1
|
Zhou B, Purmann C, Guo H, Shin G, Huang Y, Pattni R, Meng Q, Greer SU, Roychowdhury T, Wood RN, Ho M, zu Dohna H, Abyzov A, Hallmayer JF, Wong WH, Ji HP, Urban AE. Resolving the 22q11.2 deletion using CTLR-Seq reveals chromosomal rearrangement mechanisms and individual variance in breakpoints. Proc Natl Acad Sci U S A 2024; 121:e2322834121. [PMID: 39042694 PMCID: PMC11295037 DOI: 10.1073/pnas.2322834121] [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: 12/28/2023] [Accepted: 06/15/2024] [Indexed: 07/25/2024] Open
Abstract
We developed a generally applicable method, CRISPR/Cas9-targeted long-read sequencing (CTLR-Seq), to resolve, haplotype-specifically, the large and complex regions in the human genome that had been previously impenetrable to sequencing analysis, such as large segmental duplications (SegDups) and their associated genome rearrangements. CTLR-Seq combines in vitro Cas9-mediated cutting of the genome and pulse-field gel electrophoresis to isolate intact large (i.e., up to 2,000 kb) genomic regions that encompass previously unresolvable genomic sequences. These targets are then sequenced (amplification-free) at high on-target coverage using long-read sequencing, allowing for their complete sequence assembly. We applied CTLR-Seq to the SegDup-mediated rearrangements that constitute the boundaries of, and give rise to, the 22q11.2 Deletion Syndrome (22q11DS), the most common human microdeletion disorder. We then performed de novo assembly to resolve, at base-pair resolution, the full sequence rearrangements and exact chromosomal breakpoints of 22q11.2DS (including all common subtypes). Across multiple patients, we found a high degree of variability for both the rearranged SegDup sequences and the exact chromosomal breakpoint locations, which coincide with various transposons within the 22q11.2 SegDups, suggesting that 22q11DS can be driven by transposon-mediated genome recombination. Guided by CTLR-Seq results from two 22q11DS patients, we performed three-dimensional chromosomal folding analysis for the 22q11.2 SegDups from patient-derived neurons and astrocytes and found chromosome interactions anchored within the SegDups to be both cell type-specific and patient-specific. Lastly, we demonstrated that CTLR-Seq enables cell-type specific analysis of DNA methylation patterns within the deletion haplotype of 22q11DS.
Collapse
Affiliation(s)
- Bo Zhou
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
- Stanford Maternal and Child Health Research Institute, Stanford University School of Medicine, Stanford, CA94305
| | - Carolin Purmann
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
- Stanford Maternal and Child Health Research Institute, Stanford University School of Medicine, Stanford, CA94305
- Department of Genetics, Stanford University School of Medicine, Stanford, CA94305
| | - Hanmin Guo
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
- Stanford Maternal and Child Health Research Institute, Stanford University School of Medicine, Stanford, CA94305
- Department of Genetics, Stanford University School of Medicine, Stanford, CA94305
- Department of Statistics, Stanford University, Stanford, CA94305
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305
| | - GiWon Shin
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA94305
| | - Yiling Huang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
- Department of Genetics, Stanford University School of Medicine, Stanford, CA94305
| | - Reenal Pattni
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
- Department of Genetics, Stanford University School of Medicine, Stanford, CA94305
| | - Qingxi Meng
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA94305
| | - Stephanie U. Greer
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA94305
| | - Tanmoy Roychowdhury
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN55905
| | - Raegan N. Wood
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA94305
| | - Marcus Ho
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
- Department of Genetics, Stanford University School of Medicine, Stanford, CA94305
| | - Heinrich zu Dohna
- Department of Biology, American University of Beirut, Beirut1107 2020, Lebanon
| | - Alexej Abyzov
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN55905
| | - Joachim F. Hallmayer
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Wing H. Wong
- Department of Statistics, Stanford University, Stanford, CA94305
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305
| | - Hanlee P. Ji
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA94305
| | - Alexander E. Urban
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
- Stanford Maternal and Child Health Research Institute, Stanford University School of Medicine, Stanford, CA94305
- Department of Genetics, Stanford University School of Medicine, Stanford, CA94305
- Program on Genetics of Brain Function, Stanford Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA94305
| |
Collapse
|
2
|
Olson ND, Wagner J, Dwarshuis N, Miga KH, Sedlazeck FJ, Salit M, Zook JM. Variant calling and benchmarking in an era of complete human genome sequences. Nat Rev Genet 2023:10.1038/s41576-023-00590-0. [PMID: 37059810 DOI: 10.1038/s41576-023-00590-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/22/2023] [Indexed: 04/16/2023]
Abstract
Genetic variant calling from DNA sequencing has enabled understanding of germline variation in hundreds of thousands of humans. Sequencing technologies and variant-calling methods have advanced rapidly, routinely providing reliable variant calls in most of the human genome. We describe how advances in long reads, deep learning, de novo assembly and pangenomes have expanded access to variant calls in increasingly challenging, repetitive genomic regions, including medically relevant regions, and how new benchmark sets and benchmarking methods illuminate their strengths and limitations. Finally, we explore the possible future of more complete characterization of human genome variation in light of the recent completion of a telomere-to-telomere human genome reference assembly and human pangenomes, and we consider the innovations needed to benchmark their newly accessible repetitive regions and complex variants.
Collapse
Affiliation(s)
- Nathan D Olson
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Justin Wagner
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Nathan Dwarshuis
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Karen H Miga
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Fritz J Sedlazeck
- Baylor College of Medicine, Human Genome Sequencing Center, Houston, TX, USA
| | | | - Justin M Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
| |
Collapse
|
3
|
Moreno-Cabrera JM, Del Valle J, Castellanos E, Feliubadaló L, Pineda M, Serra E, Capellá G, Lázaro C, Gel B. CNVfilteR: an R/bioconductor package to identify false positives produced by germline NGS CNV detection tools. Bioinformatics 2021; 37:4227-4229. [PMID: 33983414 PMCID: PMC9502136 DOI: 10.1093/bioinformatics/btab356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/06/2021] [Accepted: 05/12/2021] [Indexed: 11/14/2022] Open
Abstract
Germline copy-number variants (CNVs) are relevant mutations for multiple genetics fields, such as the study of hereditary diseases. However, available benchmarks show that all next-generation sequencing (NGS) CNV calling tools produce false positives. We developed CNVfilteR, an R package that uses the single nucleotide variant calls usually obtained in germline NGS pipelines to identify those false positives. The package can detect both false deletions and false duplications. We evaluated CNVfilteR performance on callsets generated by 13 CNV calling tools on 3 whole-genome sequencing and 541 panel samples, showing a decrease of up to 44.8% in false positives and consistent F1-score increase. Using CNVfilteR to detect false-positive calls can improve the overall performance of existing CNV calling pipelines. AVAILABILITY CNVfilteR is released under Artistic-2.0 License. Source code and documentation are freely available at Bioconductor (http://www.bioconductor.org/packages/CNVfilteR). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- José Marcos Moreno-Cabrera
- Hereditary Cancer Group, Program for Predictive and Personalized Medicine of Cancer, Germans Trias i Pujol Research Institute (PMPPC-IGTP), Campus, Ruti Badalona Barcelona, Can Spain.,Hereditary Cancer Program, Joint Program on Hereditary Cancer, Catalan Institute of Oncology, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.,Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red Cáncer (CIBERONC), Madrid, Spain
| | - Jesús Del Valle
- Hereditary Cancer Program, Joint Program on Hereditary Cancer, Catalan Institute of Oncology, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.,Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red Cáncer (CIBERONC), Madrid, Spain
| | - Elisabeth Castellanos
- Hereditary Cancer Group, Program for Predictive and Personalized Medicine of Cancer, Germans Trias i Pujol Research Institute (PMPPC-IGTP), Campus, Ruti Badalona Barcelona, Can Spain.,Clinical Genomics Unit, Clinical Genetics Service, Northern Metropolitan Clinical Laboratory, Germans Trias i Pujol University Hospital (HUGTiP), Ruti, Campus Badalona Barcelona, Can Spain
| | - Lidia Feliubadaló
- Hereditary Cancer Program, Joint Program on Hereditary Cancer, Catalan Institute of Oncology, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.,Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red Cáncer (CIBERONC), Madrid, Spain
| | - Marta Pineda
- Hereditary Cancer Program, Joint Program on Hereditary Cancer, Catalan Institute of Oncology, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.,Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red Cáncer (CIBERONC), Madrid, Spain
| | - Eduard Serra
- Hereditary Cancer Group, Program for Predictive and Personalized Medicine of Cancer, Germans Trias i Pujol Research Institute (PMPPC-IGTP), Campus, Ruti Badalona Barcelona, Can Spain.,Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red Cáncer (CIBERONC), Madrid, Spain
| | - Gabriel Capellá
- Hereditary Cancer Program, Joint Program on Hereditary Cancer, Catalan Institute of Oncology, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.,Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red Cáncer (CIBERONC), Madrid, Spain
| | - Conxi Lázaro
- Hereditary Cancer Program, Joint Program on Hereditary Cancer, Catalan Institute of Oncology, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.,Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red Cáncer (CIBERONC), Madrid, Spain
| | - Bernat Gel
- Hereditary Cancer Group, Program for Predictive and Personalized Medicine of Cancer, Germans Trias i Pujol Research Institute (PMPPC-IGTP), Campus, Ruti Badalona Barcelona, Can Spain
| |
Collapse
|
4
|
Zhuang X, Ye R, So MT, Lam WY, Karim A, Yu M, Ngo ND, Cherny SS, Tam PKH, Garcia-Barcelo MM, Tang CSM, Sham PC. A random forest-based framework for genotyping and accuracy assessment of copy number variations. NAR Genom Bioinform 2020; 2:lqaa071. [PMID: 33575619 PMCID: PMC7671382 DOI: 10.1093/nargab/lqaa071] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 08/18/2020] [Accepted: 08/26/2020] [Indexed: 12/24/2022] Open
Abstract
Detection of copy number variations (CNVs) is essential for uncovering genetic factors underlying human diseases. However, CNV detection by current methods is prone to error, and precisely identifying CNVs from paired-end whole genome sequencing (WGS) data is still challenging. Here, we present a framework, CNV-JACG, for Judging the Accuracy of CNVs and Genotyping using paired-end WGS data. CNV-JACG is based on a random forest model trained on 21 distinctive features characterizing the CNV region and its breakpoints. Using the data from the 1000 Genomes Project, Genome in a Bottle Consortium, the Human Genome Structural Variation Consortium and in-house technical replicates, we show that CNV-JACG has superior sensitivity over the latest genotyping method, SV2, particularly for the small CNVs (≤1 kb). We also demonstrate that CNV-JACG outperforms SV2 in terms of Mendelian inconsistency in trios and concordance between technical replicates. Our study suggests that CNV-JACG would be a useful tool in assessing the accuracy of CNVs to meet the ever-growing needs for uncovering the missing heritability linked to CNVs.
Collapse
Affiliation(s)
- Xuehan Zhuang
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Rui Ye
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Man-Ting So
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Wai-Yee Lam
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Anwarul Karim
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Michelle Yu
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Ngoc Diem Ngo
- National Hospital of Pediatrics, Ha Noi 100000, Vietnam
| | - Stacey S Cherny
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Paul Kwong-Hang Tam
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | | | - Clara Sze-Man Tang
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Pak Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| |
Collapse
|
5
|
Abstract
Identifying structural variation (SV) is essential for genome interpretation but has been historically difficult due to limitations inherent to available genome technologies. Detection methods that use ensemble algorithms and emerging sequencing technologies have enabled the discovery of thousands of SVs, uncovering information about their ubiquity, relationship to disease and possible effects on biological mechanisms. Given the variability in SV type and size, along with unique detection biases of emerging genomic platforms, multiplatform discovery is necessary to resolve the full spectrum of variation. Here, we review modern approaches for investigating SVs and proffer that, moving forwards, studies integrating biological information with detection will be necessary to comprehensively understand the impact of SV in the human genome.
Collapse
Affiliation(s)
- Steve S Ho
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Alexander E Urban
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ryan E Mills
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|
6
|
Zook JM, McDaniel J, Olson ND, Wagner J, Parikh H, Heaton H, Irvine SA, Trigg L, Truty R, McLean CY, De La Vega FM, Xiao C, Sherry S, Salit M. An open resource for accurately benchmarking small variant and reference calls. Nat Biotechnol 2019; 37:561-566. [PMID: 30936564 PMCID: PMC6500473 DOI: 10.1038/s41587-019-0074-6] [Citation(s) in RCA: 188] [Impact Index Per Article: 37.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 02/19/2019] [Indexed: 12/30/2022]
Abstract
Benchmark small variant calls are required for developing, optimizing and assessing the performance of sequencing and bioinformatics methods. Here, as part of the Genome in a Bottle (GIAB) Consortium, we apply a reproducible, cloud-based pipeline to integrate multiple short- and linked-read sequencing datasets and provide benchmark calls for human genomes. We generate benchmark calls for one previously analyzed GIAB sample, as well as six genomes from the Personal Genome Project. These new genomes have broad, open consent, making this a 'first of its kind' resource that is available to the community for multiple downstream applications. We produce 17% more benchmark single nucleotide variations, 176% more indels and 12% larger benchmark regions than previously published GIAB benchmarks. We demonstrate that this benchmark reliably identifies errors in existing callsets and highlight challenges in interpreting performance metrics when using benchmarks that are not perfect or comprehensive. Finally, we identify strengths and weaknesses of callsets by stratifying performance according to variant type and genome context.
Collapse
Affiliation(s)
- Justin M Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
| | - Jennifer McDaniel
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Nathan D Olson
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Justin Wagner
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Hemang Parikh
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Haynes Heaton
- 10x Genomics, Pleasanton, CA, USA
- Wellcome Trust Sanger Institute,, Hinxton, Cambridge, UK
| | | | - Len Trigg
- Real Time Genomics, Hamilton, New Zealand
| | | | - Cory Y McLean
- Verily Life Sciences, South San Francisco, CA, USA
- Google Inc., Mountain View, CA, USA
| | - Francisco M De La Vega
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Chunlin Xiao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Stephen Sherry
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Marc Salit
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
- Joint Initiative for Metrology in Biology, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| |
Collapse
|