Detection of structural mosaicism from targeted and whole-genome sequencing data

  1. The Deciphering Developmental Disorders Study1
  1. 1Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, United Kingdom;
  2. 2Department of Clinical Genetics, Chapel Allerton Hospital, Leeds LS7 4SA, United Kingdom;
  3. 3Southwest Thames Regional Genetics Centre, St George's Healthcare NHS Trust, London SW17 0RE, United Kingdom;
  4. 4East Anglian Regional Genetics Service, Addenbrookes Hospital, Cambridge CB2 0QQ, United Kingdom;
  5. 5South East Thames Regional Genetics Centre, Guy's Hospital, London SE1 9RT, United Kingdom;
  6. 6Department of Clinical Genetics, St Michael's Hospital, Bristol BS2 8EG, United Kingdom;
  7. 7Leicester Royal Infirmary, Leicester LE1 5WW, United Kingdom
  • Corresponding author: meh{at}sanger.ac.uk
  • Abstract

    Structural mosaic abnormalities are large post-zygotic mutations present in a subset of cells and have been implicated in developmental disorders and cancer. Such mutations have been conventionally assessed in clinical diagnostics using cytogenetic or microarray testing. Modern disease studies rely heavily on exome sequencing, yet an adequate method for the detection of structural mosaicism using targeted sequencing data is lacking. Here, we present a method, called MrMosaic, to detect structural mosaic abnormalities using deviations in allele fraction and read coverage from next-generation sequencing data. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) simulations were used to calculate detection performance across a range of mosaic event sizes, types, clonalities, and sequencing depths. The tool was applied to 4911 patients with undiagnosed developmental disorders, and 11 events among nine patients were detected. For eight of these 11 events, mosaicism was observed in saliva but not blood, suggesting that assaying blood alone would miss a large fraction, possibly >50%, of mosaic diagnostic chromosomal rearrangements.

    Footnotes

    • [Supplemental material is available for this article.]

    • Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.212373.116.

    • Freely available online through the Genome Research Open Access option.

    • Received July 7, 2016.
    • Accepted July 18, 2017.

    This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.

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