ArticleCell genomics2026
Leveraging ancestral recombination graphs for scalable mixed-model analysis of complex traits.
Article in Cell genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Genetic prediction with ARG-powered linear algebra.Genetics · 2026Article
- Genealogy-based trait association with LOCATER boosts power at loci with allelic heterogeneity.Genome research · 2026Article
- Highly efficient genotype compression leveraging genealogical relatedness.bioRxiv : the preprint server for biology · 2026Article
- TS-IBD: An efficient ancestral recombination graph-based identity by descent segment detection method.Human population genetics and genomics · 2026Article
- Comprehensive gene heritability estimation reveals the genetic architecture of rare coding variants underlying complex traits.bioRxiv : the preprint server for biology · 2025Article
- On ARGs, pedigrees, and genetic relatedness matrices.bioRxiv : the preprint server for biology · 2025Article
- Evaluating ARG-estimation methods in the context of estimating population-mean polygenic score histories.bioRxiv : the preprint server for biology · 2024Article
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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.
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