Phenotype measure:   Peters4   Retic_M24

ID, description, units MPD:24380   Retic_M24   reticulocyte count (Retic)   [n/L]  at age 24mo
Data set, strains Peters4   inbred   13 strains     sex: both     age: 104wks
Procedure complete blood count
Ontology mappings

  STRAIN COMPARISON PLOT
Dimensions Width:   px    Height:   px
Download Plot   
Visualization Options

Peters4 - reticulocyte count (Retic) at age 24mo



  MEASURE SUMMARY
Measure Summary FemaleMale
Number of strains tested11 strains12 strains
Mean of the strain means374   n/L 341   n/L
Median of the strain means340   n/L 329   n/L
SD of the strain means± 107 ± 91.4
Coefficient of variation (CV)0.287 0.268
Min–max range of strain means275   –   623   n/L 258   –   596   n/L
Mean sample size per strain6.7   mice 6.6   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
sex 1 11213.8541 11213.8541 0.5035 0.4794
strain 9 780810.7812 86756.7535 3.8952 0.0002
sex:strain 9 430044.3962 47782.7107 2.1453 0.0307
Residuals 119 2650476.4395 22272.9113


Q-Q normality assessment based on residuals

  


  STRAIN MEANS (UNADJUSTED)
  
Select table page:
Strain Sex Mean SD N mice SEM CV Min, Max Z score
129S1/SvImJ f 325.0 137.0   8 48.5 0.422 157.0, 608.0 -0.46
129S1/SvImJ m 319.0 88.3   12 25.5 0.276 182.0, 457.0 -0.24
A/J m 276.0 3.54   2   2.5 0.0128 273.0, 278.0 -0.71
BALB/cByJ f 460.0 305.0   6 125.0 0.663 253.0, 1072.0 0.8
C57BL/10J f 463.0 244.0   7 92.2 0.527 253.0, 989.0 0.83
C57BL/10J m 303.0 61.7   8 21.8 0.203 256.0, 418.0 -0.42
C57BL/6J f 623.0 215.0   6 87.8 0.345 451.0, 918.0 2.32
C57BL/6J m 398.0 106.0   7 40.2 0.267 284.0, 556.0 0.62
C57BLKS/J f 340.0 85.4   6 34.8 0.251 247.0, 470.0 -0.32
C57BLKS/J m 352.0 28.3   7 10.7 0.0803 307.0, 385.0 0.12
C57BR/cdJ f 379.0 51.0   7 19.3 0.135 301.0, 434.0 0.04
C57BR/cdJ m 596.0 493.0   6 201.0 0.827 342.0, 1599.0 2.79
LP/J f 276.0 60.2   7 22.7 0.218 206.0, 375.0 -0.91
LP/J m 264.0 49.9   5 22.3 0.189 202.0, 341.0 -0.85
NON/ShiLtJ f 287.0 76.5   7 28.9 0.267 173.0, 392.0 -0.81
NON/ShiLtJ m 364.0 58.5   6 23.9 0.161 311.0, 470.0 0.25
NZW/LacJ f 397.0 136.0   7 51.2 0.341 183.0, 600.0 0.21
NZW/LacJ m 348.0 188.0   7 71.2 0.54 0.4, 558.0 0.07
PWD/PhJ f 275.0 73.4   6 30.0 0.267 193.0, 396.0 -0.92
PWD/PhJ m 278.0 17.3   6 7.06 0.0622 258.0, 303.0 -0.69
SM/J m 339.0 133.0   7 50.1 0.392 198.0, 546.0 -0.02
WSB/EiJ f 291.0 75.0   7 28.3 0.257 161.0, 387.0 -0.77
WSB/EiJ m 258.0 43.2   7 16.3 0.168 207.0, 307.0 -0.91


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ f 325.25 52.7647 429.7294 220.7706
129S1/SvImJ m 319.25 43.0822 404.5571 233.9429
C57BL/10J f 463.1429 56.4078 574.836 351.4497
C57BL/10J m 303.25 52.7647 407.7294 198.7706
C57BL/6J f 623.0 60.9274 743.6424 502.3576
C57BL/6J m 397.8571 56.4078 509.5503 286.164
C57BLKS/J f 340.1667 60.9274 460.8091 219.5243
C57BLKS/J m 352.2857 56.4078 463.9789 240.5926
C57BR/cdJ f 379.2857 56.4078 490.9789 267.5926
C57BR/cdJ m 595.8333 60.9274 716.4757 475.1909
LP/J f 275.5714 56.4078 387.2646 163.8783
LP/J m 264.2 66.7427 396.3571 132.0429
NON/ShiLtJ f 286.5714 56.4078 398.2646 174.8783
NON/ShiLtJ m 363.8333 60.9274 484.4757 243.1909
NZW/LacJ f 397.4286 56.4078 509.1217 285.7354
NZW/LacJ m 348.3429 56.4078 460.036 236.6497
PWD/PhJ f 275.3333 60.9274 395.9757 154.6909
PWD/PhJ m 278.3333 60.9274 398.9757 157.6909
WSB/EiJ f 291.4286 56.4078 403.1217 179.7354
WSB/EiJ m 257.8571 56.4078 369.5503 146.164


  LEAST SQUARES MEANS (MODEL-ADJUSTED), SEXES COMBINED
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ both 322.25 34.0595 389.6912 254.8088
C57BL/10J both 383.1964 38.6198 459.6675 306.7254
C57BL/6J both 510.4286 41.515 592.6325 428.2246
C57BLKS/J both 346.2262 41.515 428.4301 264.0223
C57BR/cdJ both 487.5595 41.515 569.7635 405.3556
LP/J both 269.8857 43.6933 356.4029 183.3686
NON/ShiLtJ both 325.2024 41.515 407.4063 242.9984
NZW/LacJ both 372.8857 39.8864 451.8647 293.9067
PWD/PhJ both 276.8333 43.0822 362.1404 191.5263
WSB/EiJ both 274.6429 39.8864 353.6218 195.6639




  GWAS USING LINEAR MIXED MODELS