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Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs
It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identificati...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379715/ https://www.ncbi.nlm.nih.gov/pubmed/34343280 http://dx.doi.org/10.1093/jas/skab226 |
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author | Hollifield, Mary Kate Lourenco, Daniela Tsuruta, Shogo Bermann, Matias Howard, Jeremy T Misztal, Ignacy |
author_facet | Hollifield, Mary Kate Lourenco, Daniela Tsuruta, Shogo Bermann, Matias Howard, Jeremy T Misztal, Ignacy |
author_sort | Hollifield, Mary Kate |
collection | PubMed |
description | It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identification are included in the model and examine if accounting for the reasons for missing traits improves the accuracy of predicting breeding values. Pedigree information was available for 1,965,077 purebred and crossbred animals. Records for 503,716 commercial three-way crossbred terminal animals from 2014 to 2019 were provided by Smithfield Premium Genetics. Two pedigree-based models were compared; model 1 (M1) was a threshold-linear model with all four traits (BW, HCW, RT, and LIV), and model 2 (M2) was a linear model including only BW and HCW. The fixed effects used in the model were contemporary group, sex, age at harvest (for HCW only), and dam parity. The random effects included direct additive genetic and random litter effects. Accuracy, dispersion, bias, and Pearson correlations were estimated using the linear regression method. The heritabilities were 0.11, 0.07, 0.02, and 0.04 for BW, HCW, RT, and LIV, respectively, with standard errors less than 0.01. No difference was observed in heritabilities or accuracies for BW and HCW between M1 and M2. Accuracies were 0.33, 0.37, 0.19, and 0.23 for BW, HCW, RT, and LIV, respectively. The genetic correlation between BW and RT was 0.34 ± 0.03, and between BW and LIV was 0.56 ± 0.03. Similarly, the genetic correlation between HCW and RT was 0.26 ± 0.04, and between HCW and LIV was 0.09 ± 0.05, respectively. The positive and moderate genetic correlations between BW and other traits imply a heavier BW resulted in a higher probability of surviving to harvest. Genetic correlations between HCW and other traits were lower due to the large quantity of missing records. Despite the heritable and correlated aspects of RT and LIV, results imply no major differences between M1 and M2; hence, it is unnecessary to include these traits in classical models for BW and HCW. |
format | Online Article Text |
id | pubmed-8379715 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-83797152021-08-23 Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs Hollifield, Mary Kate Lourenco, Daniela Tsuruta, Shogo Bermann, Matias Howard, Jeremy T Misztal, Ignacy J Anim Sci Animal Genetics and Genomics It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identification are included in the model and examine if accounting for the reasons for missing traits improves the accuracy of predicting breeding values. Pedigree information was available for 1,965,077 purebred and crossbred animals. Records for 503,716 commercial three-way crossbred terminal animals from 2014 to 2019 were provided by Smithfield Premium Genetics. Two pedigree-based models were compared; model 1 (M1) was a threshold-linear model with all four traits (BW, HCW, RT, and LIV), and model 2 (M2) was a linear model including only BW and HCW. The fixed effects used in the model were contemporary group, sex, age at harvest (for HCW only), and dam parity. The random effects included direct additive genetic and random litter effects. Accuracy, dispersion, bias, and Pearson correlations were estimated using the linear regression method. The heritabilities were 0.11, 0.07, 0.02, and 0.04 for BW, HCW, RT, and LIV, respectively, with standard errors less than 0.01. No difference was observed in heritabilities or accuracies for BW and HCW between M1 and M2. Accuracies were 0.33, 0.37, 0.19, and 0.23 for BW, HCW, RT, and LIV, respectively. The genetic correlation between BW and RT was 0.34 ± 0.03, and between BW and LIV was 0.56 ± 0.03. Similarly, the genetic correlation between HCW and RT was 0.26 ± 0.04, and between HCW and LIV was 0.09 ± 0.05, respectively. The positive and moderate genetic correlations between BW and other traits imply a heavier BW resulted in a higher probability of surviving to harvest. Genetic correlations between HCW and other traits were lower due to the large quantity of missing records. Despite the heritable and correlated aspects of RT and LIV, results imply no major differences between M1 and M2; hence, it is unnecessary to include these traits in classical models for BW and HCW. Oxford University Press 2021-08-03 /pmc/articles/PMC8379715/ /pubmed/34343280 http://dx.doi.org/10.1093/jas/skab226 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Animal Genetics and Genomics Hollifield, Mary Kate Lourenco, Daniela Tsuruta, Shogo Bermann, Matias Howard, Jeremy T Misztal, Ignacy Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs |
title | Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs |
title_full | Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs |
title_fullStr | Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs |
title_full_unstemmed | Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs |
title_short | Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs |
title_sort | impact of including the cause of missing records on genetic evaluations for growth in commercial pigs |
topic | Animal Genetics and Genomics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379715/ https://www.ncbi.nlm.nih.gov/pubmed/34343280 http://dx.doi.org/10.1093/jas/skab226 |
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