ArticleAmerican journal of human genetics2006
Mapping trait loci by use of inferred ancestral recombination graphs.
Article in American journal of human genetics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
77 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genotypic variants at 2q33 and risk of esophageal squamous cell carcinoma in China: a meta-analysis of genome-wide association studies.Human molecular genetics · 2012Pooled it
- Genealogy-based trait association with LOCATER boosts power at loci with allelic heterogeneity.Genome research · 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
- Polarising SNPs Without Outgroup.Molecular ecology resources · 2026Article
- Clade distillation for genome-wide association studies.Genetics · 2026Article
- Article
- Human ancestries simulation and inference: a review of ancestral recombination graph-based approaches.Frontiers in bioinformatics · 2026Review
- Moonshine.jl: a Julia package for genome-scale model-based ancestral recombination graph inference.Frontiers in genetics · 2026Article
- A Pandemic-Scale Ancestral Recombination Graph for SARS-CoV-2.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
- Article
- iGWAS: Image-based genome-wide association of self-supervised deep phenotyping of retina fundus images.PLoS genetics · 2024Article
- A general and efficient representation of ancestral recombination graphs.bioRxiv : the preprint server for biology · 2024Article
- The Promise of Inferring the Past Using the Ancestral Recombination Graph.Genome biology and evolution · 2024Article
- Tree-based QTL mapping with expected local genetic relatedness matrices.American journal of human genetics · 2023Article
- Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies.Nature genetics · 2023Article
- Ultrafast genome-wide inference of pairwise coalescence times.Genome research · 2023Article
- Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits.Nature genetics · 2023Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Large-scale association studies are being undertaken with the hope of uncovering the genetic determinants of complex disease. We describe a computationally efficient method for inferring genealogies from population genotype data and show how these genealogies can be used to fine map disease loci and interpret association signals. These genealogies take the form of the ancestral recombination graph (ARG). The ARG defines a genealogical tree for each locus, and, as one moves along the chromosome, the topologies of consecutive trees shift according to the impact of historical recombination events. There are two stages to our analysis. First, we infer plausible ARGs, using a heuristic algorithm, which can handle unphased and missing data and is fast enough to be applied to large-scale studies. Second, we test the genealogical tree at each locus for a clustering of the disease cases beneath a branch, suggesting that a causative mutation occurred on that branch. Since the true ARG is unknown, we average this analysis over an ensemble of inferred ARGs. We have characterized the performance of our method across a wide range of simulated disease models. Compared with simpler tests, our method gives increased accuracy in positioning untyped causative loci and can also be used to estimate the frequencies of untyped causative alleles. We have applied our method to Ueda et al.'s association study of CTLA4 and Graves disease, showing how it can be used to dissect the association signal, giving potentially interesting results of allelic heterogeneity and interaction. Similar approaches analyzing an ensemble of ARGs inferred using our method may be applicable to many other problems of inference from population genotype data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.