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PMID: 26075791 Published · ppublish English Journal Article Research Support, N.I.H., Extramural

A method to predict the impact of regulatory variants from DNA sequence.

Nature genetics ·Vol. 47 ·No. 8 ·2015-08-00 ·Pages 955-61

Lee D, Gorkin DU, Baker M, Strober BJ, Asoni AL, McCallion AS, Beer MA

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.

MeSH Terms
Animals Autoimmune Diseases/genetics Base Sequence Cell Line, Tumor Computational Biology/methods Deoxyribonuclease I/metabolism Enhancer Elements, Genetic/genetics Genetic Predisposition to Disease/genetics Genome, Human/genetics Genome-Wide Association Study/methods Hep G2 Cells Humans Mice Mutation Polymorphism, Single Nucleotide Quantitative Trait Loci/genetics Regulatory Sequences, Nucleic Acid/genetics Reproducibility of Results Risk Factors Support Vector Machine
Chemicals
Deoxyribonuclease I
Authors & Affiliations
7 authors, click to expand affiliations / ORCID
Lee Dongwon
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Gorkin David U
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Baker Maggie
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Strober Benjamin J
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Asoni Alessandro L
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
McCallion Andrew S
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Beer Michael A
1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA. [2] Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
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Article Info
Journal
Nature genetics
Abbr.
Nat Genet
ISSN
1546-1718
DOI
10.1038/ng.3331
Published
2015-08-00
Epub
2015-00-15
Pages
955-61
Language
English
Region
United States
NLM ID
9216904
PMCID
PMC4520745
Subset
IM
Grants
NIGMS NIH HHS · T32 GM007814 · United States
NIGMS NIH HHS · K12 GM068524 · United States
NINDS NIH HHS · R01 NS062972 · United States
NHGRI NIH HHS · R01 HG007348 · United States
NINDS NIH HHS · R01 NS62972 · United States
Corrections
CommentIn
PMID: 26220134
External Links
PubMed source DOI: 10.1038/ng.3331 PMC Full Text View journal details
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