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van der Sanden B, Neveling K, Pang AWC, Shukor S, Gallagher MD, Burke SL, Kamsteeg EJ, Hastie A, Hoischen A. Optical Genome Mapping for Applications in Repeat Expansion Disorders. Curr Protoc 2024; 4:e1094. [PMID: 38966883 DOI: 10.1002/cpz1.1094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/06/2024]
Abstract
Short tandem repeat (STR) expansions are associated with more than 60 genetic disorders. The size and stability of these expansions correlate with the severity and age of onset of the disease. Therefore, being able to accurately detect the absolute length of STRs is important. Current diagnostic assays include laborious lab experiments, including repeat-primed PCR and Southern blotting, that still cannot precisely determine the exact length of very long repeat expansions. Optical genome mapping (OGM) is a cost-effective and easy-to-use alternative to traditional cytogenetic techniques and allows the comprehensive detection of chromosomal aberrations and structural variants >500 bp in length, including insertions, deletions, duplications, inversions, translocations, and copy number variants. Here, we provide methodological guidance for preparing samples and performing OGM as well as running the analysis pipelines and using the specific repeat expansion workflows to determine the exact repeat length of repeat expansions expanded beyond 500 bp. Together these protocols provide all details needed to analyze the length and stability of any repeat expansion with an expected repeat size difference from the expected wild-type allele of >500 bp. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Genomic ultra-high-molecular-weight DNA isolation, labeling, and staining Basic Protocol 2: Data generation and genome mapping using the Bionano Saphyr® System Basic Protocol 3: Manual De Novo Assembly workflow Basic Protocol 4: Local guided assembly workflow Basic Protocol 5: EnFocus Fragile X workflow Basic Protocol 6: Molecule distance script workflow.
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Affiliation(s)
- Bart van der Sanden
- Department of Human Genetics, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kornelia Neveling
- Department of Human Genetics, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | - Syukri Shukor
- Bionano Genomics Clinical and Scientific Affairs, San Diego, California
| | | | - Stephanie L Burke
- Bionano Genomics Clinical and Scientific Affairs, San Diego, California
| | - Erik-Jan Kamsteeg
- Department of Human Genetics, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Alex Hastie
- Bionano Genomics Clinical and Scientific Affairs, San Diego, California
| | - Alexander Hoischen
- Department of Human Genetics, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Internal Medicine, Radboud Expertise Center for Immunodeficiency and Autoinflammation and Radboud Center for Infectious Disease (RCI), Radboud University Medical Center, Nijmegen, the Netherlands
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Leitão E, Schröder C, Depienne C. Identification and characterization of repeat expansions in neurological disorders: Methodologies, tools, and strategies. Rev Neurol (Paris) 2024; 180:383-392. [PMID: 38594146 DOI: 10.1016/j.neurol.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/11/2024]
Abstract
Tandem repeats are a common, highly polymorphic class of variation in human genomes. Their expansion beyond a pathogenic threshold is a process that contributes to a wide range of neurological and neuromuscular genetic disorders, of which over 60 have been identified to date. The last few years have seen a resurgence in repeat expansion discovery propelled by technological advancements, enabling the identification of over 20 novel repeat expansion disorders. These expansions can occur in coding or non-coding regions of genes, resulting in a range of pathogenic mechanisms. In this article, we review strategies, tools and methods that can be used for efficient detection and characterization of known repeat expansions and identification of new expansion disorders. Features that can be used to prioritize repeat expansions include anticipation, which is characterized by increased severity or earlier onset of symptoms across generations, and founder effects, which contribute to higher prevalence rates in certain populations. Classical technologies such as Southern blotting, repeat-primed polymerase chain reaction (PCR) and long-range PCR can still be used to detect known repeat expansions, although they usually have significant limitations linked to the absence of sequence context. Targeted sequencing of known expansions using either long-range PCR or CRISPR-Cas9 enrichment combined with long-read sequencing or adaptive nanopore sampling are usually better but more expensive alternatives. The development of new bioinformatics tools applied to short-read genome data can now be used to detect repeat expansions either in a targeted manner or at the genome-wide level. In addition, technological advances, particularly long-read technologies such as optical genome mapping (Bionano Genomics), Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) HiFi sequencing, offer promising avenues for the detection of repeat expansions. Despite challenges in specific DNA extraction requirements, computation resources needed and data interpretation, these technologies have an immense potential to advance our understanding of repeat expansion disorders and improve diagnostic accuracy.
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Affiliation(s)
- E Leitão
- Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - C Schröder
- Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - C Depienne
- Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany.
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Gustafson JA, Gibson SB, Damaraju N, Zalusky MPG, Hoekzema K, Twesigomwe D, Yang L, Snead AA, Richmond PA, De Coster W, Olson ND, Guarracino A, Li Q, Miller AL, Goffena J, Anderson Z, Storz SHR, Ward SA, Sinha M, Gonzaga-Jauregui C, Clarke WE, Basile AO, Corvelo A, Reeves C, Helland A, Musunuri RL, Revsine M, Patterson KE, Paschal CR, Zakarian C, Goodwin S, Jensen TD, Robb E, McCombie WR, Sedlazeck FJ, Zook JM, Montgomery SB, Garrison E, Kolmogorov M, Schatz MC, McLaughlin RN, Dashnow H, Zody MC, Loose M, Jain M, Eichler EE, Miller DE. Nanopore sequencing of 1000 Genomes Project samples to build a comprehensive catalog of human genetic variation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.05.24303792. [PMID: 38496498 PMCID: PMC10942501 DOI: 10.1101/2024.03.05.24303792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Less than half of individuals with a suspected Mendelian condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control datasets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project ONT Sequencing Consortium aims to generate LRS data from at least 800 of the 1000 Genomes Project samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37x and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs.
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Affiliation(s)
- Jonas A. Gustafson
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Sophia B. Gibson
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Nikhita Damaraju
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- Institute for Public Health Genetics, University of Washington, Seattle, WA, USA
| | - Miranda PG Zalusky
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - David Twesigomwe
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Lei Yang
- Pacific Northwest Research Institute, Seattle, WA, USA
| | | | | | - Wouter De Coster
- Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Nathan D. Olson
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Andrea Guarracino
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
- Human Technopole, Milan, Italy
| | - Qiuhui Li
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Angela L. Miller
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Joy Goffena
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Zachery Anderson
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Sophie HR Storz
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Sydney A. Ward
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Maisha Sinha
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Claudia Gonzaga-Jauregui
- International Laboratory for Human Genome Research, Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México
| | - Wayne E. Clarke
- New York Genome Center, New York, NY, USA
- Outlier Informatics Inc., Saskatoon, SK, Canada
| | | | | | | | | | | | - Mahler Revsine
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | | | - Cate R. Paschal
- Department of Laboratories, Seattle Children’s Hospital, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Christina Zakarian
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Sara Goodwin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Esther Robb
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | | | | | | | - Fritz J. Sedlazeck
- Human Genome Sequencing Center Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Justin M. Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | | | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Mikhail Kolmogorov
- Cancer Data Science Laboratory, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Michael C. Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Richard N. McLaughlin
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
- Pacific Northwest Research Institute, Seattle, WA, USA
| | - Harriet Dashnow
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Matt Loose
- Deep Seq, School of Life Sciences, University of Nottingham, Nottingham, England
| | - Miten Jain
- Department of Bioengineering, Department of Physics, Khoury College of Computer Sciences, Northeastern University, Boston, MA
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Danny E. Miller
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
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