2021•Yearbook of pediatric endocrinologyRequires access
Whole-genome sequencing of patients with rare diseases in a national health system
Ernest Turro, William J. Astle, Karyn Mégy, 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 Bennett, Paul Calleja, Keren Carss, Mark J. Caulfield, Patrick F. Chinnery, Peter Dixon, Daniel P. Gale, Roger James, Ania Koziell, Michael Laffan, Adam P. Levine, Eamonn R. Maher, Hugh S. Markus, Joannella Morales, Nicholas W. Morrell, Andrew Mumford, Elizabeth Ormondroyd, Stuart Rankin, Augusto Rendon, Sylvia Richardson, Irene Roberts, Noémi Roy, Moin A. Saleem, Kenneth G. C. Smith, Hannah Stark, Rhea Tan, Andreas C. Themistocleous, Adrian J. Thrasher, Hugh Watkins, Andrew R. Webster, Martin R. Wilkins, Catherine Williamson, James W. Whitworth, Sean Humphray, David Bentley, Nathalie Kingston, Neil Walker, John R. Bradley, Sofie Ashford, Christopher J. Penkett, Kathleen Freson, Kathleen Stirrups, F. Lucy Raymond, Willem H. Ouwehand
Abstract
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.