Evidence map›Paper›PMID 16048783›Full record

ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2005

Estimation of effective population sizes from data on genetic markers.

Jinliang Wang

Abstract readReview
In one paragraph

Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers.

0numbers the graph read from it
0cells of the map it votes in
124citing 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

124 citing papers in PubMed.

  1. Trial
  2. Article
  3. Effective population size and NBMC ecology and evolution · 2026
    Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Unraveling the Complexity of theEvolutionary applications · 2024
    Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

64 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

1 author.

Jinliang WangInstitute of Zoology, Zoological Society of London, Regent's Park, London NW1 4RY, UK. jinliang.wang@ioz.ac.uk

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The effective population size (Ne) is an important parameter in ecology, evolutionary biology and conservation biology. It is, however, notoriously difficult to estimate, mainly because of the highly stochastic nature of the processes of inbreeding and genetic drift for which Ne is usually defined and measured, and because of the many factors (such as time and spatial scales, systematic forces) confounding such processes. Many methods have been developed in the past three decades to estimate the current, past and ancient effective population sizes using different information extracted from some genetic markers in a sample of individuals. This paper reviews the methodologies proposed for estimating Ne from genetic data using information on heterozygosity excess, linkage disequilibrium, temporal changes in allele frequency, and pattern and amount of genetic variation within and between populations. For each methodology, I describe mainly the logic and genetic model on which it is based, the data required and information used, the interpretation of the estimate obtained, some results from applications to simulated or empirical datasets and future developments that are needed.

Indexed as

Evolution, MolecularGenetic VariationModels, GeneticPopulation DensityGene FrequencyGenetic Carrier ScreeningGenetic MarkersLinkage DisequilibriumGenetic Markers

Identifiers

PMID16048783
PMCPMC1847562

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.