Project measure / variable:   CSNA03   antici_correct_in_rev_Binghamton

ID, description, units MPD:112888   antici_correct_in_rev_Binghamton   mean number of anticipatory nose pokes made in correct flanking hole without prior successful nose poke in central hole (reversal learning assay), founders   [n]  reversal  
Data set, strains CSNA03   inbred w/CC8   8 strains     sex: both     age:
Procedure operant conditioning chamber
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  STRAIN COMPARISON PLOT
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CSNA03 - mean number of anticipatory nose pokes made in correct flanking hole without prior successful nose poke in central hole (reversal learning assay), founders reversal



  MEASURE SUMMARY
Measure Summary FemaleMale
Number of strains tested8 strains8 strains
Mean of the strain means0.79731   n 0.71181   n
Median of the strain means0.66448   n 0.70803   n
SD of the strain means± 0.36886 ± 0.29753
Coefficient of variation (CV)0.4626 0.418
Min–max range of strain means0.4499   –   1.4196   n 0.2271   –   1.2028   n
Mean sample size per strain5.8   mice 5.0   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
sex 1 0.0946 0.0946 0.3124 0.578
strain 7 7.0928 1.0133 3.3476 0.0039
sex:strain 7 1.8146 0.2592 0.8564 0.5451
Residuals 70 21.1879 0.3027


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 0.4499 0.2251   7 0.08508 0.5003 0.06, 0.7349 -0.94
129S1/SvImJ m 0.9228 0.61496   5 0.27502 0.6664 0.154, 1.74 0.71
A/J f 1.4196 0.78856   6 0.32193 0.5555 0.465, 2.5214 1.69
A/J m 0.84382 0.46068   6 0.18807 0.5459 0.308, 1.6607 0.44
C57BL/6J f 0.9684 0.55251   6 0.22556 0.5705 0.47727, 1.869 0.46
C57BL/6J m 0.80698 0.44216   6 0.18051 0.5479 0.3488, 1.3186 0.32
CAST/EiJ f 0.56066 0.52423   7 0.19814 0.935 0.1667, 1.65 -0.64
CAST/EiJ m 0.49783 0.43481   4 0.21741 0.8734 0.09183, 0.9 -0.72
NOD/ShiLtJ f 1.2183 0.86746   5 0.38794 0.712 0.37209, 2.65 1.14
NOD/ShiLtJ m 1.2028 0.84571   5 0.37821 0.7031 0.49091, 2.3 1.65
NZO/HlLtJ f 0.76829 0.57945   4 0.28973 0.7542 0.12195, 1.4742 -0.08
NZO/HlLtJ m 0.60907 0.42101   4 0.2105 0.6912 0.17532, 1.1818 -0.35
PWK/PhJ f 0.48284 0.55797   6 0.22779 1.1556 0.03, 1.5 -0.85
PWK/PhJ m 0.2271 0.2339   6 0.09549 1.0299 0.05, 0.669 -1.63
WSB/EiJ f 0.51052 0.3064   5 0.13702 0.6002 0.245, 0.98171 -0.78
WSB/EiJ m 0.58407 0.53705   4 0.26853 0.9195 0.23684, 1.3795 -0.43


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ f 0.4499028571 0.2079435712 0.8646332328 0.0351724815
129S1/SvImJ m 0.9228 0.2460421516 1.4135155981 0.4320844019
A/J f 1.419625 0.2246047275 1.867585004 0.971664996
A/J m 0.8438166667 0.2246047275 1.2917766707 0.3958566626
C57BL/6J f 0.9684016667 0.2246047275 1.4163616707 0.5204416626
C57BL/6J m 0.80698 0.2246047275 1.254940004 0.359019996
CAST/EiJ f 0.5606571429 0.2079435712 0.9753875185 0.1459267672
CAST/EiJ m 0.49783 0.2750834881 1.0464667175 -0.0508067175
NOD/ShiLtJ f 1.218318 0.2460421516 1.7090335981 0.7276024019
NOD/ShiLtJ m 1.20284 0.2460421516 1.6935555981 0.7121244019
NZO/HlLtJ f 0.7682875 0.2750834881 1.3169242175 0.2196507825
NZO/HlLtJ m 0.6090675 0.2750834881 1.1577042175 0.0604307825
PWK/PhJ f 0.4828383333 0.2246047275 0.9307983374 0.0348783293
PWK/PhJ m 0.2271 0.2246047275 0.675060004 -0.220860004
WSB/EiJ f 0.510518 0.2460421516 1.0012335981 0.0198024019
WSB/EiJ m 0.5840725 0.2750834881 1.1327092175 0.0354357825


  LEAST SQUARES MEANS (MODEL-ADJUSTED), SEXES COMBINED
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ both 0.6863514286 0.1610723977 1.0076001962 0.365102661
A/J both 1.1317208333 0.1588195259 1.4484763899 0.8149652768
C57BL/6J both 0.8876908333 0.1588195259 1.2044463899 0.5709352768
CAST/EiJ both 0.5292435714 0.1724177008 0.8731198327 0.1853673101
NOD/ShiLtJ both 1.210579 0.1739780738 1.5575673271 0.8635906729
NZO/HlLtJ both 0.6886775 0.1945133998 1.0766222434 0.3007327566
PWK/PhJ both 0.3549691667 0.1588195259 0.6717247232 0.0382136101
WSB/EiJ both 0.54729525 0.1845316137 0.9153319486 0.1792585514




  GWAS USING LINEAR MIXED MODELS