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Pangenome graphs improve the analysis of structural variants in rare genetic diseases

Author

Listed:
  • Cristian Groza

    (McGill University)

  • Carl Schwendinger-Schreck

    (Children’s Mercy Hospital and Research Institute)

  • Warren A. Cheung

    (Children’s Mercy Hospital and Research Institute)

  • Emily G. Farrow

    (Children’s Mercy Hospital and Research Institute)

  • Isabelle Thiffault

    (Children’s Mercy Hospital and Research Institute)

  • Juniper Lake

    (Pacific Biosciences)

  • William B. Rizzo

    (Nebraska Medical Center)

  • Gilad Evrony

    (New York University Grossman School of Medicine)

  • Tom Curran

    (Children’s Mercy Research Institute)

  • Guillaume Bourque

    (McGill University
    McGill University
    Kyoto University
    Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University)

  • Tomi Pastinen

    (Children’s Mercy Hospital and Research Institute)

Abstract

Rare DNA alterations that cause heritable diseases are only partially resolvable by clinical next-generation sequencing due to the difficulty of detecting structural variation (SV) in all genomic contexts. Long-read, high fidelity genome sequencing (HiFi-GS) detects SVs with increased sensitivity and enables assembling personal and graph genomes. We leverage standard reference genomes, public assemblies (n = 94) and a large collection of HiFi-GS data from a rare disease program (Genomic Answers for Kids, GA4K, n = 574 assemblies) to build a graph genome representing a unified SV callset in GA4K, identify common variation and prioritize SVs that are more likely to cause genetic disease (MAF

Suggested Citation

  • Cristian Groza & Carl Schwendinger-Schreck & Warren A. Cheung & Emily G. Farrow & Isabelle Thiffault & Juniper Lake & William B. Rizzo & Gilad Evrony & Tom Curran & Guillaume Bourque & Tomi Pastinen, 2024. "Pangenome graphs improve the analysis of structural variants in rare genetic diseases," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-44980-2
    DOI: 10.1038/s41467-024-44980-2
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    Cited by:

    1. Cristian Groza & Xun Chen & Travis J. Wheeler & Guillaume Bourque & Clément Goubert, 2024. "A unified framework to analyze transposable element insertion polymorphisms using graph genomes," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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