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Porubsky D, Dashnow H, Sasani TA, Logsdon GA, Hallast P, Noyes MD, Kronenberg ZN, Mokveld T, Koundinya N, Nolan C, Steely CJ, Guarracino A, Dolzhenko E, Harvey WT, Rowell WJ, Grigorev K, Nicholas TJ, Oshima KK, Lin J, Ebert P, Watkins WS, Leung TY, Hanlon VCT, McGee S, Pedersen BS, Goldberg ME, Happ HC, Jeong H, Munson KM, Hoekzema K, Chan DD, Wang Y, Knuth J, Garcia GH, Fanslow C, Lambert C, Lee C, Smith JD, Levy S, Mason CE, Garrison E, Lansdorp PM, Neklason DW, Jorde LB, Quinlan AR, Eberle MA, Eichler EE. A familial, telomere-to-telomere reference for human de novo mutation and recombination from a four-generation pedigree. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.05.606142. [PMID: 39149261 PMCID: PMC11326147 DOI: 10.1101/2024.08.05.606142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Using five complementary short- and long-read sequencing technologies, we phased and assembled >95% of each diploid human genome in a four-generation, 28-member family (CEPH 1463) allowing us to systematically assess de novo mutations (DNMs) and recombination. From this family, we estimate an average of 192 DNMs per generation, including 75.5 de novo single-nucleotide variants (SNVs), 7.4 non-tandem repeat indels, 79.6 de novo indels or structural variants (SVs) originating from tandem repeats, 7.7 centromeric de novo SVs and SNVs, and 12.4 de novo Y chromosome events per generation. STRs and VNTRs are the most mutable with 32 loci exhibiting recurrent mutation through the generations. We accurately assemble 288 centromeres and six Y chromosomes across the generations, documenting de novo SVs, and demonstrate that the DNM rate varies by an order of magnitude depending on repeat content, length, and sequence identity. We show a strong paternal bias (75-81%) for all forms of germline DNM, yet we estimate that 17% of de novo SNVs are postzygotic in origin with no paternal bias. We place all this variation in the context of a high-resolution recombination map (~3.5 kbp breakpoint resolution). We observe a strong maternal recombination bias (1.36 maternal:paternal ratio) with a consistent reduction in the number of crossovers with increasing paternal (r=0.85) and maternal (r=0.65) age. However, we observe no correlation between meiotic crossover locations and de novo SVs, arguing against non-allelic homologous recombination as a predominant mechanism. The use of multiple orthogonal technologies, near-telomere-to-telomere phased genome assemblies, and a multi-generation family to assess transmission has created the most comprehensive, publicly available "truth set" of all classes of genomic variants. The resource can be used to test and benchmark new algorithms and technologies to understand the most fundamental processes underlying human genetic variation.
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Affiliation(s)
- David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Harriet Dashnow
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Thomas A Sasani
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Glennis A Logsdon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Present address: Department of Genetics, Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pille Hallast
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Michelle D Noyes
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | | | - Nidhi Koundinya
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Cody J Steely
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Andrea Guarracino
- Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | | | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - William J Rowell
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Kirill Grigorev
- Blue Marble Space Institute of Science, Seattle, WA, USA
- Core Unit Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Thomas J Nicholas
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Keisuke K Oshima
- Present address: Department of Genetics, Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiadong Lin
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Peter Ebert
- Core Unit Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - W Scott Watkins
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Tiffany Y Leung
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada
| | | | - Sean McGee
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Brent S Pedersen
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Michael E Goldberg
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Hannah C Happ
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Hyeonsoo Jeong
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Present address: Altos Labs, San Diego, CA, USA
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Daniel D Chan
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada
| | - Yanni Wang
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada
| | - Jordan Knuth
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Gage H Garcia
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | | | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Joshua D Smith
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Shawn Levy
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA
| | - Erik Garrison
- Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | | | - Deborah W Neklason
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Lynn B Jorde
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Aaron R Quinlan
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | | | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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Bai X, Chen Z, Chen K, Wu Z, Wang R, Liu J, Chang L, Wen L, Tang F. Simultaneous de novo calling and phasing of genetic variants at chromosome-scale using NanoStrand-seq. Cell Discov 2024; 10:74. [PMID: 38977679 PMCID: PMC11231365 DOI: 10.1038/s41421-024-00694-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 05/23/2024] [Indexed: 07/10/2024] Open
Abstract
The successful accomplishment of the first telomere-to-telomere human genome assembly, T2T-CHM13, marked a milestone in achieving completeness of the human reference genome. The upcoming era of genome study will focus on fully phased diploid genome assembly, with an emphasis on genetic differences between individual haplotypes. Most existing sequencing approaches only achieved localized haplotype phasing and relied on additional pedigree information for further whole-chromosome scale phasing. The short-read-based Strand-seq method is able to directly phase single nucleotide polymorphisms (SNPs) at whole-chromosome scale but falls short when it comes to phasing structural variations (SVs). To shed light on this issue, we developed a Nanopore sequencing platform-based Strand-seq approach, which we named NanoStrand-seq. This method allowed for de novo SNP calling with high precision (99.52%) and acheived a superior phasing accuracy (0.02% Hamming error rate) at whole-chromosome scale, a level of performance comparable to Strand-seq for haplotype phasing of the GM12878 genome. Importantly, we demonstrated that NanoStrand-seq can efficiently resolve the MHC locus, a highly polymorphic genomic region. Moreover, NanoStrand-seq enabled independent direct calling and phasing of deletions and insertions at whole-chromosome level; when applied to long genomic regions of SNP homozygosity, it outperformed the strategy that combined Strand-seq with bulk long-read sequencing. Finally, we showed that, like Strand-seq, NanoStrand-seq was also applicable to primary cultured cells. Together, here we provided a novel methodology that enabled interrogation of a full spectrum of haplotype-resolved SNPs and SVs at whole-chromosome scale, with broad applications for species with diploid or even potentially polypoid genomes.
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Affiliation(s)
- Xiuzhen Bai
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
- Changping Laboratory, Beijing, China
| | - Zonggui Chen
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- Changping Laboratory, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Kexuan Chen
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- School of Life Sciences, Peking University, Beijing, China
| | - Zixin Wu
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Rui Wang
- Department of Medicine, Cancer Institute, Stanford University, Stanford, CA, USA
| | - Jun'e Liu
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
- Changping Laboratory, Beijing, China
- School of Life Sciences, Peking University, Beijing, China
| | - Liang Chang
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education Beijing, Beijing, China
- Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, China
| | - Lu Wen
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
- Changping Laboratory, Beijing, China
| | - Fuchou Tang
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China.
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China.
- Changping Laboratory, Beijing, China.
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
- School of Life Sciences, Peking University, Beijing, China.
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Henglin M, Ghareghani M, Harvey W, Porubsky D, Koren S, Eichler EE, Ebert P, Marschall T. Phasing Diploid Genome Assembly Graphs with Single-Cell Strand Sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580432. [PMID: 38529499 PMCID: PMC10962706 DOI: 10.1101/2024.02.15.580432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Haplotype information is crucial for biomedical and population genetics research. However, current strategies to produce de-novo haplotype-resolved assemblies often require either difficult-to-acquire parental data or an intermediate haplotype-collapsed assembly. Here, we present Graphasing, a workflow which synthesizes the global phase signal of Strand-seq with assembly graph topology to produce chromosome-scale de-novo haplotypes for diploid genomes. Graphasing readily integrates with any assembly workflow that both outputs an assembly graph and has a haplotype assembly mode. Graphasing performs comparably to trio-phasing in contiguity, phasing accuracy, and assembly quality, outperforms Hi-C in phasing accuracy, and generates human assemblies with over 18 chromosome-spanning haplotypes.
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Affiliation(s)
- Mir Henglin
- Institute for Medical Biometry and Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University Düsseldorf, Germany
| | - Maryam Ghareghani
- Department of Mathematics and Computer Science, Freie Universität Berlin, Germany
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - William Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Peter Ebert
- Institute for Medical Biometry and Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University Düsseldorf, Germany
- Core Unit Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University Düsseldorf, Germany
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Schloissnig S, Pani S, Rodriguez-Martin B, Ebler J, Hain C, Tsapalou V, Söylev A, Hüther P, Ashraf H, Prodanov T, Asparuhova M, Hunt S, Rausch T, Marschall T, Korbel JO. Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590093. [PMID: 38659906 PMCID: PMC11042266 DOI: 10.1101/2024.04.18.590093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Structural variants (SVs) contribute significantly to human genetic diversity and disease 1-4 . Previously, SVs have remained incompletely resolved by population genomics, with short-read sequencing facing limitations in capturing the whole spectrum of SVs at nucleotide resolution 5-7 . Here we leveraged nanopore sequencing 8 to construct an intermediate coverage resource of 1,019 long-read genomes sampled within 26 human populations from the 1000 Genomes Project. By integrating linear and graph-based approaches for SV analysis via pangenome graph-augmentation, we uncover 167,291 sequence-resolved SVs in these samples, considerably advancing SV characterization compared to population-wide short-read sequencing studies 3,4 . Our analysis details diverse SV classes-deletions, duplications, insertions, and inversions-at population-scale. LINE-1 and SVA retrotransposition activities frequently mediate transductions 9,10 of unique sequences, with both mobile element classes transducing sequences at either the 3'- or 5'-end, depending on the source element locus. Furthermore, analyses of SV breakpoint junctions suggest a continuum of homology-mediated rearrangement processes are integral to SV formation, and highlight evidence for SV recurrence involving repeat sequences. Our open-access dataset underscores the transformative impact of long-read sequencing in advancing the characterisation of polymorphic genomic architectures, and provides a resource for guiding variant prioritisation in future long-read sequencing-based disease studies.
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5
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Porubsky D, Eichler EE. A 25-year odyssey of genomic technology advances and structural variant discovery. Cell 2024; 187:1024-1037. [PMID: 38290514 DOI: 10.1016/j.cell.2024.01.002] [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: 10/07/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 02/01/2024]
Abstract
This perspective focuses on advances in genome technology over the last 25 years and their impact on germline variant discovery within the field of human genetics. The field has witnessed tremendous technological advances from microarrays to short-read sequencing and now long-read sequencing. Each technology has provided genome-wide access to different classes of human genetic variation. We are now on the verge of comprehensive variant detection of all forms of variation for the first time with a single assay. We predict that this transition will further transform our understanding of human health and biology and, more importantly, provide novel insights into the dynamic mutational processes shaping our genomes.
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Affiliation(s)
- David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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Weber T, Cosenza MR, Korbel J. MosaiCatcher v2: a single-cell structural variations detection and analysis reference framework based on Strand-seq. Bioinformatics 2023; 39:btad633. [PMID: 37851409 PMCID: PMC10628386 DOI: 10.1093/bioinformatics/btad633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/23/2023] [Accepted: 10/17/2023] [Indexed: 10/19/2023] Open
Abstract
SUMMARY Single-cell DNA template strand sequencing (Strand-seq) allows a range of various genomic analysis including chromosome length haplotype phasing and structural variation (SV) calling in individual cells. Here, we present MosaiCatcher v2, a standardized workflow and reference framework for single-cell SV detection using Strand-seq. This framework introduces a range of functionalities, including: an automated upstream Quality Control (QC) and assembly sub-workflow that relies on multiple genome assemblies and incorporates a multistep normalization module, integration of the single-cell nucleosome occupancy and genetic variation analysis SV functional characterization and of the ArbiGent SV genotyping modules, platform portability, as well as a user-friendly and shareable web report. These new features of MosaiCatcher v2 enable reproducible computational processing of Strand-seq data, which are increasingly used in human genetics and single-cell genomics, toward production environments. MosaiCatcher v2 is compatible with both container and conda environments, ensuring reproducibility and robustness and positioning the framework as a cornerstone in computational processing of Strand-seq data. AVAILABILITY AND IMPLEMENTATION MosaiCatcher v2 is a standardized workflow, implemented using the Snakemake workflow management system. The pipeline is available on GitHub: https://github.com/friendsofstrandseq/mosaicatcher-pipeline/ and on the snakemake-workflow-catalog: https://snakemake.github.io/snakemake-workflow-catalog/?usage=friendsofstrandseq/mosaicatcher-pipeline. Strand-seq example input data used in the publication can be found in the Data availability statement. Additionally, a lightweight dataset for test purposes can be found on the GitHub repository.
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Affiliation(s)
- Thomas Weber
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | | | - Jan Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Bridging Research Division on Mechanisms of Genomic Variation and Data Science, German Cancer Research Center (DKFZ), Heidelberg, Germany
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van Dijk EL, Naquin D, Gorrichon K, Jaszczyszyn Y, Ouazahrou R, Thermes C, Hernandez C. Genomics in the long-read sequencing era. Trends Genet 2023; 39:649-671. [PMID: 37230864 DOI: 10.1016/j.tig.2023.04.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/21/2023] [Accepted: 04/25/2023] [Indexed: 05/27/2023]
Abstract
Long-read sequencing (LRS) technologies have provided extremely powerful tools to explore genomes. While in the early years these methods suffered technical limitations, they have recently made significant progress in terms of read length, throughput, and accuracy and bioinformatics tools have strongly improved. Here, we aim to review the current status of LRS technologies, the development of novel methods, and the impact on genomics research. We will explore the most impactful recent findings made possible by these technologies focusing on high-resolution sequencing of genomes and transcriptomes and the direct detection of DNA and RNA modifications. We will also discuss how LRS methods promise a more comprehensive understanding of human genetic variation, transcriptomics, and epigenetics for the coming years.
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Affiliation(s)
- Erwin L van Dijk
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France.
| | - Delphine Naquin
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Kévin Gorrichon
- National Center of Human Genomics Research (CNRGH), 91000 Évry-Courcouronnes, France
| | - Yan Jaszczyszyn
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Rania Ouazahrou
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Claude Thermes
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Céline Hernandez
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
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Soto DC, Uribe-Salazar JM, Shew CJ, Sekar A, McGinty S, Dennis MY. Genomic structural variation: A complex but important driver of human evolution. AMERICAN JOURNAL OF BIOLOGICAL ANTHROPOLOGY 2023; 181 Suppl 76:118-144. [PMID: 36794631 PMCID: PMC10329998 DOI: 10.1002/ajpa.24713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 01/21/2023] [Accepted: 02/05/2023] [Indexed: 02/17/2023]
Abstract
Structural variants (SVs)-including duplications, deletions, and inversions of DNA-can have significant genomic and functional impacts but are technically difficult to identify and assay compared with single-nucleotide variants. With the aid of new genomic technologies, it has become clear that SVs account for significant differences across and within species. This phenomenon is particularly well-documented for humans and other primates due to the wealth of sequence data available. In great apes, SVs affect a larger number of nucleotides than single-nucleotide variants, with many identified SVs exhibiting population and species specificity. In this review, we highlight the importance of SVs in human evolution by (1) how they have shaped great ape genomes resulting in sensitized regions associated with traits and diseases, (2) their impact on gene functions and regulation, which subsequently has played a role in natural selection, and (3) the role of gene duplications in human brain evolution. We further discuss how to incorporate SVs in research, including the strengths and limitations of various genomic approaches. Finally, we propose future considerations in integrating existing data and biospecimens with the ever-expanding SV compendium propelled by biotechnology advancements.
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Affiliation(s)
- Daniela C. Soto
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - José M. Uribe-Salazar
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - Colin J. Shew
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - Aarthi Sekar
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - Sean McGinty
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - Megan Y. Dennis
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
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9
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Weber T, Cosenza MR, Korbel J. MosaiCatcher v2: a single-cell structural variations detection and analysis reference framework based on Strand-seq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548805. [PMID: 37503087 PMCID: PMC10370012 DOI: 10.1101/2023.07.13.548805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Single-cell DNA template strand sequencing (Strand-seq) allows a range of various genomic analysis including chromosome length haplotype phasing and structural variation (SV) calling in individual cells. Here, we present MosaiCatcher v2, a standardised workflow and reference framework for single-cell SV detection using Strand-seq. This framework introduces a range of functionalities, including: an automated upstream Quality Control (QC) and assembly sub-workflow that relies on multiple genome assemblies and incorporates a multistep normalisation module, integration of the scNOVA SV functional characterization and of the ArbiGent SV genotyping modules, platform portability, as well as a user-friendly and shareable web report. These new features of MosaiCatcher v2 enables reproducible computational processing of Strand-seq data, which are increasingly used in human genetics and single cell genomics, towards production environments.
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Affiliation(s)
- Thomas Weber
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | | | - Jan Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Bridging Research Division on Mechanisms of Genomic Variation and Data Science, German Cancer Research Center (DKFZ), Heidelberg, Germany
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10
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Laufer VA, Glover TW, Wilson TE. Applications of advanced technologies for detecting genomic structural variation. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2023; 792:108475. [PMID: 37931775 PMCID: PMC10792551 DOI: 10.1016/j.mrrev.2023.108475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 09/07/2023] [Accepted: 11/02/2023] [Indexed: 11/08/2023]
Abstract
Chromosomal structural variation (SV) encompasses a heterogenous class of genetic variants that exerts strong influences on human health and disease. Despite their importance, many structural variants (SVs) have remained poorly characterized at even a basic level, a discrepancy predicated upon the technical limitations of prior genomic assays. However, recent advances in genomic technology can identify and localize SVs accurately, opening new questions regarding SV risk factors and their impacts in humans. Here, we first define and classify human SVs and their generative mechanisms, highlighting characteristics leveraged by various SV assays. We next examine the first-ever gapless assembly of the human genome and the technical process of assembling it, which required third-generation sequencing technologies to resolve structurally complex loci. The new portions of that "telomere-to-telomere" and subsequent pangenome assemblies highlight aspects of SV biology likely to develop in the near-term. We consider the strengths and limitations of the most promising new SV technologies and when they or longstanding approaches are best suited to meeting salient goals in the study of human SV in population-scale genomics research, clinical, and public health contexts. It is a watershed time in our understanding of human SV when new approaches are expected to fundamentally change genomic applications.
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Affiliation(s)
- Vincent A Laufer
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
| | - Thomas W Glover
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
| | - Thomas E Wilson
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
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11
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Porubsky D, Harvey WT, Rozanski AN, Ebler J, Höps W, Ashraf H, Hasenfeld P, Paten B, Sanders AD, Marschall T, Korbel JO, Eichler EE. Inversion polymorphism in a complete human genome assembly. Genome Biol 2023; 24:100. [PMID: 37122002 PMCID: PMC10150506 DOI: 10.1186/s13059-023-02919-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 03/31/2023] [Indexed: 05/02/2023] Open
Abstract
The telomere-to-telomere (T2T) complete human reference has significantly improved our ability to characterize genome structural variation. To understand its impact on inversion polymorphisms, we remapped data from 41 genomes against the T2T reference genome and compared it to the GRCh38 reference. We find a ~ 21% increase in sensitivity improving mapping of 63 inversions on the T2T reference. We identify 26 misorientations within GRCh38 and show that the T2T reference is three times more likely to represent the correct orientation of the major human allele. Analysis of 10 additional samples reveals novel rare inversions at chromosomes 15q25.2, 16p11.2, 16q22.1-23.1, and 22q11.21.
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Affiliation(s)
- David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Allison N Rozanski
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Jana Ebler
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany
| | - Wolfram Höps
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117, Heidelberg, Germany
| | - Hufsah Ashraf
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany
| | - Patrick Hasenfeld
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117, Heidelberg, Germany
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Ashley D Sanders
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Helmholtz Association, 10115, Berlin, Germany
- Berlin Institute of Health (BIH), 10178, Berlin, Germany
- Charité-Universitätsmedizin, 10117, Berlin, Germany
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117, Heidelberg, Germany
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA.
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12
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Hanlon VCT, Lansdorp PM, Guryev V. A survey of current methods to detect and genotype inversions. Hum Mutat 2022; 43:1576-1589. [PMID: 36047337 DOI: 10.1002/humu.24458] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/11/2022]
Abstract
Polymorphic inversions are ubiquitous in humans, and they have been linked to both adaptation and disease. Following their discovery in Drosophila more than a century ago, inversions have proved to be more elusive than other structural variants. A wide variety of methods for the detection and genotyping of inversions have recently been developed: multiple techniques based on selective amplification by PCR, short- and long-read sequencing approaches, principal component analysis of small variant haplotypes, template strand sequencing, optical mapping, and various genome assembly methods. Many methods apply complex wet lab protocols or increasingly refined bioinformatic analyses. This review is an attempt to provide a practical summary and comparison of the methods that are in current use, with a focus on metrics such as the maximum size of segmental duplications at inversion breakpoints that each method can tolerate, the size range of inversions that they recover, their throughput, and whether the locations of putative inversions must be known beforehand. This article is protected by copyright. All rights reserved.
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Affiliation(s)
| | - Peter M Lansdorp
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, V5Z 1L3, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, The Netherlands
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13
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Lanciano S, Cristofari G. Flip-flop genomics: Charting inversions in the human population. Cell 2022; 185:1811-1813. [DOI: 10.1016/j.cell.2022.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 04/30/2022] [Accepted: 05/02/2022] [Indexed: 11/29/2022]
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14
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Porubsky D, Höps W, Ashraf H, Hsieh P, Rodriguez-Martin B, Yilmaz F, Ebler J, Hallast P, Maria Maggiolini FA, Harvey WT, Henning B, Audano PA, Gordon DS, Ebert P, Hasenfeld P, Benito E, Zhu Q, Lee C, Antonacci F, Steinrücken M, Beck CR, Sanders AD, Marschall T, Eichler EE, Korbel JO. Recurrent inversion polymorphisms in humans associate with genetic instability and genomic disorders. Cell 2022; 185:1986-2005.e26. [PMID: 35525246 PMCID: PMC9563103 DOI: 10.1016/j.cell.2022.04.017] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/14/2022] [Accepted: 04/08/2022] [Indexed: 12/13/2022]
Abstract
Unlike copy number variants (CNVs), inversions remain an underexplored genetic variation class. By integrating multiple genomic technologies, we discover 729 inversions in 41 human genomes. Approximately 85% of inversions <2 kbp form by twin-priming during L1 retrotransposition; 80% of the larger inversions are balanced and affect twice as many nucleotides as CNVs. Balanced inversions show an excess of common variants, and 72% are flanked by segmental duplications (SDs) or retrotransposons. Since flanking repeats promote non-allelic homologous recombination, we developed complementary approaches to identify recurrent inversion formation. We describe 40 recurrent inversions encompassing 0.6% of the genome, showing inversion rates up to 2.7 × 10-4 per locus per generation. Recurrent inversions exhibit a sex-chromosomal bias and co-localize with genomic disorder critical regions. We propose that inversion recurrence results in an elevated number of heterozygous carriers and structural SD diversity, which increases mutability in the population and predisposes specific haplotypes to disease-causing CNVs.
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15
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Vollger MR, Guitart X, Dishuck PC, Mercuri L, Harvey WT, Gershman A, Diekhans M, Sulovari A, Munson KM, Lewis AP, Hoekzema K, Porubsky D, Li R, Nurk S, Koren S, Miga KH, Phillippy AM, Timp W, Ventura M, Eichler EE. Segmental duplications and their variation in a complete human genome. Science 2022; 376:eabj6965. [PMID: 35357917 PMCID: PMC8979283 DOI: 10.1126/science.abj6965] [Citation(s) in RCA: 104] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Despite their importance in disease and evolution, highly identical segmental duplications (SDs) are among the last regions of the human reference genome (GRCh38) to be fully sequenced. Using a complete telomere-to-telomere human genome (T2T-CHM13), we present a comprehensive view of human SD organization. SDs account for nearly one-third of the additional sequence, increasing the genome-wide estimate from 5.4 to 7.0% [218 million base pairs (Mbp)]. An analysis of 268 human genomes shows that 91% of the previously unresolved T2T-CHM13 SD sequence (68.3 Mbp) better represents human copy number variation. Comparing long-read assemblies from human (n = 12) and nonhuman primate (n = 5) genomes, we systematically reconstruct the evolution and structural haplotype diversity of biomedically relevant and duplicated genes. This analysis reveals patterns of structural heterozygosity and evolutionary differences in SD organization between humans and other primates.
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Affiliation(s)
- Mitchell R Vollger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Xavi Guitart
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Philip C Dishuck
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Ludovica Mercuri
- Department of Biology, University of Bari, Aldo Moro, Bari 70125, Italy
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Ariel Gershman
- Department of Molecular Biology and Genetics, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Mark Diekhans
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Arvis Sulovari
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Ruiyang Li
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Sergey Nurk
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Karen H Miga
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Adam M Phillippy
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Winston Timp
- Department of Molecular Biology and Genetics, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Mario Ventura
- Department of Biology, University of Bari, Aldo Moro, Bari 70125, Italy
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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16
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Chovanec P, Yin Y. A mapping platform for mitotic crossover by single-cell multi-omics. Methods Enzymol 2021; 661:183-204. [PMID: 34776212 DOI: 10.1016/bs.mie.2021.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Mitotic crossovers have the potential to cause large-scale genome rearrangements. Here, we describe high-throughput, single-cell, whole-genome sequencing methods for mapping crossovers genome-wide at scale. The methods are generalizable to various eukaryotes and to other end points requiring high-throughput, high-coverage single cell sequencing.
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Affiliation(s)
- Peter Chovanec
- Department of Human Genetics, University of California, Los Angeles, CA, Unites States
| | - Yi Yin
- Department of Human Genetics, University of California, Los Angeles, CA, Unites States.
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17
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Hanlon VCT, Mattsson CA, Spierings DCJ, Guryev V, Lansdorp PM. InvertypeR: Bayesian inversion genotyping with Strand-seq data. BMC Genomics 2021; 22:582. [PMID: 34332539 PMCID: PMC8325862 DOI: 10.1186/s12864-021-07892-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 07/15/2021] [Indexed: 11/23/2022] Open
Abstract
Background Single cell Strand-seq is a unique tool for the discovery and phasing of genomic inversions. Conventional methods to discover inversions with Strand-seq data are blind to known inversion locations, limiting their statistical power for the detection of inversions smaller than 10 Kb. Moreover, the methods rely on manual inspection to separate false and true positives. Results Here we describe “InvertypeR”, a method based on a Bayesian binomial model that genotypes inversions using fixed genomic coordinates. We validated InvertypeR by re-genotyping inversions reported for three trios by the Human Genome Structural Variation Consortium. Although 6.3% of the family inversion genotypes in the original study showed Mendelian discordance, this was reduced to 0.5% using InvertypeR. By applying InvertypeR to published inversion coordinates and predicted inversion hotspots (n = 3701), as well as coordinates from conventional inversion discovery, we furthermore genotyped 66 inversions not previously reported for the three trios. Conclusions InvertypeR discovers, genotypes, and phases inversions without relying on manual inspection. For greater accessibility, results are presented as phased chromosome ideograms with inversions linked to Strand-seq data in the genome browser. InvertypeR increases the power of Strand-seq for studies on the role of inversions in phenotypic variation, genome instability, and human disease. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07892-9.
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Affiliation(s)
- Vincent C T Hanlon
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, V5Z 1L3, Canada.
| | - Carl-Adam Mattsson
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, V5Z 1L3, Canada
| | - Diana C J Spierings
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, The Netherlands
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, The Netherlands
| | - Peter M Lansdorp
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, V5Z 1L3, Canada.,European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, The Netherlands.,Departments of Medical Genetics and Hematology, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada
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18
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Trost B, Loureiro LO, Scherer SW. Discovery of genomic variation across a generation. Hum Mol Genet 2021; 30:R174-R186. [PMID: 34296264 PMCID: PMC8490016 DOI: 10.1093/hmg/ddab209] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/09/2021] [Accepted: 07/19/2021] [Indexed: 11/12/2022] Open
Abstract
Over the past 30 years (the timespan of a generation), advances in genomics technologies have revealed tremendous and unexpected variation in the human genome and have provided increasingly accurate answers to long-standing questions of how much genetic variation exists in human populations and to what degree the DNA complement changes between parents and offspring. Tracking the characteristics of these inherited and spontaneous (or de novo) variations has been the basis of the study of human genetic disease. From genome-wide microarray and next-generation sequencing scans, we now know that each human genome contains over 3 million single nucleotide variants when compared with the ~ 3 billion base pairs in the human reference genome, along with roughly an order of magnitude more DNA—approximately 30 megabase pairs (Mb)—being ‘structurally variable’, mostly in the form of indels and copy number changes. Additional large-scale variations include balanced inversions (average of 18 Mb) and complex, difficult-to-resolve alterations. Collectively, ~1% of an individual’s genome will differ from the human reference sequence. When comparing across a generation, fewer than 100 new genetic variants are typically detected in the euchromatic portion of a child’s genome. Driven by increasingly higher-resolution and higher-throughput sequencing technologies, newer and more accurate databases of genetic variation (for instance, more comprehensive structural variation data and phasing of combinations of variants along chromosomes) of worldwide populations will emerge to underpin the next era of discovery in human molecular genetics.
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Affiliation(s)
- Brett Trost
- The Centre for Applied Genomics and Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Livia O Loureiro
- The Centre for Applied Genomics and Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Stephen W Scherer
- The Centre for Applied Genomics and Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada.,McLaughlin Centre and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
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19
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Zhao X, Collins RL, Lee WP, Weber AM, Jun Y, Zhu Q, Weisburd B, Huang Y, Audano PA, Wang H, Walker M, Lowther C, Fu J, Gerstein MB, Devine SE, Marschall T, Korbel JO, Eichler EE, Chaisson MJP, Lee C, Mills RE, Brand H, Talkowski ME. Expectations and blind spots for structural variation detection from long-read assemblies and short-read genome sequencing technologies. Am J Hum Genet 2021; 108:919-928. [PMID: 33789087 PMCID: PMC8206509 DOI: 10.1016/j.ajhg.2021.03.014] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 03/12/2021] [Indexed: 12/13/2022] Open
Abstract
Virtually all genome sequencing efforts in national biobanks, complex and Mendelian disease programs, and medical genetic initiatives are reliant upon short-read whole-genome sequencing (srWGS), which presents challenges for the detection of structural variants (SVs) relative to emerging long-read WGS (lrWGS) technologies. Given this ubiquity of srWGS in large-scale genomics initiatives, we sought to establish expectations for routine SV detection from this data type by comparison with lrWGS assembly, as well as to quantify the genomic properties and added value of SVs uniquely accessible to each technology. Analyses from the Human Genome Structural Variation Consortium (HGSVC) of three families captured ~11,000 SVs per genome from srWGS and ~25,000 SVs per genome from lrWGS assembly. Detection power and precision for SV discovery varied dramatically by genomic context and variant class: 9.7% of the current GRCh38 reference is defined by segmental duplication (SD) and simple repeat (SR), yet 91.4% of deletions that were specifically discovered by lrWGS localized to these regions. Across the remaining 90.3% of reference sequence, we observed extremely high (93.8%) concordance between technologies for deletions in these datasets. In contrast, lrWGS was superior for detection of insertions across all genomic contexts. Given that non-SD/SR sequences encompass 95.9% of currently annotated disease-associated exons, improved sensitivity from lrWGS to discover novel pathogenic deletions in these currently interpretable genomic regions is likely to be incremental. However, these analyses highlight the considerable added value of assembly-based lrWGS to create new catalogs of insertions and transposable elements, as well as disease-associated repeat expansions in genomic sequences that were previously recalcitrant to routine assessment.
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Affiliation(s)
- Xuefang Zhao
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics and Stanley Center for Psychiatric Disorders, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Ryan L Collins
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics and Stanley Center for Psychiatric Disorders, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Division of Medical Sciences, Harvard Medical School, Boston, MA 02115, USA
| | - Wan-Ping Lee
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Alexandra M Weber
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan Medical School, 1241 East Catherine Street, Ann Arbor, MI 48109, USA
| | - Yukyung Jun
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Qihui Zhu
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Ben Weisburd
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Disorders, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Yongqing Huang
- Data Sciences Platform, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Peter A Audano
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Harold Wang
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics and Stanley Center for Psychiatric Disorders, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Mark Walker
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Disorders, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Chelsea Lowther
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics and Stanley Center for Psychiatric Disorders, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Jack Fu
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics and Stanley Center for Psychiatric Disorders, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Mark B Gerstein
- Yale University Medical School, Computational Biology and Bioinformatics Program, New Haven, CT 06520, USA
| | - Scott E Devine
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Jan O Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - Mark J P Chaisson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Graduate Studies - Life Sciences, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, South Korea; Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an 710061, Shaanxi, People's Republic of China
| | - Ryan E Mills
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan Medical School, 1241 East Catherine Street, Ann Arbor, MI 48109, USA
| | - Harrison Brand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics and Stanley Center for Psychiatric Disorders, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Michael E Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics and Stanley Center for Psychiatric Disorders, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Division of Medical Sciences, Harvard Medical School, Boston, MA 02115, USA.
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20
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Mao Y, Catacchio CR, Hillier LW, Porubsky D, Li R, Sulovari A, Fernandes JD, Montinaro F, Gordon DS, Storer JM, Haukness M, Fiddes IT, Murali SC, Dishuck PC, Hsieh P, Harvey WT, Audano PA, Mercuri L, Piccolo I, Antonacci F, Munson KM, Lewis AP, Baker C, Underwood JG, Hoekzema K, Huang TH, Sorensen M, Walker JA, Hoffman J, Thibaud-Nissen F, Salama SR, Pang AWC, Lee J, Hastie AR, Paten B, Batzer MA, Diekhans M, Ventura M, Eichler EE. A high-quality bonobo genome refines the analysis of hominid evolution. Nature 2021; 594:77-81. [PMID: 33953399 PMCID: PMC8172381 DOI: 10.1038/s41586-021-03519-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 04/07/2021] [Indexed: 12/17/2022]
Abstract
The divergence of chimpanzee and bonobo provides one of the few examples of recent hominid speciation1,2. Here we describe a fully annotated, high-quality bonobo genome assembly, which was constructed without guidance from reference genomes by applying a multiplatform genomics approach. We generate a bonobo genome assembly in which more than 98% of genes are completely annotated and 99% of the gaps are closed, including the resolution of about half of the segmental duplications and almost all of the full-length mobile elements. We compare the bonobo genome to those of other great apes1,3–5 and identify more than 5,569 fixed structural variants that specifically distinguish the bonobo and chimpanzee lineages. We focus on genes that have been lost, changed in structure or expanded in the last few million years of bonobo evolution. We produce a high-resolution map of incomplete lineage sorting and estimate that around 5.1% of the human genome is genetically closer to chimpanzee or bonobo and that more than 36.5% of the genome shows incomplete lineage sorting if we consider a deeper phylogeny including gorilla and orangutan. We also show that 26% of the segments of incomplete lineage sorting between human and chimpanzee or human and bonobo are non-randomly distributed and that genes within these clustered segments show significant excess of amino acid replacement compared to the rest of the genome. A high-quality bonobo genome assembly provides insights into incomplete lineage sorting in hominids and its relevance to gene evolution and the genetic relationship among living hominids.
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Affiliation(s)
- Yafei Mao
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | - LaDeana W Hillier
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Ruiyang Li
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Arvis Sulovari
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Jason D Fernandes
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Francesco Montinaro
- Department of Biology, University of Bari, Bari, Italy.,Estonian Biocentre, Institute of Genomics, Tartu, Estonia
| | - David S Gordon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.,Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | | | - Marina Haukness
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Ian T Fiddes
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Shwetha Canchi Murali
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.,Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Philip C Dishuck
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - PingHsun Hsieh
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Peter A Audano
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | | | | | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Carl Baker
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Tzu-Hsueh Huang
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Melanie Sorensen
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Jerilyn A Walker
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - Jinna Hoffman
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Françoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Sofie R Salama
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.,Howard Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | | | - Joyce Lee
- Bionano Genomics, San Diego, CA, USA
| | | | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Mark A Batzer
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - Mark Diekhans
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Mario Ventura
- Department of Biology, University of Bari, Bari, Italy.
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA. .,Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
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21
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Ebert P, Audano PA, Zhu Q, Rodriguez-Martin B, Porubsky D, Bonder MJ, Sulovari A, Ebler J, Zhou W, Serra Mari R, Yilmaz F, Zhao X, Hsieh P, Lee J, Kumar S, Lin J, Rausch T, Chen Y, Ren J, Santamarina M, Höps W, Ashraf H, Chuang NT, Yang X, Munson KM, Lewis AP, Fairley S, Tallon LJ, Clarke WE, Basile AO, Byrska-Bishop M, Corvelo A, Evani US, Lu TY, Chaisson MJP, Chen J, Li C, Brand H, Wenger AM, Ghareghani M, Harvey WT, Raeder B, Hasenfeld P, Regier AA, Abel HJ, Hall IM, Flicek P, Stegle O, Gerstein MB, Tubio JMC, Mu Z, Li YI, Shi X, Hastie AR, Ye K, Chong Z, Sanders AD, Zody MC, Talkowski ME, Mills RE, Devine SE, Lee C, Korbel JO, Marschall T, Eichler EE. Haplotype-resolved diverse human genomes and integrated analysis of structural variation. Science 2021; 372:eabf7117. [PMID: 33632895 PMCID: PMC8026704 DOI: 10.1126/science.abf7117] [Citation(s) in RCA: 307] [Impact Index Per Article: 102.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 02/09/2021] [Indexed: 12/14/2022]
Abstract
Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average minimum contig length needed to cover 50% of the genome: 26 million base pairs) integrate all forms of genetic variation, even across complex loci. We identified 107,590 structural variants (SVs), of which 68% were not discovered with short-read sequencing, and 278 SV hotspots (spanning megabases of gene-rich sequence). We characterized 130 of the most active mobile element source elements and found that 63% of all SVs arise through homology-mediated mechanisms. This resource enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1526 expression quantitative trait loci as well as SV candidates for adaptive selection within the human population.
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Affiliation(s)
- Peter Ebert
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Peter A Audano
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Qihui Zhu
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Bernardo Rodriguez-Martin
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Marc Jan Bonder
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Arvis Sulovari
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Jana Ebler
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Weichen Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Rebecca Serra Mari
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Feyza Yilmaz
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Xuefang Zhao
- Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - PingHsun Hsieh
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Joyce Lee
- Bionano Genomics, San Diego, CA 92121, USA
| | - Sushant Kumar
- Program in Computational Biology and Bioinformatics, Yale University, BASS 432 and 437, 266 Whitney Avenue, New Haven, CT 06520, USA
| | - Jiadong Lin
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Tobias Rausch
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Yu Chen
- Department of Genetics and Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Jingwen Ren
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Martin Santamarina
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Department of Zoology, Genetics, and Physical Anthropology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Wolfram Höps
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Hufsah Ashraf
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Nelson T Chuang
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD 21201, USA
| | - Xiaofei Yang
- School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Susan Fairley
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Luke J Tallon
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD 21201, USA
| | | | | | | | | | | | - Tsung-Yu Lu
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Mark J P Chaisson
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Junjie Chen
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | - Chong Li
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | - Harrison Brand
- Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aaron M Wenger
- Pacific Biosciences of California, Menlo Park, CA 94025, USA
| | - Maryam Ghareghani
- Max Planck Institute for Informatics, Saarland Informatics Campus E1.4, 66123 Saarbrücken, Germany
- Saarbrücken Graduate School of Computer Science, Saarland University, Saarland Informatics Campus E1.3, 66123 Saarbrücken, Germany
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Benjamin Raeder
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Patrick Hasenfeld
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Allison A Regier
- Department of Medicine, Washington University, St. Louis, MO 63108, USA
| | - Haley J Abel
- Department of Medicine, Washington University, St. Louis, MO 63108, USA
| | - Ira M Hall
- Department of Genetics, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Oliver Stegle
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Mark B Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, BASS 432 and 437, 266 Whitney Avenue, New Haven, CT 06520, USA
| | - Jose M C Tubio
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Department of Zoology, Genetics, and Physical Anthropology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Zepeng Mu
- Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - Yang I Li
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | | | - Kai Ye
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Human Genetics, University of Michigan, 1241 E. Catherine Street, Ann Arbor, MI 48109, USA
| | - Zechen Chong
- Department of Genetics and Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Ashley D Sanders
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | | | - Michael E Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ryan E Mills
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
- Department of Human Genetics, University of Michigan, 1241 E. Catherine Street, Ann Arbor, MI 48109, USA
| | - Scott E Devine
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD 21201, USA
| | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA.
- Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi, China
- Department of Graduate Studies-Life Sciences, Ewha Womans University, Ewhayeodae-gil, Seodaemun-gu, Seoul 120-750, South Korea
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tobias Marschall
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany.
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
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22
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Eimer C, Sanders AD, Korbel JO, Marschall T, Ebert P. ASHLEYS: automated quality control for single-cell Strand-seq data. Bioinformatics 2021; 37:3356-3357. [PMID: 33792647 PMCID: PMC8504637 DOI: 10.1093/bioinformatics/btab221] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/15/2021] [Accepted: 03/31/2021] [Indexed: 11/18/2022] Open
Abstract
Summary Single-cell DNA template strand sequencing (Strand-seq) enables chromosome length haplotype phasing, construction of phased assemblies, mapping sister-chromatid exchange events and structural variant discovery. The initial quality control of potentially thousands of single-cell libraries is still done manually by domain experts. ASHLEYS automates this tedious task, delivers near-expert performance and labels even large datasets in seconds. Availability and implementation github.com/friendsofstrandseq/ashleys-qc, MIT license. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Christina Eimer
- Center for Bioinformatics Saar, Saarland University, 66123 Saarbrücken, Germany
| | - Ashley D Sanders
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Peter Ebert
- Institute for Medical Biometry and Bioinformatics, Heinrich Heine University, 40225 Düsseldorf, Germany
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23
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Construction of Whole Genomes from Scaffolds Using Single Cell Strand-Seq Data. Int J Mol Sci 2021; 22:ijms22073617. [PMID: 33807210 PMCID: PMC8037727 DOI: 10.3390/ijms22073617] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 03/23/2021] [Accepted: 03/27/2021] [Indexed: 12/26/2022] Open
Abstract
Accurate reference genome sequences provide the foundation for modern molecular biology and genomics as the interpretation of sequence data to study evolution, gene expression, and epigenetics depends heavily on the quality of the genome assembly used for its alignment. Correctly organising sequenced fragments such as contigs and scaffolds in relation to each other is a critical and often challenging step in the construction of robust genome references. We previously identified misoriented regions in the mouse and human reference assemblies using Strand-seq, a single cell sequencing technique that preserves DNA directionality Here we demonstrate the ability of Strand-seq to build and correct full-length chromosomes by identifying which scaffolds belong to the same chromosome and determining their correct order and orientation, without the need for overlapping sequences. We demonstrate that Strand-seq exquisitely maps assembly fragments into large related groups and chromosome-sized clusters without using new assembly data. Using template strand inheritance as a bi-allelic marker, we employ genetic mapping principles to cluster scaffolds that are derived from the same chromosome and order them within the chromosome based solely on directionality of DNA strand inheritance. We prove the utility of our approach by generating improved genome assemblies for several model organisms including the ferret, pig, Xenopus, zebrafish, Tasmanian devil and the Guinea pig.
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24
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Porubsky D, Ebert P, Audano PA, Vollger MR, Harvey WT, Marijon P, Ebler J, Munson KM, Sorensen M, Sulovari A, Haukness M, Ghareghani M, Lansdorp PM, Paten B, Devine SE, Sanders AD, Lee C, Chaisson MJP, Korbel JO, Eichler EE, Marschall T. Fully phased human genome assembly without parental data using single-cell strand sequencing and long reads. Nat Biotechnol 2021; 39:302-308. [PMID: 33288906 PMCID: PMC7954704 DOI: 10.1038/s41587-020-0719-5] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 09/16/2020] [Indexed: 12/18/2022]
Abstract
Human genomes are typically assembled as consensus sequences that lack information on parental haplotypes. Here we describe a reference-free workflow for diploid de novo genome assembly that combines the chromosome-wide phasing and scaffolding capabilities of single-cell strand sequencing1,2 with continuous long-read or high-fidelity3 sequencing data. Employing this strategy, we produced a completely phased de novo genome assembly for each haplotype of an individual of Puerto Rican descent (HG00733) in the absence of parental data. The assemblies are accurate (quality value > 40) and highly contiguous (contig N50 > 23 Mbp) with low switch error rates (0.17%), providing fully phased single-nucleotide variants, indels and structural variants. A comparison of Oxford Nanopore Technologies and Pacific Biosciences phased assemblies identified 154 regions that are preferential sites of contig breaks, irrespective of sequencing technology or phasing algorithms.
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Affiliation(s)
- David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Peter Ebert
- Heinrich Heine University Düsseldorf, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany
| | - Peter A Audano
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Mitchell R Vollger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Pierre Marijon
- Heinrich Heine University Düsseldorf, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany
| | - Jana Ebler
- Heinrich Heine University Düsseldorf, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Melanie Sorensen
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Arvis Sulovari
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Marina Haukness
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Maryam Ghareghani
- Heinrich Heine University Düsseldorf, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany
- Center for Bioinformatics, Saarland University, and Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Peter M Lansdorp
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Scott E Devine
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ashley D Sanders
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Department of Life Science, Ewha Womans University, Seoul, Republic of Korea
| | - Mark J P Chaisson
- Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Jan O Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
| | - Tobias Marschall
- Heinrich Heine University Düsseldorf, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany.
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25
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The structure, function and evolution of a complete human chromosome 8. Nature 2021; 593:101-107. [PMID: 33828295 PMCID: PMC8099727 DOI: 10.1038/s41586-021-03420-7] [Citation(s) in RCA: 169] [Impact Index Per Article: 56.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 03/04/2021] [Indexed: 02/07/2023]
Abstract
The complete assembly of each human chromosome is essential for understanding human biology and evolution1,2. Here we use complementary long-read sequencing technologies to complete the linear assembly of human chromosome 8. Our assembly resolves the sequence of five previously long-standing gaps, including a 2.08-Mb centromeric α-satellite array, a 644-kb copy number polymorphism in the β-defensin gene cluster that is important for disease risk, and an 863-kb variable number tandem repeat at chromosome 8q21.2 that can function as a neocentromere. We show that the centromeric α-satellite array is generally methylated except for a 73-kb hypomethylated region of diverse higher-order α-satellites enriched with CENP-A nucleosomes, consistent with the location of the kinetochore. In addition, we confirm the overall organization and methylation pattern of the centromere in a diploid human genome. Using a dual long-read sequencing approach, we complete high-quality draft assemblies of the orthologous centromere from chromosome 8 in chimpanzee, orangutan and macaque to reconstruct its evolutionary history. Comparative and phylogenetic analyses show that the higher-order α-satellite structure evolved in the great ape ancestor with a layered symmetry, in which more ancient higher-order repeats locate peripherally to monomeric α-satellites. We estimate that the mutation rate of centromeric satellite DNA is accelerated by more than 2.2-fold compared to the unique portions of the genome, and this acceleration extends into the flanking sequence.
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26
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Catusi I, Bonati MT, Mainini E, Russo S, Orlandini E, Larizza L, Recalcati MP. Recombinant Chromosome 7 Driven by Maternal Chromosome 7 Pericentric Inversion in a Girl with Features of Silver-Russell Syndrome. Int J Mol Sci 2020; 21:ijms21228487. [PMID: 33187293 PMCID: PMC7698152 DOI: 10.3390/ijms21228487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/04/2020] [Accepted: 11/09/2020] [Indexed: 11/16/2022] Open
Abstract
Maternal uniparental disomy of chromosome 7 is present in 5-10% of patients with Silver-Russell syndrome (SRS), and duplication of 7p including GRB10 (Growth Factor Receptor-Bound Protein 10), an imprinted gene that affects pre-and postnatal growth retardation, has been associated with the SRS phenotype. Here, we report on a 17 year old girl referred to array-CGH analysis for short stature, psychomotor delay, and relative macrocephaly. Array-CGH analysis showed two copy number variants (CNVs): a ~12.7 Mb gain in 7p13-p11.2, involving GRB10 and an ~9 Mb loss in 7q11.21-q11.23. FISH experiments performed on the proband's mother showed a chromosome 7 pericentric inversion that might have mediated the complex rearrangement harbored by the daughter. Indeed, we found that segmental duplications, of which chromosome 7 is highly enriched, mapped at the breakpoints of both the mother's inversion and the daughter's CNVs. We postulate that pairing of highly homologous sequences might have perturbed the correct meiotic chromosome segregation, leading to unbalanced outcomes and acting as the putative meiotic mechanism that was causative of the proband's rearrangement. Comparison of the girl's phenotype to those of patients with similar CNVs supports the presence of 7p in a locus associated with features of SRS syndrome.
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Affiliation(s)
- Ilaria Catusi
- Laboratorio di Citogenetica e Genetica Molecolare, Istituto Auxologico Italiano, IRCCS, 20149 Milano, Italy; (I.C.); (E.M.); (S.R.); (L.L.)
| | - Maria Teresa Bonati
- Ambulatorio di Genetica Medica, Istituto Auxologico Italiano, IRCCS, 20149 Milano, Italy; (M.T.B.); (E.O.)
| | - Ester Mainini
- Laboratorio di Citogenetica e Genetica Molecolare, Istituto Auxologico Italiano, IRCCS, 20149 Milano, Italy; (I.C.); (E.M.); (S.R.); (L.L.)
| | - Silvia Russo
- Laboratorio di Citogenetica e Genetica Molecolare, Istituto Auxologico Italiano, IRCCS, 20149 Milano, Italy; (I.C.); (E.M.); (S.R.); (L.L.)
| | - Eleonora Orlandini
- Ambulatorio di Genetica Medica, Istituto Auxologico Italiano, IRCCS, 20149 Milano, Italy; (M.T.B.); (E.O.)
| | - Lidia Larizza
- Laboratorio di Citogenetica e Genetica Molecolare, Istituto Auxologico Italiano, IRCCS, 20149 Milano, Italy; (I.C.); (E.M.); (S.R.); (L.L.)
| | - Maria Paola Recalcati
- Laboratorio di Citogenetica e Genetica Molecolare, Istituto Auxologico Italiano, IRCCS, 20149 Milano, Italy; (I.C.); (E.M.); (S.R.); (L.L.)
- Correspondence:
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27
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Pathogenic 12-kb copy-neutral inversion in syndromic intellectual disability identified by high-fidelity long-read sequencing. Genomics 2020; 113:1044-1053. [PMID: 33157260 DOI: 10.1016/j.ygeno.2020.10.038] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/08/2020] [Accepted: 10/31/2020] [Indexed: 01/07/2023]
Abstract
We report monozygotic twin girls with syndromic intellectual disability who underwent exome sequencing but with negative pathogenic variants. To search for variants that are unrecognized by exome sequencing, high-fidelity long-read genome sequencing (HiFi LR-GS) was applied. A 12-kb copy-neutral inversion was precisely identified by HiFi LR-GS after trio-based variant filtering. This inversion directly disrupted two genes, CPNE9 and BRPF1, the latter of which attracted our attention because pathogenic BRPF1 variants have been identified in autosomal dominant intellectual developmental disorder with dysmorphic facies and ptosis (IDDDFP), which later turned out to be clinically found in the twins. Trio-based HiFi LR-GS together with haplotype phasing revealed that the 12-kb inversion occurred de novo on the maternally transmitted chromosome. This study clearly indicates that submicroscopic copy-neutral inversions are important but often uncharacterized culprits in monogenic disorders and that long-read sequencing is highly advantageous for detecting such inversions involved in genetic diseases.
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28
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Single-cell strand sequencing of a macaque genome reveals multiple nested inversions and breakpoint reuse during primate evolution. Genome Res 2020; 30:1680-1693. [PMID: 33093070 PMCID: PMC7605249 DOI: 10.1101/gr.265322.120] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 09/02/2020] [Indexed: 12/14/2022]
Abstract
Rhesus macaque is an Old World monkey that shared a common ancestor with human ∼25 Myr ago and is an important animal model for human disease studies. A deep understanding of its genetics is therefore required for both biomedical and evolutionary studies. Among structural variants, inversions represent a driving force in speciation and play an important role in disease predisposition. Here we generated a genome-wide map of inversions between human and macaque, combining single-cell strand sequencing with cytogenetics. We identified 375 total inversions between 859 bp and 92 Mbp, increasing by eightfold the number of previously reported inversions. Among these, 19 inversions flanked by segmental duplications overlap with recurrent copy number variants associated with neurocognitive disorders. Evolutionary analyses show that in 17 out of 19 cases, the Hominidae orientation of these disease-associated regions is always derived. This suggests that duplicated sequences likely played a fundamental role in generating inversions in humans and great apes, creating architectures that nowadays predispose these regions to disease-associated genetic instability. Finally, we identified 861 genes mapping at 156 inversions breakpoints, with some showing evidence of differential expression in human and macaque cell lines, thus highlighting candidates that might have contributed to the evolution of species-specific features. This study depicts the most accurate fine-scale map of inversions between human and macaque using a two-pronged integrative approach, such as single-cell strand sequencing and cytogenetics, and represents a valuable resource toward understanding of the biology and evolution of primate species.
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29
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Porubsky D, Sanders AD, Taudt A, Colomé-Tatché M, Lansdorp PM, Guryev V. breakpointR: an R/Bioconductor package to localize strand state changes in Strand-seq data. Bioinformatics 2020; 36:1260-1261. [PMID: 31504176 DOI: 10.1093/bioinformatics/btz681] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 08/12/2019] [Accepted: 08/27/2019] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION Strand-seq is a specialized single-cell DNA sequencing technique centered around the directionality of single-stranded DNA. Computational tools for Strand-seq analyses must capture the strand-specific information embedded in these data. RESULTS Here we introduce breakpointR, an R/Bioconductor package specifically tailored to process and interpret single-cell strand-specific sequencing data obtained from Strand-seq. We developed breakpointR to detect local changes in strand directionality of aligned Strand-seq data, to enable fine-mapping of sister chromatid exchanges, germline inversion and to support global haplotype assembly. Given the broad spectrum of Strand-seq applications we expect breakpointR to be an important addition to currently available tools and extend the accessibility of this novel sequencing technique. AVAILABILITY AND IMPLEMENTATION R/Bioconductor package https://bioconductor.org/packages/breakpointR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Porubsky
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Ashley D Sanders
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada.,European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Aaron Taudt
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Maria Colomé-Tatché
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Peter M Lansdorp
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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30
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Qu L, Wang L, He F, Han Y, Yang L, Wang MD, Zhu H. The Landscape of Micro-Inversions Provide Clues for Population Genetic Analysis of Humans. Interdiscip Sci 2020; 12:499-514. [PMID: 32929667 PMCID: PMC7658078 DOI: 10.1007/s12539-020-00392-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 09/02/2020] [Accepted: 09/03/2020] [Indexed: 11/04/2022]
Abstract
Background Variations in the human genome have been studied extensively. However, little is known about the role of micro-inversions (MIs), generally defined as small (< 100 bp) inversions, in human evolution, diversity, and health. Depicting the pattern of MIs among diverse populations is critical for interpreting human evolutionary history and obtaining insight into genetic diseases. Results In this paper, we explored the distribution of MIs in genomes from 26 human populations and 7 nonhuman primate genomes and analyzed the phylogenetic structure of the 26 human populations based on the MIs. We further investigated the functions of the MIs located within genes associated with human health. With hg19 as the reference genome, we detected 6968 MIs among the 1937 human samples and 24,476 MIs among the 7 nonhuman primate genomes. The analyses of MIs in human genomes showed that the MIs were rarely located in exonic regions. Nonhuman primates and human populations shared only 82 inverted alleles, and Africans had the most inverted alleles in common with nonhuman primates, which was consistent with the “Out of Africa” hypothesis. The clustering of MIs among the human populations also coincided with human migration history and ancestral lineages. Conclusions We propose that MIs are potential evolutionary markers for investigating population dynamics. Our results revealed the diversity of MIs in human populations and showed that they are essential to construct human population relationships and have a potential effect on human health. Electronic supplementary material The online version of this article (10.1007/s12539-020-00392-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Li Qu
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.,Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA, 30332, USA
| | - Luotong Wang
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.,Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - Feifei He
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.,Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - Yilun Han
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.,Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - Longshu Yang
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.,Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - May D Wang
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA, 30332, USA
| | - Huaiqiu Zhu
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China. .,Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA, 30332, USA. .,Center for Quantitative Biology, Peking University, Beijing, 100871, China.
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31
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Miga KH, Koren S, Rhie A, Vollger MR, Gershman A, Bzikadze A, Brooks S, Howe E, Porubsky D, Logsdon GA, Schneider VA, Potapova T, Wood J, Chow W, Armstrong J, Fredrickson J, Pak E, Tigyi K, Kremitzki M, Markovic C, Maduro V, Dutra A, Bouffard GG, Chang AM, Hansen NF, Wilfert AB, Thibaud-Nissen F, Schmitt AD, Belton JM, Selvaraj S, Dennis MY, Soto DC, Sahasrabudhe R, Kaya G, Quick J, Loman NJ, Holmes N, Loose M, Surti U, Risques RA, Graves Lindsay TA, Fulton R, Hall I, Paten B, Howe K, Timp W, Young A, Mullikin JC, Pevzner PA, Gerton JL, Sullivan BA, Eichler EE, Phillippy AM. Telomere-to-telomere assembly of a complete human X chromosome. Nature 2020; 585:79-84. [PMID: 32663838 PMCID: PMC7484160 DOI: 10.1038/s41586-020-2547-7] [Citation(s) in RCA: 406] [Impact Index Per Article: 101.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 05/29/2020] [Indexed: 12/15/2022]
Abstract
After two decades of improvements, the current human reference genome (GRCh38) is the most accurate and complete vertebrate genome ever produced. However, no single chromosome has been finished end to end, and hundreds of unresolved gaps persist1,2. Here we present a human genome assembly that surpasses the continuity of GRCh382, along with a gapless, telomere-to-telomere assembly of a human chromosome. This was enabled by high-coverage, ultra-long-read nanopore sequencing of the complete hydatidiform mole CHM13 genome, combined with complementary technologies for quality improvement and validation. Focusing our efforts on the human X chromosome3, we reconstructed the centromeric satellite DNA array (approximately 3.1 Mb) and closed the 29 remaining gaps in the current reference, including new sequences from the human pseudoautosomal regions and from cancer-testis ampliconic gene families (CT-X and GAGE). These sequences will be integrated into future human reference genome releases. In addition, the complete chromosome X, combined with the ultra-long nanopore data, allowed us to map methylation patterns across complex tandem repeats and satellite arrays. Our results demonstrate that finishing the entire human genome is now within reach, and the data presented here will facilitate ongoing efforts to complete the other human chromosomes.
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Affiliation(s)
- Karen H Miga
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA.
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Arang Rhie
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mitchell R Vollger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Ariel Gershman
- Department of Molecular Biology and Genetics, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Andrey Bzikadze
- Graduate Program in Bioinformatics and Systems Biology, University of California San Diego, San Diego, CA, USA
| | - Shelise Brooks
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Rockville, MD, USA
| | - Edmund Howe
- Stowers Institute for Medical Research, Kansas City, MO, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Glennis A Logsdon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Valerie A Schneider
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Tamara Potapova
- Stowers Institute for Medical Research, Kansas City, MO, USA
| | | | | | - Joel Armstrong
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | | | - Evgenia Pak
- Cytogenetic and Microscopy Core, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kristof Tigyi
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Milinn Kremitzki
- McDonnell Genome Institute at Washington University, St Louis, MO, USA
| | | | - Valerie Maduro
- Undiagnosed Diseases Program, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Amalia Dutra
- Cytogenetic and Microscopy Core, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Gerard G Bouffard
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Rockville, MD, USA
| | - Alexander M Chang
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nancy F Hansen
- Comparative Genomics Analysis Unit, Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Amy B Wilfert
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Françoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | | | | | | | - Megan Y Dennis
- Department of Biochemistry and Molecular Medicine, Genome Center, MIND Institute, University of California Davis, Davis, CA, USA
| | - Daniela C Soto
- Department of Biochemistry and Molecular Medicine, Genome Center, MIND Institute, University of California Davis, Davis, CA, USA
| | - Ruta Sahasrabudhe
- DNA Technologies Core, Genome Center, University of California Davis, Davis, CA, USA
| | - Gulhan Kaya
- Department of Biochemistry and Molecular Medicine, Genome Center, MIND Institute, University of California Davis, Davis, CA, USA
| | - Josh Quick
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Nicholas J Loman
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Nadine Holmes
- DeepSeq, School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Matthew Loose
- DeepSeq, School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Urvashi Surti
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Rosa Ana Risques
- Department of Pathology, University of Washington, Seattle, WA, USA
| | | | - Robert Fulton
- McDonnell Genome Institute at Washington University, St Louis, MO, USA
| | - Ira Hall
- McDonnell Genome Institute at Washington University, St Louis, MO, USA
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | | | - Winston Timp
- Department of Molecular Biology and Genetics, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Alice Young
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Rockville, MD, USA
| | - James C Mullikin
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Rockville, MD, USA
| | - Pavel A Pevzner
- Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA
| | | | - Beth A Sullivan
- Department of Molecular Genetics and Microbiology, Division of Human Genetics, Duke University Medical Center, Durham, NC, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Adam M Phillippy
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
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32
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Cantsilieris S, Sunkin SM, Johnson ME, Anaclerio F, Huddleston J, Baker C, Dougherty ML, Underwood JG, Sulovari A, Hsieh P, Mao Y, Catacchio CR, Malig M, Welch AE, Sorensen M, Munson KM, Jiang W, Girirajan S, Ventura M, Lamb BT, Conlon RA, Eichler EE. An evolutionary driver of interspersed segmental duplications in primates. Genome Biol 2020; 21:202. [PMID: 32778141 PMCID: PMC7419210 DOI: 10.1186/s13059-020-02074-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 06/08/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The complex interspersed pattern of segmental duplications in humans is responsible for rearrangements associated with neurodevelopmental disease, including the emergence of novel genes important in human brain evolution. We investigate the evolution of LCR16a, a putative driver of this phenomenon that encodes one of the most rapidly evolving human-ape gene families, nuclear pore interacting protein (NPIP). RESULTS Comparative analysis shows that LCR16a has independently expanded in five primate lineages over the last 35 million years of primate evolution. The expansions are associated with independent lineage-specific segmental duplications flanking LCR16a leading to the emergence of large interspersed duplication blocks at non-orthologous chromosomal locations in each primate lineage. The intron-exon structure of the NPIP gene family has changed dramatically throughout primate evolution with different branches showing characteristic gene models yet maintaining an open reading frame. In the African ape lineage, we detect signatures of positive selection that occurred after a transition to more ubiquitous expression among great ape tissues when compared to Old World and New World monkeys. Mouse transgenic experiments from baboon and human genomic loci confirm these expression differences and suggest that the broader ape expression pattern arose due to mutational changes that emerged in cis. CONCLUSIONS LCR16a promotes serial interspersed duplications and creates hotspots of genomic instability that appear to be an ancient property of primate genomes. Dramatic changes to NPIP gene structure and altered tissue expression preceded major bouts of positive selection in the African ape lineage, suggestive of a gene undergoing strong adaptive evolution.
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Affiliation(s)
- Stuart Cantsilieris
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Present Address: Centre for Eye Research Australia, Department of Surgery (Ophthalmology), University of Melbourne, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | | | - Matthew E Johnson
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Fabio Anaclerio
- Department of Biology-Genetics, University of Bari, Bari, Italy
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, 98195, USA
| | - Carl Baker
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Max L Dougherty
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Jason G Underwood
- Pacific Biosciences (PacBio) of California, Incorporated, Menlo Park, CA, 94025, USA
| | - Arvis Sulovari
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - PingHsun Hsieh
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Yafei Mao
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | | | - Maika Malig
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Present Address: Department of Molecular and Cellular Biology, University of California, Davis, CA, 95616, USA
- Present Address: Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, 95616, USA
| | - AnneMarie E Welch
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Present Address: Brain and Mitochondrial Research, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, VIC, Australia
| | - Melanie Sorensen
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Weihong Jiang
- Case Transgenic and Targeting Facility, Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Santhosh Girirajan
- Department of Biochemistry and Molecular Biology, Department of Anthropology, Pennsylvania State University, University Park, PA, 16802, USA
| | - Mario Ventura
- Department of Biology-Genetics, University of Bari, Bari, Italy
| | - Bruce T Lamb
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Ronald A Conlon
- Case Transgenic and Targeting Facility, Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA.
- Howard Hughes Medical Institute, University of Washington School of Medicine, 3720 15th Ave NE, S413C, Box 355065, Seattle, WA, 98195-5065, USA.
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33
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Recurrent inversion toggling and great ape genome evolution. Nat Genet 2020; 52:849-858. [PMID: 32541924 PMCID: PMC7415573 DOI: 10.1038/s41588-020-0646-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 05/15/2020] [Indexed: 01/14/2023]
Abstract
Inversions play an important role in disease and evolution but are difficult to characterize because their breakpoints map to large repeats. We increased by sixfold the number (n = 1,069) of previously reported great ape inversions by using single-cell DNA template strand and long-read sequencing. We find that the X chromosome is most enriched (2.5-fold) for inversions, on the basis of its size and duplication content. There is an excess of differentially expressed primate genes near the breakpoints of large (>100 kilobases (kb)) inversions but not smaller events. We show that when great ape lineage-specific duplications emerge, they preferentially (approximately 75%) occur in an inverted orientation compared to that at their ancestral locus. We construct megabase-pair scale haplotypes for individual chromosomes and identify 23 genomic regions that have recurrently toggled between a direct and an inverted state over 15 million years. The direct orientation is most frequently the derived state for human polymorphisms that predispose to recurrent copy number variants associated with neurodevelopmental disease.
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34
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Determining the impact of uncharacterized inversions in the human genome by droplet digital PCR. Genome Res 2020; 30:724-735. [PMID: 32424072 PMCID: PMC7263195 DOI: 10.1101/gr.255273.119] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 04/17/2020] [Indexed: 12/20/2022]
Abstract
Despite the interest in characterizing genomic variation, the presence of large repeats at the breakpoints hinders the analysis of many structural variants. This is especially problematic for inversions, since there is typically no gain or loss of DNA. Here, we tested novel linkage-based droplet digital PCR (ddPCR) assays to study 20 inversions ranging from 3.1 to 742 kb flanked by inverted repeats (IRs) up to 134 kb long. Of those, we validated 13 inversions predicted by different genome-wide techniques. In addition, we obtained new experimental human population information across 95 African, European, and East Asian individuals for 16 inversions, including four already validated variants without high-throughput genotyping methods. Through comparison with previous data, independent replicates and both inversion breakpoints, we demonstrate that the technique is highly accurate and reproducible. Most studied inversions are widespread across continents, and their frequency is negatively correlated with genetic length. Moreover, all except two show clear signs of being recurrent, and we could better define the factors affecting recurrence levels and estimate the inversion rate across the genome. Finally, the generated genotypes have allowed us to check inversion functional effects, validating gene expression differences reported before for two inversions and finding new candidate associations. Therefore, the developed methodology makes it possible to screen these and other complex genomic variants quickly in a large number of samples for the first time, highlighting the importance of direct genotyping to assess their potential consequences and clinical implications.
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35
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Sanders AD, Meiers S, Ghareghani M, Porubsky D, Jeong H, van Vliet MACC, Rausch T, Richter-Pechańska P, Kunz JB, Jenni S, Bolognini D, Longo GMC, Raeder B, Kinanen V, Zimmermann J, Benes V, Schrappe M, Mardin BR, Kulozik AE, Bornhauser B, Bourquin JP, Marschall T, Korbel JO. Single-cell analysis of structural variations and complex rearrangements with tri-channel processing. Nat Biotechnol 2020; 38:343-354. [PMID: 31873213 PMCID: PMC7612647 DOI: 10.1038/s41587-019-0366-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 11/20/2019] [Indexed: 02/07/2023]
Abstract
Structural variation (SV), involving deletions, duplications, inversions and translocations of DNA segments, is a major source of genetic variability in somatic cells and can dysregulate cancer-related pathways. However, discovering somatic SVs in single cells has been challenging, with copy-number-neutral and complex variants typically escaping detection. Here we describe single-cell tri-channel processing (scTRIP), a computational framework that integrates read depth, template strand and haplotype phase to comprehensively discover SVs in individual cells. We surveyed SV landscapes of 565 single cells, including transformed epithelial cells and patient-derived leukemic samples, to discover abundant SV classes, including inversions, translocations and complex DNA rearrangements. Analysis of the leukemic samples revealed four times more somatic SVs than cytogenetic karyotyping, submicroscopic copy-number alterations, oncogenic copy-neutral rearrangements and a subclonal chromothripsis event. Advancing current methods, single-cell tri-channel processing can directly measure SV mutational processes in individual cells, such as breakage-fusion-bridge cycles, facilitating studies of clonal evolution, genetic mosaicism and SV formation mechanisms, which could improve disease classification for precision medicine.
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Affiliation(s)
- Ashley D Sanders
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Sascha Meiers
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Maryam Ghareghani
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarbrücken, Germany
- Graduate School of Computer Science, Saarland University, Saarbrücken, Germany
| | - David Porubsky
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Hyobin Jeong
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | | | - Tobias Rausch
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
| | - Paulina Richter-Pechańska
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
- Department of Pediatric Oncology, Hematology, and Immunology, University of Heidelberg and Hopp Children's Cancer Center, Heidelberg, Germany
| | - Joachim B Kunz
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
- Department of Pediatric Oncology, Hematology, and Immunology, University of Heidelberg and Hopp Children's Cancer Center, Heidelberg, Germany
| | - Silvia Jenni
- Division of Pediatric Oncology, University Children's Hospital, Zürich, Switzerland
| | - Davide Bolognini
- European Molecular Biology Laboratory, Genomics Core Facility, Heidelberg, Germany
| | - Gabriel M C Longo
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Benjamin Raeder
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Venla Kinanen
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Jürgen Zimmermann
- European Molecular Biology Laboratory, Genomics Core Facility, Heidelberg, Germany
| | - Vladimir Benes
- European Molecular Biology Laboratory, Genomics Core Facility, Heidelberg, Germany
| | - Martin Schrappe
- Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Balca R Mardin
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- BioMed X Innovation Center, Heidelberg, Germany
| | - Andreas E Kulozik
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
- Department of Pediatric Oncology, Hematology, and Immunology, University of Heidelberg and Hopp Children's Cancer Center, Heidelberg, Germany
| | - Beat Bornhauser
- Division of Pediatric Oncology, University Children's Hospital, Zürich, Switzerland
| | - Jean-Pierre Bourquin
- Division of Pediatric Oncology, University Children's Hospital, Zürich, Switzerland
| | - Tobias Marschall
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany.
- Max Planck Institute for Informatics, Saarbrücken, Germany.
| | - Jan O Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany.
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36
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Balachandran P, Beck CR. Structural variant identification and characterization. Chromosome Res 2020; 28:31-47. [PMID: 31907725 PMCID: PMC7131885 DOI: 10.1007/s10577-019-09623-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 10/15/2019] [Accepted: 11/24/2019] [Indexed: 01/06/2023]
Abstract
Structural variant (SV) differences between human genomes can cause germline and mosaic disease as well as inter-individual variation. De-regulation of accurate DNA repair and genomic surveillance mechanisms results in a large number of SVs in cancer. Analysis of the DNA sequences at SV breakpoints can help identify pathways of mutagenesis and regions of the genome that are more susceptible to rearrangement. Large-scale SV analyses have been enabled by high-throughput genome-level sequencing on humans in the past decade. These studies have shed light on the mechanisms and prevalence of complex genomic rearrangements. Recent advancements in both sequencing and other mapping technologies as well as calling algorithms for detection of genomic rearrangements have helped propel SV detection into population-scale studies, and have begun to elucidate previously inaccessible regions of the genome. Here, we discuss the genomic organization of simple and complex SVs, the molecular mechanisms of their formation, and various ways to detect them. We also introduce methods for characterizing SVs and their consequences on human genomes.
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Affiliation(s)
| | - Christine R Beck
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut Health Center, Farmington, CT, 06030, USA.
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37
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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.
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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.
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38
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Mahmoud M, Gobet N, Cruz-Dávalos DI, Mounier N, Dessimoz C, Sedlazeck FJ. Structural variant calling: the long and the short of it. Genome Biol 2019; 20:246. [PMID: 31747936 PMCID: PMC6868818 DOI: 10.1186/s13059-019-1828-7] [Citation(s) in RCA: 309] [Impact Index Per Article: 61.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 09/19/2019] [Indexed: 02/08/2023] Open
Abstract
Recent research into structural variants (SVs) has established their importance to medicine and molecular biology, elucidating their role in various diseases, regulation of gene expression, ethnic diversity, and large-scale chromosome evolution-giving rise to the differences within populations and among species. Nevertheless, characterizing SVs and determining the optimal approach for a given experimental design remains a computational and scientific challenge. Multiple approaches have emerged to target various SV classes, zygosities, and size ranges. Here, we review these approaches with respect to their ability to infer SVs across the full spectrum of large, complex variations and present computational methods for each approach.
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Affiliation(s)
- Medhat Mahmoud
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, USA
| | - Nastassia Gobet
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Diana Ivette Cruz-Dávalos
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Ninon Mounier
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Christophe Dessimoz
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution & Environment, University College London, London, UK.
- Department of Computer Science, University College London, London, UK.
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, USA.
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39
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Giner-Delgado C, Villatoro S, Lerga-Jaso J, Gayà-Vidal M, Oliva M, Castellano D, Pantano L, Bitarello BD, Izquierdo D, Noguera I, Olalde I, Delprat A, Blancher A, Lalueza-Fox C, Esko T, O'Reilly PF, Andrés AM, Ferretti L, Puig M, Cáceres M. Evolutionary and functional impact of common polymorphic inversions in the human genome. Nat Commun 2019; 10:4222. [PMID: 31530810 PMCID: PMC6748972 DOI: 10.1038/s41467-019-12173-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 08/27/2019] [Indexed: 12/21/2022] Open
Abstract
Inversions are one type of structural variants linked to phenotypic differences and adaptation in multiple organisms. However, there is still very little information about polymorphic inversions in the human genome due to the difficulty of their detection. Here, we develop a new high-throughput genotyping method based on probe hybridization and amplification, and we perform a complete study of 45 common human inversions of 0.1–415 kb. Most inversions promoted by homologous recombination occur recurrently in humans and great apes and they are not tagged by SNPs. Furthermore, there is an enrichment of inversions showing signatures of positive or balancing selection, diverse functional effects, such as gene disruption and gene-expression changes, or association with phenotypic traits. Therefore, our results indicate that the genome is more dynamic than previously thought and that human inversions have important functional and evolutionary consequences, making possible to determine for the first time their contribution to complex traits. Inversions are a little-studied type of genomic variation that could contribute to phenotypic traits. Here the authors characterize 45 common polymorphic inversions in human populations and investigate their evolutionary and functional impact.
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Affiliation(s)
- Carla Giner-Delgado
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain.,Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain
| | - Sergi Villatoro
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain
| | - Jon Lerga-Jaso
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain
| | - Magdalena Gayà-Vidal
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain.,CIBIO/InBIO Research Center in Biodiversity and Genetic Resources, Universidade do Porto, Vairão, Distrito do Porto, 4485-661, Portugal
| | - Meritxell Oliva
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain
| | - David Castellano
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain
| | - Lorena Pantano
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain
| | - Bárbara D Bitarello
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Saxony, 04103, Germany
| | - David Izquierdo
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain
| | - Isaac Noguera
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain
| | - Iñigo Olalde
- Institute of Evolutionary Biology, CSIC-Universitat Pompeu Fabra, Barcelona, 08003, Spain
| | - Alejandra Delprat
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain
| | - Antoine Blancher
- Laboratoire d'immunologie, CHU de Toulouse, IFB Hôpital Purpan, Toulouse, 31059, France.,Centre de Physiopathologie Toulouse-Purpan (CPTP), Université de Toulouse, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (Inserm), Université Paul Sabatier (UPS), Toulouse, 31024, France
| | - Carles Lalueza-Fox
- Institute of Evolutionary Biology, CSIC-Universitat Pompeu Fabra, Barcelona, 08003, Spain
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Paul F O'Reilly
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Aida M Andrés
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Saxony, 04103, Germany.,UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK
| | - Luca Ferretti
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
| | - Marta Puig
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain
| | - Mario Cáceres
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain. .,ICREA, Barcelona, 08010, Spain.
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40
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Ruiz-Arenas C, Cáceres A, López-Sánchez M, Tolosana I, Pérez-Jurado L, González JR. scoreInvHap: Inversion genotyping for genome-wide association studies. PLoS Genet 2019; 15:e1008203. [PMID: 31269027 PMCID: PMC6608898 DOI: 10.1371/journal.pgen.1008203] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 05/17/2019] [Indexed: 02/02/2023] Open
Abstract
Polymorphic inversions contribute to adaptation and phenotypic variation. However, large multi-centric association studies of inversions remain challenging. We present scoreInvHap, a method to genotype inversions from SNP data for genome-wide association studies (GWASs), overcoming important limitations of current methods and outperforming them in accuracy and applicability. scoreInvHap calls individual inversion-genotypes from a similarity score to the SNPs of experimentally validated references. It can be used on different sources of SNP data, including those with low SNP coverage such as exome sequencing, and is easily adaptable to genotype new inversions, either in humans or in other species. We present 20 human inversions that can be reliably and easily genotyped with scoreInvHap to discover their role in complex human traits, and illustrate a first genome-wide association study of experimentally-validated human inversions. scoreInvHap is implemented in R and it is freely available from Bioconductor. Chromosomal inversions are structural variants consisting on an orientation change of a chromosome segment. Inversions have been linked to some phenotypic differences between individuals and to genetic divergence. However, their overall contribution to complex diseases is largely underdetermined as there are no high-throughput methods to call inversion-genotypes in large cohort studies. Here, we propose a new method, scoreInvHap, to call individual inversion genotypes from their haplotype similarity. We show that scoreInvHap has a high performance when analyzing heterogeneous sources of SNP data. Our current implementation contains 20 human inversions that can be readily genotyped in existing GWAS datasets. We exemplify the utility of scoreInvHap by running the first-genome wide association of experimentally validated inversions and a multi-centric inversion association study. All in all, scoreInvHap can substantially contribute to increase our knowledge of the role of chromosomal inversions in complex diseases by re-analyzing data from existing genetic association studies.
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Affiliation(s)
- Carlos Ruiz-Arenas
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Alejandro Cáceres
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Marcos López-Sánchez
- Genetics Unit, Universitat Pompeu Fabra, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain
| | - Ignacio Tolosana
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Luis Pérez-Jurado
- Genetics Unit, Universitat Pompeu Fabra, Barcelona, Spain
- Hospital del Mar Research Institute (IMIM), Barcelona, Spain
- SA Clinical Genetics, Women's and Children's Hospital & University of Adelaide, Adelaide, South Australia Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia Australia
| | - Juan R. González
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- * E-mail:
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41
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Multi-platform discovery of haplotype-resolved structural variation in human genomes. Nat Commun 2019. [PMID: 30992455 DOI: 10.1038/s41467‐018‐08148‐z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs (≥50 bp) per genome. We also discover 156 inversions per genome and 58 of the inversions intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a three to sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The methods and the dataset presented serve as a gold standard for the scientific community allowing us to make recommendations for maximizing structural variation sensitivity for future genome sequencing studies.
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42
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Multi-platform discovery of haplotype-resolved structural variation in human genomes. Nat Commun 2019; 10:1784. [PMID: 30992455 PMCID: PMC6467913 DOI: 10.1038/s41467-018-08148-z] [Citation(s) in RCA: 489] [Impact Index Per Article: 97.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 12/20/2018] [Indexed: 12/30/2022] Open
Abstract
The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs (≥50 bp) per genome. We also discover 156 inversions per genome and 58 of the inversions intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a three to sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The methods and the dataset presented serve as a gold standard for the scientific community allowing us to make recommendations for maximizing structural variation sensitivity for future genome sequencing studies.
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43
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Maggiolini FAM, Cantsilieris S, D’Addabbo P, Manganelli M, Coe BP, Dumont BL, Sanders AD, Pang AWC, Vollger MR, Palumbo O, Palumbo P, Accadia M, Carella M, Eichler EE, Antonacci F. Genomic inversions and GOLGA core duplicons underlie disease instability at the 15q25 locus. PLoS Genet 2019; 15:e1008075. [PMID: 30917130 PMCID: PMC6436712 DOI: 10.1371/journal.pgen.1008075] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 03/07/2019] [Indexed: 11/19/2022] Open
Abstract
Human chromosome 15q25 is involved in several disease-associated structural rearrangements, including microdeletions and chromosomal markers with inverted duplications. Using comparative fluorescence in situ hybridization, strand-sequencing, single-molecule, real-time sequencing and Bionano optical mapping analyses, we investigated the organization of the 15q25 region in human and nonhuman primates. We found that two independent inversions occurred in this region after the fission event that gave rise to phylogenetic chromosomes XIV and XV in humans and great apes. One of these inversions is still polymorphic in the human population today and may confer differential susceptibility to 15q25 microdeletions and inverted duplications. The inversion breakpoints map within segmental duplications containing core duplicons of the GOLGA gene family and correspond to the site of an ancestral centromere, which became inactivated about 25 million years ago. The inactivation of this centromere likely released segmental duplications from recombination repression typical of centromeric regions. We hypothesize that this increased the frequency of ectopic recombination creating a hotspot of hominid inversions where dispersed GOLGA core elements now predispose this region to recurrent genomic rearrangements associated with disease.
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Affiliation(s)
| | - Stuart Cantsilieris
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, United States of America
| | - Pietro D’Addabbo
- Dipartimento di Biologia, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Michele Manganelli
- Dipartimento di Biologia, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Bradley P. Coe
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, United States of America
| | - Beth L. Dumont
- The Jackson Laboratory, Bar Harbor, ME, United States of America
| | - Ashley D. Sanders
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, Heidelberg, Germany
| | | | - Mitchell R. Vollger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, United States of America
| | - Orazio Palumbo
- Medical Genetics Unit, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo (FG), Italy
| | - Pietro Palumbo
- Medical Genetics Unit, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo (FG), Italy
| | - Maria Accadia
- Medical Genetics Service, Hospital “Cardinale G. Panico”, Via San Pio X n°4, Tricase, LE, Italy
| | - Massimo Carella
- Medical Genetics Unit, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo (FG), Italy
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, United States of America
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, United States of America
| | - Francesca Antonacci
- Dipartimento di Biologia, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
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44
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Saini N, Gordenin DA. Somatic mutation load and spectra: A record of DNA damage and repair in healthy human cells. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2018; 59:672-686. [PMID: 30152078 PMCID: PMC6188803 DOI: 10.1002/em.22215] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 06/07/2018] [Accepted: 06/11/2018] [Indexed: 05/31/2023]
Abstract
Somatic genome instability is a hallmark of cancer genomes and has been linked to aging and a variety of other pathologies. Large-scale cancer genome and exome sequencing have revealed that mutation load and spectra in cancers can be influenced by environmental exposures, the anatomical site of exposures, and tissue type. There is now an abundance of data favoring the hypothesis that a substantial portion of the mutations in cancers originate prior to carcinogenesis in stem cells of the healthy individual. Rapid advances in sequencing of noncancer cells from healthy humans have shown that their mutation loads and spectra resemble cancer data. Similar to cancer genomes, mutation profiles of healthy cells show marked intra-individual variation, thus providing a metric of the various factors-environmental and endogenous-involved in mutagenesis in these individuals. This review focuses on the current methodologies to measure mutation loads and to determine mutation signatures for evaluating the environmental and endogenous sources of DNA damage in human somatic cells. We anticipate that in future, such large-scale studies aimed at exploring the landscapes of somatic mutations across different cell types in healthy people would provide a valuable resource for designing personalized preventative strategies against diseases associated with somatic genome instability. Environ. Mol. Mutagen. 59:672-686, 2018. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
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Affiliation(s)
- Natalie Saini
- Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, US National Institutes of Health, Research Triangle Park, North Carolina, USA
| | - Dmitry A. Gordenin
- Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, US National Institutes of Health, Research Triangle Park, North Carolina, USA
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45
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Hehir-Kwa JY, Tops BBJ, Kemmeren P. The clinical implementation of copy number detection in the age of next-generation sequencing. Expert Rev Mol Diagn 2018; 18:907-915. [PMID: 30221560 DOI: 10.1080/14737159.2018.1523723] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION The role of copy number variants (CNVs) in disease is now well established. In parallel NGS technologies, such as long-read technologies, there is continual development and data analysis methods continue to be refined. Clinical exome sequencing data is now a reality for many diagnostic laboratories in both congenital genetics and oncology. This provides the ability to detect and report both SNVs and structural variants, including CNVs, using a single assay for a wide range of patient cohorts. Areas covered: Currently, whole-genome sequencing is mainly restricted to research applications and clinical utility studies. Furthermore, detecting the full-size spectrum of CNVs as well as somatic events remains difficult for both exome and whole-genome sequencing. As a result, the full extent of genomic variants in an individual's genome is still largely unknown. Recently, new sequencing technologies have been introduced which maintain the long-range genomic context, aiding the detection of CNVs and structural variants. Expert commentary: The development of long-read sequencing promises to resolve many CNV and SV detection issues but is yet to become established. The current challenge for clinical CNV detection is how to fully exploit all the data which is generated by high throughput sequencing technologies.
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Affiliation(s)
- Jayne Y Hehir-Kwa
- a Princess Máxima Center for Pediatric Oncology , Utrecht , Netherlands
| | - Bastiaan B J Tops
- a Princess Máxima Center for Pediatric Oncology , Utrecht , Netherlands
| | - Patrick Kemmeren
- a Princess Máxima Center for Pediatric Oncology , Utrecht , Netherlands
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46
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Ghareghani M, Porubskỳ D, Sanders AD, Meiers S, Eichler EE, Korbel JO, Marschall T. Strand-seq enables reliable separation of long reads by chromosome via expectation maximization. Bioinformatics 2018; 34:i115-i123. [PMID: 29949971 PMCID: PMC6022540 DOI: 10.1093/bioinformatics/bty290] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Motivation Current sequencing technologies are able to produce reads orders of magnitude longer than ever possible before. Such long reads have sparked a new interest in de novo genome assembly, which removes reference biases inherent to re-sequencing approaches and allows for a direct characterization of complex genomic variants. However, even with latest algorithmic advances, assembling a mammalian genome from long error-prone reads incurs a significant computational burden and does not preclude occasional misassemblies. Both problems could potentially be mitigated if assembly could commence for each chromosome separately. Results To address this, we show how single-cell template strand sequencing (Strand-seq) data can be leveraged for this purpose. We introduce a novel latent variable model and a corresponding Expectation Maximization algorithm, termed SaaRclust, and demonstrates its ability to reliably cluster long reads by chromosome. For each long read, this approach produces a posterior probability distribution over all chromosomes of origin and read directionalities. In this way, it allows to assess the amount of uncertainty inherent to sparse Strand-seq data on the level of individual reads. Among the reads that our algorithm confidently assigns to a chromosome, we observed more than 99% correct assignments on a subset of Pacific Bioscience reads with 30.1× coverage. To our knowledge, SaaRclust is the first approach for the in silico separation of long reads by chromosome prior to assembly. Availability and implementation https://github.com/daewoooo/SaaRclust.
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Affiliation(s)
- Maryam Ghareghani
- Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, Saarbrücken, 66123, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus E1.4, Saarbrücken, Germany
- Graduate School of Computer Science, Saarland University, Saarland Informatics Campus E1.3, Saarbrücken, Germany
| | - David Porubskỳ
- Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, Saarbrücken, 66123, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus E1.4, Saarbrücken, Germany
| | - Ashley D Sanders
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Sascha Meiers
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Tobias Marschall
- Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, Saarbrücken, 66123, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus E1.4, Saarbrücken, Germany
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Kronenberg ZN, Fiddes IT, Gordon D, Murali S, Cantsilieris S, Meyerson OS, Underwood JG, Nelson BJ, Chaisson MJP, Dougherty ML, Munson KM, Hastie AR, Diekhans M, Hormozdiari F, Lorusso N, Hoekzema K, Qiu R, Clark K, Raja A, Welch AE, Sorensen M, Baker C, Fulton RS, Armstrong J, Graves-Lindsay TA, Denli AM, Hoppe ER, Hsieh P, Hill CM, Pang AWC, Lee J, Lam ET, Dutcher SK, Gage FH, Warren WC, Shendure J, Haussler D, Schneider VA, Cao H, Ventura M, Wilson RK, Paten B, Pollen A, Eichler EE. High-resolution comparative analysis of great ape genomes. Science 2018; 360:eaar6343. [PMID: 29880660 PMCID: PMC6178954 DOI: 10.1126/science.aar6343] [Citation(s) in RCA: 237] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 04/02/2018] [Indexed: 12/22/2022]
Abstract
Genetic studies of human evolution require high-quality contiguous ape genome assemblies that are not guided by the human reference. We coupled long-read sequence assembly and full-length complementary DNA sequencing with a multiplatform scaffolding approach to produce ab initio chimpanzee and orangutan genome assemblies. By comparing these with two long-read de novo human genome assemblies and a gorilla genome assembly, we characterized lineage-specific and shared great ape genetic variation ranging from single- to mega-base pair-sized variants. We identified ~17,000 fixed human-specific structural variants identifying genic and putative regulatory changes that have emerged in humans since divergence from nonhuman apes. Interestingly, these variants are enriched near genes that are down-regulated in human compared to chimpanzee cerebral organoids, particularly in cells analogous to radial glial neural progenitors.
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Affiliation(s)
- Zev N Kronenberg
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Ian T Fiddes
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - David Gordon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - Shwetha Murali
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - Stuart Cantsilieris
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Olivia S Meyerson
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jason G Underwood
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
- Pacific Biosciences (PacBio) of California, Inc., Menlo Park, CA 94025, USA
| | - Bradley J Nelson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Mark J P Chaisson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
- Computational Biology and Bioinformatics, University of Southern California, Los Angeles, CA 90089, USA
| | - Max L Dougherty
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | | | - Mark Diekhans
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Fereydoun Hormozdiari
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, CA 95817, USA
| | - Nicola Lorusso
- Department of Biology, University of Bari, Aldo Moro, Bari 70121, Italy
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Ruolan Qiu
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Karen Clark
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Archana Raja
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - AnneMarie E Welch
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Melanie Sorensen
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Carl Baker
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Robert S Fulton
- Departments of Medicine and Genetics, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Joel Armstrong
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Tina A Graves-Lindsay
- Departments of Medicine and Genetics, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Ahmet M Denli
- The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Emma R Hoppe
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - PingHsun Hsieh
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Christopher M Hill
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | | | - Joyce Lee
- Bionano Genomics, San Diego, CA 92121, USA
| | | | - Susan K Dutcher
- Departments of Medicine and Genetics, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Fred H Gage
- The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Wesley C Warren
- Departments of Medicine and Genetics, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - David Haussler
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
- Howard Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Valerie A Schneider
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Han Cao
- Bionano Genomics, San Diego, CA 92121, USA
| | - Mario Ventura
- Department of Biology, University of Bari, Aldo Moro, Bari 70121, Italy
| | - Richard K Wilson
- Departments of Medicine and Genetics, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Alex Pollen
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
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48
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O'Neill K, Hills M, Gottlieb M, Borkowski M, Karsan A, Lansdorp PM. Assembling draft genomes using contiBAIT. Bioinformatics 2018; 33:2737-2739. [PMID: 28475666 PMCID: PMC5860061 DOI: 10.1093/bioinformatics/btx281] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 05/03/2017] [Indexed: 11/14/2022] Open
Abstract
Summary Massively parallel sequencing is now widely used, but data interpretation is only as good as the reference assembly to which it is aligned. While the number of reference assemblies has rapidly expanded, most of these remain at intermediate stages of completion, either as scaffold builds, or as chromosome builds (consisting of correctly ordered, but not necessarily correctly oriented scaffolds separated by gaps). Completion of de novo assemblies remains difficult, as regions that are repetitive or hard to sequence prevent the accumulation of larger scaffolds, and create errors such as misorientations and mislocalizations. Thus, complementary methods for determining the orientation and positioning of fragments are important for finishing assemblies. Strand-seq is a method for determining template strand inheritance in single cells, information that can be used to determine relative genomic distance and orientation between scaffolds, and find errors within them. We present contiBAIT, an R/Bioconductor package which uses Strand-seq data to repair and improve existing assemblies. Availability and Implementation contiBAIT is available on Bioconductor. Source files available from GitHub. Contact koneill@bcgsc.ca or mark.hills@stemcell.com. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kieran O'Neill
- Michael Smith Genome Sciences Centre, BC Cancer Agency.,Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | - Mike Gottlieb
- Michael Smith Genome Sciences Centre, BC Cancer Agency.,Bioinformatics Training Program, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | - Aly Karsan
- Michael Smith Genome Sciences Centre, BC Cancer Agency.,Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Peter M Lansdorp
- Michael Smith Genome Sciences Centre, BC Cancer Agency.,Hematology Division, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.,European Research Institute for the Biology of Ageing, University Medical Centre Groningen, Groningen, The Netherlands
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49
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Catacchio CR, Maggiolini FAM, D'Addabbo P, Bitonto M, Capozzi O, Lepore Signorile M, Miroballo M, Archidiacono N, Eichler EE, Ventura M, Antonacci F. Inversion variants in human and primate genomes. Genome Res 2018; 28:910-920. [PMID: 29776991 PMCID: PMC5991517 DOI: 10.1101/gr.234831.118] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Accepted: 04/26/2018] [Indexed: 02/06/2023]
Abstract
For many years, inversions have been proposed to be a direct driving force in speciation since they suppress recombination when heterozygous. Inversions are the most common large-scale differences among humans and great apes. Nevertheless, they represent large events easily distinguishable by classical cytogenetics, whose resolution, however, is limited. Here, we performed a genome-wide comparison between human, great ape, and macaque genomes using the net alignments for the most recent releases of genome assemblies. We identified a total of 156 putative inversions, between 103 kb and 91 Mb, corresponding to 136 human loci. Combining literature, sequence, and experimental analyses, we analyzed 109 of these loci and found 67 regions inverted in one or multiple primates, including 28 newly identified inversions. These events overlap with 81 human genes at their breakpoints, and seven correspond to sites of recurrent rearrangements associated with human disease. This work doubles the number of validated primate inversions larger than 100 kb, beyond what was previously documented. We identified 74 sites of errors, where the sequence has been assembled in the wrong orientation, in the reference genomes analyzed. Our data serve two purposes: First, we generated a map of evolutionary inversions in these genomes representing a resource for interrogating differences among these species at a functional level; second, we provide a list of misassembled regions in these primate genomes, involving over 300 Mb of DNA and 1978 human genes. Accurately annotating these regions in the genome references has immediate applications for evolutionary and biomedical studies on primates.
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Affiliation(s)
| | | | - Pietro D'Addabbo
- Dipartimento di Biologia, Università degli Studi di Bari "Aldo Moro," Bari 70125, Italy
| | - Miriana Bitonto
- Dipartimento di Biologia, Università degli Studi di Bari "Aldo Moro," Bari 70125, Italy
| | - Oronzo Capozzi
- Dipartimento di Biologia, Università degli Studi di Bari "Aldo Moro," Bari 70125, Italy
| | | | - Mattia Miroballo
- Dipartimento di Biologia, Università degli Studi di Bari "Aldo Moro," Bari 70125, Italy
| | | | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA.,Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA
| | - Mario Ventura
- Dipartimento di Biologia, Università degli Studi di Bari "Aldo Moro," Bari 70125, Italy
| | - Francesca Antonacci
- Dipartimento di Biologia, Università degli Studi di Bari "Aldo Moro," Bari 70125, Italy
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50
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Porubsky D, Garg S, Sanders AD, Korbel JO, Guryev V, Lansdorp PM, Marschall T. Dense and accurate whole-chromosome haplotyping of individual genomes. Nat Commun 2017; 8:1293. [PMID: 29101320 PMCID: PMC5670131 DOI: 10.1038/s41467-017-01389-4] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 09/14/2017] [Indexed: 12/15/2022] Open
Abstract
The diploid nature of the human genome is neglected in many analyses done today, where a genome is perceived as a set of unphased variants with respect to a reference genome. This lack of haplotype-level analyses can be explained by a lack of methods that can produce dense and accurate chromosome-length haplotypes at reasonable costs. Here we introduce an integrative phasing strategy that combines global, but sparse haplotypes obtained from strand-specific single-cell sequencing (Strand-seq) with dense, yet local, haplotype information available through long-read or linked-read sequencing. We provide comprehensive guidance on the required sequencing depths and reliably assign more than 95% of alleles (NA12878) to their parental haplotypes using as few as 10 Strand-seq libraries in combination with 10-fold coverage PacBio data or, alternatively, 10X Genomics linked-read sequencing data. We conclude that the combination of Strand-seq with different technologies represents an attractive solution to chart the genetic variation of diploid genomes.
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Affiliation(s)
- David Porubsky
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Building 3226, 9713 AV, Groningen, The Netherlands
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Shilpa Garg
- Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, 66123, Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus E1.4, 66123, Saarbrücken, Germany
- Graduate School of Computer Science, Saarland University, Saarland Informatics Campus E1.3, 66123, Saarbrücken, Germany
| | - Ashley D Sanders
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117, Heidelberg, Germany
- Terry Fox Laboratory, BC Cancer Agency, 601 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Building 3226, 9713 AV, Groningen, The Netherlands
| | - Peter M Lansdorp
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Building 3226, 9713 AV, Groningen, The Netherlands
- Terry Fox Laboratory, BC Cancer Agency, 601 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada
- Department of Medical Genetics, University of British Columbia, 2350 Health Science Mall, Vancouver, BC, V6T 1Z3, Canada
| | - Tobias Marschall
- Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, 66123, Saarbrücken, Germany.
- Max Planck Institute for Informatics, Saarland Informatics Campus E1.4, 66123, Saarbrücken, Germany.
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