Evidence map›Paper›PMID 42724483›Full record

ReviewFrontiers in bioinformatics2026

Human ancestries simulation and inference: a review of ancestral recombination graph-based approaches.

Patrick Fournier, Fabrice Larribe

Abstract readReview
In one paragraph

Review in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Patrick FournierDépartement de Mathématiques, Université du Québec à Montréal, Montréal, QC, Canada.
Fabrice LarribeDépartement de Mathématiques, Université du Québec à Montréal, Montréal, QC, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The importance of the ancestral recombination graph (ARG) in population genetics is undeniable. An important theoretical tool, the main obstacle to its widespread usage is the computational cost required to match the ever-increasing scale of the data being analyzed. Many of these difficulties have been overcome in the past 2 decades, which have consequently seen the development of increasingly sophisticated ARG simulation and inference software. Nonetheless, challenges remain, especially in the area of ancestry inference. This study is a comprehensive review of ARG simulation and inference programs that have emerged in the past 3 decades to meet the need for scalable and flexible ancestry simulation and inference solutions. It specifically focuses on their performance, usability, and the biological realism of the underlying algorithm and primarily aims to provide a technical overview of the field for researchers seeking to design and implement their own coalescent-with-recombination algorithm. As a complement to this article, we have compiled the links to software, source code, and documentation and made them available at https://patrickfournier.ca/publications/arg-software-review/graph.

Indexed as

ancestral recombination graphancestral recombination graph inferenceancestral recombination graph simulationcoalescent theorysoftware review

Identifiers

PMID42724483
PMCPMC13559533

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.