Correcting within-family pre-selection in genetic evaluation of growth—A simulation study on rainbow trout

• Breeders may select fish in stages causing traits to be recorded only on selected fish.
• Pre-selected data with missing records result in seriously biased genetic parameters.
• Individual measurements of culled fish remove selection bias in genetic evaluation.
• Selection can be corrected by giving the culled fish their replicated family average.
• Family correction produces reliable EBVs for growth but residual variance is biased.

Genetic improvement programs for some fish species apply a two-stage selection scheme in which phenotypic selection is first practiced within families based on early body size. Pre-selection improves genetic gain in the breeding objective traits correlated with the pre-selection criteria, and it can also reduce management costs of a program. In this study, stochastic simulation of a rainbow trout, Oncorhynchus mykiss, breeding scheme with 150 full-sib families (2:2 mating design) was utilized to explore how within-family pre-selection and different information on the culled fish affect variance estimates and accuracy of genetic evaluation in grow-out body weights. The bias in genetic parameters and breeding values (EBVs) was quantified for fingerling weight at id-tagging (BW1), used as the criterion for pre-selection, and for two harvest weights recorded at the freshwater nucleus (BW2) and sea test station (BW2sea) in a split-family design. At tagging, fish from each full-sib family were either randomly sampled (R) or pre-selected, and the BW1 records of the culled fish were either individually measured (S + IND), augmented with the replicated family-specific averages of the culled fish (S + AVER), or were treated as missing (S–MIS). These four alternative data treatments were compared using a fixed initial family size of 100 individuals before tagging and two different pre-selection intensities (40% or 21% of fish per family selected). Variance estimates in R and S + IND did not diverge from the simulated a priori values in either of the selection intensities studied. The strategy S + AVER resulted in unbiased genetic variance estimates but decreased the residual variance, especially for BW1 and BW2. The accuracy of EBVs was, nevertheless, equally high for R, S + IND and S + AVER, and these values did not essentially differ between the two selection intensities. For S–MIS, the variance estimates were strongly biased in each trait, and the EBV accuracies were, on average, lower than in the other three treatments. Common environment variances were consistently overestimated and residual variances underestimated, whereas genetic variances were biased in both directions depending on the trait and pre-selection intensity. Further, for S–MIS, frequent convergence problems occurred in the estimation of variance components. For fish breeding schemes applying within-family pre-selection, data augmentation for culled fish by their average values of BW1 will sufficiently control for selection bias in genetic evaluation of growth. For accurate estimation of variance components either random samples from families or individual records from all culled fish are preferable.