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
AUTHORS (62)
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.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (0)
CITATIONS (2)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....