A study by researchers from the Wellcome Sanger Institute, University of Exeter, and University of Cambridge proposes a revolutionary shift in diagnosing rare developmental disorders in children. Published in Genetics in Medicine, the study suggests that a single genetic test, utilizing exome sequencing, could supplant the current two-step diagnostic approach, offering earlier diagnoses and conserving vital NHS resources.
The research analyzed genetic data from nearly 10,000 families involved in the Deciphering Developmental Disorders study. It demonstrated that exome sequencing, which specifically reads protein-coding DNA, is as accurate, if not superior, to standard microarrays in identifying disease-causing structural genetic variations.
This advancement presents hope for quicker and more precise diagnoses of rare genetic diseases. Additionally, it promises substantial cost savings for the NHS, contingent upon further training for specialists in generating and analyzing the data.
Genetic alterations can range from minor single-letter changes to the deletion or duplication of larger DNA segments, known as copy number variations (CNVs). These CNVs, while typically benign and contributing to genetic diversity, can underlie various neurodevelopmental disorders.
Currently, children suspected of genetic diseases undergo a prolonged testing process involving multiple diagnostic methods. This typically begins with a microarray test and progresses to broader genome-wide sequencing tests. However, the new study aimed to streamline this process by developing a single-assay approach using data from genome-wide exome sequencing assays.
Genome-Wide Exome Sequencing Assay implies a broader approach that encompasses not only exome sequencing but also additional analyses or techniques that cover the entire exome in a genome-wide manner. This include methods for capturing and sequencing all protein-coding regions, as well as approaches to analyze the data comprehensively across the exome. Essentially, it emphasizes the comprehensive nature of the sequencing assay, ensuring that all exonic regions across the entire genome are sequenced and analyzed.
The research team combined four algorithms utilizing machine learning techniques to analyze exome sequencing data. Comparison with standard clinical methods revealed the new approach’s ability to reliably detect 305 large-scale pathogenic mutations, including 91 previously undetectable by microarrays.
Professor Caroline Wright of the University of Exeter, a study author, emphasized the significance of using exome sequencing to detect both small genetic variants and clinically important large-scale changes. She anticipates this advancement will simplify and enhance genetic testing accessibility.
Professor Matthew Hurles, senior author of the study, advocates for widespread adoption of the single-test approach in NHS clinical practice, underscoring the importance of bioinformatics training to support its implementation.