Project measure / variable:   Wiltshire4   monocytes_BEZ_D


  STRAIN COMPARISON PLOT
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Wiltshire4 - monocytes, percent viability dose: 11.1 µM



  MEASURE SUMMARY
Measure Summary Male
Number of strains tested34 strains
Mean of the strain means98.7   %
Median of the strain means95.5   %
SD of the strain means± 24.3
Coefficient of variation (CV)0.246
Min–max range of strain means71.8   –   214   %
Mean sample size per strain4.0   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
strain 33 63817.8358 1933.8738 14.9973 < 0.0001
Residuals 99 12765.8568 128.948


Q-Q normality assessment based on residuals

  


  STRAIN MEANS (UNADJUSTED)
  
Select table page:
Strain Sex Mean SD N mice SEM CV Min, Max Z score
129S1/SvImJ m 135.0 23.1   4 11.6 0.171 102.0, 152.0 1.49
129X1/SvJ m 84.0 6.19   4 3.09 0.0736 74.9, 88.2 -0.6
A/J m 83.3 10.3   4 5.14 0.123 72.9, 94.7 -0.63
AKR/J m 84.7 11.3   4 5.64 0.133 70.4, 96.2 -0.58
BALB/cByJ m 109.0 1.59   4 0.797 0.0146 107.0, 110.0 0.42
BTBR T+ Itpr3tf/J m 101.0 16.2   4 8.08 0.159 81.0, 118.0 0.09
BUB/BnJ m 99.0 3.43   4 1.72 0.0347 95.8, 103.0 0.01
C3H/HeJ m 81.2 5.34   4 2.67 0.0658 74.0, 86.9 -0.72
C57BL/6J m 80.4 8.94   4 4.47 0.111 67.5, 88.2 -0.75
C57BLKS/J m 112.0 9.7   4 4.85 0.0868 103.0, 123.0 0.55
C58/J m 119.0 14.1   2   9.98 0.118 109.0, 129.0 0.83
CBA/J m 94.4 16.7   4 8.34 0.177 78.0, 115.0 -0.18
CZECHII/EiJ m 103.0 12.1   4 6.05 0.118 87.1, 116.0 0.18
DBA/2J m 111.0 19.9   4 9.94 0.18 84.5, 132.0 0.51
FVB/NJ m 83.1 13.2   4 6.59 0.159 64.3, 95.0 -0.64
I/LnJ m 96.5 2.86   4 1.43 0.0296 94.6, 101.0 -0.09
KK/HlJ m 91.8 6.79   4 3.39 0.074 81.9, 96.5 -0.28
LG/J m 101.0 7.32   4 3.66 0.0724 90.8, 108.0 0.09
LP/J m 95.8 7.25   4 3.63 0.0757 85.6, 103.0 -0.12
MA/MyJ m 98.7 1.88   4 0.94 0.019 96.8, 101.0 0.0
NOD/ShiLtJ m 84.8 17.2   4 8.58 0.202 71.1, 109.0 -0.57
NON/ShiLtJ m 98.2 12.5   4 6.25 0.127 87.7, 116.0 -0.02
NZB/BlNJ m 71.8 12.7   4 6.37 0.178 57.0, 86.5 -1.11
NZO/HlLtJ m 94.6 2.29   4 1.15 0.0242 92.3, 96.7 -0.17
NZW/LacJ m 89.8 3.49   4 1.75 0.0389 87.0, 94.6 -0.37
PERA/EiJ m 98.0 12.2   4 6.1 0.124 80.3, 106.0 -0.03
PL/J m 95.3 6.45   4 3.23 0.0677 88.7, 103.0 -0.14
PWD/PhJ m 112.0 9.08   4 4.54 0.0809 99.0, 118.0 0.55
PWK/PhJ m 86.5 14.2   4 7.11 0.165 71.0, 100.0 -0.5
RIIIS/J m 103.0 12.6   4 6.31 0.123 93.2, 120.0 0.18
SEA/GnJ m 74.2 15.6   4 7.78 0.21 52.3, 87.1 -1.01
SJL/J m 78.5 11.7   4 5.87 0.15 61.9, 88.0 -0.83
SM/J m 91.4 5.74   4 2.87 0.0628 84.1, 98.0 -0.3
WSB/EiJ m 214.0 10.3   3 5.92 0.048 203.0, 223.0 4.74


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ m 135.255 5.6778 146.5209 123.9891
129X1/SvJ m 84.025 5.6778 95.2909 72.7591
A/J m 83.2525 5.6778 94.5184 71.9866
AKR/J m 84.685 5.6778 95.9509 73.4191
BALB/cByJ m 108.9975 5.6778 120.2634 97.7316
BTBR T+ Itpr3tf/J m 101.3725 5.6778 112.6384 90.1066
BUB/BnJ m 98.955 5.6778 110.2209 87.6891
C3H/HeJ m 81.1625 5.6778 92.4284 69.8966
C57BL/6J m 80.3975 5.6778 91.6634 69.1316
C57BLKS/J m 111.725 5.6778 122.9909 100.4591
C58/J m 119.18 8.0296 135.1124 103.2476
CBA/J m 94.415 5.6778 105.6809 83.1491
CZECHII/EiJ m 102.8725 5.6778 114.1384 91.6066
DBA/2J m 110.6175 5.6778 121.8834 99.3516
FVB/NJ m 83.0775 5.6778 94.3434 71.8116
I/LnJ m 96.55 5.6778 107.8159 85.2841
KK/HlJ m 91.81 5.6778 103.0759 80.5441
LG/J m 101.1325 5.6778 112.3984 89.8666
LP/J m 95.8275 5.6778 107.0934 84.5616
MA/MyJ m 98.695 5.6778 109.9609 87.4291
NOD/ShiLtJ m 84.7825 5.6778 96.0484 73.5166
NON/ShiLtJ m 98.1725 5.6778 109.4384 86.9066
NZB/BlNJ m 71.7775 5.6778 83.0434 60.5116
NZO/HlLtJ m 94.6 5.6778 105.8659 83.3341
NZW/LacJ m 89.805 5.6778 101.0709 78.5391
PERA/EiJ m 98.025 5.6778 109.2909 86.7591
PL/J m 95.35 5.6778 106.6159 84.0841
PWD/PhJ m 112.265 5.6778 123.5309 100.9991
PWK/PhJ m 86.455 5.6778 97.7209 75.1891
RIIIS/J m 102.5625 5.6778 113.8284 91.2966
SEA/GnJ m 74.23 5.6778 85.4959 62.9641
SJL/J m 78.4625 5.6778 89.7284 67.1966
SM/J m 91.4325 5.6778 102.6984 80.1666
WSB/EiJ m 213.9033 6.5561 226.9121 200.8946




  GWAS USING LINEAR MIXED MODELS