Evidence map›Paper›PMID 38236805›Full record

ReviewPLoS genetics2024

The era of the ARG: An introduction to ancestral recombination graphs and their significance in empirical evolutionary genomics.

Alexander L Lewanski, Michael C Grundler, Gideon S Bradburd

Abstract readReview
In one paragraph

Review in PLoS genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.

0numbers the graph read from it
0cells of the map it votes in
70citing 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

70 citing papers in PubMed.

  1. Article
  2. Genetics and genomics of hybridization.Nature reviews. Genetics · 2026
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10 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Alexander L LewanskiDepartment of Integrative Biology, Michigan State University, East Lansing, Michigan, United States of America.ORCID 0000-0001-5843-0837
Michael C GrundlerDepartment of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America.ORCID 0000-0003-0729-6687
Gideon S BradburdW.K. Kellogg Biological Station, Michigan State University, Hickory Corners, Michigan, United States of America.ORCID 0000-0001-8009-0154

Funding

Incorporating geography into statistical methods for analysis of population genomic DNAR35GM137919 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Gideon Bradburd · 2020 to 2026
$2.3M
NIGMS NIH HHS R35 GM137919
6 · The paper itself

Abstract

In the presence of recombination, the evolutionary relationships between a set of sampled genomes cannot be described by a single genealogical tree. Instead, the genomes are related by a complex, interwoven collection of genealogies formalized in a structure called an ancestral recombination graph (ARG). An ARG extensively encodes the ancestry of the genome(s) and thus is replete with valuable information for addressing diverse questions in evolutionary biology. Despite its potential utility, technological and methodological limitations, along with a lack of approachable literature, have severely restricted awareness and application of ARGs in evolution research. Excitingly, recent progress in ARG reconstruction and simulation have made ARG-based approaches feasible for many questions and systems. In this review, we provide an accessible introduction and exploration of ARGs, survey recent methodological breakthroughs, and describe the potential for ARGs to further existing goals and open avenues of inquiry that were previously inaccessible in evolutionary genomics. Through this discussion, we aim to more widely disseminate the promise of ARGs in evolutionary genomics and encourage the broader development and adoption of ARG-based inference.

Indexed as

AlgorithmsRecombination, GeneticEvolution, MolecularGenomeGenomicsModels, GeneticPhylogeny

Identifiers

PMID38236805
PMCPMC10796009

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Registered trials

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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.