Project measure / variable:   Brown2   odorR_dig_pct

ID, description, units MPD:22532   odorR_dig_pct   percent digging odor pot, conditioned stimulus rose   [%]  
Data set, strains Brown2   inbred   13 strains     sex: both     age: 10-18wks
Procedure olfactory discrimination task
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  STRAIN COMPARISON PLOT
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Brown2 - percent digging odor pot, conditioned stimulus rose



  MEASURE SUMMARY
Measure Summary FemaleMale
Number of strains tested13 strains13 strains
Mean of the strain means78.1   % 84.7   %
Median of the strain means79.7   % 86.6   %
SD of the strain means± 15.2 ± 11.8
Coefficient of variation (CV)0.194 0.140
Min–max range of strain means49.1   –   100   % 62.0   –   100   %
Mean sample size per strain5.5   mice 5.1   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
sex 1 1551.1359 1551.1359 1.6115 0.2069
strain 11 14145.409 1285.9463 1.336 0.214
sex:strain 11 6288.0423 571.6402 0.5939 0.8305
Residuals 113 108769.7666 962.5643


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 79.7 40.0   6 16.3 0.502 0.1
129S1/SvImJ m 62.0 48.8   6 19.9 0.787 -1.91
A/J f 49.1 53.7   6 21.9 1.1 -1.91
A/J m 78.4 34.2   10 10.8 0.436 -0.53
AKR/J f 77.8 23.4   6 9.54 0.301 33.2, 100.0 -0.02
AKR/J m 74.1 36.8   6 15.0 0.496 -0.89
BALB/cByJ f 84.6 17.3   9 5.76 0.204 57.4, 100.0 0.42
BALB/cByJ m 98.3 3.35   4 1.67 0.0341 93.3, 100.0 1.15
BALB/cJ f 61.9 32.4   11 9.77 0.524 8.58, 100.0 -1.07
BALB/cJ m 83.0 11.9   5 5.34 0.144 68.7, 100.0 -0.14
C3H/HeJ f 92.3 16.2   5 7.23 0.175 63.4, 100.0 0.93
C3H/HeJ m 91.6 13.8   5 6.18 0.151 68.2, 100.0 0.58
C57BL/6J f 100.0 0.0   1   0.0 0.0 100.0, 100.0 1.44
C57BL/6J m 100.0 0.0   2   0.0 0.0 100.0, 100.0 1.29
CAST/EiJ f 91.2 17.6   4 8.78 0.192 64.9, 100.0 0.86
CAST/EiJ m 86.6 20.3   4 10.1 0.234 57.2, 100.0 0.16
DBA/2J f 71.3 26.3   6 10.7 0.369 31.6, 95.6 -0.45
DBA/2J m 73.7 30.9   6 12.6 0.419 35.6, 100.0 -0.93
FVB/NJ f 66.7 57.7   3 33.3 0.866 -0.75
FVB/NJ m 73.3 25.9   5 11.6 0.353 44.6, 100.0 -0.96
MOLF/EiJ f 97.4 5.77   5 2.58 0.0592 87.1, 100.0 1.27
MOLF/EiJ m 100.0 0.0   4 0.0 0.0 100.0, 100.0 1.29
SJL/J f 80.5 43.5   5 19.5 0.541 2.64, 100.0 0.15
SJL/J m 91.4 12.2   5 5.46 0.134 73.8, 100.0 0.57
SM/J f 63.4 44.0   5 19.7 0.694 8.31, 100.0 -0.97
SM/J m 88.5 25.8   6 10.5 0.292 35.9, 100.0 0.32


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ f 79.7333 12.666 104.827 54.6397
129S1/SvImJ m 61.9833 12.666 87.077 36.8897
A/J f 49.05 12.666 74.1436 23.9564
A/J m 78.44 9.811 97.8774 59.0026
AKR/J f 77.75 12.666 102.8436 52.6564
AKR/J m 74.0667 12.666 99.1603 48.973
BALB/cByJ f 84.6111 10.3417 105.1 64.1223
BALB/cByJ m 98.325 15.5126 129.0583 67.5917
BALB/cJ f 61.8618 9.3545 80.3947 43.329
BALB/cJ m 83.0 13.8749 110.4887 55.5113
C3H/HeJ f 92.28 13.8749 119.7687 64.7913
C3H/HeJ m 91.58 13.8749 119.0687 64.0913
CAST/EiJ f 91.225 15.5126 121.9583 60.4917
CAST/EiJ m 86.625 15.5126 117.3583 55.8917
DBA/2J f 71.3 12.666 96.3936 46.2064
DBA/2J m 73.6833 12.666 98.777 48.5897
FVB/NJ f 66.6667 17.9124 102.1544 31.1789
FVB/NJ m 73.32 13.8749 100.8087 45.8313
MOLF/EiJ f 97.42 13.8749 124.9087 69.9313
MOLF/EiJ m 100.0 15.5126 130.7333 69.2667
SJL/J f 80.528 13.8749 108.0167 53.0393
SJL/J m 91.42 13.8749 118.9087 63.9313
SM/J f 63.362 13.8749 90.8507 35.8733
SM/J m 88.4833 12.666 113.577 63.3897


  LEAST SQUARES MEANS (MODEL-ADJUSTED), SEXES COMBINED
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ both 70.8583 8.9562 88.6022 53.1145
A/J both 63.745 8.0107 79.6156 47.8744
AKR/J both 75.9083 8.9562 93.6522 58.1645
BALB/cByJ both 91.4681 9.3219 109.9365 72.9997
BALB/cJ both 72.4309 8.3669 89.0072 55.8546
C3H/HeJ both 91.93 9.811 111.3674 72.4926
CAST/EiJ both 88.925 10.9691 110.6567 67.1933
DBA/2J both 72.4917 8.9562 90.2355 54.7478
FVB/NJ both 69.9933 11.3288 92.4377 47.5489
MOLF/EiJ both 98.71 10.4062 119.3265 78.0935
SJL/J both 85.974 9.811 105.4114 66.5366
SM/J both 75.9227 9.3934 94.5326 57.3127




  GWAS USING LINEAR MIXED MODELS