Background The UK 100,000 Genomes Project is in the process of investigating the role of genome sequencing of patients with undiagnosed rare disease following usual care, and the alignment of research with healthcare implementation in the UK’s national health service. (Other parts of this Project focus on patients with cancer and infection.) Methods We enrolled participants, collected clinical features with human phenotype ontology terms, undertook genome sequencing and applied automated variant prioritization based on virtual gene panels (PanelApp) and phenotypes (Exomiser), alongside identification of novel pathogenic variants through research analysis. We report results on a pilot study of 4660 participants from 2183 families with 161 disorders covering a broad spectrum of rare disease. Results Diagnostic yields varied by family structure and were highest in trios and larger pedigrees. Likely monogenic disorders had much higher diagnostic yields (35%) with intellectual disability, hearing and vision disorders, achieving yields between 40 and 55%. Those with more complex etiologies had an overall 25% yield. Combining research and automated approaches was critical to 14% of diagnoses in which we found etiologic non-coding, structural and mitochondrial genome variants and coding variants poorly covered by exome sequencing. Cohort-wide burden testing across 57,000 genomes enabled discovery of 3 new disease genes and 19 novel associations. Of the genetic diagnoses that we made, 24% had immediate ramifications for the clinical decision-making for the patient or their relatives. Conclusion Our pilot study of genome sequencing in a national health care system demonstrates diagnostic uplift across a range of rare diseases. (Funded by National Institute for Health Research and others)

SummaryAccurate diagnosis of rare inherited anaemias is challenging, requiring a series of complex and expensive laboratory tests. Targeted next‐generation‐sequencing (NGS) has been used to investigate these disorders, but the selection of genes on individual panels has been narrow and the validation strategies used have fallen short of the standards required for clinical use. Clinical‐grade validation of negative results requires the test to distinguish between lack of adequate sequencing reads at the locations of known mutations and a real absence of mutations. To achieve a clinically‐reliable diagnostic test and minimize false‐negative results we developed an open‐source tool (CoverMi) to accurately determine base‐coverage and the ‘discoverability’ of known mutations for every sample. We validated our 33‐gene panel using Sanger sequencing and microarray. Our panel demonstrated 100% specificity and 99·7% sensitivity. We then analysed 57 clinical samples: molecular diagnoses were made in 22/57 (38·6%), corresponding to 32 mutations of which 16 were new. In all cases, accurate molecular diagnosis had a positive impact on clinical management. Using a validated NGS‐based platform for routine molecular diagnosis of previously undiagnosed congenital anaemias is feasible in a clinical diagnostic setting, improves precise diagnosis and enhances management and counselling of the patient and their family.

If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections.

Summary Transfusion‐dependent myelodysplastic (MDS) patients are prone to iron overload. We evaluated 43 transfused MDS patients with T2* magnetic resonance imaging scans. 81% had liver and 16·8% cardiac iron overload. Liver R2* (1000/T2*), but not cardiac R2*, was correlated with number of units transfused (r = 0·72, P < 0·0001) and ferritin (r = 0·53, P < 0·0001). The area under the curve of a time‐ferritin plot was found to be much greater in patients with cardiac iron loading (median 53·7 × 105 Megaunits vs. 12·2 × 105 Megaunits, P = 0·002). HFE, HFE2, HAMP or SLC40A1 genotypes were not predictors of iron overload in these patients.