Better Data Framework Needed to Improve Rare Disease Diagnostic Rates
April 20, 2020
Rare Daily Staff
A better framework for the reanalysis of genetic data could improve diagnostic rates of rare disease by as much as 32 percent, according to a new study.
Reanalysis is when laboratories re-run previously analyzed genomic data to check for new genes associated with particular conditions and new variants in genes previously reported.
The study, led by the Murdoch Children’s Research and the University of Melbourne, and published in the journal Familial Cancer, found broad variation in the ways that reanalysis of patient data was initiated. The researchers said this raised concerns about the responsibilities of laboratories and clinicians, as well as patients’ abilities to advocate for themselves.
Previous studies have shown that despite the high diagnostic yield associated with genomic sequencing (up to 68 percent depending on the genetic condition), a sizable proportion of patients still do not receive a genetic diagnosis at the time of the initial analysis. Even though reanalysis of genomic sequencing data could potentially change treatment or management, particularly in rare disease and inherited cancer cases, there is currently no responsibility for laboratories to reanalyze data.
“Studies show that systematic data reanalysis leads to considerable increases in genetic diagnosis rates between 4 and 32 percent. Yet it is time intensive and is not currently feasible for most laboratories to implement,” said Danya Vears, Murdoch Children’s Research’s lead researcher. “Few policies address whether laboratories have a duty to reanalyze and it is unclear until now how this has impacted clinical practice.”
The study interviewed 31 genetic counsellors and clinical geneticists across Europe, Australia, and Canada about their experiences with data reanalysis and reinterpretation practices after requesting genomic sequencing for their patients. It found that a combination of patient-, clinician-, and laboratory-initiated reanalysis practice models were used to trigger reanalysis of patient data. Patient-initiated reanalysis, where clinicians instruct patients to return to the genetic service for reassessment after a period of time or if new information came to light, was the most common.
While some participants felt that the system was working well, others raised questions around patients’ abilities to request reanalysis. This could be due to their lack of understanding of what a negative result might mean, or because it places additional pressure on patients or families to remember to return when they are dealing with a complex medical situation.
Genetic health professionals felt a laboratory-initiated model would be ideal, but many acknowledged the technology to make this a reality was not yet available.
Vears said, in many cases, there was no clear pathway for the initiation of reanalysis and that it could occur through multiple channels, which could lead to confusion.
“Regardless of the model that a genetic service adopts,” Vears said, “roles and responsibilities need to be clearly outlined so patients do not miss the opportunity to receive ongoing information about their genetic diagnosis.”
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