Evidence map›Paper›PMID 24204310›Full record

ArticlePLoS genetics2013

Robust demographic inference from genomic and SNP data.

Laurent Excoffier, Isabelle Dupanloup, Emilia Huerta-Sánchez, Vitor C Sousa, Matthieu Foll

Abstract read
In one paragraph

Article in PLoS genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 617 papers.

0numbers the graph read from it
0cells of the map it votes in
617citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

617 citing papers in PubMed.

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  18. Pervasive cryptic selection in the human noncoding genome.bioRxiv : the preprint server for biology · 2026
    Article
  19. Article
  20. Methods to estimate marine functional connectivity: A primer.Ecological applications : a publication of the Ecological Society of America · 2026
    Review

557 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

5 authors.

Laurent ExcoffierCMPG, Institute of Ecology and Evolution, Berne, Switzerland ; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
Isabelle Dupanloup
Emilia Huerta-Sánchez
Vitor C Sousa
Matthieu Foll

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets. We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods. For simple scenarios, our approach compares favorably in terms of accuracy and speed with ∂a∂i, the current reference in the field, while showing better convergence properties for complex models. We first apply our methodology to non-coding genomic SNP data from four human populations. To infer their demographic history, we compare neutral evolutionary models of increasing complexity, including unsampled populations. We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations, our framework can correctly infer parameters of more complex models including the divergence of several populations, bottlenecks and migration. We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models. The two SNP panels lead to globally very similar estimates and confidence intervals, and suggest an ancient divergence (>110 Ky) between Yoruba and San populations. Our methodology appears well suited to the study of complex scenarios from large genomic data sets.

Indexed as

Computer SimulationDemographyGenetics, PopulationGenome, HumanGenomicsHumansPolymorphism, Single NucleotidePopulation Groups

Identifiers

PMID24204310
PMCPMC3812088

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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.