Evidence map›Paper›PMID 39264367›Full record

ArticleeLife2024

Improved inference of population histories by integrating genomic and epigenomic data.

Thibaut Sellinger, Frank Johannes, Aurélien Tellier

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Coalescence and translation: A language model for population genetics.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  4. Review
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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

3 authors.

Thibaut SellingerProfessorship for Population Genetics, Department of Life Science Systems, Technical University of Munich, Munich, Germany.
Frank JohannesProfessorship for Plant Epigenomics, Department of Molecular Life Sciences, Technical University of Munich, Freising, Germany.ORCID https://orcid.org/0000-0002-7962-2907
Aurélien TellierProfessorship for Population Genetics, Department of Life Science Systems, Technical University of Munich, Munich, Germany.ORCID https://orcid.org/0000-0002-8895-0785

Funding

Austrian Science Fund project no. TAI 151-BDeutsche Forschungsgemeinschaft 317616126 (TE809/7-1)
6 · The paper itself

Abstract

With the availability of high-quality full genome polymorphism (SNPs) data, it becomes feasible to study the past demographic and selective history of populations in exquisite detail. However, such inferences still suffer from a lack of statistical resolution for recent, for example bottlenecks, events, and/or for populations with small nucleotide diversity. Additional heritable (epi)genetic markers, such as indels, transposable elements, microsatellites, or cytosine methylation, may provide further, yet untapped, information on the recent past population history. We extend the Sequential Markovian Coalescent (SMC) framework to jointly use SNPs and other hyper-mutable markers. We are able to (1) improve the accuracy of demographic inference in recent times, (2) uncover past demographic events hidden to SNP-based inference methods, and (3) infer the hyper-mutable marker mutation rates under a finite site model. As a proof of principle, we focus on demographic inference in

Indexed as

ArabidopsisDNA MethylationEpigenomicsGenetics, PopulationGenomicsPolymorphism, Single NucleotideA. thalianacoalescent theoryepigenomicsevolutionary biologyevolutionary modellingstatistical inference

Identifiers

PMID39264367
PMCPMC11392530

What OpenQuestion holds

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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.