2025/10/30 by Yueyang Huang, Riddhik Basu, Wenbin Lu +3 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Genetic Associations and Epidemiology #Genomics and Rare Diseases #Genomic variations and chromosomal abnormalities
paper · pdf · doi:10.1101/2025.10.28.684891
openalex created_date 2025/10/30 · openalex publication_date 2025/10/30 · openalex updated_date 2026/08/01
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.