Applications are now open.
A vital challenge to incorporating genomic data into clinical practice is the high cost of generating NGS (specifically WES and WGS) data, bioinformatics processing, and data storage. While WGS and WES currently provide the most comprehensive genomic data, they are not currently cost-effective techniques for clinical screening on a national volume of subjects. In this context, NGS also generates large quantities of extraneous data alongside relevant data, which makes analyses particularly complex and resource intensive.
This will be a giant step
forward for the following reasons: (a) early diagnosis and identification of
mutation carriers many years before the onset of clinical disease (population
screening), (b) helping people to comprehensively determine their risk for
passing on one of these diseases (pre-marital screening), and (c) determining
the risk that a fetus will be born with certain genetic abnormalities (prenatal
screening). Thus, the test proposed here
would be based on results from WGS of whole populations but is more cost
effective and clinically relevant than WES or WGS and will result in improved
patient care and significant reductions in medical costs for patients and for