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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Identifying noncoding risk variants using disease-relevant gene regulatory networks.

Long Gao¹, Yasin Uzun^2,3, Peng Gao^2,3

¹Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Nature Communications

|February 18, 2018

Summary

This summary is machine-generated.

We developed ARVIN, a computational tool that uses gene regulatory networks to accurately identify noncoding variants linked to disease risk. This method improves upon existing approaches and aids in understanding autoimmune disease mechanisms.

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Area of Science:

Genomics
Computational Biology
Systems Biology

Background:

Identifying noncoding variants associated with disease risk is a significant challenge in genetic research.
Noncoding variants influence gene regulation within complex gene regulatory networks (GRNs).

Purpose of the Study:

To develop and validate a computational framework, ARVIN, for improved prediction of causal noncoding risk variants.
To leverage disease-relevant GRNs to enhance the accuracy of noncoding variant identification.

Main Methods:

ARVIN integrates novel regulatory network-based features with sequence-based features for variant prediction.
The framework was tested using known causal variants in promoters and enhancers across various diseases.
Reporter assays were employed for experimental validation of ARVIN's predictions.

Main Results:

ARVIN demonstrated superior performance compared to state-of-the-art methods relying solely on sequence-based features.
Experimental validation confirmed the accuracy of ARVIN in predicting causal noncoding variants.
Application to autoimmune diseases provided insights into perturbed gene subnetworks.

Conclusions:

ARVIN offers a robust computational approach for identifying noncoding risk variants by incorporating GRN information.
The framework enhances our understanding of the genetic basis of diseases, particularly autoimmune conditions.
ARVIN facilitates a comprehensive view of genetic perturbations in disease-related biological networks.