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

0301 basic medicine Erythrocytes Internationality Databases, Factual National Health Programs [SDV]Life Sciences [q-bio] VARIANTS State Medicine NIHR BioResource for the 100,000 Genomes Project Receptors Genetics research GATA1 Transcription Factor 0303 health sciences Multidisciplinary Disease genetics Adaptor Proteins ASSOCIATION 3. Good health Multidisciplinary Sciences [SDV] Life Sciences [q-bio] Phenotype Thrombopoietin disease genetics Science & Technology - Other Topics Receptors, Thrombopoietin General Science & Technology Quantitative Trait Loci 610 DIAGNOSIS computational biology and bioinformatics Actin-Related Protein 2-3 Complex LINKS Databases 03 medical and health sciences Rare Diseases 616 Humans Factual Alleles Adaptor Proteins, Signal Transducing Science & Technology Whole Genome Sequencing MUTATIONS THROMBOCYTOPENIA Signal Transducing United Kingdom Computational biology and bioinformatics MACROTHROMBOCYTOPENIA genetics research
DOI: 10.1530/ey.18.14.6 Publication Date: 2021-09-22T02:14:57Z
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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