In a pilot clinical study in the U.S. researchers have applied whole-genome sequencing (WGS) to diagnose genetic diseases in critically ill newborns in under two days, rather than the 4-6 weeks turnaround time needed for genetic analysis using current methods. The approach, called STAT-Seq by its developers at Children’s Mercy Hospital in Kansas City, combines WGS sequencing of blood DNA, with a newly developed bioinformatics platform that marries the clinical symptoms of the patient with a list of possible diseases, so that analysis of the genome sequence is narrowed down to those regions harboring the mutations responsible for the candidate diseases. Taking just 50 hours to complete from sample to diagnostic result, the technology could dramatically improve the speed of diagnosing genetic disorders in neonates, potentially saving lives and reducing the numbers of unnecessary tests carried out.

About 20% of infant deaths in the U.S. are caused by congenital birth defects and chromosomal abnormalities, report the Children’s Mercy Hospital team. In the case of genetic diseases, it’s imperative that a correct diagnosis is made as soon as possible so that the infant can be treated, if a suitable therapy is available. And for those neonatal genetic diseases for which there are no treatments, a fast diagnosis avoids inappropriate therapy, means parents can swiftly be given counselling, and is critical for research to develop management guidelines.

Unfortunately, diagnosing a suspected genetic disorder isn’t always possible just by looking at symptoms, and newborns might not yet exhibit the full clinical phenotype. On top of this, neonatal screens are currently only available for a handful of genetic disorders, and serial gene sequencing is too slow to be clinically useful in a neonatal intensive care unit (NICU) setting.

To speed diagnosis of genetic diseases in the NICU the Children’s Mercy Hospital team have developed and trialled an approach that combines WGS carried out on Illumnina’s HiSeq 2500 system (which can sequence an entire genome at high coverage in about 25 hours), with a new bioinformatics platform called symptom-and-sign-assisted genome analysis (SSAGA). The clinicopathological correlation tool effectively maps the clinical features of nearly 600 genetic diseases, and how they present in infants, to phenotypes and genes known to cause the same types of symptoms. Effectively, the clinician inputs the patient’s symptoms according to specified terminology, and the software identifies a set of possible diseases.

The team developed SSAGA to carry out two tasks when combined with WGS. Firstly, to evaluate the data from WGS analysis restricted to a set of gene-associated regions relevant to the clinical presentation. And secondly, to prioritize clinical information to help interpret the WGS data itself. Basically, SSAGA maps the clinical features presented to disease genes, and hones down the analysis to only those relevant parts of the genome.

And its the combination of automating the main components of WGS, with the use of bioinformatics-based gene-variant characterization and clinical interpretation that has allowed the overall process time to be pared down to under two days. The total “hands on” time for technical staff is about five hours, and while sample preparation for WGS is being undertaken (the time for this has been shortened from 16 to 4.5 hours), the clinician enters clinical terms that described the neonates’ illnesses into the SSAGA software.

Headed by Stephen Francis Kingsmore, M.D., the investigators applied the STAT-Seq platform to retrospectively diagnose two infants with diseases that had already been confirmed by other established methods, and then prospectively to diagnose disease in five newborns with clinical symptoms that suggested they may be harboring a genetic disorder. To verify the accuracy of the WGS, the sequencing results were compared with those resulting from deep-targeted sequencing approaches.

In the blinded, retrospective analyses the rapid WGS technology correctly identified the known diagnoses. And in four out of the five prospective cases, the technology provided a definitive or likely molecular diagnosis within about 50 hours.

The investigators admit that the platform will need refining and can’t yet detect every type of genetic abnormality. “WGS is not yet effective for clinical-grade detection of all mutation types,” they write in their published paper in Science Translational Medicine. “Copy number variations and large deletions require clinical validation of research methods. Long, simple sequence-repeat expansions and complex rearrangements are problematic.” And promising as the WGS approach is for diagnosing genetic diseases in the neonatal setting, clinical validation of rapid WGS will take a while yet, they note.

Nevertheless, the authors conclude, “the specific diseases, genes, exons, and mutation classes that are qualified for analysis, interpretation, and clinical reporting with WGS can be precisely predicted.” The investigators hope that with by the end of the year they will also have reduced the testing time from 50 hours to just 36 hours.

Dr. Kingsmore et al., describe the technology and their studies in a paper titled “Rapid Whole-Genome Sequencing for Genetic Disease Diagnosis in Neonatal Intensive Care Units”.

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