ArticleAmerican journal of human genetics2022
A genealogical estimate of genetic relationships.
Article in American journal of human genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 46 citations in OpenAlex.
- Allelic association analyses: estimation recommendations.Genetics · 2026Article
- Observational epidemiological studies can mitigate genetic confounding with a genetic relatedness matrix.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Genetic prediction with ARG-powered linear algebra.Genetics · 2026Article
- Leveraging ancestral recombination graphs for scalable mixed-model analysis of complex traits.Cell genomics · 2026Article
- Spatial modelling improves genomic evaluation in Tanzanian smallholder admixed dairy cattle.Genetics, selection, evolution : GSE · 2026Article
- On ARGs, pedigrees, and genetic relatedness matrices.Genetics · 2026Article
- Clade distillation for genome-wide association studies.Genetics · 2026Article
- Likelihoods for a general class of ARGs under the SMC.Genetics · 2026Article
- Characterizing and mitigating confounding by unreplicated evolutionary events in phylogenetic regression.bioRxiv : the preprint server for biology · 2025Article
- A Pandemic-Scale Ancestral Recombination Graph for SARS-CoV-2.bioRxiv : the preprint server for biology · 2025Article
- Genealogical Analysis of Replicate Flower Colour Hybrid Zones in Antirrhinum.Molecular ecology · 2025Article
- Robust and accurate Bayesian inference of genome-wide genealogies for hundreds of genomes.Nature genetics · 2025Article
- Methodological opportunities in genomic data analysis to advance health equity.Nature reviews. Genetics · 2025Review
- Recent Statistical Innovations in Human Genetics.Annals of human genetics · 2025Review
- randPedPCA: rapid approximation of principal components from large pedigrees.Genetics, selection, evolution : GSE · 2025Article
- A genealogy-based approach for revealing ancestry-specific structures in admixed populations.American journal of human genetics · 2025Article
- Tsbrowse: an interactive browser for ancestral recombination graphs.Bioinformatics (Oxford, England) · 2025Article
- Accurate determination of breed origin of alleles in a simulated smallholder crossbred dairy cattle population.Genetics, selection, evolution : GSE · 2025Article
- Evaluating ARG-estimation methods in the context of estimating population-mean polygenic score histories.Genetics · 2025Article
- A likelihood-based framework for demographic inference from genealogical trees.Nature genetics · 2025Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The application of genetic relationships among individuals, characterized by a genetic relationship matrix (GRM), has far-reaching effects in human genetics. However, the current standard to calculate the GRM treats linked markers as independent and does not explicitly model the underlying genealogical history of the study sample. Here, we propose a coalescent-informed framework, namely the expected GRM (eGRM), to infer the expected relatedness between pairs of individuals given an ancestral recombination graph (ARG) of the sample. Through extensive simulations, we show that the eGRM is an unbiased estimate of latent pairwise genome-wide relatedness and is robust when computed with ARG inferred from incomplete genetic data. As a result, the eGRM better captures the structure of a population than the canonical GRM, even when using the same genetic information. More importantly, our framework allows a principled approach to estimate the eGRM at different time depths of the ARG, thereby revealing the time-varying nature of population structure in a sample. When applied to SNP array genotypes from a population sample from Northern and Eastern Finland, we find that clustering analysis with the eGRM reveals population structure driven by subpopulations that would not be apparent via the canonical GRM and that temporally the population model is consistent with recent divergence and expansion. Taken together, our proposed eGRM provides a robust tree-centric estimate of relatedness with wide application to genetic studies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.