Back

Implementing a consultation service for translating genomic research findings into the clinic: lessons from the SickKids Genome Board

Pan, A.; Pulsifer, K.; Axford, M.; Dolman, L.; Gallinger, B.; Liston, E.; Stephenson, E. A.; Szuto, A.; Zahavich, L.; Costain, G.

2024-11-19 genetic and genomic medicine
10.1101/2024.11.18.24317509 medRxiv
Show abstract

ObjectivesGenome-wide sequencing (GWS) is now used across the breadth of paediatric research. There is a greater potential to identify unexpected, clinically relevant findings with GWS than with the targeted genetic techniques used in prior decades. Individual research teams may not have the expertise to evaluate and manage these findings. The Hospital for Sick Children (SickKids) Genome Board is a no-cost consultation service for researchers with questions arising from genetic aspects of their studies. MethodsWe reviewed all submissions to and recommendations from the Genome Board over the first four years, to identify common questions, themes, and trends. ResultsThere were 67 submissions, and a year-over-year increase in volumes. The most common request (60%) was to assess variants identified by GWS for pathogenicity, clinical actionability, and returnability to a study participant. Overall, 23 of 48 reviewed variants were recommended for clinical confirmation and return with genetic counselling. Other categories of submissions included requests to researchers from study participants to release their "raw" genomic data, and for input on protocols related to clinical translation of findings. ConclusionThe Genome Board provides a generalizable model for centralized triage of clinical questions arising from genomic research at a paediatric centre. Clinicians should be aware that patient participation in genetic research studies can have downstream consequences for their healthcare.

Published in Paediatrics & Child Health · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.