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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 the government.

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