Background: Empirically assessing the impact of preselection on genetic evaluation of preselected animals requires comparing scenarios that take different approaches into account, including scenarios without preselection. However, preselection is almost always performed in animal breeding programs, so it is difficult to have a dataset without preselection. Hence, most studies on preselection have used simulated datasets, and have concluded that genomic estimated breeding values (GEBV) from subsequent single-step genomic best linear unbiased prediction (ssGBLUP) evaluations are unbiased. The aim of this study was to investigate the impact of genomic preselection (GPS) on accuracy and bias in subsequent ssGBLUP evaluations, using data from a commercial pig breeding program.
Methods: We used data on average daily gain during performance testing, average daily gain throughout life, backfat thickness, and loin depth from one sire line and one dam line of pigs. As these traits have different weights in the breeding goals of the two lines, we analyzed the lines separately. For each line, we implemented a reference GPS scenario that kept all available data, against which the next two scenarios were compared. We then implemented two other scenarios with additional layers of GPS by removing all animals without progeny either (i) only in the validation generation, or (ii) in all generations. We conducted subsequent ssGBLUP evaluations for each GPS scenario, using all the data remaining after implementing the GPS scenario. Accuracy and bias were computed by comparing GEBV against progeny yield deviations of validation animals.
Results: Results for all traits and in both lines showed a marginal loss in accuracy due to the additional layers of GPS. Average accuracies across all GPS scenarios in the two lines were 0.39, 0.47, 0.56, and 0.60, for average daily gain during performance testing and throughout life, backfat thickness, and loin depth, respectively. Biases were largely absent, and when present, did not differ greatly between the GPS scenarios.
Conclusions: We conclude that the impact of preselection on accuracy and bias in subsequent ssGBLUP evaluations of selection candidates in pigs is generally minimal. We expect this conclusion to apply for other animal breeding programs as well, since preselection of any type or intensity generally has the same effect in animal breeding programs.
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http://dx.doi.org/10.1186/s12711-022-00727-5 | DOI Listing |
Poult Sci
January 2025
Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China. Electronic address:
Low-coverage whole genome sequencing (lcWGS) is an effective low-cost genotyping technology when combined with genotype imputation approaches. It facilitates cost-effective genomic selection (GS) programs in agricultural animal populations. GS based on lcWGS data has been successfully applied to livestock such as pigs and donkeys.
View Article and Find Full Text PDFJ Dairy Sci
January 2025
College of Animal Science and Technology, Northwest A&F University, 22 nt, Xinong Road, Yangling, Shaanxi, China. Electronic address:
Low-coverage whole-genome sequencing (LcWGS), a cost-effective genotyping method, offers greater flexibility in variant detection than does single-nucleotide polymorphism (SNP) chips. However, to our knowledge, no studies have explored the application of LcWGS in sheep. This study aimed to evaluate the feasibility of implementing LcWGS and genotype imputation and assess their applicability in genomic studies of body weight and milk yield in sheep.
View Article and Find Full Text PDFJ Dairy Sci
January 2025
Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602; Council on Dairy Cattle Breeding, Bowie, MD 20716.
The US dairy cattle genetic evaluation is currently a multistep process, including multibreed traditional BLUP estimations followed by single-breed SNP effects estimation. Single-step GBLUP (ssGBLUP) combines pedigree and genomic data for all breeds in one analysis. Unknown parent groups (UPG) or metafounders (MF) can be used to address missing pedigree information.
View Article and Find Full Text PDFJ Anim Sci
January 2024
Zoetis Inc, Livestock Genetics and Precision Animal Health VMRD, Kalamazoo, MI 49007, USA.
BMC Genomics
November 2024
ARO, The Volcani Center, Rishon LeZion, 15159, Israel.
Background: Routine genomic-estimated breeding values (gEBVs) are computed for the Israeli dairy cattle population by a two-step methodology in combination with the much larger Dutch population. Only sire genotypes are included. This work evaluated the contribution of cow genotypes obtained from the Israeli Holstein population to enhance gEBVs predictions via single-step genomic best-linear unbiased prediction (ssGBLUP).
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