Phenotype measure:   Brown2   acq_lat_d3


  STRAIN COMPARISON PLOT
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Brown2 - acquistion training mean latency to find hidden platform, day 3



  MEASURE SUMMARY
Measure Summary FemaleMale
Number of strains tested13 strains14 strains
Mean of the strain means36.9   s 38.4   s
Median of the strain means40.6   s 42.0   s
SD of the strain means± 11.0 ± 11.4
Coefficient of variation (CV)0.298 0.297
Min–max range of strain means17.9   –   52.7   s 18.5   –   54.3   s
Mean sample size per strain10.4   mice 10.0   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
sex 1 88.1282 88.1282 0.5222 0.4706
strain 12 27566.806 2297.2338 13.6123 < 0.0001
sex:strain 12 1087.3618 90.6135 0.5369 0.8894
Residuals 239 40333.9694 168.7614


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 17.9 8.38   12 2.42 0.469 8.22, 35.7 -1.73
129S1/SvImJ m 18.5 9.56   12 2.76 0.516 8.75, 43.4 -1.74
A/J f 48.9 11.0   6 4.5 0.226 32.1, 60.0 1.1
A/J m 48.2 11.2   18 2.65 0.233 26.8, 59.9 0.86
AKR/J f 31.7 15.9   12 4.58 0.501 8.68, 60.0 -0.47
AKR/J m 33.5 12.5   12 3.6 0.373 14.2, 51.0 -0.43
BALB/cByJ f 39.2 16.0   15 4.13 0.408 15.3, 60.0 0.21
BALB/cByJ m 35.9 14.8   6 6.04 0.412 17.3, 60.0 -0.22
BALB/cJ f 40.6 12.1   14 3.23 0.297 14.6, 60.0 0.34
BALB/cJ m 45.4 13.2   10 4.17 0.291 25.3, 60.0 0.61
C3H/HeJ f 41.0 13.4   10 4.23 0.326 19.9, 60.0 0.38
C3H/HeJ m 44.7 13.0   9 4.34 0.291 24.3, 60.0 0.55
C57BL/6J f 24.5 12.8   12 3.7 0.524 8.73, 48.5 -1.13
C57BL/6J m 25.4 17.0   12 4.92 0.672 6.54, 60.0 -1.14
CAST/EiJ f 45.7 18.4   10 5.81 0.403 5.74, 60.0 0.8
CAST/EiJ m 52.0 9.29   11 2.8 0.179 31.3, 60.0 1.19
DBA/2J f 22.7 9.14   6 3.73 0.402 14.8, 39.2 -1.29
DBA/2J m 22.3 8.78   6 3.58 0.394 13.0, 33.4 -1.41
FVB/NJ f 52.7 7.22   6 2.95 0.137 40.4, 60.0 1.44
FVB/NJ m 39.5 15.2   6 6.19 0.384 15.2, 60.0 0.1
MOLF/EiJ f 46.2 10.4   11 3.14 0.225 22.1, 59.9 0.85
MOLF/EiJ m 44.4 11.7   4 5.87 0.264 33.0, 57.2 0.53
SJL/J f 41.0 14.7   12 4.24 0.358 22.7, 60.0 0.38
SJL/J m 46.0 14.6   12 4.23 0.318 10.8, 60.0 0.67
SM/J f 27.2 13.2   9 4.4 0.486 17.3, 59.9 -0.88
SM/J m 27.5 11.6   12 3.34 0.421 7.73, 53.4 -0.96
SPRET/EiJ m 54.3 7.15   12 2.06 0.132 35.8, 60.0 1.39


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ f 17.8933 3.7501 25.2809 10.5058
129S1/SvImJ m 18.5042 3.7501 25.8917 11.1166
A/J f 48.85 5.3035 59.2975 38.4025
A/J m 48.2111 3.062 54.243 42.1792
AKR/J f 31.665 3.7501 39.0525 24.2775
AKR/J m 33.4583 3.7501 40.8459 26.0708
BALB/cByJ f 39.1533 3.3542 45.7609 32.5457
BALB/cByJ m 35.9 5.3035 46.3475 25.4525
BALB/cJ f 40.6143 3.4719 47.4538 33.7748
BALB/cJ m 45.42 4.1081 53.5126 37.3274
C3H/HeJ f 41.05 4.1081 49.1426 32.9574
C3H/HeJ m 44.6556 4.3303 53.1859 36.1252
C57BL/6J f 24.4858 3.7501 31.8734 17.0983
C57BL/6J m 25.3842 3.7501 32.7717 17.9966
CAST/EiJ f 45.674 4.1081 53.7666 37.5814
CAST/EiJ m 51.9636 3.9169 59.6797 44.2476
DBA/2J f 22.7333 5.3035 33.1809 12.2858
DBA/2J m 22.3 5.3035 32.7475 11.8525
FVB/NJ f 52.6667 5.3035 63.1142 42.2191
FVB/NJ m 39.5 5.3035 49.9475 29.0525
MOLF/EiJ f 46.1636 3.9169 53.8797 38.4476
MOLF/EiJ m 44.4 6.4954 57.1956 31.6044
SJL/J f 41.0083 3.7501 48.3959 33.6208
SJL/J m 46.0333 3.7501 53.4209 38.6458
SM/J f 27.1778 4.3303 35.7082 18.6474
SM/J m 27.5192 3.7501 34.9067 20.1316


  LEAST SQUARES MEANS (MODEL-ADJUSTED), SEXES COMBINED
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ both 18.1988 2.6517 23.4225 12.975
A/J both 48.5306 3.062 54.5624 42.4987
AKR/J both 32.5617 2.6517 37.7854 27.3379
BALB/cByJ both 37.5267 3.1376 43.7075 31.3458
BALB/cJ both 43.0171 2.6894 48.315 37.7193
C3H/HeJ both 42.8528 2.9844 48.7319 36.9736
C57BL/6J both 24.935 2.6517 30.1588 19.7112
CAST/EiJ both 48.8188 2.838 54.4096 43.228
DBA/2J both 22.5167 3.7501 29.9042 15.1291
FVB/NJ both 46.0833 3.7501 53.4709 38.6958
MOLF/EiJ both 45.2818 3.7925 52.7528 37.8108
SJL/J both 43.5208 2.6517 48.7446 38.2971
SM/J both 27.3485 2.8642 32.9908 21.7062




  GWAS USING LINEAR MIXED MODELS