ArticleNature communications2018
Identifying noncoding risk variants using disease-relevant gene regulatory networks.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Leverage Large-Scale Biological Networks to Decipher the Genetic Basis of Human Diseases Using Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2021Pooled it
- Functional genomics integration of glycolysis-related gene networks reveals prognostic biomarkers and immune microenvironment regulation in breast cancer.Scientific reports · 2026Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibility.HGG advances · 2024Article
- Constructing eRNA-mediated gene regulatory networks to explore the genetic basis of muscle and fat-relevant traits in pigs.Genetics, selection, evolution : GSE · 2024Article
- Decoding Non-coding Variants: Recent Approaches to Studying Their Role in Gene Regulation and Human Diseases.Frontiers in bioscience (Scholar edition) · 2024Review
- sscNOVA: a semi-supervised convolutional neural network for predicting functional regulatory variants in autoimmune diseases.Frontiers in immunology · 2024Article
- Application of whole exome sequencing in the diagnosis of muscular disorders: a study of Taiwanese pediatric patients.Frontiers in genetics · 2024Article
- VarSCAT: A computational tool for sequence context annotations of genomic variants.PLoS computational biology · 2023Article
- Single-cell dissection of aggression in honeybee colonies.Nature ecology & evolution · 2023Article
- Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation.PLoS computational biology · 2023Article
- Exploration of Tools for the Interpretation of Human Non-Coding Variants.International journal of molecular sciences · 2022Review
- Demystifying non-coding GWAS variants: an overview of computational tools and methods.Human molecular genetics · 2022Review
- Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure.Nature communications · 2022Article
- VarSAn: associating pathways with a set of genomic variants using network analysis.Nucleic acids research · 2021Article
- Discover novel disease-associated genes based on regulatory networks of long-range chromatin interactions.Methods (San Diego, Calif.) · 2021Article
- Systematic Evaluation of DNA Sequence Variations onFrontiers in genetics · 2021Article
- Epigenomics and genotype-phenotype association analyses reveal conserved genetic architecture of complex traits in cattle and human.BMC biology · 2020Article
- Missing heritability in Parkinson's disease: the emerging role of non-coding genetic variation.Journal of neural transmission (Vienna, Austria : 1996) · 2020Review
- CERENKOV3: Clustering and molecular network-derived features improve computational prediction of functional noncoding SNPs.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.