Determining the stability of accuracy of genomic estimated breeding values in future generations in commercial pig populations

Abstract

Genomic information has a limited dimensionality (Me) related to the effective population size. Under the additive model, the persistence of genomic accuracies over generations should be high when the nongenomic information (pedigree and phenotypes) is equivalent to Me animals with high accuracy. The objective of this study was to evaluate the decay in accuracy over time and to compare the magnitude of decay with varying quantities of data, and with traits of low and moderate heritability. The dataset included 161,897 phenotypic records for a growth trait (GT) and 27,669 phenotypic records for a fitness trait related to prolificacy (FT) in a population with dimensionality around 5,000. The pedigree included 404,979 animals from 2008 to 2020, of which 55,118 were genotyped. Two single-trait models were used with all ancestral data and sliding subsets of 3-, 2-, and 1- generation intervals. Single-step GBLUP (ssGBLUP) was used to compute genomic estimated breeding values (GEBV). Estimated accuracies were calculated by the linear regression (LR) method. The validation population consisted of single generations succeeding the training population and continued forward for all generations available. The average accuracy for the first generation after training with all ancestral data was 0.69 and 0.46 for GT and FT, respectively. The average decay in accuracy from the first generation after training to generation 9 was -0.13, and -0.19 for GT and FT, respectively. The persistence of accuracy improves with more data. Old data has a limited impact on predictions for young animals for a trait with a large amount of information but a bigger impact for a trait with less information.

Correcting for base-population differences and unknown parent groups in single-step genomic predictions of Norwegian Red cattle

“…In setting parents of the validation animals to missing, the relationship between genotyped and non-genotyped animals might become zero (i.e., A 12 = 0) and in such situation, H −1 will not contribute to the estimation of group effects ( Tsuruta et al, 2019 ). Truncation of the data set, e.g., only considering data after the year 2000, may also reduce the number of genetic groups, and has been found to reduce prediction biases ( Cesarani et al, 2021 ; Hidalgo et al, 2021 ; Hollifield et al, 2021 ; Macedo et al, 2022 ).…”

Estimation of heritabilities and genetic correlations by time slices using predictivity in large genomic models

“…For pigs, a higher heritability growth trait and a lower heritability fitness trait were examined. The initial heritability was 0.21 for the growth trait and 0.05 for the fitness trait (Hollifield et al 2021), and the calculated heritability was slightly higher (0.26) for the growth trait and almost the same (0.06) for the fitness trait.…”

“…The formula for heritability was tested with data extracted from several studies, which included milk yield in Holsteins (Cesarani et al 2021), growth and fitness traits in pigs (Hollifield et al 2021), and a growth trait in broiler chicken (Hidalgo et al 2021). The information extracted included initial heritabilities used in the calculations, approximate number of genotyped animals with phenotypes in both reference and validation populations, and calculated accuracy or predictivity.…”

Quality of breeding value predictions from longitudinal analyses, with application to residual feed intake in pigs

“…The lack of effect of genomic information on SBV prediction in the present study is confirmed by the high correlation between ESBV and GSBV and the high regression coefficient of GSBV on ESBV that we obtained. In our study, the small benefit from genomic information is likely due to the small number of available genotyped animals with accurate phenotype information, much smaller than the expected number (1690, [40]) that is necessary to represent the genomic structure of the population [41]. Consequently, too little of the genetic variance could be captured by the genomic information in this dataset to have a significant impact on ESBV.…”