Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
Nat Commun. 2018 Feb 16;9(1):702. doi: 10.1038/s41467-018-03133-y.
Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.
鉴定非编码风险变异仍然是一项具有挑战性的任务。由于非编码变异在基因调控网络（GRN）的背景下发挥作用，我们假设明确使用与疾病相关的 GRN 可以显著提高非编码风险变异的推断准确性。我们描述了使用综合网络进行调控变异注释（ARVIN），这是一种用于预测因果非编码变异的通用计算框架。它采用了一组新的基于调控网络的特征，结合基于序列的特征来推断非编码风险变异。使用多种疾病中基因启动子和增强子中的已知因果变异，我们表明 ARVIN 优于仅使用基于序列的特征的最先进方法。使用报告基因检测的额外实验验证进一步证明了 ARVIN 的准确性。将 ARVIN 应用于七种自身免疫性疾病，提供了受整个风险非编码突变组合作用干扰的基因子网络的整体视图。
