Evidence map›Paper›PMID 40113903›Full record

ArticleNature genetics2025

A likelihood-based framework for demographic inference from genealogical trees.

Caoqi Fan, Jordan L Cahoon, Bryan L Dinh, Diego Ortega-Del Vecchyo, Christian D Huber, Michael D Edge, Nicholas Mancuso, Charleston W K Chiang

Erratum issuedAbstract read
In one paragraph

Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Physics-Inspired Single-Particle Tracking Accelerated with Parallelism.bioRxiv : the preprint server for biology · 2025
    Article
  11. Article
  12. Likelihoods for a general class of ARGs under the SMC.bioRxiv : the preprint server for biology · 2025
    Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. A general and efficient representation of ancestral recombination graphs.bioRxiv : the preprint server for biology · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Caoqi FanCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. fcq1116@gmail.com.ORCID http://orcid.org/0009-0003-4548-1637
Jordan L CahoonDepartment of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
Bryan L DinhCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Diego Ortega-Del VecchyoLaboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Querétaro, México.ORCID http://orcid.org/0000-0003-4054-3766
Christian D HuberDepartment of Biology, Penn State University, University Park, PA, USA.ORCID http://orcid.org/0000-0002-2267-2604
Michael D EdgeDepartment of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0001-8773-2906
Nicholas MancusoCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Charleston W K ChiangCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. charleston.chiang@med.usc.edu.ORCID http://orcid.org/0000-0002-0668-7865

Funding

Statistical Methods for Integrative Genomics in CancerP01CA196569 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI David V Conti · 2016 to 2026
$25.5M
Leveraging the Evolutionary History to Improve Identification of Trait-Associated Alleles and Risk Stratification Models in Native HawaiiansR01HG011646 · NHGRI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Charleston Chiang · 2022 to 2026
$4.0M
Characterizing the evolutionary architecture of complex disease within and across diverse populationsR01HG012133 · NHGRI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI MANCUSO, NICHOLAS · 2021 to 2025
$3.6M
Traits on trees: Population genomics for understanding complex phenotypesR35GM137758 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Michael Donald Edge · 2020 to 2026
$2.5M
An evolutionary framework to elucidate and interpret the genetic architecture of complex traits in diverse populations - diversity supplementR35GM142783 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI CHIANG, CHARLESTON · 2021 to 2025
$2.2M
A genome-wide genealogical framework for statistical and population genetic analysisR01HG012605 · NHGRI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Charleston Chiang · 2023 to 2026
$2.1M
NCI NIH HHS P01 CA196569NHGRI NIH HHS R01 HG011646NHGRI NIH HHS R01 HG012133NHGRI NIH HHS R01 HG012605NIGMS NIH HHS R35 GM137758NIGMS NIH HHS R35 GM142783U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) R01HG012133U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) R01HG12605U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM137758U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM142783U.S. Department of Health & Human Services | NIH | NCI | Division of Cancer Epidemiology and Genetics, National Cancer Institute (National Cancer Institute Division of Cancer Epidemiology and Genetics) P01CA196569
6 · The paper itself

Abstract

The demographic history of a population underlies patterns of genetic variation and is encoded in the gene-genealogical trees of the sampled haplotypes. Here we propose a demographic inference framework called the genealogical likelihood (gLike). Our method uses a graph-based structure to summarize the relationships among all lineages in a gene-genealogical tree with all possible trajectories of population memberships through time and derives the full likelihood across trees under a parameterized demographic model. We show through simulations and empirical applications that for populations that have experienced multiple admixtures, gLike can accurately estimate dozens of demographic parameters, including ancestral population sizes, admixture timing and admixture proportions, and it outperforms conventional demographic inference methods using the site frequency spectrum. Taken together, our proposed gLike framework harnesses underused genealogical information to offer high sensitivity and accuracy in inferring complex demographies for humans and other species.

Indexed as

DemographyGenetics, PopulationModels, GeneticAlgorithmsComputer SimulationGenealogy and HeraldryGenetic VariationHaplotypesHumansLikelihood FunctionsPedigree

Identifiers

PMID40113903
PMCPMC12283123

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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.