Evidence map›Paper›PMID 20067940›Full record

ReviewGenome research2010

Population genetic inference from genomic sequence variation.

John E Pool, Ines Hellmann, Jeffrey D Jensen, Rasmus Nielsen

Abstract readReview
In one paragraph

Review in Genome research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers.

0numbers the graph read from it
0cells of the map it votes in
99citing 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

99 citing papers in PubMed.

  1. Article
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  14. Nonparametric coalescent inference of mutation spectrum history and demography.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  15. Article
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  18. Review
  19. Article
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39 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

4 authors.

John E PoolDepartment of Integrative Biology, University of California, Berkeley, Berkeley, California 94720, USA.
Ines Hellmann
Jeffrey D Jensen
Rasmus Nielsen

Funding

Population Genetic Inferences From Dense Genotype DataR01HG003229 · NHGRI · CORNELL UNIVERSITY ITHACA · PI BUSTAMANTE, CARLOS DANIEL, CLARK, ANDREW G · 2004 to 2013
$4.8M
Dimension Reduction Approaches for Genome-wide Association TestingU01HL084706 · NHLBI · CORNELL UNIVERSITY · PI CLARK, ANDREW G · 2006 to 2008
$921k
The use of recombining genetic markers for demographic inferenceF32HG004182 · NHGRI · UNIVERSITY OF COPENHAGEN · PI POOL, JOHN E · 2006 to 2009
$139k
NHGRI NIH HHS F32 HG004182NHGRI NIH HHS R01 HG003229NHLBI NIH HHS U01 HL084706NHLBI NIH HHS UO1HL084706
6 · The paper itself

Abstract

Population genetics has evolved from a theory-driven field with little empirical data into a data-driven discipline in which genome-scale data sets test the limits of available models and computational analysis methods. In humans and a few model organisms, analyses of whole-genome sequence polymorphism data are currently under way. And in light of the falling costs of next-generation sequencing technologies, such studies will soon become common in many other organisms as well. Here, we assess the challenges to analyzing whole-genome sequence polymorphism data, and we discuss the potential of these data to yield new insights concerning population history and the genomic prevalence of natural selection.

Indexed as

Genetics, PopulationGenetic VariationPolymorphism, GeneticSelection, GeneticBase SequenceChromosome MappingGenesGenomeHumansPopulation Groups

Identifiers

PMID20067940
PMCPMC2840988

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.