A method to predict the impact of regulatory variants from DNA sequence.
| Title: | A method to predict the impact of regulatory variants from DNA sequence. |
|---|---|
| Authors: | Lee, Dongwon1, Gorkin, David U1, Baker, Maggie1, McCallion, Andrew S1, Strober, Benjamin J2, Asoni, Alessandro L2, Beer, Michael A3 |
| Source: | Nature Genetics. Aug2015, Vol. 47 Issue 8, p955-961. 7p. 1 Diagram, 2 Charts, 5 Graphs. |
| Subject Terms: | *DNA, *GENOMICS, *MUTAGENESIS, *AUTOIMMUNE disease diagnosis, *CHROMATIN |
| Abstract: | Most variants implicated in common human disease by genome-wide association studies (GWAS) lie in noncoding sequence intervals. Despite the suggestion that regulatory element disruption represents a common theme, identifying causal risk variants within implicated genomic regions remains a major challenge. Here we present a new sequence-based computational method to predict the effect of regulatory variation, using a classifier (gkm-SVM) that encodes cell type-specific regulatory sequence vocabularies. The induced change in the gkm-SVM score, deltaSVM, quantifies the effect of variants. We show that deltaSVM accurately predicts the impact of SNPs on DNase I sensitivity in their native genomic contexts and accurately predicts the results of dense mutagenesis of several enhancers in reporter assays. Previously validated GWAS SNPs yield large deltaSVM scores, and we predict new risk-conferring SNPs for several autoimmune diseases. Thus, deltaSVM provides a powerful computational approach to systematically identify functional regulatory variants. [ABSTRACT FROM AUTHOR] |
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| Items | – Name: Title Label: Title Group: Ti Data: A method to predict the impact of regulatory variants from DNA sequence. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lee%2C+Dongwon%22">Lee, Dongwon</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gorkin%2C+David+U%22">Gorkin, David U</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Baker%2C+Maggie%22">Baker, Maggie</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22McCallion%2C+Andrew+S%22">McCallion, Andrew S</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Strober%2C+Benjamin+J%22">Strober, Benjamin J</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Asoni%2C+Alessandro+L%22">Asoni, Alessandro L</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Beer%2C+Michael+A%22">Beer, Michael A</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Nature+Genetics%22">Nature Genetics</searchLink>. Aug2015, Vol. 47 Issue 8, p955-961. 7p. 1 Diagram, 2 Charts, 5 Graphs. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22DNA%22">DNA</searchLink><br />*<searchLink fieldCode="DE" term="%22GENOMICS%22">GENOMICS</searchLink><br />*<searchLink fieldCode="DE" term="%22MUTAGENESIS%22">MUTAGENESIS</searchLink><br />*<searchLink fieldCode="DE" term="%22AUTOIMMUNE+disease+diagnosis%22">AUTOIMMUNE disease diagnosis</searchLink><br />*<searchLink fieldCode="DE" term="%22CHROMATIN%22">CHROMATIN</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Most variants implicated in common human disease by genome-wide association studies (GWAS) lie in noncoding sequence intervals. Despite the suggestion that regulatory element disruption represents a common theme, identifying causal risk variants within implicated genomic regions remains a major challenge. Here we present a new sequence-based computational method to predict the effect of regulatory variation, using a classifier (gkm-SVM) that encodes cell type-specific regulatory sequence vocabularies. The induced change in the gkm-SVM score, deltaSVM, quantifies the effect of variants. We show that deltaSVM accurately predicts the impact of SNPs on DNase I sensitivity in their native genomic contexts and accurately predicts the results of dense mutagenesis of several enhancers in reporter assays. Previously validated GWAS SNPs yield large deltaSVM scores, and we predict new risk-conferring SNPs for several autoimmune diseases. Thus, deltaSVM provides a powerful computational approach to systematically identify functional regulatory variants. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Nature Genetics is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/ng.3331 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 955 Subjects: – SubjectFull: DNA Type: general – SubjectFull: GENOMICS Type: general – SubjectFull: MUTAGENESIS Type: general – SubjectFull: AUTOIMMUNE disease diagnosis Type: general – SubjectFull: CHROMATIN Type: general Titles: – TitleFull: A method to predict the impact of regulatory variants from DNA sequence. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lee, Dongwon – PersonEntity: Name: NameFull: Gorkin, David U – PersonEntity: Name: NameFull: Baker, Maggie – PersonEntity: Name: NameFull: McCallion, Andrew S – PersonEntity: Name: NameFull: Strober, Benjamin J – PersonEntity: Name: NameFull: Asoni, Alessandro L – PersonEntity: Name: NameFull: Beer, Michael A IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 10614036 Numbering: – Type: volume Value: 47 – Type: issue Value: 8 Titles: – TitleFull: Nature Genetics Type: main |
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