Evidence map›Paper›PMID 20858254›Full record

ArticleBMC genetics2010

A search for quantitative trait loci controlling within-individual variation of physical activity traits in mice.

Larry J Leamy, Daniel Pomp, J Timothy Lightfoot

Abstract read
In one paragraph

Article in BMC genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Differential skeletal muscle proteome of high- and low-active mice.Journal of applied physiology (Bethesda, Md. : 1985) · 2014
    Article
  5. Genetic determinants of voluntary exercise.Trends in genetics : TIG · 2013
    Review
  6. Article
  7. Review
  8. Article
  9. Article
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

3 authors.

Larry J LeamyDepartment of Biology, University of North Carolina at Charlotte, Charlotte, North Carolina 28223, USA. ljleamy@uncc.edu
Daniel Pomp
J Timothy Lightfoot

Funding

UNC-CH CENTER FOR ENVIRONMENTAL HEALTH &SUSCEPTIBILITYP30ES010126 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Melissa A. Troester · 2001 to 2026
$36.3M
GENETIC FACTORS RESPONSIBLE FOR EXERCISE ENDURANCER01AR050085 · NIAMS · UNIVERSITY OF NORTH CAROLINA CHARLOTTE · PI LIGHTFOOT, J TIMOTHY · 2005 to 2009
$1.6M
Genetic Architecture of Voluntary Exercise in MiceR01DK076050 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI POMP, DANIEL · 2007 to 2010
$966k
Control of Physical Activity by Genetic FactorsR15DK061635 · NIDDK · UNIVERSITY OF NORTH CAROLINA CHARLOTTE · PI LIGHTFOOT, J TIMOTHY · 2003 to 2003
$133k
NIAMS NIH HHS AR050085NIDDK NIH HHS DK076050NIDDK NIH HHS DK61635NIEHS NIH HHS P30 ES010126
6 · The paper itself

Abstract

backgroundIn recent years it has become increasingly apparent that physical inactivity can predispose individuals to a host of health problems. While many studies have analyzed the effect of various environmental factors on activity, we know much less about the genetic control of physical activity. Some studies in mice have discovered quantitative trait loci (QTL) influencing various physical activity traits, but mostly have analyzed inter-individual variation rather than variation in activity within individuals over time. We conducted a genome scan to identify QTLs controlling the distance, duration, and time run by mice over seven consecutive three-day intervals in an F2 population created by crossing two inbred strains (C57L/J and C3H/HeJ) that differed widely (average of nearly 300%) in their activity levels. Our objectives were (a) to see if we would find QTLs not originally discovered in a previous investigation that assessed these traits over the entire 21-day period and (b) to see if some of these QTLs discovered might affect the activity traits only in the early or in the late time intervals.

resultsThis analysis uncovered 39 different QTLs, over half of which were new. Some QTLs affected the activity traits only in the early time intervals and typically exhibited significant dominance effects whereas others affected activity only in the later age intervals and exhibited less dominance. We also analyzed the regression slopes of the activity traits over the intervals, and found several QTLs affecting these traits that generally mapped to unique genomic locations.

conclusionsIt was concluded that the genetic architecture of physical activity in mice is much more complicated than has previously been recognized, and may change considerably depending on the age at which various activity measures are assessed.

Indexed as

Genetic VariationQuantitative Trait LociAnimalsGenotypeMiceMice, Inbred C3HMice, Inbred C57BLMotor ActivityPolymorphism, Single NucleotideRegression Analysis

Identifiers

PMID20858254
PMCPMC2949740

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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.