Identifying noncoding risk variants using disease-relevant gene regulatory networks

Current methods for prioritization of non-coding genetic risk variants are based on sequence and chromatin features. Here, Gao et al. develop ARVIN, which predicts causal regulatory variants using disease-relevant gene-regulatory networks, and validate this approach in reporter gene assays.

Bibliographic Details
Main Authors:	Long Gao, Yasin Uzun, Peng Gao, Bing He, Xiaoke Ma, Jiahui Wang, Shizhong Han, Kai Tan
Format:	Article
Language:	English
Published:	Nature Publishing Group 2018-02-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-018-03133-y

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spelling	doaj-5472bfb79e7346c78601c129c375ba8e2021-05-11T09:28:24ZengNature Publishing GroupNature Communications2041-17232018-02-019111210.1038/s41467-018-03133-yIdentifying noncoding risk variants using disease-relevant gene regulatory networksLong Gao0Yasin Uzun1Peng Gao2Bing He3Xiaoke Ma4Jiahui Wang5Shizhong Han6Kai Tan7Department of Genetics, Perelman School of Medicine, University of PennsylvaniaDivision of Oncology and Center for Childhood Cancer Research, Children’s Hospital of PhiladelphiaDivision of Oncology and Center for Childhood Cancer Research, Children’s Hospital of PhiladelphiaDivision of Oncology and Center for Childhood Cancer Research, Children’s Hospital of PhiladelphiaSchool of Computer Science and Technology, Xidian UniversityThe Jackson LaboratoryDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of MedicineDepartment of Genetics, Perelman School of Medicine, University of PennsylvaniaCurrent methods for prioritization of non-coding genetic risk variants are based on sequence and chromatin features. Here, Gao et al. develop ARVIN, which predicts causal regulatory variants using disease-relevant gene-regulatory networks, and validate this approach in reporter gene assays.https://doi.org/10.1038/s41467-018-03133-y
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Long Gao Yasin Uzun Peng Gao Bing He Xiaoke Ma Jiahui Wang Shizhong Han Kai Tan
spellingShingle	Long Gao Yasin Uzun Peng Gao Bing He Xiaoke Ma Jiahui Wang Shizhong Han Kai Tan Identifying noncoding risk variants using disease-relevant gene regulatory networks Nature Communications
author_facet	Long Gao Yasin Uzun Peng Gao Bing He Xiaoke Ma Jiahui Wang Shizhong Han Kai Tan
author_sort	Long Gao
title	Identifying noncoding risk variants using disease-relevant gene regulatory networks
title_short	Identifying noncoding risk variants using disease-relevant gene regulatory networks
title_full	Identifying noncoding risk variants using disease-relevant gene regulatory networks
title_fullStr	Identifying noncoding risk variants using disease-relevant gene regulatory networks
title_full_unstemmed	Identifying noncoding risk variants using disease-relevant gene regulatory networks
title_sort	identifying noncoding risk variants using disease-relevant gene regulatory networks
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2018-02-01
description	Current methods for prioritization of non-coding genetic risk variants are based on sequence and chromatin features. Here, Gao et al. develop ARVIN, which predicts causal regulatory variants using disease-relevant gene-regulatory networks, and validate this approach in reporter gene assays.
url	https://doi.org/10.1038/s41467-018-03133-y
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Identifying noncoding risk variants using disease-relevant gene regulatory networks

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