A method to predict the impact of regulatory variants from DNA sequence.
A method to predict the impact of regulatory variants from DNA sequence.
复制标题
DOI：
10.1038/ng.3331
复制
发表时间：
2015-08
期刊：
影响因子：
30.8
通讯作者：
Beer MA
中科院分区：
文献类型：
--
作者：
Lee D;Gorkin DU;Baker M;Strober BJ;Asoni AL;McCallion AS;Beer MA
全基因组关联研究（GWAS）发现的与人类常见疾病有关的大多数变异都位于非编码序列区间。尽管有人认为调控元件破坏是一个共同的主题，但在被起诉的基因组区域内识别因果风险变体仍然是一个重大挑战。在这里，我们提出了一种新型的基于序列的计算方法来预测调节变异的影响，使用编码细胞特异性调节序列词汇表的分类器（gkm-SVM）。gkm-SVM评分的诱导变化deltaSVM量化了变体的影响。我们表明，deltaSVM准确地预测了单核苷酸多态性对DNA酶I敏感性的影响，在他们的天然基因组背景下，并准确地预测了报告分析中的几个增强子的密集诱变的结果。先前验证的GWAS SNP产生大的deltaSVM分数，我们预测了几种自身免疫性疾病的新风险SNP。因此，deltaSVM提供了一个强大的计算方法，系统地确定功能的调节变体。
Most variants implicated in common human disease by Genome-Wide Association Studies (GWAS) lie in non-coding sequence intervals. Despite the suggestion that regulatory element disruption represents a common theme, identifying causal risk variants within indicted genomic regions remains a significant challenge. Here we present a novel sequence-based computational method to predict the effect of regulatory variation, using a classifier (gkm-SVM) which encodes cell-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 context, 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 novel risk SNPs for several autoimmune diseases. Thus, deltaSVM provides a powerful computational approach for systematically identifying functional regulatory variants.