With the increased use of genetic testing, whether through broadening of testing criteria or the well documented ordering of tests by physicians because of patient demand, and furthermore the commoditization of measurements of thousands of variants on a single individual, the number of healthy individuals undergoing genetic testing is accelerating. The growth in the number of false positives is projected to rise dramatically because many of the current annotations for known mutations have not been developed for the asymptomatic, well population. With the false positive results, patients will be alarmed unnecessarily, and also clinicians will order unnecessary and occasionally risky tests. Furthermore, insurance companies will find the growth in unnecessary secondary testing triggered by this tsunami of false positive tests (""""""""the incidentalome"""""""") as an unanticipated financial risk and thereby endanger the very real benefits that the sound use of genetic testing and long-anticipated genomically-enable """"""""personalized medicine"""""""" can bring. We propose to demonstrate that this risk of the incidentalome is substantial by automatically mining the biomedical literature, by scanning public genomic data sets, and computationally predicting the effects of mutations to identify a set of candidate mutations that are predicted to not contribute to disease in the general population. This despite their annotation in authoritative genetic databases and texts as """"""""highly penetrant"""""""" in causing disease congenitally or in childhood. Two thousand adult patients known not to have these diseases will be then tested for each of these candidate mutations with the hypothesis that we will demonstrate the presence of some of these mutations in these health individuals. Evidence supporting this hypothesis will both serve as an important caution in the use of genetic testing and will also demonstrate the value of population studies to find the broader clinical significance of genetic mutations.

Public Health Relevance

The first rule of medicine is to do no harm. The current state of annotation of genetic tests is inadequate to support clinical application to large segments of our patient population. In the context of increased use of genetic testing, we are poised to unintentionally inflict anxiety, unnecessary and costly and risky additional testing. This proposal will provide a demonstration of the reality of this risk and point the way to developing evidence-based annotations that can be safely used for the interpretation of genetic tests in the general population.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
5R01LM010125-02
Application #
7828226
Study Section
Special Emphasis Panel (ZLM1-AP-E (M3))
Program Officer
Ye, Jane
Project Start
2009-07-01
Project End
2012-06-30
Budget Start
2010-07-01
Budget End
2012-06-30
Support Year
2
Fiscal Year
2010
Total Cost
$663,869
Indirect Cost
Name
Harvard University
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
047006379
City
Boston
State
MA
Country
United States
Zip Code
02115
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