Evidence map›Paper›PMID 35189878›Full record

ArticleBMC biology2022

Genomic characterization of the world's longest selection experiment in mouse reveals the complexity of polygenic traits.

Sergio E Palma-Vera, Henry Reyer, Martina Langhammer, Norbert Reinsch, Lorena Derezanin, Joerns Fickel, Saber Qanbari, Joachim M Weitzel, Soeren Franzenburg, Georg Hemmrich-Stanisak and 1 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in BMC biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

8 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 1 country.

Sergio E Palma-VeraInstitute of Reproductive Biology, Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany. serpalma.v@gmail.com.ORCID http://orcid.org/0000-0002-1249-4867
Henry ReyerInstitute of Genome Biology, Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
Martina LanghammerInstitute of Genetics and Biometry, Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
Norbert ReinschInstitute of Genetics and Biometry, Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
Lorena DerezaninInstitute of Reproductive Biology, Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
Joerns FickelDepartment of Evolutionary Genetics, Research Institute for Zoo and Wildlife Research (IZW), Berlin, Germany.
Saber QanbariInstitute of Genetics and Biometry, Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
Joachim M WeitzelInstitute of Reproductive Biology, Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
Soeren FranzenburgInstitute of Clinical Molecular Biology (IKMB), Kiel, Germany.
Georg Hemmrich-StanisakInstitute of Clinical Molecular Biology (IKMB), Kiel, Germany.
Jennifer SchoenInstitute of Reproductive Biology, Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
Research Institute for Farm Animal Biology (FBN) · DEZimmer Biomet (Germany) · DEInstitute of Molecular Biology · DEUniversity of Potsdam · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLong-term selection experiments are a powerful tool to understand the genetic background of complex traits. The longest of such experiments has been conducted in the Research Institute for Farm Animal Biology (FBN), generating extreme mouse lines with increased fertility, body mass, protein mass and endurance. For >140 generations, these lines have been maintained alongside an unselected control line, representing a valuable resource for understanding the genetic basis of polygenic traits. However, their history and genomes have not been reported in a comprehensive manner yet. Therefore, the aim of this study is to provide a summary of the breeding history and phenotypic traits of these lines along with their genomic characteristics. We further attempt to decipher the effects of the observed line-specific patterns of genetic variation on each of the selected traits.

resultsOver the course of >140 generations, selection on the control line has given rise to two extremely fertile lines (>20 pups per litter each), two giant growth lines (one lean, one obese) and one long-distance running line. Whole genome sequencing analysis on 25 animals per line revealed line-specific patterns of genetic variation among lines, as well as high levels of homozygosity within lines. This high degree of distinctiveness results from the combined effects of long-term continuous selection, genetic drift, population bottleneck and isolation. Detection of line-specific patterns of genetic differentiation and structural variation revealed multiple candidate genes behind the improvement of the selected traits.

conclusionsThe genomes of the Dummerstorf trait-selected mouse lines display distinct patterns of genomic variation harbouring multiple trait-relevant genes. Low levels of within-line genetic diversity indicate that many of the beneficial alleles have arrived to fixation alongside with neutral alleles. This study represents the first step in deciphering the influence of selection and neutral evolutionary forces on the genomes of these extreme mouse lines and depicts the genetic complexity underlying polygenic traits.

Indexed as

Multifactorial InheritancePolymorphism, Single NucleotideAllelesAnimalsGenomicsMicePhenotypeSelection, GeneticBody massBottleneckEnduranceFertilityGenetic driftMouseSelective breedingSingle-nucleotide polymorphismStructural variationWhole genome sequencing

Identifiers

PMID35189878
PMCPMC8862358
OpenAlexW4213014407

What OpenQuestion holds

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LicenceCC BY
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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.