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Table 2 Mean (SD) of the “true” variance components and heritability in the base (founder) population (P-base) and the base population for the genomic phasea and estimates of variance components and heritabilities from P-AM, ssGBLUP, and ssBR based on three scenarios of phenotypingb

From: Bias in estimates of variance components in populations undergoing genomic selection: a simulation study

MethodScenariob\( {\sigma}_g^2 \)(\( {\sigma}_{\varepsilon}^2 \))\( {\sigma}_e^2 \)\( {\sigma}_{\alpha}^2\sum \limits_{j=1}^m2{p}_j\left(1-{p}_j\right) \)h2c
True VCs in P-base 3.59 (0.03)5.05 (0.09)0.42 (0.004)
True VCs in G-base 2.82 (0.09)5.10 (0.07)0.36 (0.009)
P-AMPheno13.62 (0.12)5.02 (0.02)0.42 (0.009)
Pheno1 + 23.52 (0.06)5.07 (0.03)0.41 (0.004)
Pheno23.19 (0.20)*5.21 (0.10)0.38 (0.018)*
ssGBLUPPheno12.99 (0.12)***5.54 (0.01)***0.35 (0.009)***
Pheno1 + 23.39 (0.06)***5.17 (0.03)0.40 (0.009)***
Pheno23.36 (0.15)***5.19 (0.05)0.39 (0.013)*
ssBRPheno13.44 (0.15)5.10 (0.04)4.20 (0.10)***0.40 (0.012)
Pheno1 + 23.14 (0.10)***5.24 (0.02)*3.71 (0.11)0.37 (0.008)***
Pheno22.99 (0.20)5.27 (0.06)**2.97 (0.09)0.36 (0.017)
  1. aP-AM: traditional pedigree-based animal model; ssGBLUP: single-step genomic BLUP; ssBR: single-step Bayesian regression. \( {\sigma}_g^2 \) is the genetic variance used in P-AM, \( {\sigma}_{\varepsilon}^2 \) is the total genetic variance used in ssBR; \( {\sigma}_e^2 \) is the residual variance; \( {\sigma}_{\alpha}^2 \) is the marker variance; \( {\sigma}_{\alpha}^2\sum \limits_{j=1}^m2{p}_j\left(1-{p}_j\right) \) is used to calculate genetic variance via the estimated marker variance in ssBR, where pj is the observed allele frequency at locus j, and m is the total number of markers
  2. bPheno1: phenotypes from only the conventional phase (1–20 years) were used; Pheno1 + 2: phenotypes from both the conventional phase and genomic selection phase (1–35 years) were used; Pheno2: phenotypes from only the genomic selection phase were used (21–35 years)
  3. cHeritabilities (h2) from P-AM, ssGBLUP, and ssBR calculated as \( \frac{\sigma_{g\left(\varepsilon \right)}^2}{\left({\sigma}_{g\left(\varepsilon \right)}^2+{\sigma}_e^2\right)} \)
  4. The significance test was performed to determine whether the estimated parameter differs from the simulated parameter. * significant at P < 0.01; ** significant at P < 0.005; *** significant at P < 0.001