Multistage selection for maximum economic return with an application to beef cattle breeding

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

In animal breeding, parents of the next generation are usually selected in multiple stages, and the initial stages of this selection are called preselection. Preselection reduces the information available for subsequent evaluation of preselected animals and this sometimes leads to bias. The objective of this study was to establish the minimum information required to subsequently evaluate genomically preselected animals without bias arising from preselection, with single-step genomic best linear unbiased prediction (ssGBLUP). We simulated a nucleus of a breeding program in which a recent population of 15 generations was produced. In each generation, parents of the next generation were selected in a single-stage selection based on pedigree BLUP. However, in generation 15, 10% of male and 15% of female offspring were preselected on their genomic estimated breeding values (GEBV). These GEBV were estimated using ssGBLUP, including the pedigree of all animals in generations 0-15, genotypes of all animals in generations 13-15 and phenotypes of all animals in generations 11-14. In subsequent ssGBLUP evaluation of these preselected animals, genotypes and phenotypes from various groups of animals were excluded one after another. We found that GEBV of the preselected animals were only estimated without preselection bias when genotypes and phenotypes of all animals in generations 13 and 14 and of the preselected animals were included in the subsequent evaluation. We also found that genotypes of the animals discarded at preselection only helped in reducing preselection bias in GEBV of their preselected sibs when genotypes of their parents were absent or excluded from the subsequent evaluation. We concluded that to prevent preselection bias in subsequent ssGBLUP evaluation of genomically preselected animals, information representative of the reference data used in the evaluation at preselection and genotypes and phenotypes of the preselected animals are needed in the subsequent evaluation.

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

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).

Combined Multistage Linear Genomic Selection Indices To Predict the Net Genetic Merit in Plant Breeding

A combined multistage linear genomic selection index (CMLGSI) is a linear combination of phenotypic and genomic estimated breeding values useful for predicting the individual net genetic merit, which in turn is a linear combination of the true unobservable breeding values of the traits weighted by their respective economic values. The CMLGSI is a cost-saving strategy for improving multiple traits because the breeder does not need to measure all traits at each stage. The optimum (OCMLGSI) and decorrelated (DCMLGSI) indices are the main CMLGSIs. Whereas the OCMLGSI takes into consideration the index correlation values among stages, the DCMLGSI imposes the restriction that the index correlation values among stages be zero. Using real and simulated datasets, we compared the efficiency of both indices in a two-stage context. The criteria we applied to compare the efficiency of both indices were that the total selection response of each index must be lower than or equal to the single-stage combined linear genomic selection index (CLGSI) response and that the correlation of each index with the net genetic merit should be maximum. Using four different total proportions for the real dataset, the estimated total OCMLGSI and DCMLGSI responses explained 97.5% and 90%, respectively, of the estimated single-stage CLGSI selection response. In addition, at stage two, the estimated correlations of the OCMLGSI and the DCMLGSI with the net genetic merit were 0.84 and 0.63, respectively. We found similar results for the simulated datasets. Thus, we recommend using the OCMLGSI when performing multistage selection.

Optimising multistage dairy cattle breeding schemes including genomic selection using decorrelated or optimum selection indices

Background

The prediction of the outcomes from multistage breeding schemes is especially important for the introduction of genomic selection in dairy cattle. Decorrelated selection indices can be used for the optimisation of such breeding schemes. However, they decrease the accuracy of estimated breeding values and, therefore, the genetic gain to an unforeseeable extent and have not been applied to breeding schemes with different generation intervals and selection intensities in each selection path.

Methods

A grid search was applied in order to identify optimum breeding plans to maximise the genetic gain per year in a multistage, multipath dairy cattle breeding program. In this program, different values of the accuracy of estimated genomic breeding values and of their costs per individual were applied, whereby the total breeding costs were restricted. Both decorrelated indices and optimum selection indices were used together with fast multidimensional integration algorithms to produce results.

Results

In comparison to optimum indices, the genetic gain with decorrelated indices was up to 40% less and the proportion of individuals undergoing genomic selection was different. Additionally, the interaction between selection paths was counter-intuitive and difficult to interpret. Independent of using decorrelated or optimum selection indices, genomic selection replaced traditional progeny testing when maximising the genetic gain per year, as long as the accuracy of estimated genomic breeding values was ≥ 0.45. Overall breeding costs were mainly generated in the path "dam-sire". Selecting males was still the main source of genetic gain per year.

Conclusion

Decorrelated selection indices should not be used because of misleading results and the availability of accurate and fast algorithms for exact multidimensional integration. Genomic selection is the method of choice when maximising the genetic gain per year but genotyping females may not allow for a reduction in overall breeding costs. Furthermore, the economic justification of genotyping females remains questionable.

Association of serum protein levels with egg productivity in Taiwan red-feathered country chickens

This study investigated the relationship between serum protein levels and egg productivity in Taiwan red-feathered country chickens (TRFCC). The total egg numbers of TRFCC (n=157) were recorded from 25 to 48 weeks of age. Serum samples were collected at 24 and 35 weeks of age, then classified by total egg number into four groups according to mean+/-1S.D. Serum X protein levels were evaluated by protein chip technology and with an insulin-like growth factor-I (IGF-I) immunoassay. Other serum proteins (apolipoprotein A-I, ovotransferrin and vitellogenin) were found at different levels between the most productive and the least productive groups of TRFCC, were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and were quantified by age. The results showed that levels of vitellogenin were positively correlated with total egg number at 24 and at 35 weeks old (p<0.01). Levels of apolipoprotein A-I and an IGF-1-related marker (termed X protein) in serum at 35 weeks old were correlated with total egg number (p<0.01). Serum ovotransferrin levels remained constant regardless of total egg number. To investigate the concentration differences among the four groups of TRFCC, serum protein levels of each group were analyzed by one-way ANOVA. The results showed that those protein levels, except for ovotransferrin, in the least productive group significantly differed from the other three groups (p<0.05). Although the correlation between those protein levels and the total egg number was not significant at the time of initial egg production, the selection strategy of those protein levels at later stages of egg production should provide a screening model to improve selection.

Selection index updating for maximizing rate of annual genetic gain in laying hens

Four selection strategies aimed at maximizing egg production in laying hens were compared with respect to expected annual genetic gain (GA). The selection strategies were: 1) (S1P) Traditional single-stage selection based on a single-house production system using partial records for both the individual and its ancestors, 2) Single-stage selection based on a single-house production system using full records for both the individual and its ancestors (S1F), 3) Single-stage selection based on a two-house production system using partial records for the individual and full records for its ancestors (S2P), and 4) Multistage selection based on a two-house production system using partial records for the individual and all available ancestral records (M2P). Strategy M2P resulted in the shortest generation interval (0.538 yr) and was the most efficient (deltaGA4 = 3.620 eggs per year), whereas strategy S1F generated the longest generation interval (2 yr) and was the least efficient (deltaGA2 = 1.334 eggs per year). Strategies S1P and S2P resulted in generation intervals of 1 yr, and were intermediate in efficiency (deltaGA1 = 2.232 eggs per year, deltaGA3 = 2.593 eggs per year). It was concluded that a two-house production system utilizing multistage selection was the most effective selection methodology. Further, selection based on M2P is expected to improve persistency of lay, whereas selection on S1P will not.