ArticleAmerican journal of human genetics2023
Tree-based QTL mapping with expected local genetic relatedness matrices.
Article in American journal of human genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 27 citations in OpenAlex.
- ALGI: Sparse Convolutional Denoising Autoencoder Utilizing Local Genomic Information for Genotype Imputation.Animals : an open access journal from MDPI · 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
- Genealogy-based trait association with LOCATER boosts power at loci with allelic heterogeneity.Genome research · 2026Article
- Leveraging ancestral recombination graphs for scalable mixed-model analysis of complex traits.Cell genomics · 2026Article
- Clade distillation for genome-wide association studies.Genetics · 2026Article
- On ARGs, pedigrees, and genetic relatedness matrices.Genetics · 2026Article
- Robust and accurate Bayesian inference of genome-wide genealogies for hundreds of genomes.Nature genetics · 2025Article
- Tsbrowse: an interactive browser for ancestral recombination graphs.Bioinformatics (Oxford, England) · 2025Article
- Mathematical bounds on rTheoretical population biology · 2025Article
- Evaluating ARG-estimation methods in the context of estimating population-mean polygenic score histories.Genetics · 2025Article
- On ARGs, pedigrees, and genetic relatedness matrices.bioRxiv : the preprint server for biology · 2025Article
- Constructing ancestral recombination graphs through reinforcement learning.Frontiers in genetics · 2025Article
- Inference and applications of ancestral recombination graphs.Nature reviews. Genetics · 2025Review
- Evaluating ARG-estimation methods in the context of estimating population-mean polygenic score histories.bioRxiv : the preprint server for biology · 2024Article
- Mathematical bounds onbioRxiv : the preprint server for biology · 2024Article
- Tree Sequences as a General-Purpose Tool for Population Genetic Inference.Molecular biology and evolution · 2024Article
- Tree sequences as a general-purpose tool for population genetic inference.bioRxiv : the preprint server for biology · 2024Article
- Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations.PLoS biology · 2024Article
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7 authors at 1 institution in 1 country.
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Abstract
Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide association studies (GWASs) are a powerful way to find genetic loci associated with phenotypes. GWASs are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix (local eGRM) given the ARG. Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to analyze two chromosomes containing known body size loci in a sample of Native Hawaiians. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.
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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.