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Triple categorical regression for genomic selection: application to cassava breeding

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Table 2 – Allele frequencies (freq), genotypic values (GVs),  parametric (theoretical) genetic additive effects (u a ), dominance  effects (u d ), and variances obtained from the triple categorical  regression method
Table 3 shows the average results of accuracy,  prediction bias, and heritability obtained by the TCR,  G-BLUP, and BLASSO methods, associated with the  predicted additive genomic values that considered the  absence of dominance and complete dominance
Table 5 shows the average results of additive  heritability and heritability due to dominance  asso-ciated with the heritability estimation through total  genotype variation
Table 6 – Additive heritability ( h 2 a M ), predictive ability (r ây ), and  bias (b yâ ) of the additive genomic values estimated by the triple  categorical regression (TCR), genomic best linear unbiased  predictor (G-BLUP), and TCR/G-BLUP models on real

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