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Detection of structural mosaicism from targeted and whole-genome sequencing data
Author(s) -
Daniel A. King,
Alejandro Sifrim,
Tomas Fitzgerald,
Raheleh Rahbari,
Emma Hobson,
Tessa Homfray,
Sahar Mansour,
Sarju Mehta,
Mohammed Shehla,
Susan Tomkins,
Pradeep Vasudevan,
Matthew E. Hurles
Publication year - 2017
Publication title -
genome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.556
H-Index - 297
eISSN - 1549-5469
pISSN - 1088-9051
DOI - 10.1101/gr.212373.116
Subject(s) - biology , exome sequencing , genetics , dna sequencing , exome , structural variation , genome , whole genome sequencing , computational biology , genomics , copy number variation , human genetics , mutation , gene
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.

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