Evidence map›Paper›PMID 22956910›Full record

ArticlePLoS genetics2012

Genome patterns of selection and introgression of haplotypes in natural populations of the house mouse (Mus musculus).

Fabian Staubach, Anna Lorenc, Philipp W Messer, Kun Tang, Dmitri A Petrov, Diethard Tautz

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In one paragraph

Article in PLoS genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.

0numbers the graph read from it
0cells of the map it votes in
75citing papers in PubMed
12.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

75 citing papers in PubMed, 156 citations in OpenAlex.

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15 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

6 authors at 3 institutions in 3 countries.

Fabian StaubachMax Planck Institute for Evolutionary Biology, Plön, Germany.
Anna Lorenc
Philipp W Messer
Kun Tang
Dmitri A Petrov
Diethard Tautz
Stanford University · USMax Planck Institute for Evolutionary Biology · DEShanghai Institutes for Biological Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

General parameters of selection, such as the frequency and strength of positive selection in natural populations or the role of introgression, are still insufficiently understood. The house mouse (Mus musculus) is a particularly well-suited model system to approach such questions, since it has a defined history of splits into subspecies and populations and since extensive genome information is available. We have used high-density single-nucleotide polymorphism (SNP) typing arrays to assess genomic patterns of positive selection and introgression of alleles in two natural populations of each of the subspecies M. m. domesticus and M. m. musculus. Applying different statistical procedures, we find a large number of regions subject to apparent selective sweeps, indicating frequent positive selection on rare alleles or novel mutations. Genes in the regions include well-studied imprinted loci (e.g. Plagl1/Zac1), homologues of human genes involved in adaptations (e.g. alpha-amylase genes) or in genetic diseases (e.g. Huntingtin and Parkin). Haplotype matching between the two subspecies reveals a large number of haplotypes that show patterns of introgression from specific populations of the respective other subspecies, with at least 10% of the genome being affected by partial or full introgression. Using neutral simulations for comparison, we find that the size and the fraction of introgressed haplotypes are not compatible with a pure migration or incomplete lineage sorting model. Hence, it appears that introgressed haplotypes can rise in frequency due to positive selection and thus can contribute to the adaptive genomic landscape of natural populations. Our data support the notion that natural genomes are subject to complex adaptive processes, including the introgression of haplotypes from other differentiated populations or species at a larger scale than previously assumed for animals. This implies that some of the admixture found in inbred strains of mice may also have a natural origin.

Indexed as

Genetics, PopulationSelection, GeneticAllelesAnimalsGenetic SpeciationGenomeGenomicsHaplotypesHumansMiceMutationPolymorphism, Single Nucleotide

Identifiers

PMID22956910
PMCPMC3431316
OpenAlexW2149085533

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.