Expectations and blind spots for structural variation detection from long-read assemblies and short-read genome sequencing technologies

Xuefang Zhao; Ryan L Collins; Wan-Ping Lee; Alexandra M Weber; Yukyung Jun; Qihui Zhu; Ben Weisburd; Yongqing Huang; Peter A Audano; Harold Wang; Mark Walker; Chelsea Lowther; Jack Fu; Human Genome Structural Variation Consortium; Mark B Gerstein; Scott E Devine; Tobias Marschall; Jan O Korbel; Evan E Eichler; Mark J P Chaisson; Charles Lee; Ryan E Mills; Harrison Brand; Michael E Talkowski

doi:10.1016/j.ajhg.2021.03.014

Expectations and blind spots for structural variation detection from long-read assemblies and short-read genome sequencing technologies

Am J Hum Genet. 2021 May 6;108(5):919-928. doi: 10.1016/j.ajhg.2021.03.014. Epub 2021 Mar 30.

Authors

Xuefang Zhao¹, Ryan L Collins², Wan-Ping Lee³, Alexandra M Weber⁴, Yukyung Jun³, Qihui Zhu³, Ben Weisburd⁵, Yongqing Huang⁶, Peter A Audano⁷, Harold Wang⁸, Mark Walker⁹, Chelsea Lowther¹, Jack Fu¹; Human Genome Structural Variation Consortium; Mark B Gerstein¹⁰, Scott E Devine¹¹, Tobias Marschall¹², Jan O Korbel¹³, Evan E Eichler¹⁴, Mark J P Chaisson¹⁵, Charles Lee¹⁶, Ryan E Mills⁴, Harrison Brand¹, Michael E Talkowski¹⁷

Affiliations

¹ 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.
² 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.
³ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
⁴ 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.
⁵ 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.
⁶ Data Sciences Platform, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
⁷ Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA.
⁸ 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.
⁹ 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.
¹⁰ Yale University Medical School, Computational Biology and Bioinformatics Program, New Haven, CT 06520, USA.
¹¹ Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
¹² Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany.
¹³ 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.
¹⁴ 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.
¹⁵ 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.
¹⁶ 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.
¹⁷ 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. Electronic address: talkowsk@broadinstitute.org.

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.

Keywords: copy number variation; genome assembly; long-read technology; segmental duplication; simple repeats; structural variation; whole-genome sequencing.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

DNA Copy Number Variations
Exons / genetics
Genome, Human / genetics*
Genomic Structural Variation*
Genomics / methods*
Goals*
Humans
Research Design
Segmental Duplications, Genomic
Sequence Alignment
Whole Genome Sequencing / methods*
Whole Genome Sequencing / standards*

Abstract

Publication types

MeSH terms

Grants and funding