2 research outputs found
PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels
I
ndividual genomes contain millions
of genetic variants. When considering
which variants may be causative for a
given rare genetic disease, applying filtering
criteria (such as allele frequency, predicted
variant consequence, familial segregation
and mode of inheritance) decreases this
number to hundreds of variants. However,
such a number remains labor intensive
for a diagnostic genetic testing laboratory
to interpret as part of routine service for
each patient or family. A list of genes
with evidence of disease causation in the
condition being assessed aids in prioritizing
and ranking the variants. This prioritization
decreases the number of candidates that
laboratories or clinical geneticists must
assess to identify the likely causative
variants for clinical reporting. Established
lists of genes with clear evidence of disease
causation (referred to herein as virtual gene
panels) are therefore a highly effective tool
in variant prioritization.M. Caulfield was funded by the National Institute for Health Research (NIHR) as part of the portfolio of translational research of the NIHR Biomedical Research Center at Barts and The London School of Medicine and Dentistry. He is supported as an NIHR senior investigator, and this work was funded by the MRC eMedLab award. This research was made possible through access to the data and findings generated by the 100,000 Genomes Project. The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health). The 100,000 Genomes Project is funded by the NIHR and NHSE. The Wellcome Trust, Cancer Research UK and the Medical Research Council have also funded research infrastructur
100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - Preliminary Report.
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.)