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

被引：0
|
作者：
Dongwon Lee
David U Gorkin
Maggie Baker
Benjamin J Strober
Alessandro L Asoni
Andrew S McCallion
Michael A Beer
机构：
[1] McKusick-Nathans Institute of Genetic Medicine,Department of Biomedical Engineering
[2] Johns Hopkins University,undefined
[3] Johns Hopkins University,undefined
[4] Present address: Ludwig Institute for Cancer Research,undefined
[5] University of California,undefined
[6] San Diego,undefined
[7] La Jolla,undefined
[8] California,undefined
[9] USA.,undefined
来源：
Nature Genetics | 2015年 / 47卷
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摘要：
Michael Beer and colleagues report a metric based on a regulatory region annotation method, gkm-SVM, and use this to predict the effects of regulatory variants from sequencing and DNase I–hypersensitive site data. They apply their method to autoimmune disease GWAS data and report several new predictions for causal SNPs.
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页码：955 / 961
页数：6
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