Additional file 1 of Statistical model and testing designs to increase response to selection with constrained inbreeding in genomic breeding programs for pigs affected by social genetic effects
Creators: name: Chu, Thinh Tuan
name: Henryon, Mark
name: Jensen, Just
name: Ask, Birgitte
name: Christensen, Ole Fredslund
Publisher: name: figshare
Year: 2021
Subjects: subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences

description: Additional file 1: Table S1. Accuracies and bias of predicted genetic effects (mean ± standard error over 100 replicates) for the three breeding schemes assuming a social genetic variance ( $$\sigma_{{u_{S} }}^{2}$$ σ u S 2 ) of 0.01. The means and standard errors over 100 replicates are shown for the accuracies and bias of predicted genetic effects between the three breeding schemes: SGM_DGE used a social genetic model (SGM) with selection criteria based on direct genetic values (DGE); SGM_TBV used a SGM with selection criteria based on total breeding values (TBV); and CGM_DGE used the classical genetic model (CGM) with selection criteria based on DGE. The three breeding schemes were compared for traits with an SGE variance ( $$\sigma_{{u_{S} }}^{2}$$ σ u S 2 ) equal to 0.01. Table S2. Accuracies and bias of predicted genetic effects (mean ± standard error over 100 replicates) for the three breeding schemes assuming a social genetic variance ( $$\sigma_{{u_{S} }}^{2}$$ σ u S 2 ) of 0.001. The means and standard errors over 100 replicates are shown for the accuracies and bias of predicted genetic effects between the three breeding schemes: SGM_DGE used a social genetic model (SGM) with selection criteria based on direct genetic values (DGE); SGM_TBV used a SGM with selection criteria based on total breeding values (TBV); and CGM_DGE used the classical genetic model (CGM) with selection criteria based on DGE. The three breeding schemes were compared for traits with an SGE variance ( $$\sigma_{{u_{S} }}^{2}$$ σ u S 2 ) equal to 0.001. Table S3. Percentage of converged replicates in scenarios assuming a social genetic variance ( $$\sigma_{{u_{S} }}^{2}$$ σ u S 2 ) of 0.001 or 0.01, and a correlation ( $$r_{{u_{DS} }}$$ r u DS ) between direct and social genetic effects at -0.5, 0 or 0.5. The table shows the convergence of the model for the estimation of variance components based on the average information (AI) restricted maximum likelihood (REML) estimation method (DMUAI module), and for solving BLUP equations based on the preconditioned conjugate gradient method (DMU5 module). Table S4. Accuracies of predicted genetic effects and bias in prediction (mean ± standard error over 100 replicates) when group members were composed at random versus composed of four families per group. The means and standard errors over 100 replicates are shown for the accuracies and bias of predicted genetic effects between family groups and random groups. These two designs were compared under three breeding schemes: SGM_DGE used a social genetic model (SGM) with selection criteria based on direct genetic values (DGE); SGM_TBV used a SGM with selection criteria based on total breeding values (TBV); and CGM_DGE used the classical genetic model (CGM) with selection criteria based on DGE. The trait was simulated with an SGE variance of 0.01 and a correlation between SGE and DGE of 0. Table S5. Response to selection per 1% of increase in true inbreeding (mean ± standard error over 100 replicates) in the three breeding schemes. The table shows response to selection per 1% of increase in true inbreeding for different breeding schemes using either pedigree-based or genomic-based relationship matrices. The three breeding schemes were compared for traits with an SGE variance ( $$\sigma_{{u_{S} }}^{2}$$ σ u S 2 ) equal to 0.01 and correlations ( $$r_{{u_{DS} }}$$ r u DS ) between SGE and DGE of -0.5, 0, and 0.5. Group members were allocated at random or composed of families.