A search for quantitative trait loci controlling within-individual variation of physical activity traits in mice
1 Department of Biology, University of North Carolina at Charlotte, Charlotte, North Carolina 28223, USA
2 Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
3 Department of Nutrition, University of North Carolina, Chapel Hill, NC 27599, USA
4 Department of Cell and Molecular Physiology, University of North Carolina, Chapel Hill, NC 27599, USA
5 Carolina Center for Genome Science, University of North Carolina, Chapel Hill, NC 27599, USA
6 Department of Health and Kinesiology, Texas A&M University, College Station, Texas 77845, USA
BMC Genetics 2010, 11:83 doi:10.1186/1471-2156-11-83Published: 21 September 2010
In 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.
This 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.
It 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.