Project measure / variable:   Rice1   Krt31

ID, description, units MPD:49221   Krt31   KRT31, spectral counts, hair proteomics    
Data set, strains Rice1   inbred   11 strains     sex: both     age: 10-19wks
Procedure proteomic profiling
Ontology mappings

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  STRAIN COMPARISON PLOT
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Rice1 - KRT31, spectral counts, hair proteomics



  MEASURE SUMMARY
Measure Summary FemaleMale
Number of strains tested11 strains11 strains
Mean of the strain means75.2   None 75.1   None
Median of the strain means77.0   None 56.6   None
SD of the strain means± 44.8 ± 42.8
Coefficient of variation (CV)0.596 0.569
Min–max range of strain means31.5   –   182   None 39.0   –   181   None
Mean sample size per strain2.0   mice 2.0   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
sex 1 0.0032 0.0032 0.0 0.9988
strain 10 57189.6952 5718.9695 3.9024 0.0037
sex:strain 10 19495.5639 1949.5564 1.3303 0.2752
Residuals 22 32240.6153 1465.4825


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
C3H/HeJ f 55.1 30.7   2   21.7 0.557 33.4, 76.8 -0.45
C3H/HeJ m 56.6 6.13   2   4.33 0.108 52.3, 61.0 -0.43
C57BL/6J f 31.6 9.94   2   7.03 0.315 24.5, 38.6 -0.97
C57BL/6J m 39.0 25.8   2   18.2 0.661 20.8, 57.3 -0.84
CAST/EiJ f 118.0 12.0   2   8.52 0.102 110.0, 127.0 0.96
CAST/EiJ m 42.0 24.6   2   17.4 0.585 24.6, 59.4 -0.77
DBA/1J f 182.0 43.3   2   30.6 0.239 151.0, 212.0 2.38
DBA/1J m 117.0 79.3   2   56.1 0.675 61.3, 173.0 0.98
MRL/MpJ f 77.0 20.7   2   14.7 0.269 62.3, 91.6 0.04
MRL/MpJ m 62.2 9.35   2   6.61 0.15 55.6, 68.8 -0.3
MRL/MpJ Faslpr-Foxq1sa-J/J f 79.2 56.0   2   39.6 0.707 39.6, 119.0 0.09
MRL/MpJ Faslpr-Foxq1sa-J/J m 80.3 4.55   2   3.22 0.0567 77.1, 83.6 0.12
NOD/ShiLtJ f 51.3 23.8   2   16.8 0.464 34.5, 68.2 -0.53
NOD/ShiLtJ m 51.8 10.8   2   7.61 0.208 44.2, 59.4 -0.54
NZW/LacJ f 32.8 8.34   2   5.9 0.255 26.9, 38.7 -0.95
NZW/LacJ m 55.2 32.1   2   22.7 0.581 32.5, 77.9 -0.47
SB/LeJ f 31.5 7.53   2   5.32 0.239 26.1, 36.8 -0.98
SB/LeJ m 44.0 2.7   2   1.91 0.0614 42.1, 45.9 -0.73
STOCK a/a Tmem79ma Flgft/J f 90.0 98.7   2   69.8 1.1 20.2, 160.0 0.33
STOCK a/a Tmem79ma Flgft/J m 181.0 53.6   2   37.9 0.296 143.0, 219.0 2.48
WSB/EiJ f 78.6 28.6   2   20.2 0.364 58.3, 98.8 0.08
WSB/EiJ m 96.9 51.5   2   36.4 0.531 60.5, 133.0 0.51


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
C3H/HeJ f 55.106 27.0692 111.2441 0.0
C3H/HeJ m 56.627 27.0692 112.7651 0.4889
C57BL/6J f 31.558 27.0692 87.6961 0.0
C57BL/6J m 39.025 27.0692 95.1631 0.0
CAST/EiJ f 118.0365 27.0692 174.1746 61.8984
CAST/EiJ m 41.9865 27.0692 98.1246 0.0
DBA/1J f 181.519 27.0692 237.6571 125.3809
DBA/1J m 117.3905 27.0692 173.5286 61.2524
MRL/MpJ f 76.9805 27.0692 133.1186 20.8424
MRL/MpJ m 62.2205 27.0692 118.3586 6.0824
MRL/MpJ Faslpr-Foxq1sa-J/J f 79.2265 27.0692 135.3646 23.0884
MRL/MpJ Faslpr-Foxq1sa-J/J m 80.3345 27.0692 136.4726 24.1964
NOD/ShiLtJ f 51.3065 27.0692 107.4446 0.0
NOD/ShiLtJ m 51.817 27.0692 107.9551 0.0
NZW/LacJ f 32.7615 27.0692 88.8996 0.0
NZW/LacJ m 55.2015 27.0692 111.3396 0.0
SB/LeJ f 31.4575 27.0692 87.5956 0.0
SB/LeJ m 43.9635 27.0692 100.1016 0.0
STOCK a/a Tmem79ma Flgft/J f 89.952 27.0692 146.0901 33.8139
STOCK a/a Tmem79ma Flgft/J m 181.2005 27.0692 237.3386 125.0624
WSB/EiJ f 78.5915 27.0692 134.7296 22.4534
WSB/EiJ m 96.9175 27.0692 153.0556 40.7794


  LEAST SQUARES MEANS (MODEL-ADJUSTED), SEXES COMBINED
Strain Sex Mean SEM UpperCL LowerCL
C3H/HeJ both 55.8665 19.1408 95.5621 16.1709
C57BL/6J both 35.2915 19.1408 74.9871 0.0
CAST/EiJ both 80.0115 19.1408 119.7071 40.3159
DBA/1J both 149.4548 19.1408 189.1504 109.7591
MRL/MpJ both 69.6005 19.1408 109.2961 29.9049
MRL/MpJ Faslpr-Foxq1sa-J/J both 79.7805 19.1408 119.4761 40.0849
NOD/ShiLtJ both 51.5618 19.1408 91.2574 11.8661
NZW/LacJ both 43.9815 19.1408 83.6771 4.2859
SB/LeJ both 37.7105 19.1408 77.4061 0.0
STOCK a/a Tmem79ma Flgft/J both 135.5763 19.1408 175.2719 95.8806
WSB/EiJ both 87.7545 19.1408 127.4501 48.0589




  GWAS USING LINEAR MIXED MODELS