Table 3 Comparison of natural bin and artificial bin analyses for eight traits in rice
Type of binParameterYDTPGNKGWGLGWHDOsC1
Phenotypic variance19.73791.4774372.70346.38870.30810.047763.42830.2455
Natural binNo. bins16191619161916191619161916191619
Mean squared error (MSE)16.68840.7674226.29781.35490.06460.012747.44790.0048
Residual error variance9.07430.1897102.70380.29610.01000.005739.38230.0002
Embedded Image (proportion)0.15450.48050.39280.78790.79020.73370.25190.9801
Embedded Image (Pearson)0.16250.48100.39320.78480.79250.73440.26360.9807
No. nonzero effects541017410113961142
Artificial binOptimal bin size (Mb)0.100.200.750.050.502.000.300.20
Optimal no. bins37291869501745175019112471869
MSE16.36070.7551211.64791.36480.05840.013046.90940.0009
Residual error variance9.41650.187677.65180.34240.01350.004439.67480.0007
Embedded Image (proportion)0.17110.48890.43210.78630.81010.72580.26040.9962
Embedded Image (Pearson)0.17410.49340.43840.78710.81080.72900.27210.9962
No. nonzero effects791638726011288192
  • YD, yield per plant; TP, tiller number per plant; GN, number of grains per panicle; KGW, 1000-grain weight; GL, grain length; GW, grain width; HD, heading date; OsC1, apicule color (a binary trait controlled by a single gene that has been cloned); Embedded Image (proportion) = 1 – MSE/phenotypic variance (proportion of phenotypic valiance explained by markers); Embedded Image (Pearson), the squared Pearson correlation between predicted and observed phenotypic values.