Project measure / variable:   Schughart1   pctBW_d1

ID, description, units MPD:39301   pctBW_d1   body weight as percent of baseline   [%]  post-infection day 1  
influenza A (H1N1) virus study
Data set, strains Schughart1   inbred   9 strains     sex: both     age: 10-12wks
Procedure body weight
Ontology mappings

  STRAIN COMPARISON PLOT
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Schughart1 - body weight as percent of baseline post-infection day 1



  MEASURE SUMMARY
Measure Summary Female
Number of strains tested9 strains
Mean of the strain means98.4   %
Median of the strain means98.9   %
SD of the strain means± 1.94
Coefficient of variation (CV)0.0197
Min–max range of strain means96.0   –   102   %
Mean sample size per strain10.9   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
sex 1 0.3887 0.3887 0.0414 0.8393
strain 1 135.5433 135.5433 14.4543 0.0003
sex:strain 1 1.3978 1.3978 0.1491 0.7008
Residuals 62 581.3951 9.3773


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
A/JOlaHsd f 99.1 1.87   9 0.622 0.0188 97.2, 103.0 0.36
B6D2F1/J f 96.0 2.99   11 0.9 0.0311 92.0, 100.0 -1.24
BALB/cByJ f 97.5 1.87   10 0.592 0.0192 95.0, 100.0 -0.46
C57BL/6J f 99.2 3.33   22 0.709 0.0335 93.3, 105.0 0.41
CBA/J f 97.0 2.79   9 0.931 0.0288 92.0, 100.0 -0.72
DBA/2J f 96.1 3.04   20 0.681 0.0317 89.1, 103.0 -1.19
DBA/2NHsd f 98.9 2.23   7 0.842 0.0225 95.3, 103.0 0.26
FVB/NJ f 99.8 2.68   10 0.846 0.0268 97.1, 105.0 0.72
SJL/JOrlCrl f 102.0 3.59   7 1.36 0.0352 95.9, 106.0 1.86


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
C57BL/6J f 99.2136 0.6529 100.5187 97.9086
C57BL/6J m 98.825 0.884 100.5921 97.0579
DBA/2J f 96.125 0.6847 97.4938 94.7562
DBA/2J m 96.3417 0.884 98.1087 94.5746


  LEAST SQUARES MEANS (MODEL-ADJUSTED), SEXES COMBINED
Strain Sex Mean SEM UpperCL LowerCL
C57BL/6J both 99.0193 0.5495 100.1177 97.9209
DBA/2J both 96.2333 0.5591 97.3509 95.1157




  GWAS USING LINEAR MIXED MODELS