Variant interpretation through Bayesian fusion of frequency and genomic knowledge
| dc.citation.articleNumber | 4 | en_US |
| dc.citation.journalTitle | Genome Medicine | en_US |
| dc.citation.volumeNumber | 7 | en_US |
| dc.contributor.author | Shaw, Chad A. | en_US |
| dc.contributor.author | Campbell, Ian M. | en_US |
| dc.date.accessioned | 2017-05-22T18:57:18Z | en_US |
| dc.date.available | 2017-05-22T18:57:18Z | en_US |
| dc.date.issued | 2015 | en_US |
| dc.description.abstract | Variant interpretation is a central challenge in genomic medicine. A recent study demonstrates the power of Bayesian statistical approaches to improve interpretation of variants in the context of specific genes and syndromes. Such Bayesian approaches combine frequency (in the form of observed genetic variation in cases and controls) with biological annotations to determine a probability of pathogenicity. These Bayesian approaches complement other efforts to catalog human variation. | en_US |
| dc.identifier.citation | Shaw, Chad A. and Campbell, Ian M.. "Variant interpretation through Bayesian fusion of frequency and genomic knowledge." <i>Genome Medicine,</i> 7, (2015) BioMed Central: https://doi.org/10.1186/s13073-015-0129-3. | en_US |
| dc.identifier.doi | https://doi.org/10.1186/s13073-015-0129-3 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/94327 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | BioMed Central | en_US |
| dc.rights | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.title | Variant interpretation through Bayesian fusion of frequency and genomic knowledge | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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