Evidence map›Paper›PMID 41079782›Full record

ArticleJournal of the American Statistical Association2024

An efficient coalescent model for heterochronously sampled molecular data.

Lorenzo Cappello, Amandine Véber, Julia A Palacios

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Efficient Bayesian Phylogenetics under the Infinite Sites Model.bioRxiv : the preprint server for biology · 2025
    Article
  2. Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022
    Article
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.

Lorenzo CappelloDepartment of Economics and Business, Universitat Pompeu Fabra.
Amandine VéberUniversité Paris Cité, CNRS, MAP5, F-75006 Paris.
Julia A PalaciosDepartment of Statistics, Stanford University.

Funding

Novel Coalescent Approaches for Studying Evolutionary ProcessesR35GM148338 · NIGMS · STANFORD UNIVERSITY · PI Julia Palacios · 2023 to 2026
$1.5M
Scalable Coalescent Inference for Large Data SetsR01GM131404 · NIGMS · STANFORD UNIVERSITY · PI PALACIOS, JULIA · 2018 to 2021
$1.2M
NIGMS NIH HHS R01 GM131404NIGMS NIH HHS R35 GM148338
6 · The paper itself

Abstract

Molecular sequence variation at a locus informs about the evolutionary history of the sample and past population size dynamics. The Kingman coalescent is used in a generative model of molecular sequence variation to infer evolutionary parameters. However, it is well understood that inference under this model does not scale well with sample size. Here, we build on recent work based on a lower resolution coalescent process, the Tajima coalescent, to model longitudinal samples. While the Kingman coalescent models the ancestry of labeled individuals, we model the ancestry of individuals labeled by their sampling time. We propose a new inference scheme for the reconstruction of effective population size trajectories based on this model and the infinite-sites mutation model. Modeling of longitudinal samples is necessary for applications (

Indexed as

ancient DNABayesian nonparametricEffective population sizeGaussian processKingman n-coalescentMarkov lumping

Identifiers

PMID41079782
PMCPMC12509649

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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.