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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 |
J Anim Sci
September 2025
U.S. Meat Animal Research Center, USDA, ARS, Clay Center, NE 68933, USA.
Liver abscesses are a concern in feedlot cattle, and little is known about the role of genetics in their development. This study aimed to estimate genetic parameters and to identify single nucleotide polymorphisms (SNP) associated with liver abscesses. Crossbred cattle representing 18 breeds in the United States Meat Animal Research Center Germplasm Evaluation Program were phenotyped for liver abscesses at slaughter (n = 9,044).
View Article and Find Full Text PDFJ Dairy Sci
September 2025
Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602.
Random regression models (RRM) combined with single-step genomic best linear unbiased prediction (ssGBLUP) are widely used for genomic evaluations in dairy cattle. This study aimed to efficiently implement RRM with ssGBLUP for national dairy cattle evaluations. Data from the Czech Holstein population were used, including 30 million test-day records for milk yield across 3 lactations.
View Article and Find Full Text PDFAnimals (Basel)
August 2025
College of Animal Science and Technology, Inner Mongolia Agricultural University, Hohhot 010018, China.
Although genomic selection can accelerate livestock breeding, its application in many countries is hindered due to the limited size of reference populations. To address this issue, researchers have explored methods of combining multiple breeds to create reference populations, aiming to enhance the accuracy of genomic prediction. The main objective of this study was to evaluate the impact of the construction of mixed reference populations at different genetic distance levels on the accuracy of multi-breed genome prediction in multi-breed beef cattle populations using three evaluation methods: GBLUP, ssGBLUP, and wGBLUP.
View Article and Find Full Text PDFJ Anim Breed Genet
August 2025
Centre for Animal Nutrition and Welfare, University of Veterinary Medicine, Vienna, Austria.
In the present study, a genome-wide association study (GWAS), estimation of genetic parameters, and prediction of breeding values for semen quality and quantity traits in Pietrain pigs were performed. The traits inferred were total number of sperm, motility, volume, and density. Traits were recorded from 2012 to 2021 using a CASA system (computer aided sperm analysis) and provided data from 96,815 ejaculates from 1647 Pietrain boars.
View Article and Find Full Text PDFAnimal
July 2025
State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China. Electronic address:
Robust animals, which are generally less susceptible to disease and require fewer medications, have greater resilience. As big data collection technologies have progressed, discovering new indicators of resilience by examining longitudinal data has become feasible. Environmental factor-induced variability in reproductive traits may affect an animal's ability to adjust to changing environmental circumstances.
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