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.
Main Authors: | , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2018-02-01
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Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-018-03133-y |
Summary: | 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. |
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ISSN: | 2041-1723 |