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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
Published on: May 6, 2010
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Hi-C informed kernel association test: integrating 3-dimensional genome structure into variant-set association for
Yueyang Huang1, Riddhik Basu2, Wenbin Lu2
1Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.
Biorxiv : the Preprint Server for Biology
|November 24, 2025
Summary
This study introduces a novel Hi-C informed kernel association test for whole genome sequence data. This method enhances the detection of rare variant sets by integrating 3D genome architecture into genetic association testing.
Area of Science:
- Genomics
- Bioinformatics
- Statistical Genetics
Background:
- Variant-set association analysis is crucial for whole genome sequence (WGS) data, particularly for rare variants.
- Three-dimensional (3D) genome architecture significantly influences gene transcription and regulatory processes.
- Existing methods primarily focus on gene-centric association tests, limiting whole-genome applications.
Purpose of the Study:
- To extend 3D genome-guided association testing from gene-centric to gene-agnostic, whole-genome analysis.
- To develop a novel Hi-C informed kernel association test for improved rare variant detection.
- To integrate 3D genome architecture into genetic similarity kernels for enhanced statistical power.
Main Methods:
- Developed a principled procedure to convert Hi-C contact confidence into borrowing weights.
- Integrated these weights into genetic similarity kernels for association testing.
- Introduced a controlling parameter for adaptive information borrowing from interacting loci.
Main Results:
- The Hi-C informed kernel association test demonstrated improved performance in simulations.
- The method showed advantages in detecting rare-variant sets using WGS data.
- Successfully applied the test to the ARIC study data from the Trans-Omics for Precision Medicine (TOPMed) program.
Conclusions:
- The Hi-C informed kernel association test offers a powerful gene-agnostic approach for WGS data analysis.
- Integrating 3D genome architecture enhances the detection of rare variant sets.
- This method holds promise for advancing genetic studies and precision medicine.
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