 Host Genetics Initiative to investigate similarities and differences in their genetic signals. We identified 9 loci associated with susceptibility (one with two independent GWAS signals; one with an ethnicity-specific signal), 14 associated with severity (one with two independent GWAS signals; two with ethnicity-specific signals) and one harboring two discrepant GWAS signals (one for susceptibility; one for severity). Utilizing colocalization we also identified 45 GTEx tissues that had eQTL(s) for 18 genes strongly associated with GWAS signals in eleven loci (1-4 genes per locus). Some of these genes showed tissue-specific altered expression and others showed altered expression in up to 41 different tissue types. Our study provides insights into the complex molecular mechanisms underlying inherited predispositions to COVID-19-disease phenotypes.
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Affiliation(s)
- Matteo D’Antonio
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
| | | | - Timothy D. Arthur
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Jennifer P. Nguyen
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | | | - Kelly A. Frazer
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
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34
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Dannemann M, Gallego Romero I. Harnessing pluripotent stem cells as models to decipher human evolution. FEBS J 2021; 289:2992-3010. [PMID: 33876573 DOI: 10.1111/febs.15885] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/18/2021] [Accepted: 04/16/2021] [Indexed: 12/13/2022]
Abstract
The study of human evolution, long constrained by a lack of experimental model systems, has been transformed by the emergence of the induced pluripotent stem cell (iPSC) field. iPSCs can be readily established from noninvasive tissue sources, from both humans and other primates; they can be maintained in the laboratory indefinitely, and they can be differentiated into other tissue types. These qualities mean that iPSCs are rapidly becoming established as viable and powerful model systems with which it is possible to address questions in human evolution that were until now logistically and ethically intractable, especially in the quest to understand humans' place among the great apes, and the genetic basis of human uniqueness. In this review, we discuss the key lessons and takeaways of this nascent field; from the types of research, iPSCs make possible to lingering challenges and likely future directions. We provide a comprehensive overview of how the seemingly unlikely combination of iPSCs and explicit evolutionary frameworks is transforming what is possible in our understanding of humanity's past and present.
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Affiliation(s)
| | - Irene Gallego Romero
- Institute of Genomics, University of Tartu, Estonia.,Melbourne Integrative Genomics, The University of Melbourne, Parkville, Australia.,School of BioSciences, The University of Melbourne, Parkville, Australia.,The Centre for Stem Cell Systems, The University of Melbourne, Parkville, Australia
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35
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Jerber J, Seaton DD, Cuomo ASE, Kumasaka N, Haldane J, Steer J, Patel M, Pearce D, Andersson M, Bonder MJ, Mountjoy E, Ghoussaini M, Lancaster MA, Marioni JC, Merkle FT, Gaffney DJ, Stegle O. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat Genet 2021; 53:304-312. [PMID: 33664506 PMCID: PMC7610897 DOI: 10.1038/s41588-021-00801-6] [Citation(s) in RCA: 111] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 01/25/2021] [Indexed: 02/06/2023]
Abstract
Studying the function of common genetic variants in primary human tissues and during development is challenging. To address this, we use an efficient multiplexing strategy to differentiate 215 human induced pluripotent stem cell (iPSC) lines toward a midbrain neural fate, including dopaminergic neurons, and use single-cell RNA sequencing (scRNA-seq) to profile over 1 million cells across three differentiation time points. The proportion of neurons produced by each cell line is highly reproducible and is predictable by robust molecular markers expressed in pluripotent cells. Expression quantitative trait loci (eQTL) were characterized at different stages of neuronal development and in response to rotenone-induced oxidative stress. Of these, 1,284 eQTL colocalize with known neurological trait risk loci, and 46% are not found in the Genotype-Tissue Expression (GTEx) catalog. Our study illustrates how coupling scRNA-seq with long-term iPSC differentiation enables mechanistic studies of human trait-associated genetic variants in otherwise inaccessible cell states.
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Affiliation(s)
- Julie Jerber
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Daniel D Seaton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Anna S E Cuomo
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Natsuhiko Kumasaka
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - James Haldane
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Juliette Steer
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Minal Patel
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Daniel Pearce
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Malin Andersson
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Marc Jan Bonder
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Ed Mountjoy
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Maya Ghoussaini
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - John C Marioni
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
| | - Florian T Merkle
- Metabolic Research Laboratories and Medical Research Council Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
| | - Daniel J Gaffney
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
| | - Oliver Stegle
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center, Heidelberg, Germany.
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36
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Armitage LH, Stimpson SE, Santostefano KE, Sui L, Ogundare S, Newby BN, Castro-Gutierrez R, Huber MK, Taylor JP, Sharma P, Radichev IA, Perry DJ, Fredette NC, Savinov AY, Wallet MA, Terada N, Brusko TM, Russ HA, Chen J, Egli D, Mathews CE. Use of Induced Pluripotent Stem Cells to Build Isogenic Systems and Investigate Type 1 Diabetes. Front Endocrinol (Lausanne) 2021; 12:737276. [PMID: 34858326 PMCID: PMC8630743 DOI: 10.3389/fendo.2021.737276] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 10/05/2021] [Indexed: 11/13/2022] Open
Abstract
Type 1 diabetes (T1D) is a disease that arises due to complex immunogenetic mechanisms. Key cell-cell interactions involved in the pathogenesis of T1D are activation of autoreactive T cells by dendritic cells (DC), migration of T cells across endothelial cells (EC) lining capillary walls into the islets of Langerhans, interaction of T cells with macrophages in the islets, and killing of β-cells by autoreactive CD8+ T cells. Overall, pathogenic cell-cell interactions are likely regulated by the individual's collection of genetic T1D-risk variants. To accurately model the role of genetics, it is essential to build systems to interrogate single candidate genes in isolation during the interactions of cells that are essential for disease development. However, obtaining single-donor matched cells relevant to T1D is a challenge. Sourcing these genetic variants from human induced pluripotent stem cells (iPSC) avoids this limitation. Herein, we have differentiated iPSC from one donor into DC, macrophages, EC, and β-cells. Additionally, we also engineered T cell avatars from the same donor to provide an in vitro platform to study genetic influences on these critical cellular interactions. This proof of concept demonstrates the ability to derive an isogenic system from a single donor to study these relevant cell-cell interactions. Our system constitutes an interdisciplinary approach with a controlled environment that provides a proof-of-concept for future studies to determine the role of disease alleles (e.g. IFIH1, PTPN22, SH2B3, TYK2) in regulating cell-cell interactions and cell-specific contributions to the pathogenesis of T1D.
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Affiliation(s)
- Lucas H. Armitage
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- University of Florida Diabetes Institute, University of Florida, Gainesville, FL, United States
| | - Scott E. Stimpson
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- University of Florida Diabetes Institute, University of Florida, Gainesville, FL, United States
| | - Katherine E. Santostefano
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- Center for Cellular Reprogramming, College of Medicine, University of Florida, Gainesville, FL, United States
- Century Therapeutics, iPSC Biology, Philadelphia, PA, United States
| | - Lina Sui
- Department of Pediatrics, Naomi Berrie Diabetes Center, Columbia Stem Cell Initiative, Columbia University, New York, NY, United States
| | - Similoluwa Ogundare
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- University of Florida Diabetes Institute, University of Florida, Gainesville, FL, United States
| | - Brittney N. Newby
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- University of Florida Diabetes Institute, University of Florida, Gainesville, FL, United States
| | - Roberto Castro-Gutierrez
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States
| | - Mollie K. Huber
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- University of Florida Diabetes Institute, University of Florida, Gainesville, FL, United States
| | - Jared P. Taylor
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Prerana Sharma
- Children’s Health Research Center, Sanford Research, Sioux Falls, SD, United States
| | - Ilian A. Radichev
- Children’s Health Research Center, Sanford Research, Sioux Falls, SD, United States
| | - Daniel J. Perry
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- University of Florida Diabetes Institute, University of Florida, Gainesville, FL, United States
| | - Natalie C. Fredette
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- Center for Cellular Reprogramming, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Alexei Y. Savinov
- Children’s Health Research Center, Sanford Research, Sioux Falls, SD, United States
| | - Mark A. Wallet
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- University of Florida Diabetes Institute, University of Florida, Gainesville, FL, United States
- Century Therapeutics, Immunology, Philadelphia, PA, United States
| | - Naohiro Terada
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- Center for Cellular Reprogramming, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Todd M. Brusko
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- University of Florida Diabetes Institute, University of Florida, Gainesville, FL, United States
| | - Holger A. Russ
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States
| | - Jing Chen
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- University of Florida Diabetes Institute, University of Florida, Gainesville, FL, United States
| | - Dieter Egli
- Department of Pediatrics, Naomi Berrie Diabetes Center, Columbia Stem Cell Initiative, Columbia University, New York, NY, United States
| | - Clayton E. Mathews
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
- University of Florida Diabetes Institute, University of Florida, Gainesville, FL, United States
- Center for Cellular Reprogramming, College of Medicine, University of Florida, Gainesville, FL, United States
- *Correspondence: Clayton E. Mathews,
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