Genetic influence on daily wheel running activity level

J. Timothy Lightfoot1, Michael J. Turner1, Meredith Daves1, Anna Vordermark1 and Steven R. Kleeberger2

1 Department of Kinesiology, University of North Carolina Charlotte, Charlotte
2 Laboratory of Respiratory Biology, National Institute of Environmental Health Sciences, Durham, North Carolina


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
This project was designed to determine the genetic (between-strain) and environmental (within-strain) variance in daily running wheel activity level in inbred mice. Five male and five female mice, 9.7–15.3 wk old, from each of 13 strains (A/J, AKR/J, BALB/cJ, C3H/HeJ, C57Bl/6J, C57L/J, C3Heb/FeJ, CBA/J, DBA/2J, SWR/J, MRL/MpJ, SPRET/Ei, and CAST/Ei) as well as five female NZB/BinJ mice were housed individually. A running wheel in each cage was interfaced with a magnetic sensor to measure total daily distance and exercise time for each animal every 24 h for 21 consecutive days (3 wk). Average daily distance (km), duration (min), and velocity (m/min) for each strain was then calculated. Significant interstrain differences in average daily distance (P < 0.001), average daily exercise duration (P < 0.0001), and average daily exercise velocity (P < 0.0001) were found, with C57L/J mice running farther and faster than the other strains. Sex was a significant factor in daily running wheel activity, with female mice running an average of 20% farther (P = 0.01) and 38% faster (P < 0.0001) than male mice. The male mice ran 15% longer duration on a daily basis (P = 0.0091). Weight was only associated with exercise velocity in the female mice, but this relationship was not significant when subdivided by strain. Broad-sense heritability estimates on the physical activity differed by sex (for distance, male 31–48% and female 12–22%; for duration, male 44–61% and female 12–21%; for velocity, male 49–66% and female 44–61%). In conclusion, these data indicate that daily running wheel activity level in mice is significantly affected by genetic background and sex.

genetics; physical activity; mice; inbred strains; weight; sex


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
IT WAS RECENTLY ESTIMATED by Booth et al. (2) that ~250,000 deaths/yr in the United States could be directly attributed to physical inactivity. Increasing physical activity level is a well-known treatment that prevents and ameliorates the effects of a host of diseases including cardiovascular disease (26), some forms of diabetes (27), and some forms of cancer (29). However, the average daily physical activity level of individuals in our society continues to decrease (2), with concomitant increasing rates of obesity and hypokinetic disease (30).

Physical activity level is usually considered the total amount of activity a person accomplishes in the course of a day and includes not only formal exercise but movement that is part of activities of daily living (e.g., walking up stairs, dressing, bathing, etc.). Although it has been common knowledge for several years that a wide range of environmental and behavioral factors such as habitual behavior and "behavior settings" can directly impact physical activity level (32, 36), some suggest that physical activity levels in humans may be partially controlled by biological mechanisms (33, 37). Therefore, it is not surprising that several studies in humans (16, 22, 33) and animals (14, 19, 23, 39) have presented early evidence that there is a genetic contribution to physical activity level.

Multiple ethical and technical concerns limit the use of humans in genetic investigations. Because of the genetic similarity of mice and humans, we have chosen to use a mouse model to determine the genetic and environmental variance in daily running wheel activity level between 14 strains of inbred mice. Another goal of the study was to determine the impact of sex upon daily running wheel activity and whether sex affected the heritability of daily activity. The use of a mouse model allows the control of environmental factors that may affect daily running wheel activity, and, as a result, analysis of the genetic contribution to variation in daily running wheel activity levels is possible.


    METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Animals.
All procedures used in this study were reviewed and approved by the UNC Charlotte Institutional Animal Care and Use Committee for appropriate treatment of animal subjects as outlined by the United States Department of Agriculture, the Animal Welfare Act, and the National Research Council (31). All mice were housed in the University Vivarium with 12:12-h light/dark cycles and were provided water and food ad libitum. All mice were fed a standard diet (Teklad 8604 rodent diet, 24.5% protein, 4.4% fat, 3.7% fiber, and 48.6% nitrogen-free extract; Harlan, Madison, WI). All cages were kept in the same room in the University Vivarium, which was maintained at 18–21°C and 20–40% humidity. Each mouse was weighed weekly to determine whether body weight was a covariate.

We measured the daily wheel running activity of 5 female and 5 male mice in 13 different inbred strains (A/J, AKR/J, BALB/cJ, C3H/HeJ, C57Bl/6J, C57L/J, C3Heb/FeJ, CBA/J, DBA/2J, SWR/J, MRL/MpJ, SPRET/Ei, CAST/Ei, NZB/BinJ). Male NZB/BinJ mice were unavailable for investigation. Because daily activity reaches a peak and plateaus at 9–10 wk of age in mice (39), we monitored daily wheel running activity for 3 wk (i.e., 21 consecutive days) starting at ~9 wk of age where possible. All mice were purchased from the Jackson Laboratory (Bar Harbor, ME).

Daily wheel running activity level.
Although the amount of wheel running exercise can be substantially different between individual outbred mice (5), the amount of wheel running in selected strains appears to be consistent and repeatable within a generation of animals (14, 39). To prevent confounding due to the mice learning to run on the wheels (9, 23) as well as to account for any fluctuations in daily wheel running due to menstrual cycling in the female mice (1), daily activity was monitored for 3 wk (i.e., 21 consecutive days). Mice were housed individually upon receipt (age range from 5–9 wk), and in each cage a solid surface running wheel (127 mm; Ware Manufacturing, Phoenix, AZ) was mounted. The running wheel was interfaced to a magnetic sensor (either BC600 or BC500; Sigma Sport, Olney, IL) that counted the total wheel revolutions and time spent exercising by each mouse (23). Each sensor was calibrated, and total daily distance (kilometers) and total daily exercise time (minutes) was noted every 24 h for each mouse. The wheels were checked on a daily basis to ensure that they turned freely. The average daily running velocity (meters/minute) of each mouse was calculated by dividing the total daily distance by total daily duration of exercise.

Statistics.
Average daily distance (km), average daily exercise time (min), and average daily exercise velocity (m/min) were calculated for each mouse from the 21 days of data collection. General linear modeling (GLM, JMP 5.1; SAS Institute, Cary, NC) was used to determine the contribution of strain, sex, weight, age, and interactions upon each activity phenotype (i.e., daily distance, duration, and exercise velocity). Alpha values were set a priori at P < 0.05, and variables that did not contribute significantly to the model were dropped and the analysis repeated. To determine pairwise differences (e.g., between sexes and strains) in a phenotype, subsequent post hoc testing was completed using Tukey-Kramer HSD. Data are presented as means ± SE.

Determination of the heritability of daily running wheel activity level was calculated by estimating broad-sense heritability. Broad-sense heritability is usually considered the degree to which the phenotype is determined by the genotype (7). Interclass correlations and the coefficient of genetic determination, both measures of heritability in the broad sense, were calculated using methods outlined by Festing (8). Intraclass correlations are defined as the "proportion of the total variation that is accounted for by differences between strains" and are estimated by the following formula:

where r1 = the intraclass correlation estimate, MSB = the mean square of the between strain comparison, MSW = the mean square of the within-strain comparison, and n = number of animals tested per strain with appropriate corrections for differences in animal numbers per strain (7, 8). The coefficient of genetic determination, which takes into account the doubling of the additive genetic variance with inbreeding, was calculated by using the following formula:

where g2 = the coefficient of genetic determination estimate, MSB = the mean square of the between strain comparison, MSW = the mean square of the within-strain comparison, and n = number of animals tested per strain (8). Since the coefficient of genetic determination results in more conservative heritability estimates, it has been noted to be a better indicator of broad-sense heritability (7). However, we also calculated the intraclass correlation, which is a more liberal heritability estimate, because it is commonly used as a heritability indicator (11, 17, 18, 23, 24).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Demographic data.
Data from 133 of the 135 mice that began the protocol are presented because one female A/J and one male MRL/MpJ mice died inexplicably during the 3 wk exercise period (Table 1). MRL/MpJ mice were significantly heavier than all of the other strains, and the CAST/Ei and SPRET/Ei mice were lighter than the other strains (Table 1). Male mice tended to be heavier than the female mice in each strain (Table 1). Female mice were slightly older than the strain-matched male mice at the beginning and throughout the 3-wk exercise period (P = 0.001).


View this table:
[in this window]
[in a new window]
 
Table 1. Demographic data at end of 3-wk exercise period

 
Indices of daily running wheel activity.
Significant strain (P < 0.0001) and sex (P = 0.01; Fig. 1) effects but not a strain x sex effect (P = 0.11) were found in average daily distance run. Regardless of sex, C57L/J ran the farthest on a daily basis (7.9 ± 3.0 km; Table 2), which was 395% more than the strain that ran the shortest distance (NZB/BinJ = 2.0 ± 0.91 km). Female mice ran 20% farther on average than the males, although post hoc analysis found no between-sex differences within any of the strains (Fig. 1). Neither age (P = 0.49) nor weight (P = 0.38) contributed significantly to the distance run by the mice. Additionally, body weight per se was not significantly associated with daily distance run (Table 3).



View larger version (33K):
[in this window]
[in a new window]
 
Fig. 1. Average daily exercise distance (km) parsed by sex within strain. Open bars = females; solid bars = males. There were no significant differences between sexes within each strain upon post hoc testing.

 

View this table:
[in this window]
[in a new window]
 
Table 2. Strain differences in wheel-running phenotypes

 

View this table:
[in this window]
[in a new window]
 
Table 3. Association of physical activity and body weight

 
Strain (P < 0.0001), sex (P = 0.009), and strain x sex interaction (P = 0.06) contributed significantly to the statistical model that explained 41% of the variation in exercise duration. Regardless of sex, CBA/J mice had the highest duration of exercise [342.03 ± 18.51 min (5.7 h) of daily wheel activity; Table 2], which was 342% more than NZB/BinJ mice, which had the lowest average duration of exercise (99.89 ± 16.34 min/day; 1.67 h/day). C57L/J mice, which ran the farthest on a daily basis (Table 2), had the third highest duration (310.2 ± 15.13 min/day; 5.17 h/day). Overall, male mice had 15% higher exercise durations than female mice, but none of the post hoc comparisons revealed a sex difference within any of the strains (Fig. 2). Neither age (P = 0.88) nor weight (P = 0.30) contributed significantly to the variation in the daily exercise duration of the mice. When considered separately, body weight was not associated with daily exercise duration (Table 3).



View larger version (34K):
[in this window]
[in a new window]
 
Fig. 2. Average daily exercise duration (min) parsed by sex within strain. Open bars = females; solid bars = males. There were no significant differences between sexes within each strain upon post hoc testing.

 
As was the case with the other wheel activity phenotypes, exercise velocity was significantly affected by strain (P < 0.0001), sex (P < 0.0001), and strain x sex interaction (P < 0.0001), without significant influence by either age (P = 0.82) or weight (P = 0.37). Regardless of sex, C57L/J mice ran 176% faster (25.7 ± 3.0 m/min) during their activity periods than did the slowest strain (C3H/HeJ; 14.6 ± 1.2 m/min; Table 2). Interestingly, C3Heb/FeJ mice, which ran the fourth longest on a daily basis, was the second slowest strain (50% slower than C57L/J mice). The strongest effect of sex was found for velocity of daily exercise. Overall, female mice ran 38% faster than male mice, and significant differences in exercise velocity between sexes were found in eight strains (Fig. 3). When considered separately, body weight was significantly associated with velocity of exercise (Table 3) but exhibited a relatively poor predictive fit (r2 = 0.16). When split by sex, only the body weight of the female was associated with exercise velocity (Table 3). Despite this significant association between weight and velocity in the female mice, the r value was still relatively low, with a corresponding low predictive value as exhibited by the r2 value (0.09).



View larger version (34K):
[in this window]
[in a new window]
 
Fig. 3. Average daily exercise velocity (m/min) parsed by sex within strain. Open bars = females; solid bars = males. *Significantly different (P < 0.05) between sexes.

 
Wheel running heritability estimates.
Heritability estimates for each daily wheel running index were subdivided by sex (Table 4). Overall, when sex was not considered, all measures of wheel running were low to moderately influenced by heritability (range = 14–30%). However, subdividing by sex increased the heritability estimates, particularly in male mice in regard to distance run (31–48%) and duration of exercise (44–61%), as well as significantly increasing the heritability statistics for exercise velocity in both male (49–66%) and female (44–61%) mice.


View this table:
[in this window]
[in a new window]
 
Table 4. Broad-sense heritability estimates

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Multiple behavioral or environmental factors influencing daily physical activity have been studied. The significant finding of this study is that genetic background also plays a role in determining daily running wheel activity in inbred mouse strains. Furthermore, it appears that sex influences to some extent the heritability of daily running wheel activity level. These findings can serve as the basis for future studies to identify the genes that control daily running wheel activity, as well as the role sex plays in influencing activity.

Despite their limitations, human studies have begun to suggest that physical activity may have a genetic component. One of the earliest estimations of the heritability of physical activity level came from the large Finnish Twin Registry study (16), which surveyed 1,537 monozygous male twins and 3,057 dizygous male twins regarding their daily physical activity levels. After adjusting for age and using inter-pair correlations, Kaprio et al. (16) estimated that heritability of physical activity was 62% and that the common environmental effects were zero (0%). Perusse and coworkers (33) collected 3-day activity records from 1,610 subjects from 375 different families from Quebec city and concluded that heritability of physical activity level was between 20% and 29% (depending on the statistical model used) while the environmental effect was ~12%. More recently, Lauderdale et al. (22) surveyed 3,344 twin pairs in the Vietnam Era Twin Registry by using sets of questions that dealt with moderate (e.g., climbing stairs, walking) and intense (e.g., running, racquet sports, cycling) physical activities. They found 38% heritability for their overall index of moderate activity, with intense activity resulting in higher heritability estimates (39–58%). Although these studies all indicated a significant genetic effect on daily physical activity, Simonen et al. (37) noted that maximal heritability values of activity levels were only 16–25% after measuring physical activity using a 3-day activity diary and a questionnaire in 696 subjects enrolled in the Quebec Family study.

Limited estimates of the inheritance of daily physical activity levels exist in animal models. Festing (9) noted that the broad-sense heritability of daily distance run in 26 inbred strains (intraclass correlations, sex not reported) ranged from 0.26 (7-day running) to 0.29 (48-h running), similar to our estimates of 0.30 (intraclass correlation) for distance run in all of the mice, regardless of sex. Similar to our heritability estimates (Table 4), Lerman et al. (23), reported significant heritability estimates for distance run (39–56%), duration of exercise (42–59%), and exercise velocity (24–38%) in male mice in seven inbred strains. Interestingly, exercise duration, distance, and velocity differ between the Lerman study and the current study for the three common strains (C3H/HeJ, DBA/2J, and C57BL/6J). Because the method of measurement of daily physical activity in both studies was identical, it appears that either laboratory environment or age of the mice (20–24 wk vs. 10–13 wk) possibly played a role in the differing results. We have preliminary evidence that daily activity decreases at differing rates for these three strains over the first 9 mo of their life, indicating that age must be considered in any cross-study comparisons (M. Turner, unpublished observation, 2004). Furthermore, Crabbe and coworkers (3) observed differences in locomotive activity in nine different mouse strains not only by strain, but also by location of laboratory. For example, mice tested in Edmonton, Alberta, were more active than mice tested in Albany, New York or Portland, Oregon. The investigators suggested that differences in personnel at each laboratory could have influenced behavior of the mice, given that the methods were rigorously standardized among the three laboratories. Therefore, although interstrain differences in daily physical activity appear to be fairly robust and estimations of the magnitude of heritability of daily activity are similar among studies, unique differences in laboratory environment (see below) may complicate direct comparisons between studies, especially where strain differences are minimal (3).

Differences in activity by sex have been suggested by previous studies. Animal studies that control the majority of environmental influences indicate that female mice tend to be more active than males. While Swallow et al. (39) tested only male mice because "females tend to run more on the wheels", similar to our study, Koteja and coworkers (19) observed that females in a strain of mice bred for high activity tended to run 58% farther, have 29% more periods of activity daily, and run 21% faster on a daily basis than did male high-activity mice. They noted that this trend was also present in the control mice, with the control female mice running 50% farther and having 43% more periods of activity daily but being only 3% faster than the control male mice. Koteja et al. (19) suggested that female mice may be more economical runners than males or that females differed in behavioral patterns that would affect the measurement of physical activity. Since it has been noted that female rats are more active during proestrus (early follicular phase) and less active during metestrus (luteal phase) (1), it is also possible that the hormonal differences between the sexes are responsible for differences in daily activity.

The role of body weight in determining daily wheel running activity remains unclear, with weight being shown to have no association with daily activity (4, 10), a positive association with duration and distance (23), or a negative association with wheel performance (40). Although we found that body weight did not contribute significantly to any of the overall statistical models used (i.e., that included strain, sex, and interactions), when body weight alone was correlated with exercise velocity, we did observe a significant negative correlation in the female mice strains (Table 3). Although Lerman et al. (23) found no relationship between weight and exercise velocity in male mice, which was similar to our study, no other study with the exception of Friedman et al. (10) has considered the association between weight and daily activity in female mice. Given the multiple strains tested by Lerman et al. (23) and the 14 strains in the current study (only 3 of which are common with the Lerman study), it appears that using multiple strains to determine whether a relationship exists between body weight and daily wheel running activity is difficult, due to interstrain differences. It has been observed in at least two studies (6, 41) that ICR mice selectively bred over several generations for high activity are lighter than control ICR mice, thereby indicating that selective breeding for activity may result in smaller body size. Interestingly, we observed that the SPRET/Ei and CAST/Ei strains, which were ranked first and third on a scale for difficulty in capturing and holding (i.e., a "wildness scale"; Ref. 43), exhibited the second and third highest daily exercise velocities while also being the lightest two strains we tested. These findings support data from the laboratory of Garland and colleagues (6, 41) suggesting that mice bred for activity were lighter. Although those investigations (6, 41) have not shown an association between weight and activity, they have shown that active mice have more lean tissue than sedentary mice. Therefore, the role of weight in determining daily wheel running activity is still unclear, with our data and the literature suggesting that this association depends significantly upon the strain tested with an unknown contribution of sex.

Although animal studies to this point have shown that genetic background can have a significant role in the determination of daily physical activity level, suggestions as to the specific genes or other factors involved in this regulation are limited. Tsao et al. (42) found that relative to controls, mice overexpressing GLUT4, a type of glucose transporter, ran fourfold farther ({approx}3.7 km/day total) on an average daily basis. Although these distances would rank fourth lowest among the strains if compared with the present study, the study of Tsao et al. (42) did support the hypothesis that a particular physiological factor controlled by genetic influences could significantly affect daily activity.

A variety of environmental factors could influence activity of mice. Food composition, temperature, and housing conditions can affect behavioral responses of mice. It has been suggested that both food composition (44) and volume (35) may affect physiological parameters that could directly impact daily activity in mice. However, we controlled for this possible extraneous variable by providing the same diet to all mice ad libitum.

Gordon et al. (12) showed that female CD-1 mice preferred ambient temperatures of 26.2–29.5°C and were more active when their preferred ambient temperatures were cooler. These preferred ambient temperatures were actually higher than the 18–26°C recommended for rodent housing vivarium operations (31) and higher than the housing temperature used in the current study (18–21°C). However, although the mice of the current study may have been more active because of the cooler housing temperatures, all mice were exposed to the same housing temperature, and thus temperature should have affected all mice equally.

Since it is generally agreed that mice are highly social, the type of housing (i.e., single housed or group housed) can alter baseline physiology of mice and pheromone effects of close housing may exist. Gordon et al. (12) noted that individually housed mice (i.e., single-housed mice) preferred warmer ambient temperatures and appeared to have less activity when in a group housing situation. Single-housed male mice have been noted to have increased heart rate but not an increased activity level after "several weeks" of acclimation to isolated housing (38), whereas male mice isolated for 4 mo had higher locomotor activity than isolated female or group-housed mice (13). Therefore, it is possible that group housing of the strains would result in different strain distribution patterns of daily activity. However, it would be technically difficult to separate individual mouse activity with group housing, a difficulty clearly delineated by Gordon et al. (12), and thus repeating this study with group housing would probably not yield further information than the single-housing model we used in this study.

The physiological impact of pheromones from adjoining mouse cages, whether in single- or group-housed conditions, is still largely undocumented. To our knowledge, no data exist regarding the level of mouse activity with the near presence of other male or female mice. It has been shown that male pheromones will induce the estrous cycle in female mice (effect attenuated in single-housed mice; Ref. 15) as well as inducing aggressive behavior in other male mice (20), and these pheromone effects may be mediated through a c-fos immunoreactivity in the accessory olfactory bulb (20, 28). Interestingly, Rhodes et al. (34) have shown that an increased fos immunoreactivity in the caudate-putamen complex, the medial frontal cortex, and the lateral hypothalamus may "play a role in the motivation to run" of mice that were selectively bred for high daily wheel running activity. Thus it is possible that pheromones from either male or female mice in adjoining cages could influence daily wheel running in single-housed mice of either sex, a possibility that has implications for proximity of housing of mice in future studies. However, critical questions regarding range of airborne distribution of pheromones as well as appropriate blockage of pheromones to control their influence are unanswered at this time and thus cannot be knowledgably controlled in the interpretation of these data or any other activity data where mice are housed in the same facility.

It should be noted that daily physical activity level is not analogous to maximal exercise endurance, but appears to be a distinct phenotype. Our earlier strain screen for maximal exercise endurance in female mice in 10 of the 14 strains tested in this study (25) showed the following strain distribution pattern from highest to lowest exercise endurance: BALB/cJ > SWR/J > CBA/J > C57L/J > C3H/HeJ > C3Heb/FeJ > C57BL/6J > AKR/J > DBA/2J > A/J.

Compared with the results from the female mice in the current study, the two strain distribution patterns are not concordant and thus implicate different genetic contributions to these two phenotypes. Our observation that daily physical activity level and maximal exercise endurance are two distinct phenotypes is supported by other studies. Lerman et al. (23) showed no significant correlation between voluntary wheel activity and treadmill performance (r values range from 0.27–0.68), and Friedman et al. (10) noted that two different measurements of wheel running activity did not significantly correlate (r = 0.15 and r = 0.38) with maximal oxygen consumption in 35 male ICR mice. Furthermore, Lambert et al. (21) showed that treadmill exercise performance was not correlated with subsequent voluntary wheel running performance in rats (r = –0.15, P = 0.53). Given these data, it appears that physical activity level and maximal exercise endurance/aerobic capacity are two distinct phenotypes with differing genetic control.

In summary, both between-strain (i.e., genetic) and sex variation was found on 3 different measures of daily running wheel activity in 14 inbred strains of mice. The influence of heritability on the various indices of physical activity rose substantially when the mice were partitioned by both sex and strain, indicating a significant role for sex in the determination of daily wheel running activity. These results lay the foundation for future investigations to identify the genes responsible for the control of daily running wheel activity in both male and female mice.


    GRANTS
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
This work was supported by National Institutes of Health Grants DK-61635 (to J. T. Lightfoot, M. Daves, and A. Vordermark), AG-22417 (M. J. Turner), and by the National Institute of Environmental Health Sciences (to S. R. Kleeberger).


    ACKNOWLEDGMENTS
 
We acknowledge the technical skills of Sherin Salama, Mark Lindley, Amber Lowe, and Sarah Carter in the development and collection of the physical activity data, as well as the willingness of Dr. B. Harrison to share his design for the measurement method of daily physical activity.


    FOOTNOTES
 
Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

Address for reprint requests and other correspondence: J. T. Lightfoot, Dept. of Kinesiology, UNC Charlotte, 9201 Univ. City Blvd., Charlotte, NC 28223 (E-mail: jtlightf{at}email.uncc.edu).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

  1. Anantharaman-Barr HG and Decombaz J. The effect of wheel running and the estrous cycle on energy expenditure in female rats. Physiol Behav 46: 259–263, 1989.[CrossRef][ISI][Medline]
  2. Booth FW, Gordon SE, Carlson CJ, and Hamilton MT. Waging war on modern chronic diseases: primary prevention through exercise biology. J Appl Physiol 88: 774–787, 2000.[Abstract/Free Full Text]
  3. Crabbe JC, Wahlsten D, and Dudek BC. Genetics of mouse behavior: interactions with laboratory environment. Science 284: 1670–1672, 1999.[Abstract/Free Full Text]
  4. Dohm MR, Hayes JP, and TG Jr. Quantitative genetics of sprint running speed and swimming endurance in laboratory house mice (Mus domesticus). Evolution 50: 1688–1701, 1996.[ISI]
  5. Dohm MR, Richardson CS, and Garland T Jr. Exercise physiology of wild and random-bred laboratory house mice and their reciprocal hybrids. Am J Physiol Regul Integr Comp Physiol 267: R1098–R1108, 1994.[Abstract/Free Full Text]
  6. Dumke CL, Rhodes JS, Garland T, Maslowski E, Swallow JG, Wetter AC, and Cartee GD. Genetic selection of mice for high voluntary wheel running: effect on skeletal muscle glucose uptake. J Appl Physiol 91: 1289–1297, 2001.[Abstract/Free Full Text]
  7. Falconer DS and Mackay TFC. Introduction to Quantitative Genetics. Essex, UK: Longman, 1996.
  8. Festing MFW. Notes on genetic analysis. In: Inbred Strains in Biomedical Research. New York: Oxford University Press, 1979, p. 80–98.
  9. Festing MFW. Wheel activity in 26 strains of mouse. Lab Anim 11: 257–258, 1977.[ISI][Medline]
  10. Friedman WA, Garland T Jr, and Dohm MR. Individual variation in locomotor behavior and maximal oxygen consumption in mice. Physiol Behav 52: 97–104, 1992.[CrossRef][ISI][Medline]
  11. Gedda L, Milani-Comparetti M, and Brenci G. A preliminary report on research made during the games of the XVIIth Olympiad Rome, 1960. In: International Research in Sport and Physical Education, edited by Jokl E and Simon E. Springfield, IL: Thomas, 1964, p. 693–697.
  12. Gordon CJ, Becker P, and Ali JS. Behavioral thermoregulatory responses of single- and group-housed mice. Physiol Behav 65: 255–262, 1998.[CrossRef][ISI][Medline]
  13. Guo M, Wu C, Liu W, Yang JY, and Chen D. Sex difference in psychological behavior changes induced by long-term social isolation in mice. Prog Neuropsychopharmacol Biol Psychiatry 28: 115–121, 2004.[CrossRef][ISI][Medline]
  14. Houle-Leroy P, Garland T Jr, Swallow JG, and Guderley H. Effects of voluntary activity and genetic selection on muscle metabolic capacities in house mice Mus domesticus. J Appl Physiol 89: 1608–1616, 2000.[Abstract/Free Full Text]
  15. Jemiolo B, Harvey S, and Novotny M. promotion of the Whitten effect in female mice by synthetic analogs of male urinary constituents. Proc Natl Acad Sci USA 83: 4576–4579, 1986.[Abstract]
  16. Kaprio JM, Koskenvuo M, and Sarna S. Cigarette smoking, use of alcohol and leisure-time activity among same-sexed adult male twins. In: Progress in Clinical and Biological Research. Twin Research 3: Epidemiological and Clinical Studies, edited by Gedda L, Parisi P, and Nance WE. New York: Liss, 1981, p. 37–46.
  17. Klissouras V. Heritability of adaptive variation. J Appl Physiol 31: 338–344, 1971.[Free Full Text]
  18. Klissouras V, Pirnay F, and Petit JM. Adaptation to maximal effort: genetics and age. J Appl Physiol 35: 288–293, 1973.[Free Full Text]
  19. Koteja P, Swallow JG, Carter PA, and Garland T Jr. Energy cost of wheel running in house mice: implications for coadaptation of locomotion and energy budgets. Physiol Biochem Zool 72: 238–249, 1999.[CrossRef][ISI][Medline]
  20. Kumar A, Dudley C, and Moss R. Functional dichotomy within the vomeronasal system: distinct zones of neuronal activity in the accessory olfactory bulb correlate with sex-specific behaviors. J Neurosci 19: RC32, 1999.[Medline]
  21. Lambert MI, VanZyl C, Jaunky R, Lambert EV, and Noakes TD. Tests of running performance do not predict subsequent spontaneous running in rats. Physiol Behav 60: 171–178, 1996.[CrossRef][ISI][Medline]
  22. Lauderdale DS, Fabsitz R, Meyer JM, Sholinsky P, Ramakrishnan V, and Goldberg J. Familial determinants of moderate and intense physical activity: a twin study. Med Sci Sports Exerc 29: 1062–1068, 1997.[ISI][Medline]
  23. Lerman I, Harrison BC, Freeman K, Hewett TE, Allen DL, Robbins J, and Leinwand LA. Genetic variability in forced and voluntary endurance exercise performance in seven inbred mouse strains. J Appl Physiol 92: 2245–2255, 2002; doi:10.1152/japplphysiol.01045.2001.[Abstract/Free Full Text]
  24. Lesage R, Simoneau JA, Jobin J, Leblanc J, and Bouchard C. Familial resemblance in maximal heart rate, blood lactate and aerobic power. Hum Hered 35: 182–189, 1985.[ISI][Medline]
  25. Lightfoot JT, Turner MJ, DeBate KA, and Kleeberger SR. Interstrain variation in murine aerobic capacity. Med Sci Sports Exerc 33: 2053–2057, 2001.[CrossRef][ISI][Medline]
  26. Manson JE, Hu FB, Rich-Edwards JW, Colditz GA, Stampfer MJ, Willett WC, Speizer FE, and Hennekens CH. A prospective study of walking compared with vigorous exercise in the prevention of coronary heart disease in women. N Engl J Med 341: 650–658, 1999.[Abstract/Free Full Text]
  27. Manson JE, Nathan DM, Krolewski AS, Stampfer MJ, Willett WC, and Hennekens CH. A prospective study of exercise and incidence of diabetes among US male physicians. JAMA 268: 63–67, 1992.[Abstract]
  28. Matsuoka M, Yokosuka M, Mori Y, and Ichikawa M. Specific expression pattern of Fos in the accessory olfactory bulb of male mice after exposure to soiled bedding females. Neurosci Res 35: 189–195, 1999.[CrossRef][ISI][Medline]
  29. McTiernan A, Ulrich C, Slater S, and Potter J. Physical activity and cancer etiology: associations and mechanisms. Cancer Causes Control 9: 487–509, 1993.[CrossRef]
  30. Mokdad AH, Serdula MK, Dietz WH, Bowman BA, Marks JS, and Koplan JP. The spread of the obesity epidemic in the United States, 1991–1998. JAMA 282, 1999.
  31. National Research Council. Animal environment, housing, and management. In: Guide for the Care and Use of Laboratory Animals. Washington, DC: National Academy Press, 1996, chapt. 2, p. 32.
  32. Owen N, Leslie E, Salmon J, and Fotheringham MJ. Environmental determinants of physical activity and sedentary behavior. Exerc Sport Sci Rev 28: 153–158, 2000.[Medline]
  33. Perusse L, Tremblay A, LeBlanc C, and Bouchard C. Genetic and environmental influences on level of habitual physical activity and exercise participation. Am J Epidemiol 129: 1012–1022, 1989.[Abstract]
  34. Rhodes JS, Gammie SC, and Garland T Jr. Patterns of brain activity associated with variation in voluntary wheel-running behavior. Behav Neurosci 117: 1243–1256, 2003.[CrossRef][ISI][Medline]
  35. Rikke BA, Yerg JE III, Battaglia ME, Nagy TR, Allison DB, and Johnson TE. Strain variation in the response of body temperature to dietary restriction. Mech Ageing Dev 124: 663–678, 2003.[CrossRef][ISI][Medline]
  36. Rowland TW. The biological basis of physical activity. Med Sci Sports Exerc 30: 392–299, 1998.[ISI][Medline]
  37. Simonen RL, Perusse L, Rankinen T, Rice T, Rao DC, and Bouchard C. Familial aggregation of physical activity levels in the Quebec family study. Med Sci Sports Exerc 34: 1137–1142, 2002.[ISI][Medline]
  38. Spani D, Arras M, Konig B, and Rulicke T. Higher heart rate of laboratory mice housed individually vs in pairs. Lab Anim 37: 54–62, 2003.[CrossRef][ISI][Medline]
  39. Swallow JG, Garland T Jr, Carter PA, Zhan WZ, and Sieck GC. Effects of voluntary activity and genetic selection on aerobic capacity in house mice (Mus domesticus). J Appl Physiol 84: 69–76, 1998.[Abstract/Free Full Text]
  40. Swallow JG, Koteja P, Carter PA, and Garland T. Artificial selection for increased wheel-running activity in house mice results in decreased body mass at maturity. J Exp Biol 202: 2513–2520, 1999.[Abstract/Free Full Text]
  41. Swallow JG, Koteja P, Carter PA, and Garland T Jr. Food consumption and body composition in mice selected for high wheel-running activity. J Comp Physiol [B] 171: 651–659, 2001.[ISI][Medline]
  42. Tsao TS, Li J, Change KS, Stenbit AE, Galuska D, Anderson JE, Zierath JR, McCarter RJ, and Charron MJ. Metabolic adaptations in skeletal muscle overexpressing GLUT4: effects on muscle and physical activity. FASEB J 15: 958–969, 2001.[Abstract/Free Full Text]
  43. Wahlsten D, Metten P, and Crabbe JC. A rating scale for wildness and ease of handling laboratory mice: results for 21 inbred strains tested in two laboratories. Genes Brain Behav 2: 71–79, 2003.[ISI][Medline]
  44. Yashiro M and Kimura S. Effect of voluntary exercise on physiological function and feeding behavior of mice on a 20% casein diet or a 10% casein diet. J Nutr Sci Vitaminol (Tokyo) 25: 23–32, 1979.[Medline]