Project measure / variable:   CSNA03   phase_in

ID, description, units MPD:112540   phase_in   specific time point when bioluminescence reaches highest circadian rhythm amplitude (cycle number), in fibroblast cultures, founders   [n]  
Data set, strains CSNA03   inbred w/CC8   8 strains     sex: both     age: 8-12wks
Procedure in vitro assay
Ontology mappings

  STRAIN COMPARISON PLOT
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CSNA03 - specific time point when bioluminescence reaches highest circadian rhythm amplitude (cycle number), in fibroblast cultures, founders

A/JC57BL/6JWSB/EiJPWK/PhJNOD/ShiLtJ129S1/SvImJNZO/HlLtJCAST/EiJ n 0.51.01.52.02.53.03.54.04.55.05.56.06.5femalemale


  MEASURE SUMMARY
Measure Summary FemaleMale
Number of strains tested8 strains8 strains
Mean of the strain means3.1529   n 3.4864   n
Median of the strain means2.9845   n 3.3824   n
SD of the strain means± 0.77817 ± 0.7578
Coefficient of variation (CV)0.2468 0.2174
Min–max range of strain means1.978   –   4.5035   n 2.0932   –   4.3578   n
Mean sample size per strain9.4   mice 9.0   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
sex 1 4.0129 4.0129 4.0858 0.0453
strain 7 60.7179 8.674 8.8317 < 0.0001
sex:strain 7 18.6024 2.6575 2.7058 0.0119
Residuals 131 128.6601 0.9821


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 2.7396 0.86203   10 0.2726 0.3147 1.2015, 3.9857 -0.53
129S1/SvImJ m 3.3415 0.65622   9 0.21874 0.1964 2.7477, 4.879 -0.19
A/J f 3.9739 1.4927   9 0.49755 0.3756 0.96967, 5.7873 1.05
A/J m 4.3578 1.3637   9 0.45458 0.3129 2.1573, 6.211 1.15
C57BL/6J f 4.5035 0.94989   10 0.30038 0.2109 3.3537, 6.4568 1.74
C57BL/6J m 3.4233 0.69286   10 0.2191 0.2024 2.5543, 4.4967 -0.08
CAST/EiJ f 1.978 0.90551   10 0.28635 0.4578 0.84725, 3.4713 -1.51
CAST/EiJ m 2.0932 0.83365   10 0.26362 0.3983 0.58775, 3.5928 -1.84
NOD/ShiLtJ f 3.2399 1.0086   9 0.33619 0.3113 2.178, 4.787 0.11
NOD/ShiLtJ m 3.2767 0.86524   9 0.28841 0.2641 1.3388, 4.36 -0.28
NZO/HlLtJ f 2.8196 1.0878   8 0.3846 0.3858 0.78933, 3.886 -0.43
NZO/HlLtJ m 3.0046 1.2124   7 0.45825 0.4035 1.1, 4.1555 -0.64
PWK/PhJ f 2.9007 0.47471   10 0.15012 0.1637 1.9387, 3.524 -0.32
PWK/PhJ m 4.2627 1.2099   10 0.3826 0.2838 2.3375, 5.7205 1.02
WSB/EiJ f 3.0683 1.026   9 0.34201 0.3344 1.4805, 4.7633 -0.11
WSB/EiJ m 4.1312 0.83626   8 0.29566 0.2024 3.0187, 5.1455 0.85


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ f 2.73964 0.3133908223 3.3596018025 2.1196781975
129S1/SvImJ m 3.3415 0.3303429321 3.9949971194 2.6880028806
A/J f 3.9738522222 0.3303429321 4.6273493416 3.3203551029
A/J m 4.3577666667 0.3303429321 5.011263786 3.7042695473
C57BL/6J f 4.50349 0.3133908223 5.1234518025 3.8835281975
C57BL/6J m 3.42326 0.3133908223 4.0432218025 2.8032981975
CAST/EiJ f 1.978005 0.3133908223 2.5979668025 1.3580431975
CAST/EiJ m 2.093185 0.3133908223 2.7131468025 1.4732231975
NOD/ShiLtJ f 3.2398888889 0.3303429321 3.8933860083 2.5863917695
NOD/ShiLtJ m 3.2767111111 0.3303429321 3.9302082305 2.6232139917
NZO/HlLtJ f 2.81955375 0.3503815911 3.5126921169 2.1264153831
NZO/HlLtJ m 3.0046 0.3745736767 3.745596083 2.263603917
PWK/PhJ f 2.90068 0.3133908223 3.5206418025 2.2807181975
PWK/PhJ m 4.26271 0.3133908223 4.8826718025 3.6427481975
WSB/EiJ f 3.0683222222 0.3303429321 3.7218193416 2.4148251029
WSB/EiJ m 4.13125 0.3503815911 4.8243883669 3.4381116331


  LEAST SQUARES MEANS (MODEL-ADJUSTED), SEXES COMBINED
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ both 3.04057 0.227673154 3.4909618076 2.5901781924
A/J both 4.1658094444 0.2335877274 4.627901689 3.7037171999
C57BL/6J both 3.963375 0.2216007756 4.4017541946 3.5249958054
CAST/EiJ both 2.035595 0.2216007756 2.4739741946 1.5972158054
NOD/ShiLtJ both 3.2583 0.2335877274 3.7203922446 2.7962077554
NZO/HlLtJ both 2.912076875 0.2564530652 3.4194022121 2.4047515379
PWK/PhJ both 3.581695 0.2216007756 4.0200741946 3.1433158054
WSB/EiJ both 3.5997861111 0.2407767182 4.0760998944 3.1234723278




  GWAS USING LINEAR MIXED MODELS

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