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Whole-genome sequencing of patients with rare diseases in a national health system

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NIHR BioResource for the 100,000 Genomes Project, Ernest Turro, William J. Astle, Karyn Megy, Stefan Gräf, Daniel Greene, Olga Shamardina, Hana Lango Allen, Alba Sanchis-Juan, Mattia Frontini, Chantal Thys, Jonathan Stephens, Rutendo Mapeta, Oliver S. Burren, Kate Downes, Matthias Haimel, Salih Tuna, Sri V.V. Deevi, Timothy J. Aitman, David L. Bennett & 31 more Paul Calleja, Keren Carss, Mark J. Caulfield, Patrick F. Chinnery, Peter H. Dixon, Daniel P. Gale, Roger James, Ania Koziell, Michael A. Laffan, Adam P. Levine, Eamonn R. Maher, Hugh S. Markus, Joannella Morales, Nicholas W. Morrell, Andrew D. Mumford, Elizabeth Ormondroyd, Stuart Rankin, Augusto Rendon, Sylvia Richardson, David L. Bennett, Teofila Bueser, Gerald Carr-White, Frances A. Flinter, Melita Irving, Dragana Josifova, Ania Koziell, Shehla N. Mohammed, Ellen Thomas, Matthew Traylor, Richard Trembath, Catherine Williamson

Original languageEnglish
Pages (from-to)96-102
Number of pages7
JournalNature
Volume583
Issue number7814
Early online date24 Jun 2020
DOIs
Accepted/In press5 May 2020
E-pub ahead of print24 Jun 2020
Published2 Jul 2020

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Abstract

Most patients with rare diseases do not receive a molecular diagnosis and the aetiological variants and causative genes for more than half such disorders remain to be discovered1. Here we used whole-genome sequencing (WGS) in a national health system to streamline diagnosis and to discover unknown aetiological variants in the coding and non-coding regions of the genome. We generated WGS data for 13,037 participants, of whom 9,802 had a rare disease, and provided a genetic diagnosis to 1,138 of the 7,065 extensively phenotyped participants. We identified 95 Mendelian associations between genes and rare diseases, of which 11 have been discovered since 2015 and at least 79 are confirmed to be aetiological. By generating WGS data of UK Biobank participants2, we found that rare alleles can explain the presence of some individuals in the tails of a quantitative trait for red blood cells. Finally, we identified four novel non-coding variants that cause disease through the disruption of transcription of ARPC1B, GATA1, LRBA and MPL. Our study demonstrates a synergy by using WGS for diagnosis and aetiological discovery in routine healthcare.

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