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Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP using real data from pigs

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,...

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Autores principales: Jibrila, Ibrahim, Vandenplas, Jeremie, ten Napel, Jan, Bergsma, Rob, Veerkamp, Roel F., Calus, Mario P. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9238012/
https://www.ncbi.nlm.nih.gov/pubmed/35764921
http://dx.doi.org/10.1186/s12711-022-00727-5
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author Jibrila, Ibrahim
Vandenplas, Jeremie
ten Napel, Jan
Bergsma, Rob
Veerkamp, Roel F.
Calus, Mario P. L.
author_facet Jibrila, Ibrahim
Vandenplas, Jeremie
ten Napel, Jan
Bergsma, Rob
Veerkamp, Roel F.
Calus, Mario P. L.
author_sort Jibrila, Ibrahim
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-022-00727-5.
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spelling pubmed-92380122022-06-29 Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP using real data from pigs Jibrila, Ibrahim Vandenplas, Jeremie ten Napel, Jan Bergsma, Rob Veerkamp, Roel F. Calus, Mario P. L. Genet Sel Evol Research Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-022-00727-5. BioMed Central 2022-06-28 /pmc/articles/PMC9238012/ /pubmed/35764921 http://dx.doi.org/10.1186/s12711-022-00727-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Jibrila, Ibrahim
Vandenplas, Jeremie
ten Napel, Jan
Bergsma, Rob
Veerkamp, Roel F.
Calus, Mario P. L.
Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP using real data from pigs
title Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP using real data from pigs
title_full Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP using real data from pigs
title_fullStr Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP using real data from pigs
title_full_unstemmed Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP using real data from pigs
title_short Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP using real data from pigs
title_sort impact of genomic preselection on subsequent genetic evaluations with ssgblup using real data from pigs
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9238012/
https://www.ncbi.nlm.nih.gov/pubmed/35764921
http://dx.doi.org/10.1186/s12711-022-00727-5
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