Project measure / variable:   Auwerx2   vo2_day_CD

ID, description, units MPD:114569   vo2_day_CD   oxygen consumption, 24h test, day time average   [mL/kg/min]  CD  
Data set, strains Auwerx2   inbred w/CC8   9 strains     sex: both     age: 14wks
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  STRAIN COMPARISON PLOT
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Auwerx2 - oxygen consumption, 24h test, day time average CD



  MEASURE SUMMARY
Measure Summary FemaleMale
Number of strains tested9 strains9 strains
Mean of the strain means3607.0   mL/kg/min 3201.0   mL/kg/min
Median of the strain means3790.0   mL/kg/min 3024.0   mL/kg/min
SD of the strain means± 476.1 ± 491.2
Coefficient of variation (CV)0.132 0.1535
Min–max range of strain means2707.0   –   4235.0   mL/kg/min 2609.0   –   4172.0   mL/kg/min
Mean sample size per strain4.3   mice 4.6   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
sex 1 3033603.3684 3033603.3684 25.4212 < 0.0001
strain 7 10911691.1773 1558813.0253 13.0627 < 0.0001
sex:strain 7 1023216.4525 146173.7789 1.2249 0.3035
Residuals 60 7160005.6333 119333.4272


Q-Q normality assessment based on residuals

  


  STRAIN MEANS (UNADJUSTED)
  
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Strain Sex Mean SD N mice SEM CV Min, Max Z score
129S1/SvImJ f 3792.0 189.0   5 84.5 0.0498 3590.0, 4060.0 0.39
129S1/SvImJ m 2998.0 137.2   5 61.35 0.0458 2802.0, 3184.0 -0.41
A/J f 3366.0 517.1   4 258.5 0.1536 3057.0, 4137.0 -0.51
A/J m 2989.0 35.119   3 20.276 0.0117 2956.0, 3026.0 -0.43
C57BL/6J f 3790.0 253.8   5 113.5 0.067 3438.0, 4060.0 0.38
C57BL/6J m 3024.0 137.0   5 61.26 0.0453 2816.0, 3167.0 -0.36
CAST/EiJ f 4235.0 0.0   1   0.0 0.0 4235.0, 4235.0 1.32
CAST/EiJ m 4172.0 359.7   3 207.7 0.0862 3761.0, 4431.0 1.98
DBA/2J f 3785.0 337.5   5 150.9 0.0892 3548.0, 4315.0 0.37
DBA/2J m 3388.0 826.8   5 369.8 0.244 2523.0, 4747.0 0.38
NOD/ShiLtJ f 2707.0 172.0   7 65.02 0.0636 2480.0, 2994.0 -1.89
NOD/ShiLtJ m 2609.0 191.4   5 85.59 0.0733 2393.0, 2844.0 -1.2
NZO/HlLtJ f 3049.0 79.18   4 39.588 0.026 2940.0, 3116.0 -1.17
NZO/HlLtJ m 2652.0 206.7   5 92.43 0.0779 2391.0, 2861.0 -1.12
PWK/PhJ f 3850.0 338.5   5 151.4 0.0879 3350.0, 4141.0 0.51
PWK/PhJ m 3587.0 160.9   5 71.97 0.0449 3447.0, 3795.0 0.79
WSB/EiJ f 3892.0 738.2   3 426.2 0.1896 3109.0, 4575.0 0.6
WSB/EiJ m 3386.0 335.1   5 149.9 0.099 3014.0, 3845.0 0.38


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ f 3792.4 154.4884637908 4101.4229376471 3483.3770623529
129S1/SvImJ m 2997.6 154.4884637908 3306.6229376471 2688.5770623529
A/J f 3366.25 172.7233533879 3711.7481475928 3020.7518524072
A/J m 2989.3333333333 199.4437491477 3388.2802303678 2590.3864362989
C57BL/6J f 3789.8 154.4884637908 4098.8229376471 3480.7770623529
C57BL/6J m 3024.2 154.4884637908 3333.2229376471 2715.1770623529
DBA/2J f 3785.0 154.4884637908 4094.0229376471 3475.9770623529
DBA/2J m 3388.4 154.4884637908 3697.4229376471 3079.3770623529
NOD/ShiLtJ f 2707.0 130.5665824793 2968.1720505611 2445.8279494389
NOD/ShiLtJ m 2609.4 154.4884637908 2918.4229376471 2300.3770623529
NZO/HlLtJ f 3049.25 172.7233533879 3394.7481475928 2703.7518524072
NZO/HlLtJ m 2651.6 154.4884637908 2960.6229376471 2342.5770623529
PWK/PhJ f 3850.4 154.4884637908 4159.4229376471 3541.3770623529
PWK/PhJ m 3587.2 154.4884637908 3896.2229376471 3278.1770623529
WSB/EiJ f 3892.3333333333 199.4437491477 4291.2802303678 3493.3864362989
WSB/EiJ m 3386.4 154.4884637908 3695.4229376471 3077.3770623529


  LEAST SQUARES MEANS (MODEL-ADJUSTED), SEXES COMBINED
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ both 3395.0 109.2398403616 3613.5122147524 3176.4877852476
A/J both 3177.7916666667 131.9196401978 3441.6702356352 2913.9130976982
C57BL/6J both 3407.0 109.2398403616 3625.5122147524 3188.4877852476
DBA/2J both 3586.7 109.2398403616 3805.2122147524 3368.1877852476
NOD/ShiLtJ both 2658.2 101.1364399027 2860.5030004636 2455.8969995364
NZO/HlLtJ both 2850.425 115.8663478431 3082.1922032353 2618.6577967647
PWK/PhJ both 3718.8 109.2398403616 3937.3122147524 3500.2877852476
WSB/EiJ both 3639.3666666667 126.139302478 3891.6828386838 3387.0504946496




  GWAS USING LINEAR MIXED MODELS