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| Content Provider | PubMed Central |
|---|---|
| Author | Miosge, Lisa A. Field, Matthew A. Sontani, Yovina Cho, Vicky Johnson, Simon Anna, Palkova Bhavani, Balakishnan Liang, Rong Zhang, Yafei Lyon, Stephen Beutler, Bruce Whittle, Belinda Bertram, Edward M. Enders, Anselm Goodnow, Christopher C. Andrews, T. Daniel |
| Abstract | Computational tools applied to any human genome sequence identify hundreds of genetic variants predicted to disrupt the function of individual proteins as the result of a single codon change. These tools have been trained on disease mutations and common polymorphisms but have yet to be tested against an unbiased spectrum of random mutations arising de novo. Here we perform such a test comparing the predicted and actual effects of de novo mutations in 23 genes with essential functions for normal immunity and all possible mutations in the TP53 tumor suppressor gene. These results highlight an important gap in our ability to relate genotype to phenotype in clinical genome sequencing: the inability to differentiate immediately clinically relevant mutations from nearly neutral mutations. |
| Related Links | http://dx.doi.org/10.1073/pnas.1511585112 |
| Starting Page | 5189 |
| File Format | |
| ISSN | 00278424 |
| e-ISSN | 10916490 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Issue Number | 37 |
| Volume Number | 112 |
| Language | English |
| Publisher | National Academy of Sciences |
| Publisher Date | 2015-09-15 |
| Access Restriction | Open |
| Rights Holder | National Academy of Sciences |
| Subject Keyword | General Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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