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

Abstract

Most patients with rare diseases do not receive a molecular diagnosis and the aetiological variants and mediating genes for more than half such disorders remain to be discovered. We implemented whole-genome sequencing (WGS) in a national healthcare system to streamline diagnosis and to discover unknown aetiological variants, in the coding and non-coding regions of the genome. In a pilot study for the 100,000 Genomes Project, 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 patients with detailed phenotypic data. We identified 95 Mendelian associations between genes and rare diseases, of which 11 have been discovered since 2015 and at least 79 are confirmed aetiological. Using WGS of UK Biobank 1 , we showed that rare alleles can explain the presence of some individuals in the tails of a quantitative red blood cell (RBC) trait. Finally, we reported 4 novel non-coding variants which 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. 3. Ferreira CR. The burden of rare diseases.

Long-read sequencing resolves structural variants inSERPINC1causing antithrombin deficiency and identifies a complex rearrangement and a retrotransposon insertion not characterized by routine diagnostic methods

“…An explanation may be that the causal variant was missed due to low coverage, or alternatively the variant is located in an unidentified transacting gene or in a regulatory element for SERPINC1, as we have recently reported for other genes. 14 The observation that the ATD patients without causal SVs have significantly higher anti-FXa activity than those with SVs ( Figure 1D) is supportive of the notion that causal variants may regulate gene expression.…”

Rare disease awareness and perspectives of physicians in China: a questionnaire-based study

“…The establishment of a rare disease registration platform can form a rare disease knowledge base, realize multi-level sharing of rare disease data, reduce the time for physicians to diagnose, and improve the accuracy of diagnosis [16]. At the same time, the establishment of the system is conducive to the formation of uni ed rare disease registration technical standards and norms, uniting superior units to form a collaborative network of rare diseases, carrying out rare disease registration research nationwide, and establishing a rare disease direct reporting system [17].…”

“…Based on this, hospitals are designated as Primary (< 100 beds), Secondary (100-500 beds), or Tertiary institutions (> 500 beds). Further, based on the level of service provision, size, medical technology, medical equipment, and management and medical quality, these 3 grades are further subdivided into 3 subsidiary levels: A, B and C. Out of 224 physicians, 163 (72.8%) were from Tertiary A (the highest level), 16 Figure.2A, international sources, e.g., NORD (10.7%), Orphanet (11.6%), Global Genes (8.5%), EURORDIS (4.5%), were not as well-known in China. It has been shown that among pediatricians in Australia, the percentage of awareness of Orphanet, NORD, and EURORDIS was 50%, 35%, and 21%, respectively [10].…”

Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes

“…Four different groups of participants were recruited: healthy day controls (N=20; from which metabolically healthy where then selected), familiar partial lipodistrophy type 2 (N=10; hereafter lipodystrophy; carrying mutations in PPARG or LMNA genes, as verified by whole genome sequence (Turro et al, 2020)), obese referred for bariatric surgery (N=11; without clear genetic causes (Turro et al, 2020)) and blood donors (hereafter "BD"; N=202) (Chen et al, 2016). To determine the metabolic health of the individuals in these groups, we collected data on age and body weight (BW), and performed plasma biochemistry assays for the following (all values and differences reported in Table S1): leptin, adiponectin, insulin, free fatty acid (FFA), glucose (GLC), serum lipid (triglycerides (TG), total cholesterol (TC), high density lipoprotein (HDL-C), low-density lipoprotein (LDL-C)), activity of alanine and aspartate amino-transferases (ALT and AST, respectively) and high-sensitivity C-reactive Protein (hsCRP).…”