Evidence map›Paper›PMID 16140989›Full record

ArticleGenome research2005

Fitting background-selection predictions to levels of nucleotide variation and divergence along the human autosomes.

Floyd A Reed, Joshua M Akey, Charles F Aquadro

Abstract read
In one paragraph

Article in Genome research, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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  5. Article
  6. Article
  7. Background selection as null hypothesis in population genomics: insights and challenges fromPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017
    Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. The case for junk DNA.PLoS genetics · 2014
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Article
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

3 authors.

Floyd A ReedDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA. freed@umd.edu
Joshua M Akey
Charles F Aquadro

Funding

MOLECULAR POPULATION GENETICS OF DROSOPHILA MELANOGASTERR01GM036431 · NIGMS · CORNELL UNIVERSITY ITHACA · PI AQUADRO, CHARLES F · 1986 to 2010
$2.6M
NIGMS NIH HHS GM36431NIGMS NIH HHS R01 GM036431
6 · The paper itself

Abstract

The roles of positive directional selection (selective sweeps) and negative selection (background selection) in shaping the genome-wide distribution of genetic variation in humans remain largely unknown. Here, we optimize the parameter values of a model of the removal of deleterious mutations (background selection) to observed levels of human polymorphism, controlling for mutation rate heterogeneity by using interspecific divergence. A point of "best fit" was found between background-selection predictions and estimates of human effective population sizes, with reasonable parameter estimates whose uncertainty was assessed by bootstrapping. The results suggest that the purging of deleterious alleles has had some influence on shaping levels of human variation, although the effects may be subtle over the majority of the human genome. A significant relationship was found between background-selection predictions and measures of skew in the allele frequency distribution. The genome-wide action of selection (positive and/or negative) is required to explain this observation.

Indexed as

Genetic VariationSelection, GeneticAllelesDNAGenetics, PopulationHumansModels, GeneticMutationPolymorphism, GeneticPopulation DensityRecombination, GeneticDNA

Identifiers

PMID16140989
PMCPMC1199535

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.