Project measure / variable:   Auwerx2   vco2_day_CD

ID, description, units MPD:114573   vco2_day_CD   carbon dioxide production, 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 - carbon dioxide production, 24h test, day time average CD



  MEASURE SUMMARY
Measure Summary FemaleMale
Number of strains tested9 strains9 strains
Mean of the strain means2908.0   mL/kg/min 2514.0   mL/kg/min
Median of the strain means3021.0   mL/kg/min 2504.0   mL/kg/min
SD of the strain means± 554.9 ± 394.6
Coefficient of variation (CV)0.1908 0.1569
Min–max range of strain means2050.0   –   3938.0   mL/kg/min 2060.0   –   3394.0   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 2106225.0526 2106225.0526 23.8733 < 0.0001
strain 7 7701312.7775 1100187.5396 12.4703 < 0.0001
sex:strain 7 1061227.4308 151603.9187 1.7184 0.1218
Residuals 60 5293498.5286 88224.9755


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 3092.0 323.8   5 144.8 0.1047 2845.0, 3462.0 0.33
129S1/SvImJ m 2491.0 98.88   5 44.22 0.0397 2379.0, 2601.0 -0.06
A/J f 2517.0 328.7   4 164.3 0.1306 2308.0, 3004.0 -0.71
A/J m 2245.0 51.22   3 29.569 0.0228 2186.0, 2278.0 -0.68
C57BL/6J f 3231.0 182.0   5 81.38 0.0563 2999.0, 3403.0 0.58
C57BL/6J m 2504.0 159.0   5 71.13 0.0635 2362.0, 2760.0 -0.03
CAST/EiJ f 3938.0 0.0   1   0.0 0.0 3938.0, 3938.0 1.86
CAST/EiJ m 3394.0 473.5   3 273.4 0.1395 2863.0, 3773.0 2.23
DBA/2J f 2945.0 382.1   5 170.9 0.1297 2619.0, 3420.0 0.07
DBA/2J m 2629.0 683.2   5 305.5 0.2599 1920.0, 3755.0 0.29
NOD/ShiLtJ f 2050.0 109.1   7 41.22 0.0532 1928.0, 2257.0 -1.55
NOD/ShiLtJ m 2109.0 216.1   5 96.62 0.1024 1859.0, 2319.0 -1.03
NZO/HlLtJ f 2326.0 58.27   4 29.133 0.025 2274.0, 2395.0 -1.05
NZO/HlLtJ m 2060.0 153.0   5 68.43 0.0743 1834.0, 2207.0 -1.15
PWK/PhJ f 3021.0 350.1   5 156.6 0.1159 2541.0, 3345.0 0.2
PWK/PhJ m 2616.0 123.4   5 55.2 0.0472 2502.0, 2781.0 0.26
WSB/EiJ f 3056.0 564.5   3 325.9 0.1847 2410.0, 3454.0 0.27
WSB/EiJ m 2580.0 260.2   5 116.3 0.1008 2304.0, 2839.0 0.17


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ f 3091.6 132.8344650128 3357.3084910535 2825.8915089465
129S1/SvImJ m 2491.2 132.8344650128 2756.9084910535 2225.4915089465
A/J f 2516.75 148.5134467617 2813.8211240973 2219.6788759027
A/J m 2245.0 171.4885569323 2588.0281869321 1901.9718130679
C57BL/6J f 3230.8 132.8344650128 3496.5084910535 2965.0915089465
C57BL/6J m 2504.0 132.8344650128 2769.7084910535 2238.2915089465
DBA/2J f 2945.4 132.8344650128 3211.1084910535 2679.6915089465
DBA/2J m 2628.6 132.8344650128 2894.3084910535 2362.8915089465
NOD/ShiLtJ f 2049.7142857143 112.2656132802 2274.2789474457 1825.1496239829
NOD/ShiLtJ m 2109.4 132.8344650128 2375.1084910535 1843.6915089465
NZO/HlLtJ f 2326.25 148.5134467617 2623.3211240973 2029.1788759027
NZO/HlLtJ m 2060.0 132.8344650128 2325.7084910535 1794.2915089465
PWK/PhJ f 3020.6 132.8344650128 3286.3084910535 2754.8915089465
PWK/PhJ m 2615.8 132.8344650128 2881.5084910535 2350.0915089465
WSB/EiJ f 3056.0 171.4885569323 3399.0281869321 2712.9718130679
WSB/EiJ m 2579.6 132.8344650128 2845.3084910535 2313.8915089465


  LEAST SQUARES MEANS (MODEL-ADJUSTED), SEXES COMBINED
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ both 2791.4 93.9281509858 2979.2842758428 2603.5157241572
A/J both 2380.875 113.4290185841 2607.7668188269 2153.9831811731
C57BL/6J both 2867.4 93.9281509858 3055.2842758428 2679.5157241572
DBA/2J both 2787.0 93.9281509858 2974.8842758428 2599.1157241572
NOD/ShiLtJ both 2079.5571428571 86.9605701172 2253.5041818636 1905.6101038507
NZO/HlLtJ both 2193.125 99.6258487596 2392.4063682902 1993.8436317098
PWK/PhJ both 2818.2 93.9281509858 3006.0842758428 2630.3157241572
WSB/EiJ both 2817.8 108.4588865123 3034.7500744687 2600.8499255313




  GWAS USING LINEAR MIXED MODELS