A single genetic test could potentially replace the current two-step approach to diagnosing rare developmental disorders in children. This change could enable earlier diagnoses for families and save vital resources for the NHS.
Researchers at the Wellcome Sanger Institute and their collaborators at the University of Exeter and the University of Cambridge were able to re-evaluate the genetic data of almost 10,000 families from the Deciphering Developmental Disorders study.
In a new study, recently published in Genetics in Medicineshow for the first time that using exome sequencing, which reads only protein-coding DNA, is as accurate, if not better, than standard microarrays in identifying structural genetic variations that cause disease.
Its adoption offers hope for faster and more accurate diagnoses of rare genetic diseases. It could also generate substantial cost savings for the NHS, although more training is needed for specialists to generate and analyze the data, the researchers say.
Changes to our genetic code can range from single letter changes to the deletion or duplication of larger stretches of DNA. These larger changes, called copy number variations (CNVs), can be more difficult for clinical teams to detect and understand in sequencing data, which is why microarrays are used. While generally harmless and a major source of genetic diversity in humans, these large-scale variations can sometimes cause various neurodevelopmental disorders, including Angelman syndrome, DiGeorge syndrome, and Williams syndrome. -Beuren.
Currently, children suspected of having genetic diseases resulting from these large DNA deletions or duplications go through a lengthy process of testing and waiting for results from multiple diagnostic approaches, starting with a microarray test before moving on to further testing. extensive genome sequencing (such as exome or genome sequencing). In this new study, scientists set out to develop a unique approach to detect these structural changes, using data available from genome-wide exome sequencing assays.
Using data from the Deciphering Developmental Disorders study, the team developed a unique trial approach that combined four algorithms using machine learning methods to analyze exome sequencing data.
Comparison of the new single-assay approach with current standard clinical methods revealed that it could reliably detect 305 pathogenic mutations on a large scale, including 91 that could not previously be detected using standard clinical microarrays. The findings suggest it could replace current methods.
Caroline Wright, Professor of Genomic Medicine at the University of Exeter and author of the study, said: “Using exome sequencing data to detect large-scale clinically important changes, as well as small genetic variants, marks an important step forward. “by making genetic testing simpler, cheaper and more accessible.”
Helen Firth, professor of clinical genomics at the University of Cambridge, senior doctor and author of the study, said: “Under the current system, children often endure a long and gradual process of different genetic tests before reaching a diagnosis. This research “It brings hope that, in the near future, families will only need one test.”
Professor Matthew Hurles, director of the Wellcome Sanger Institute and lead author of the study, said: “We are still learning how large-scale genetic variations affect human health. This study demonstrates that with the right computational methods, a single test can accurately detect accuracy “Our findings support its widespread adoption into NHS clinical practice and appropriate bioinformatics training to support this.”