Evidence map›Paper›PMID 17623807›Full record

ArticleGenome research2007

Recombination rate estimation in the presence of hotspots.

Adam Auton, Gil McVean

Abstract read
In one paragraph

Article in Genome research, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 169 papers.

0numbers the graph read from it
0cells of the map it votes in
169citing 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

169 citing papers in PubMed.

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  6. GENERAL MOMENT CLOSURE FOR THE NEUTRAL TWO-LOCUS WRIGHT-FISHER DYNAMICS.bioRxiv : the preprint server for biology · 2026
    Article
  7. Fine-scale recombination rates inferred using the canFam4 assembly are strongly correlated with previous maps of dog recombination.Mammalian genome : official journal of the International Mammalian Genome Society · 2025
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109 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

2 authors.

Adam AutonDepartment of Statistics, University of Oxford, Oxford, UK. auton@stats.ox.ac.uk
Gil McVean

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fine-scale estimation of recombination rates remains a challenging problem. Experimental techniques can provide accurate estimates at fine scales but are technically challenging and cannot be applied on a genome-wide scale. An alternative source of information comes from patterns of genetic variation. Several statistical methods have been developed to estimate recombination rates from randomly sampled chromosomes. However, most such methods either make poor assumptions about recombination rate variation, or simply assume that there is no rate variation. Since the discovery of recombination hotspots, it is clear that recombination rates can vary over many orders of magnitude at the fine scale. We present a method for the estimation of recombination rates in the presence of recombination hotspots. We demonstrate that the method is able to detect and accurately quantify recombination rate heterogeneity, and is a substantial improvement over a commonly used method. We then use the method to reanalyze genetic variation data from the HLA and MS32 regions of the human genome and demonstrate that the method is able to provide accurate rate estimates and simultaneously detect hotspots.

Indexed as

Recombination, GeneticBayes TheoremComputer SimulationGenome, HumanHumansMarkov ChainsMonte Carlo MethodPolymorphism, Single NucleotideSoftware

Identifiers

PMID17623807
PMCPMC1933511

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.