Evidence map›Paper›PMID 31387977›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2019

Inference of complex population histories using whole-genome sequences from multiple populations.

Matthias Steinrücken, Jack Kamm, Jeffrey P Spence, Yun S Song

Abstract readHistorical Article
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing 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

45 citing papers in PubMed.

  1. Review
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  7. Computational Genomics and Its Applications to Anthropological Questions.American journal of biological anthropology · 2024
    Review
  8. Review
  9. Exact Decoding of a Sequentially Markov Coalescent Model in Genetics.Journal of the American Statistical Association · 2024
    Article
  10. Article
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  14. Scaling the Discrete-time Wright Fisher model to biobank-scale datasets.bioRxiv : the preprint server for biology · 2023
    Article
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  16. 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

4 authors.

Matthias SteinrückenDepartment of Ecology and Evolution, University of Chicago, Chicago, IL 60637.
Jack KammDepartment of Statistics, University of California, Berkeley, CA 94720.
Jeffrey P SpenceComputational Biology Graduate Group, University of California, Berkeley, CA 94720.
Yun S SongDepartment of Statistics, University of California, Berkeley, CA 94720; yss@berkeley.edu.ORCID 0000-0002-0734-9868

Funding

Mathematical Models and Statistical Methods for Large-Scale Population GenomicsR01GM094402 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI SONG, YUN S · 2010 to 2018
$2.2M
NIGMS NIH HHS R01 GM094402
6 · The paper itself

Abstract

There has been much interest in analyzing genome-scale DNA sequence data to infer population histories, but inference methods developed hitherto are limited in model complexity and computational scalability. Here we present an efficient, flexible statistical method, diCal2, that can use whole-genome sequence data from multiple populations to infer complex demographic models involving population size changes, population splits, admixture, and migration. Applying our method to data from Australian, East Asian, European, and Papuan populations, we find that the population ancestral to Australians and Papuans started separating from East Asians and Europeans about 100,000 y ago, and that the separation of East Asians and Europeans started about 50,000 y ago, with pervasive gene flow between all pairs of populations.

Indexed as

Gene FlowGenome-Wide Association StudyHuman MigrationModels, GeneticWhole Genome SequencingAustraliaGenetics, PopulationHistory, AncientHumanscoalescentdemographypopulation geneticsstatistical inference

Identifiers

PMID31387977
PMCPMC6708337

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.