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Predicting the impact of genotype-by-genotype interaction on the purebred–crossbred genetic correlation from phenotype and genotype marker data of parental lines

BACKGROUND: The genetic correlation between purebred (PB) and crossbred (CB) performances ([Formula: see text] ) partially determines the response in CB when selection is on PB performance in the parental lines. An earlier study has derived expressions for an upper and lower bound of [Formula: see t...

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Autores principales: Duenk, Pascal, Wientjes, Yvonne C. J., Bijma, Piter, Iversen, Maja W., Lopes, Marcos S., Calus, Mario P. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837999/
https://www.ncbi.nlm.nih.gov/pubmed/36639760
http://dx.doi.org/10.1186/s12711-022-00773-z
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author Duenk, Pascal
Wientjes, Yvonne C. J.
Bijma, Piter
Iversen, Maja W.
Lopes, Marcos S.
Calus, Mario P. L.
author_facet Duenk, Pascal
Wientjes, Yvonne C. J.
Bijma, Piter
Iversen, Maja W.
Lopes, Marcos S.
Calus, Mario P. L.
author_sort Duenk, Pascal
collection PubMed
description BACKGROUND: The genetic correlation between purebred (PB) and crossbred (CB) performances ([Formula: see text] ) partially determines the response in CB when selection is on PB performance in the parental lines. An earlier study has derived expressions for an upper and lower bound of [Formula: see text] , using the variance components of the parental purebred lines, including e.g. the additive genetic variance in the sire line for the trait expressed in one of the dam lines. How to estimate these variance components is not obvious, because animals from one parental line do not have phenotypes for the trait expressed in the other line. Thus, the aim of this study was to propose and compare three methods for approximating the required variance components. The first two methods are based on (co)variances of genomic estimated breeding values (GEBV) in the line of interest, either accounting for shrinkage (VC(GEBV-S)) or not (VC(GEBV)). The third method uses restricted maximum likelihood (REML) estimates directly from univariate and bivariate analyses (VC(REML)) by ignoring that the variance components should refer to the line of interest, rather than to the line in which the trait is expressed. We validated these methods by comparing the resulting predicted bounds of [Formula: see text] with the [Formula: see text] estimated from PB and CB data for five traits in a three-way cross in pigs. RESULTS: With both VC(GEBV) and VC(REML), the estimated [Formula: see text] (plus or minus one standard error) was between the upper and lower bounds in 14 out of 15 cases. However, the range between the bounds was much smaller with VC(REML) (0.15–0.22) than with VC(GEBV) (0.44–0.57). With VC(GEBV-S), the estimated [Formula: see text] was between the upper and lower bounds in only six out of 15 cases, with the bounds ranging from 0.21 to 0.44. CONCLUSIONS: We conclude that using REML estimates of variance components within and between parental lines to predict the bounds of [Formula: see text] resulted in better predictions than methods based on GEBV. Thus, we recommend that the studies that estimate [Formula: see text] with genotype data also report estimated genetic variance components within and between the parental lines. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-022-00773-z.
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spelling pubmed-98379992023-01-14 Predicting the impact of genotype-by-genotype interaction on the purebred–crossbred genetic correlation from phenotype and genotype marker data of parental lines Duenk, Pascal Wientjes, Yvonne C. J. Bijma, Piter Iversen, Maja W. Lopes, Marcos S. Calus, Mario P. L. Genet Sel Evol Research Article BACKGROUND: The genetic correlation between purebred (PB) and crossbred (CB) performances ([Formula: see text] ) partially determines the response in CB when selection is on PB performance in the parental lines. An earlier study has derived expressions for an upper and lower bound of [Formula: see text] , using the variance components of the parental purebred lines, including e.g. the additive genetic variance in the sire line for the trait expressed in one of the dam lines. How to estimate these variance components is not obvious, because animals from one parental line do not have phenotypes for the trait expressed in the other line. Thus, the aim of this study was to propose and compare three methods for approximating the required variance components. The first two methods are based on (co)variances of genomic estimated breeding values (GEBV) in the line of interest, either accounting for shrinkage (VC(GEBV-S)) or not (VC(GEBV)). The third method uses restricted maximum likelihood (REML) estimates directly from univariate and bivariate analyses (VC(REML)) by ignoring that the variance components should refer to the line of interest, rather than to the line in which the trait is expressed. We validated these methods by comparing the resulting predicted bounds of [Formula: see text] with the [Formula: see text] estimated from PB and CB data for five traits in a three-way cross in pigs. RESULTS: With both VC(GEBV) and VC(REML), the estimated [Formula: see text] (plus or minus one standard error) was between the upper and lower bounds in 14 out of 15 cases. However, the range between the bounds was much smaller with VC(REML) (0.15–0.22) than with VC(GEBV) (0.44–0.57). With VC(GEBV-S), the estimated [Formula: see text] was between the upper and lower bounds in only six out of 15 cases, with the bounds ranging from 0.21 to 0.44. CONCLUSIONS: We conclude that using REML estimates of variance components within and between parental lines to predict the bounds of [Formula: see text] resulted in better predictions than methods based on GEBV. Thus, we recommend that the studies that estimate [Formula: see text] with genotype data also report estimated genetic variance components within and between the parental lines. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-022-00773-z. BioMed Central 2023-01-13 /pmc/articles/PMC9837999/ /pubmed/36639760 http://dx.doi.org/10.1186/s12711-022-00773-z Text en © The Author(s) 2023 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
Duenk, Pascal
Wientjes, Yvonne C. J.
Bijma, Piter
Iversen, Maja W.
Lopes, Marcos S.
Calus, Mario P. L.
Predicting the impact of genotype-by-genotype interaction on the purebred–crossbred genetic correlation from phenotype and genotype marker data of parental lines
title Predicting the impact of genotype-by-genotype interaction on the purebred–crossbred genetic correlation from phenotype and genotype marker data of parental lines
title_full Predicting the impact of genotype-by-genotype interaction on the purebred–crossbred genetic correlation from phenotype and genotype marker data of parental lines
title_fullStr Predicting the impact of genotype-by-genotype interaction on the purebred–crossbred genetic correlation from phenotype and genotype marker data of parental lines
title_full_unstemmed Predicting the impact of genotype-by-genotype interaction on the purebred–crossbred genetic correlation from phenotype and genotype marker data of parental lines
title_short Predicting the impact of genotype-by-genotype interaction on the purebred–crossbred genetic correlation from phenotype and genotype marker data of parental lines
title_sort predicting the impact of genotype-by-genotype interaction on the purebred–crossbred genetic correlation from phenotype and genotype marker data of parental lines
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837999/
https://www.ncbi.nlm.nih.gov/pubmed/36639760
http://dx.doi.org/10.1186/s12711-022-00773-z
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