100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - Preliminary Report

Abstract

Background: The U.K. 100,000 Genomes Project is in the process of investigating the role of genome sequencing in patients with undiagnosed rare diseases after usual care and the alignment of this research with health care implementation in the U.K. National Health Service. Other parts of this project focus on patients with cancer and infection.

Methods: We conducted a pilot study involving 4660 participants from 2183 families, among whom 161 disorders covering a broad spectrum of rare diseases were present. We collected data on clinical features with the use of Human Phenotype Ontology terms, undertook genome sequencing, applied automated variant prioritization on the basis of applied virtual gene panels and phenotypes, and identified novel pathogenic variants through research analysis.

Results: Diagnostic yields varied among family structures and were highest in family trios (both parents and a proband) and families with larger pedigrees. Diagnostic yields were much higher for disorders likely to have a monogenic cause (35%) than for disorders likely to have a complex cause (11%). Diagnostic yields for intellectual disability, hearing disorders, and vision disorders ranged from 40 to 55%. We made genetic diagnoses in 25% of the probands. A total of 14% of the diagnoses were made by means of the combination of research and automated approaches, which was critical for cases in which we found etiologic noncoding, structural, and mitochondrial genome variants and coding variants poorly covered by exome sequencing. Cohortwide burden testing across 57,000 genomes enabled the discovery of three new disease genes and 19 new associations. Of the genetic diagnoses that we made, 25% had immediate ramifications for clinical decision making for the patients or their relatives.

Conclusions: Our pilot study of genome sequencing in a national health care system showed an increase in diagnostic yield across a range of rare diseases. (Funded by the National Institute for Health Research and others.).

Figure 1.. Overview of the Diagnostic and Research Pipeline and Source of Diagnoses.

Results from 2183 probands in the pilot study were returned for presentation to the Genomic Medicine Centres (GMCs) of the recruiting hospitals. A total of 25% of the probands received a positive diagnosis, and 10% had a variant or variants of unknown significance in genes that were determined by clinical geneticists at the recruiting site to be consistent with the phenotype but that required further functional validation. The remaining 65% of the probands received a negative report at the time but will be reassessed. The numbers and sources of these positive diagnoses are shown at each stage of the automated diagnostic pipeline, and the additional research is shown for diagnoses that were not immediately obvious. CCR denotes constrained coding region, indel insertion or deletion, mtDNA mitochondrial DNA, SNV single-nucleotide variant, and SV structural variant.

Figure 2.. Diagnoses in the Rare Disease Pilot Study.

Panel A shows diagnostic yield for any disease and according to family structure and cause of disease. The diagnostic yield was 35% for diseases likely to have a monogenic cause and 11% for diseases likely to have a complex cause. The values above the bars are the numbers of probands. Singleton refers to a proband for whom no other family member was recruited, family duo to a parent–proband pair, family trio to both parents and a proband, and family quad to a proband, sibling, and parents. Panel B shows diagnostic yield according to disease category. The values above the bars are the numbers of probands. Panel C shows the diagnostic yield among probands according to previous genetic testing and most extensive testing type: chromosomal (karyotyping, array-based comparative genomic hybridization, single-nucleotide polymorphism arrays), targeted (targeted single-gene tests), next-generation sequencing (NGS) panels, or whole-exome sequencing (WES). The values above the bars are the numbers of probands. Panel D shows the performance of virtual panel-based and Exomiser-based prioritization for identifying the diagnoses. “Disease panel only” indicates the use of a single virtual panel for the recruited disease category. “Applied panels” indicates the use of all applied virtual gene panels used in the pipeline, including the recruited disease–associated panel as well as 0 or more additional panels selected on the basis of the patient’s phenotypes (Human Phenotype Ontology terms). “Exomiser top” indicates Exomiser use in the top-ranked candidate variants, “Exomiser top 3” use in the top three candidates, and “Exomiser top 5” use in the top five candidates. Sensitivity is the percentage of true positive diagnoses based on SNVs or indels that were identified, and the positive predictive value is the percentage of prioritized variants that led to a positive diagnosis. The values above the bars are percentages. In this analysis, the diagnosed variant or variants are true positives, and the other candidate variants that were returned are false positives.