Project measure / variable:   Brown1   groom_LD

ID, description, units MPD:9417   groom_LD   grooming duration   [s]  
Data set, strains Brown1   inbred   14 strains     sex: both     age: 10wks
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  STRAIN COMPARISON PLOT
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Brown1 - grooming duration

SPRET/EiJDBA/2JSJL/JAKR/JCAST/EiJSM/JC57BL/6JC3H/HeJ129S1/SvImJBALB/cByJA/JMOLF/EiJBALB/cJFVB/NJ s 0102030405060708090femalemale


  MEASURE SUMMARY
Measure Summary FemaleMale
Number of strains tested14 strains14 strains
Mean of the strain means6.13   s 5.02   s
Median of the strain means4.37   s 4.58   s
SD of the strain means± 5.35 ± 3.08
Coefficient of variation (CV)0.873 0.613
Min–max range of strain means0.961   –   20.5   s 1.44   –   13.1   s
Mean sample size per strain12.9   mice 13.2   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
sex 1 88.3696 88.3696 1.3148 0.2523
strain 13 5206.6587 400.5122 5.959 < 0.0001
sex:strain 13 1255.726 96.5943 1.4372 0.14
Residuals 348 23389.4574 67.2111


Q-Q normality assessment based on residuals

  


  STRAIN MEANS (UNADJUSTED)
  
Select table page: 1 - 10 / 28 (28)
Strain
Sex
Mean
SD
N mice
SEM
CV
Min, Max
Z score
129S1/SvImJ f 1.24 2.36   12 0.68 1.9 -0.91
129S1/SvImJ m 6.96 7.72   12 2.23 1.11 0.63
A/J f 3.19 4.52   12 1.3 1.42 -0.55
A/J m 2.74 4.22   20 0.944 1.54 -0.74
AKR/J f 6.95 7.58   12 2.19 1.09 0.15
AKR/J m 6.17 6.27   19 1.44 1.02 0.38
BALB/cByJ f 4.93 7.04   22 1.5 1.43 -0.22
BALB/cByJ m 1.44 2.32   12 0.669 1.61 -1.16
BALB/cJ f 2.26 1.88   14 0.502 0.83 -0.72
BALB/cJ m 2.06 4.18   12 1.21 2.03 -0.96
C3H/HeJ f 2.93 3.6   19 0.825 1.23 -0.6
C3H/HeJ m 5.54 5.99   18 1.41 1.08 0.17
C57BL/6J f 3.8 3.43   12 0.991 0.904 -0.44
C57BL/6J m 6.61 5.13   13 1.42 0.776 0.52
CAST/EiJ f 8.67 9.42   9 3.14 1.09 0.47
CAST/EiJ m 3.4 5.13   12 1.48 1.51 -0.53
DBA/2J f 12.9 23.8   14 6.35 1.84 1.26
DBA/2J m 5.73 7.36   13 2.04 1.28 0.23
FVB/NJ f 0.961 1.2   12 0.347 1.25 -0.97
FVB/NJ m 1.87 1.4   12 0.404 0.747 -1.02
MOLF/EiJ f 2.11 4.43   12 1.28 2.1 -0.75
MOLF/EiJ m 3.62 4.55   11 1.37 1.26 -0.45
SJL/J f 8.25 7.72   13 2.14 0.936 0.4
SJL/J m 7.44 6.34   12 1.83 0.852 0.79
SM/J f 7.13 5.15   10 1.63 0.722 1.65, 17.3 0.19
SM/J m 3.55 2.83   11 0.854 0.798 -0.48
SPRET/EiJ f 20.5 21.5   13 5.97 1.05 2.68
SPRET/EiJ m 13.1 11.8   13 3.27 0.904 2.63


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ f 1.2418 2.3666 5.8965 0.0
129S1/SvImJ m 6.9567 2.3666 11.6114 2.302
A/J f 3.1875 2.3666 7.8422 0.0
A/J m 2.7372 1.8332 6.3427 0.0
AKR/J f 6.9492 2.3666 11.6039 2.2945
AKR/J m 6.17 1.8808 9.8692 2.4708
BALB/cByJ f 4.9276 1.7479 8.3654 1.4899
BALB/cByJ m 1.4418 2.3666 6.0965 0.0
BALB/cJ f 2.2629 2.1911 6.5723 0.0
BALB/cJ m 2.0608 2.3666 6.7155 0.0
C3H/HeJ f 2.9254 1.8808 6.6245 0.0
C3H/HeJ m 5.545 1.9323 9.3455 1.7445
C57BL/6J f 3.7983 2.3666 8.453 0.0
C57BL/6J m 6.6145 2.2738 11.0865 2.1424
CAST/EiJ f 8.67 2.7327 14.0448 3.2952
CAST/EiJ m 3.3998 2.3666 8.0545 0.0
DBA/2J f 12.9314 2.1911 17.2408 8.622
DBA/2J m 5.73 2.2738 10.2021 1.2579
FVB/NJ f 0.9609 2.3666 5.6156 0.0
FVB/NJ m 1.8725 2.3666 6.5272 0.0
MOLF/EiJ f 2.1085 2.3666 6.7632 0.0
MOLF/EiJ m 3.6235 2.4719 8.4852 0.0
SJL/J f 8.2478 2.2738 12.7199 3.7758
SJL/J m 7.4375 2.3666 12.0922 2.7828
SM/J f 7.13 2.5925 12.229 2.031
SM/J m 3.5482 2.4719 8.4098 0.0
SPRET/EiJ f 20.5246 2.2738 24.9967 16.0525
SPRET/EiJ m 13.0531 2.2738 17.5252 8.581


  LEAST SQUARES MEANS (MODEL-ADJUSTED), SEXES COMBINED
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ both 4.0992 1.6735 7.3906 0.8079
A/J both 2.9623 1.4968 5.9062 0.0184
AKR/J both 6.5596 1.5115 9.5324 3.5868
BALB/cByJ both 3.1847 1.4711 6.078 0.2915
BALB/cJ both 2.1618 1.6126 5.3335 0.0
C3H/HeJ both 4.2352 1.3483 6.887 1.5834
C57BL/6J both 5.2064 1.641 8.4338 1.9789
CAST/EiJ both 6.0349 1.8075 9.59 2.4798
DBA/2J both 9.3307 1.5788 12.436 6.2255
FVB/NJ both 1.4167 1.6735 4.7081 0.0
MOLF/EiJ both 2.866 1.7111 6.2314 0.0
SJL/J both 7.8427 1.641 11.0701 4.6152
SM/J both 5.3391 1.791 8.8617 1.8165
SPRET/EiJ both 16.7888 1.6078 19.9511 13.6266




  GWAS USING LINEAR MIXED MODELS

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