Investigating the impact of preselection on subsequent single-step genomic BLUP evaluation of preselected animals

Abstract

Background: Preselection of candidates, hereafter referred to as preselection, is a common practice in breeding programs. Preselection can cause bias and accuracy loss in subsequent pedigree-based best linear unbiased prediction (PBLUP). However, the impact of preselection on subsequent single-step genomic BLUP (ssGBLUP) is not completely clear yet. Therefore, in this study, we investigated, across different heritabilities, the impact of intensity and type of preselection on subsequent ssGBLUP evaluation of preselected animals. Methods: We simulated a nucleus of a breeding programme, in which a recent population of 15 generations was produced with PBLUP-based selection. In generation 15 of this recent population, the parents of the next generation were preselected using several preselection scenarios. These scenarios were combinations of three intensities of preselection (no, high or very high preselection) and three types of preselection (genomic, parental average or random), across three heritabilities (0.5, 0.3 or 0.1). Following each preselection scenario, a subsequent evaluation was performed using ssGBLUP by excluding all the information from the preculled animals, and these genetic evaluations were compared in terms of accuracy and bias for the preselected animals, and in terms of realized genetic gain. Results: Type of preselection affected selection accuracy at both preselection and subsequent evaluation stages. While preselection accuracy decreased, accuracy in the subsequent ssGBLUP evaluation increased, from genomic to parent average to random preselection scenarios. Bias was always negligible. Genetic gain decreased from genomic to parent average to random preselection scenarios. Genetic gain also decreased with increasing intensity of preselection, but only by a maximum of 0.1 additive genetic standard deviation from no to very high genomic preselection scenarios. Conclusions: Using ssGBLUP in subsequent evaluations prevents preselection bias, irrespective of intensity and type of preselection, and heritability. With GPS, in addition to reducing the phenotyping effort considerably, the use of ssGBLUP in subsequent evaluations realizes only a slightly lower genetic gain than that realized without preselection. This is especially the case for traits that are expensive to measure (e.g. feed intake of individual broiler chickens), and traits for which phenotypes can only be measured at advanced stages of life (e.g. litter size in pigs).

Avoiding preselection bias in subsequent single‐step genomic BLUP evaluations of genomically preselected animals

“…This suggests that, in contrast with subsequent PBLUP evaluations, information from preculled animals is not strictly needed to prevent preselection bias in subsequent ssGBLUP evaluation of their preselected sibs. Based on the literature and our previous work (Jibrila et al., 2020; Koivula et al., 2018; Shabalina et al., 2017), we hypothesize that the impact of genotypes of preculled animals in subsequent ssGBLUP evaluations depends on whether genotypes of their parents are included in the subsequent evaluations. The objective of this study was to establish, through simulation, the minimum information required in subsequent ssGBLUP evaluations to estimate GEBV of genomically preselected animals without bias associated with preselection.…”

“…We used QMSim (Sargolzaei & Schenkel, 2009) to simulate the datasets, and the details of the simulation can be found in Jibrila et al. (2020).…”

“…The MiXBLUP instruction file for the ssGBLUP analysis can be found in Jibrila et al. (2020).…”

“…Jibrila et al. (2020) showed that using ssGBLUP in subsequent evaluations prevents GEBV of preselected animals from becoming biased due to preselection, even if genotypes of preculled animals are excluded. This suggests that, in contrast with subsequent PBLUP evaluations, information from preculled animals is not strictly needed to prevent preselection bias in subsequent ssGBLUP evaluation of their preselected sibs.…”

Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping

“…Second, starting with an initial 10,000 training population gave an adequate accuracy that was additionally boosted by using all available information jointly through the single-step genomic prediction. Using single-step genomic prediction also removed the bias due to pre-selection ( Jibrila et al, 2020 ).…”