Evidence map›Paper›PMID 20071383›Full record

ArticleProceedings. Biological sciences2010

Even small SNP clusters are non-randomly distributed: is this evidence of mutational non-independence?

William Amos

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
3.2field-weighted citation impact, top 7% of its field
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

37 citing papers in PubMed, 95 citations in OpenAlex.

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  14. Substitutions of short heterologous DNA segments of intragenomic or extragenomic origins produce clustered genomic polymorphisms.Proceedings of the National Academy of Sciences of the United States of America · 2016
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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 at 1 institution in 1 country.

William AmosDepartment of Zoology, Cambridge University, , Downing Street, Cambridge CB2 3EJ, UK.
University of Cambridge · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single nucleotide polymorphisms (SNPs) are distributed highly non-randomly in the human genome through a variety of processes from ascertainment biases (i.e. the preferential development of SNPs around interesting genes) to the action of mutation hotspots and natural selection. However, with more systematic SNP development, one might expect an increasing proportion of SNPs to be distributed more or less randomly. Here, I test this null hypothesis using stochastic simulations and compare this output with that of an alternative hypothesis that mutations are more likely to occur near existing SNPs, a possibility suggested both by molecular studies of meiotic mismatch repair in yeast and by data showing that SNPs cluster around heterozygous deletions. A purely Poisson process generates SNP clusters that differ from equivalent data from human chromosome 1 in both the frequency of different-sized clusters and the SNP density within each cluster, even for small clusters of just four or five SNPs, while clusters on the X chromosome differ from those on the autosomes. In contrast, modest levels of mutational non-independence generate a reasonable fit to the real data for both cluster frequency and density, and also exhibit the evolutionary transience noted for 'mutation hotspots'. Mutational non-independence therefore provides an interesting new hypothesis that appears capable of explaining the distribution of SNPs in the human genome.

Indexed as

MutationGenome, HumanHumansModels, GeneticPolymorphism, Single NucleotideStochastic Processes

Identifiers

PMID20071383
PMCPMC2871933
OpenAlexW2161315143

What OpenQuestion holds

Textmetadata
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.