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Table 3 Regression equation model for prediction of rice grain size

From: Influence of Multi-Gene Allele Combinations on Grain Size of Rice and Development of a Regression Equation Model to Predict Grain Parameters

    Grain length Grain width Grain length to width ratio 1000-grain weight
Gene name Primer name Variable Parameter estimate T value Parameter estimate T value Parameter estimate T value Parameter estimate T value
GS3 GS3-PstI GS3-1 -0.901±0.17 -5.24** 0.1169±0.06 1.84 -0.4724±0.08 -5.34** -1.2166±0.94 -1.29
GS3-2 -1.4351±0.17 -8.17** 0.229±0.06 3.52** -0.7206±0.09 -7 97** -1.9387±0.96 -2*
GS5 GS5-TaqI, GS5-SalI GS5-1 - - -0.0607±0.05 1.15 -0.0769±0.07 -1.05 -  
GS5-2 - - -0.0632±0.07 -0.81 0.0764±0.1 0.7 -  
GS6 indel-GS6 GS6 0.0008±0.12 0.01 -0.1564±0.04 -3.28** 0.1532±0.06 2.3* -2.4581±0.68 -3.59**
GW2 GW2-ScaI GW2 - - 0.4838±0.16 2.87** - - 20.9539±2.53 8.26**
qSW5/GW5 N1212del qSW5-1 0.7148±0.15 4.53** -0.4591±0.05 -7.89** 0.7302±0.08 8.99** - -
qSW5-2 0.0862±0.18 0.46 -0.0188±0.07 -0.27 0.1054±0.09 1.08 - -
GW8/OsSPL16 indel-GW8, seq-GW8 GW8-1 0.5161±0.15 3.32** -0.1267±0.05 -2.21* 0.2907±0.07 3.64** 0.7881±0.78 1
GW8-2 0.2797±0.17 1.61 -0.2075±0.06 -3.24** 0.2845±0.08 3.18** -1.4489±0.92 -1.57
  intercept   8.7539±0.24 35.67** 3.2556±0.09 35.95** 2.7589±0.12 21.83** 29.4539±0.91 32.13**
  total R   0.4391 0.5913 0.6339 0.3286
  1. ** and * indicate the significance at 1 % and 5 % level, respectively
  2. A Dummy variable substitution;
  3. GS3: A-allele (0,0), B-allele (1,0), and C-allele (0,1)
  4. GS5: H94 type (0,0), Zhonghua 11 type (1,0), and Zhenshan 97 type (0,1)
  5. GS6: Type I (0) and Type II/III (1)
  6. GW2: FAZ1 allele (0) and WY3 allele (1)
  7. qSW5/GW5: Indica II type (0,0), Kasalath type (1,0), and Nipponbare type (0,1)
  8. GW8/OsSPL16: Basmati allele (0,0), HJX74 allele (1,0), and TN1 allele (0,1)