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Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population

SIMPLE SUMMARY: The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model to estimate heritability and genetic correlations of traits such as 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matt...

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Autores principales: Önder, Hasan, Sitskowska, Beata, Kurnaz, Burcu, Piwczyński, Dariusz, Kolenda, Magdalena, Şen, Uğur, Tırınk, Cem, Çanga Boğa, Demet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572056/
https://www.ncbi.nlm.nih.gov/pubmed/37835676
http://dx.doi.org/10.3390/ani13193070
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author Önder, Hasan
Sitskowska, Beata
Kurnaz, Burcu
Piwczyński, Dariusz
Kolenda, Magdalena
Şen, Uğur
Tırınk, Cem
Çanga Boğa, Demet
author_facet Önder, Hasan
Sitskowska, Beata
Kurnaz, Burcu
Piwczyński, Dariusz
Kolenda, Magdalena
Şen, Uğur
Tırınk, Cem
Çanga Boğa, Demet
author_sort Önder, Hasan
collection PubMed
description SIMPLE SUMMARY: The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model to estimate heritability and genetic correlations of traits such as 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matter percentage in the Polish Holstein population. Results showed that strong accuracies for the predictions were achieved. The genetic relations with milk yield were negative, as expected, because increasing milk yield decreases the milk components percentage. In conclusion, multi-trait genomic prediction can be more beneficial than single-trait genomic prediction. ABSTRACT: The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model that accounts for interactions between marker effects to estimate heritability and genetic correlations of traits including 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matter percentage in the Polish Holstein Friesian cow population. For this aim, 14,742 SNP genotype records for 586 Polish Holstein Friesian dairy cows from Poland were used. Single-Trait-ssGBLUP (ST) and Multi-Trait-ssGBLUP (MT) methods were used for estimation. We examined 305-day milk yield (MY, kg), milk fat percentage (MF, %), milk protein percentage (MP, %), milk lactose percentage (ML, %), and milk dry matter percentage (MDM, %). The results showed that the highest marker effect rank correlation was found between milk fat percentage and milk dry matter. The weakest marker effect rank correlation was found between ML and all other traits. Obtained accuracies of this study were between 0.770 and 0.882, and 0.773 and 0.876 for MT and ST, respectively, which were acceptable values. All estimated bias values were positive, which is proof of underestimation. The highest heritability value was obtained for MP (0.3029) and the lowest heritability value was calculated for ML (0.2171). Estimated heritability values were low for milk yield and milk composition as expected. The strongest genetic correlation was estimated between MDM and MF (0.4990) and the weakest genetic correlation was estimated between MY and ML (0.001). The genetic relations with milk yield were negative and can be ignored as they were not significant. In conclusion, multi-trait genomic prediction can be more beneficial than single-trait genomic prediction.
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spelling pubmed-105720562023-10-14 Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population Önder, Hasan Sitskowska, Beata Kurnaz, Burcu Piwczyński, Dariusz Kolenda, Magdalena Şen, Uğur Tırınk, Cem Çanga Boğa, Demet Animals (Basel) Article SIMPLE SUMMARY: The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model to estimate heritability and genetic correlations of traits such as 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matter percentage in the Polish Holstein population. Results showed that strong accuracies for the predictions were achieved. The genetic relations with milk yield were negative, as expected, because increasing milk yield decreases the milk components percentage. In conclusion, multi-trait genomic prediction can be more beneficial than single-trait genomic prediction. ABSTRACT: The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model that accounts for interactions between marker effects to estimate heritability and genetic correlations of traits including 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matter percentage in the Polish Holstein Friesian cow population. For this aim, 14,742 SNP genotype records for 586 Polish Holstein Friesian dairy cows from Poland were used. Single-Trait-ssGBLUP (ST) and Multi-Trait-ssGBLUP (MT) methods were used for estimation. We examined 305-day milk yield (MY, kg), milk fat percentage (MF, %), milk protein percentage (MP, %), milk lactose percentage (ML, %), and milk dry matter percentage (MDM, %). The results showed that the highest marker effect rank correlation was found between milk fat percentage and milk dry matter. The weakest marker effect rank correlation was found between ML and all other traits. Obtained accuracies of this study were between 0.770 and 0.882, and 0.773 and 0.876 for MT and ST, respectively, which were acceptable values. All estimated bias values were positive, which is proof of underestimation. The highest heritability value was obtained for MP (0.3029) and the lowest heritability value was calculated for ML (0.2171). Estimated heritability values were low for milk yield and milk composition as expected. The strongest genetic correlation was estimated between MDM and MF (0.4990) and the weakest genetic correlation was estimated between MY and ML (0.001). The genetic relations with milk yield were negative and can be ignored as they were not significant. In conclusion, multi-trait genomic prediction can be more beneficial than single-trait genomic prediction. MDPI 2023-09-29 /pmc/articles/PMC10572056/ /pubmed/37835676 http://dx.doi.org/10.3390/ani13193070 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Önder, Hasan
Sitskowska, Beata
Kurnaz, Burcu
Piwczyński, Dariusz
Kolenda, Magdalena
Şen, Uğur
Tırınk, Cem
Çanga Boğa, Demet
Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population
title Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population
title_full Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population
title_fullStr Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population
title_full_unstemmed Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population
title_short Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population
title_sort multi-trait single-step genomic prediction for milk yield and milk components for polish holstein population
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572056/
https://www.ncbi.nlm.nih.gov/pubmed/37835676
http://dx.doi.org/10.3390/ani13193070
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