Evidence map›Paper›PMID 26290347›Full record

ArticleMolecular ecology2016

Detecting recent selective sweeps while controlling for mutation rate and background selection.

Christian D Huber, Michael DeGiorgio, Ines Hellmann, Rasmus Nielsen

Abstract read
In one paragraph

Article in Molecular ecology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
83citing papers in PubMed, 1 pooled it
–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

83 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. ThebioRxiv : the preprint server for biology · 2026
    Article
  5. Selection scans and downstream analysis with selscan.Human population genetics and genomics · 2026
    Article
  6. Selection scans and downstream analysis with selscan.bioRxiv : the preprint server for biology · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Repeated global adaptation across plant species.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  16. Digital Image Processing to Detect Adaptive Evolution.Molecular biology and evolution · 2024
    Article
  17. Computational Genomics and Its Applications to Anthropological Questions.American journal of biological anthropology · 2024
    Review
  18. Article
  19. Article
  20. Article

23 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.

Christian D HuberMax F. Perutz Laboratory, University of Vienna, Vienna, Austria.
Michael DeGiorgioDepartments of Biology and Statistics, Pennsylvania State University, University Park, PA, USA.
Ines HellmannDepartment Biologie II, Ludwig-Maximilians-Universität München, Großhaderner Str. 2, 82152, Planegg-Martinsried, Germany.
Rasmus NielsenDepartments of Integrative Biology and Statistics, University of California, Berkeley, CA, USA.

Funding

Austrian Science Fund FWF W 1225
6 · The paper itself

Abstract

A composite likelihood ratio test implemented in the program sweepfinder is a commonly used method for scanning a genome for recent selective sweeps. sweepfinder uses information on the spatial pattern (along the chromosome) of the site frequency spectrum around the selected locus. To avoid confounding effects of background selection and variation in the mutation process along the genome, the method is typically applied only to sites that are variable within species. However, the power to detect and localize selective sweeps can be greatly improved if invariable sites are also included in the analysis. In the spirit of a Hudson-Kreitman-Aguadé test, we suggest adding fixed differences relative to an out-group to account for variation in mutation rate, thereby facilitating more robust and powerful analyses. We also develop a method for including background selection, modelled as a local reduction in the effective population size. Using simulations, we show that these advances lead to a gain in power while maintaining robustness to mutation rate variation. Furthermore, the new method also provides more precise localization of the causative mutation than methods using the spatial pattern of segregating sites alone.

Indexed as

Genetics, PopulationModels, GeneticMutation RateSelection, GeneticGene FrequencyHumansbackground selectionHudson-Kreitman-Aguadé testpopulation bottleneckssweep detectionsweepfinder

Identifiers

PMID26290347
PMCPMC5082542

What OpenQuestion holds

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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.