Hi-C informed kernel association test: integrating 3-dimensional genome structure into variant-set association for whole-genome sequencing data

Basu, R.; Huang, Y.; Tzeng, J. Y.; Lu, W.; Holloway, S.; Li, Y.

doi:10.1101/2025.10.28.684891

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bioRxiv · 10.1101/2025.10.28.684891

Hi-C informed kernel association test: integrating 3-dimensional genome structure into variant-set association for whole-genome sequencing data

Basu, R.·Huang, Y.·Tzeng, J. Y.·Lu, W.·Holloway, S.·Li, Y.

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Abstract

Variant-set association analysis is a powerful strategy for genetic studies of whole genome sequence (WGS) data, especially for rare variants. By aggregating variant signals, variant-set analysis can improve statistical power, result interpretability, and study replicability. Motivated by evidence that three-dimensional (3D) genome architecture plays a critical role in regulating gene transcription, several works have incorporated 3D genome architecture into gene-based association tests and demonstrated great promise. In this work, we extend the idea of 3D-genome guided test from gene-centric to gene-agnostic, whole-genome testing by introducing a Hi-C informed kernel association test. We present a principled procedure that converts Hi-C contact confidence into borrowing weights and integrates these weights into genetic similarity kernels so that higher-confidence interacting loci contribute more to the association test of the target variant set. We use a controlling parameter to adaptively determine the appropriate degree of information borrowing from its interacting loci during association testing. We assess the performance of the Hi-C informed test using simulations and illustrate its advantage in detecting rare-variant sets using WGS data from the ARIC study in the Trans-Omics for Precision Medicine (TOPMed) program.

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Basu, R., Huang, Y., Tzeng, J. Y., Lu, W., Holloway, S., Li, Y.. 2025-10-30. Hi-C informed kernel association test: integrating 3-dimensional genome structure into variant-set association for whole-genome sequencing data. https://doi.org/10.1101/2025.10.28.684891

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