Evidence map›Paper›PMID 28024154›Full record

ArticleNature genetics2017

Robust and scalable inference of population history from hundreds of unphased whole genomes.

Jonathan Terhorst, John A Kamm, Yun S Song

Abstract read
In one paragraph

Article in Nature genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 503 papers.

0numbers the graph read from it
0cells of the map it votes in
503citing 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

503 citing papers in PubMed.

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  15. High-coverage ancient genomes reveal divergent population histories and prehistoric starch-related genetic variation in Japan.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  16. Article
  17. Fixation Probabilities of Mutant Alleles in an Ecological Context.bioRxiv : the preprint server for biology · 2026
    Article
  18. Article
  19. Population Structure and Local Adaptation ofAnimals : an open access journal from MDPI · 2026
    Article
  20. Article

443 more citing papers are in PubMed but not listed here.

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.

Jonathan TerhorstDepartment of Statistics, University of California, Berkeley, Berkeley, California, USA.
John A KammDepartment of Statistics, University of California, Berkeley, Berkeley, California, USA.
Yun S SongDepartment of Statistics, University of California, Berkeley, Berkeley, California, USA.

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
Population Genetic Consequences of Recent Explosive Population Growth in HumansR01GM108805 · NIGMS · CORNELL UNIVERSITY · PI CLARK, ANDREW G, KEINAN, ALON · 2014 to 2017
$2.2M
NIGMS NIH HHS R01 GM094402NIGMS NIH HHS R01 GM108805
6 · The paper itself

Abstract

It has recently been demonstrated that inference methods based on genealogical processes with recombination can uncover past population history in unprecedented detail. However, these methods scale poorly with sample size, limiting resolution in the recent past, and they require phased genomes, which contain switch errors that can catastrophically distort the inferred history. Here we present SMC++, a new statistical tool capable of analyzing orders of magnitude more samples than existing methods while requiring only unphased genomes (its results are independent of phasing). SMC++ can jointly infer population size histories and split times in diverged populations, and it employs a novel spline regularization scheme that greatly reduces estimation error. We apply SMC++ to analyze sequence data from over a thousand human genomes in Africa and Eurasia, hundreds of genomes from a Drosophila melanogaster population in Africa, and tens of genomes from zebra finch and long-tailed finch populations in Australia.

Indexed as

AfricaAnimalsAustraliaComputer SimulationDrosophila melanogasterEquidaeGenetics, PopulationGenomeHumansModels, GeneticPopulation Density

Identifiers

PMID28024154
PMCPMC5470542

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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.