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Table 1 Estimates of residual variancea (\( {\boldsymbol{\sigma}}_{\boldsymbol{e}\;}^2 \)), additive genetic varianceb (\( {\boldsymbol{V}}_{\boldsymbol{A}\;}^{\boldsymbol{\prime}} \)) and heritabilityc (\( {\boldsymbol{h}}^2 \)) with their standard errors (±SE) for SRS resistance phenotypes DAYS and STATUS using different modelsd

From: Genomic predictions can accelerate selection for resistance against Piscirickettsia salmonis in Atlantic salmon (Salmo salar)

Model

Trait

Days

Status

\( {\boldsymbol{\sigma}}_{\boldsymbol{e}\;}^2 \)

\( {\boldsymbol{V}}_{\boldsymbol{A}\;}^{\boldsymbol{\prime}} \)

\( {\boldsymbol{h}}^2\pm \boldsymbol{S}\boldsymbol{E} \)

\( {\boldsymbol{V}}_{\boldsymbol{A}\;}^{\boldsymbol{\prime}} \)

\( {\boldsymbol{h}}^2\pm \boldsymbol{S}\boldsymbol{E} \)

PBLUP

73.079

16.640

0.185 ± 0.038

0.358

0.260 ± 0.037

GBLUP

65.323

24.295

0.271 ± 0.041

0.661

0.393 ± 0.040

BAYESC

65.454

17.392

0.210 ± 0.031

0.442

0.303 ± 0.054

PBAYESC

65.348

19.318

0.228 ± 0.032

0.417

0.290 ± 0.053

BLASSO

64.963

18.037

0.217 ± 0.030

0.439

0.300 ± 0.063

PBLASSO

65.118

19.530

0.231 ± 0.034

0.374

0.269 ± 0.052

  1. aResidual variance for binary survival STATUS was set to 1
  2. b, cTotal additive genetic variance \( {\boldsymbol{V}}_{\boldsymbol{A}\;}^{\boldsymbol{\prime}} \): PBLUP and GBLUP was \( {\boldsymbol{\sigma}}_{\boldsymbol{u}\;}^2 \); BAYESC and BLASSO was \( 2{\sigma}_{a\;}^2\pi \sum {p}_i{q}_i \); PBAYESC and PBLASSO was \( 2{\sigma}_{a\;}^2\pi \sum {p}_i{q}_i+{\sigma}_{u\;}^2 \)
  3. cHeritability \( {\boldsymbol{h}}^2 \) : PBLUP and GBLUP \( \frac{\sigma_{u\;}^2}{\sigma_{u\;}^2+{\sigma}_{e\;}^2} \); BAYESC, BLASSO, PBAYESC and PBLASSO \( \frac{V_{A\;}^{\prime }}{V_{A\;}^{\prime }+{\sigma}_{e\;}^2} \)
  4. dModels with pedigree: pedigree based BLUP (PBLUP), genomic BLUP (GBLUP) and Bayesian estimation methods with additive SNP effects and polygenic pedigree (PBAYESC and PBLASSO); Models with only additive SNP effects: Bayesian estimation methods (BAYESC and BLASSO)