Leveraging Ancestral Recombination Graphs for Scalable Mixed-Model Analysis of Complex Traits

Authors

Jiazheng Zhu
Georgios Kalantzis
Ali Pazokitoroudi
Árni Freyr Gunnarsson
Hrushikesh Loya
Han Chen
Sriram Sankararaman
Pier Francesco Palamara

Language

English

Publication Date

2-11-2026

Journal

Cell Genomics

DOI

10.1016/j.xgen.2025.101072

PMID

41380687

PMCID

PMC12903391

PubMedCentral® Posted Date

12-10-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Recent algorithmic advances have enabled the inference of genome-wide ancestral recombination graphs (ARGs) from large genomic cohorts, providing detailed models of genealogical relatedness along the genome. These inferred ARGs can complement genotype imputation by capturing the effects of unobserved variants, but their use in large-scale linear mixed-model analyses has been computationally prohibitive. Here, we develop methods that leverage the ARG to perform genotype-matrix multiplications in sublinear time and implement scalable randomized algorithms for mixed-model analyses. We introduce ARG-RHE, a randomized Haseman-Elston approach for estimating narrow-sense heritability and performing region-based association testing using ARGs, enabling parallel analysis of multiple quantitative traits. Through extensive simulations, we demonstrate the computational efficiency and statistical power of this approach. Applied to 21,159 genes and 52 blood traits in 337,464 UK Biobank participants, ARG-RHE identifies 8% more gene-trait associations than imputation alone, demonstrating that genome-wide genealogies may be leveraged to complement genotype imputation in complex trait analyses.

Keywords

Humans, Algorithms, Models, Genetic, Genome-Wide Association Study, Recombination, Genetic, Genotype, Quantitative Trait Loci, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Computer Simulation, ancestral recombination graph, complex traits, heritability, variance components, association, GWAS

Published Open-Access

yes

Recommended Citation

Zhu, Jiazheng; Kalantzis, Georgios; Pazokitoroudi, Ali; et al., "Leveraging Ancestral Recombination Graphs for Scalable Mixed-Model Analysis of Complex Traits" (2026). Faculty, Staff and Student Publications. 5470.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5470