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Related Experiment Video

Updated: Aug 24, 2025

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

Annotating genetic variants to target genes using H-MAGMA.

Nancy Y A Sey^1,2, Brandon M Pratt³, Hyejung Won^4,5

¹UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA.

Nature Protocols

|October 27, 2022

Summary

We developed Hi-C-coupled multi-marker analysis of genomic annotation (H-MAGMA) to link genetic variants to genes using 3D genome structure. This method aids in understanding complex traits and brain disorders by identifying key biological pathways.

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Published on: August 21, 2016

Area of Science:

Genomics
Computational Biology
Genetics

Background:

Predicting functional outcomes of noncoding genetic variation in complex traits is a key challenge in genomics.
Traditional gene-based analysis tools like MAGMA assign variants to genes but do not incorporate 3D genome structure.

Purpose of the Study:

To develop and present a novel method, H-MAGMA (Hi-C-coupled multi-marker analysis of genomic annotation), for assigning variants to putative target genes using 3D chromatin conformation.
To demonstrate the utility of H-MAGMA in identifying biological pathways associated with complex brain disorders and complementing other functional genomic resources.

Main Methods:

Developed H-MAGMA by integrating Hi-C chromatin interaction data with traditional MAGMA gene-based variant annotation.
Generated H-MAGMA variant-gene annotation files using adult human brain Hi-C data.
Applied H-MAGMA to genome-wide association study (GWAS) summary statistics for Parkinson's disease.

Main Results:

Successfully identified key biological pathways implicated in various brain disorders.
Demonstrated H-MAGMA's complementary role to expression quantitative trait loci (eQTL) based variant annotation.
Generated comprehensive variant-gene annotation files for 28 human tissues and cell types.

Conclusions:

H-MAGMA provides a powerful approach to functionally annotate noncoding variants by leveraging 3D genome architecture.
The generated H-MAGMA annotation files serve as a valuable resource for researchers studying complex genetic disorders across diverse tissues.
H-MAGMA analysis is computationally efficient, requiring less than 2 hours for any cell type with available Hi-C data.