Hi-C informed kernel association test for integrating 3D genome structure into variant-set analysis

Area of Science:

• Genetics
• Genomics
• Bioinformatics

Background:

• Variant-set association analysis improves statistical power for genetic studies using whole-genome sequence (WGS) data, particularly for rare variants.
• 3D genome architecture is crucial for gene transcription regulation, prompting integration into genetic association tests.
• Existing methods primarily focus on gene-centric association tests, leaving potential for genome-wide approaches.

Purpose of the Study:

• To extend 3D genome-guided association testing from gene-centric to gene-agnostic, whole-genome analysis.
• To introduce the Hi-C informed kernel association test (HiC-KAT) for improved rare variant detection.
• To develop a principled method for integrating 3D genome architecture into association testing.

Main Methods:

• Developed HiC-KAT, a kernel association test informed by Hi-C contact data.
• Converted Hi-C contact confidence into borrowing weights for genetic similarity kernels.
• Integrated adaptive borrowing weights to prioritize interacting loci in association testing.

Main Results:

• Simulations demonstrated the effectiveness of HiC-KAT in detecting rare variant sets.
• HiC-KAT showed advantages in analyzing whole-genome sequence data.
• The method successfully applied to WGS data from the ARIC study within the Trans-Omics for Precision Medicine program.

Conclusions:

• HiC-KAT provides a novel gene-agnostic, whole-genome approach for variant-set association analysis.
• The method effectively leverages 3D genome architecture to enhance the detection of rare variants.
• HiC-KAT offers a promising tool for genetic studies utilizing WGS data.