Evidence map›Paper›PMID 21041556›Full record

ArticleGenetics2011

Distinguishing positive selection from neutral evolution: boosting the performance of summary statistics.

Kao Lin, Haipeng Li, Christian Schlötterer, Andreas Futschik

Abstract read
In one paragraph

Article in Genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing 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

66 citing papers in PubMed.

  1. Genomic evidence for shared common ancestry of East African hunting-gathering populations and insights into local adaptation.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Trial
  2. Review
  3. Article
  4. Article
  5. Neural posterior estimation for population genetics.bioRxiv : the preprint server for biology · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Accessible, realistic genome simulation with selection using stdpopsim.bioRxiv : the preprint server for biology · 2025
    Article
  10. Article
  11. Article
  12. Digital Image Processing to Detect Adaptive Evolution.Molecular biology and evolution · 2024
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. IntroUNET: identifying introgressed alleles via semantic segmentation.bioRxiv : the preprint server for biology · 2024
    Article
  18. Review
  19. Article
  20. Article

6 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.

Kao LinCAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Haipeng Li
Christian Schlötterer
Andreas Futschik

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Summary statistics are widely used in population genetics, but they suffer from the drawback that no simple sufficient summary statistic exists, which captures all information required to distinguish different evolutionary hypotheses. Here, we apply boosting, a recent statistical method that combines simple classification rules to maximize their joint predictive performance. We show that our implementation of boosting has a high power to detect selective sweeps. Demographic events, such as bottlenecks, do not result in a large excess of false positives. A comparison to other neutrality tests shows that our boosting implementation performs well compared to other neutrality tests. Furthermore, we evaluated the relative contribution of different summary statistics to the identification of selection and found that for recent sweeps integrated haplotype homozygosity is very informative whereas older sweeps are better detected by Tajima's π. Overall, Watterson's was found to contribute the most information for distinguishing between bottlenecks and selection.

Indexed as

Evolution, MolecularSelection, GeneticAlgorithmsBayes TheoremGenomicsHumansStatistics as Topic

Identifiers

PMID21041556
PMCPMC3018323

What OpenQuestion holds

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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.