Evidence map›Paper›PMID 21464928›Full record

ArticlePloS one2011

Similarity in recombination rate estimates highly correlates with genetic differentiation in humans.

Hafid Laayouni, Ludovica Montanucci, Martin Sikora, Marta Melé, Giovanni Marco Dall'Olio, Belén Lorente-Galdos, Kate M McGee, Jan Graffelman, Philip Awadalla, Elena Bosch and 5 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.4field-weighted citation impact, top 18% 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

11 citing papers in PubMed, 20 citations in OpenAlex.

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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

15 authors at 4 institutions in 3 countries.

Hafid LaayouniIBE, Institute of Evolutionary Biology UPF-CSIC, CEXS-UPF-PRBB, Barcelona, Catalonia, Spain.
Ludovica Montanucci
Martin Sikora
Marta Melé
Giovanni Marco Dall'Olio
Belén Lorente-Galdos
Kate M McGee
Jan Graffelman
Philip Awadalla
Elena Bosch
David Comas
Arcadi Navarro
Francesc Calafell
Ferran Casals
Jaume Bertranpetit
Institut de Biologia Evolutiva · ESNational Institutes of Health · USUniversitat Politècnica de Catalunya · ESUniversité de Montréal · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recombination varies greatly among species, as illustrated by the poor conservation of the recombination landscape between humans and chimpanzees. Thus, shorter evolutionary time frames are needed to understand the evolution of recombination. Here, we analyze its recent evolution in humans. We calculated the recombination rates between adjacent pairs of 636,933 common single-nucleotide polymorphism loci in 28 worldwide human populations and analyzed them in relation to genetic distances between populations. We found a strong and highly significant correlation between similarity in the recombination rates corrected for effective population size and genetic differentiation between populations. This correlation is observed at the genome-wide level, but also for each chromosome and when genetic distances and recombination similarities are calculated independently from different parts of the genome. Moreover, and more relevant, this relationship is robustly maintained when considering presence/absence of recombination hotspots. Simulations show that this correlation cannot be explained by biases in the inference of recombination rates caused by haplotype sharing among similar populations. This result indicates a rapid pace of evolution of recombination, within the time span of differentiation of modern humans.

Indexed as

Genetics, PopulationRecombination, GeneticChromosomes, HumanComputer SimulationGene FrequencyHumansPolymorphism, Single NucleotidePopulation Density

Identifiers

PMID21464928
PMCPMC3065460
OpenAlexW2022850700

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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.